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  • 1.
    Abrahamsson, Annelie
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Oncology.
    Rzepecka, Anna
    Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping.
    Romu, Thobias
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Lundberg, Peter
    Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Kihlberg, Johan
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping.
    Dabrosin, Charlotta
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Oncology.
    Dense breast tissue in postmenopausal women is associated with a pro-inflammatory microenvironment in vivo2016In: Oncoimmunology, ISSN 2162-4011, E-ISSN 2162-402X, Vol. 5, no 10, article id e1229723Article in journal (Refereed)
    Abstract [en]

    Inflammation is one of the hallmarks of carcinogenesis. High mammographic density has been associated with increased risk of breast cancer but the mechanisms behind are poorly understood. We evaluated whether breasts with different mammographic densities exhibited differences in the inflammatory microenvironment.Postmenopausal women attending the mammography-screening program were assessed having extreme dense, n = 20, or entirely fatty breasts (nondense), n = 19, on their regular mammograms. Thereafter, the women were invited for magnetic resonance imaging (MRI), microdialysis for the collection of extracellular molecules in situ and a core tissue biopsy for research purposes. On the MRI, lean tissue fraction (LTF) was calculated for a continuous measurement of breast density. LTF confirmed the selection from the mammograms and gave a continuous measurement of breast density. Microdialysis revealed significantly increased extracellular in vivo levels of IL-6, IL-8, vascular endothelial growth factor, and CCL5 in dense breast tissue as compared with nondense breasts. Moreover, the ratio IL-1Ra/IL-1 was decreased in dense breasts. No differences were found in levels of IL-1, IL-1Ra, CCL2, leptin, adiponectin, or leptin:adiponectin ratio between the two breast tissue types. Significant positive correlations between LTF and the pro-inflammatory cytokines as well as between the cytokines were detected. Stainings of the core biopsies exhibited increased levels of immune cells in dense breast tissue.Our data show that dense breast tissue in postmenopausal women is associated with a pro-inflammatory microenvironment and, if confirmed in a larger cohort, suggests novel targets for prevention therapies for women with dense breast tissue.

  • 2.
    Agebratt, Christian
    et al.
    Linköping University, Department of Medical and Health Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Ström, Edvin
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences.
    Romu, Thobias
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Dahlqvist Leinhard, Olof
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Borga, Magnus
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Leandersson, Per
    Linköping University, Department of Clinical and Experimental Medicine, Division of Neuro and Inflammation Science. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Occupational and Environmental Medicine Center.
    Nyström, Fredrik H.
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Endocrinology.
    A Randomized Study of the Effects of Additional Fruit and Nuts Consumption on Hepatic Fat Content, Cardiovascular Risk Factors and Basal Metabolic Rate2016In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 11, no 1, p. e0147149-Article in journal (Refereed)
    Abstract [en]

    Background

    Fruit has since long been advocated as a healthy source of many nutrients, however, the high content of sugars in fruit might be a concern.

    Objectives

    To study effects of an increased fruit intake compared with similar amount of extra calories from nuts in humans.

    Methods

    Thirty healthy non-obese participants were randomized to either supplement the diet with fruits or nuts, each at +7 kcal/kg bodyweight/day for two months. Major endpoints were change of hepatic fat content (HFC, by magnetic resonance imaging, MRI), basal metabolic rate (BMR, with indirect calorimetry) and cardiovascular risk markers.

    Results

    Weight gain was numerically similar in both groups although only statistically significant in the group randomized to nuts (fruit: from 22.15±1.61 kg/m2 to 22.30±1.7 kg/m2, p = 0.24 nuts: from 22.54±2.26 kg/m2 to 22.73±2.28 kg/m2, p = 0.045). On the other hand BMR increased in the nut group only (p = 0.028). Only the nut group reported a net increase of calories (from 2519±721 kcal/day to 2763±595 kcal/day, p = 0.035) according to 3-day food registrations. Despite an almost three-fold reported increased fructose-intake in the fruit group (from 9.1±6.0 gram/day to 25.6±9.6 gram/day, p<0.0001, nuts: from 12.4±5.7 gram/day to 6.5±5.3 gram/day, p = 0.007) there was no change of HFC. The numerical increase in fasting insulin was statistical significant only in the fruit group (from 7.73±3.1 pmol/l to 8.81±2.9 pmol/l, p = 0.018, nuts: from 7.29±2.9 pmol/l to 8.62±3.0 pmol/l, p = 0.14). Levels of vitamin C increased in both groups while α-tocopherol/cholesterol-ratio increased only in the fruit group.

    Conclusions

    Although BMR increased in the nut-group only this was not linked with differences in weight gain between groups which potentially could be explained by the lack of reported net caloric increase in the fruit group. In healthy non-obese individuals an increased fruit intake seems safe from cardiovascular risk perspective, including measurement of HFC by MRI.

  • 3.
    Andersson, Thord
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Läthén, Gunnar
    Linköping University, Department of Science and Technology, Digital Media. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Lenz, Reiner
    Linköping University, Department of Science and Technology, Digital Media. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    A Fast Optimization Method for Level Set Segmentation2009In: Image Analysis: 16th Scandinavian Conference, SCIA 2009, Oslo, Norway, June 15-18, 2009. Proceedings / [ed] A.-B. Salberg, J.Y. Hardeberg, and R. Jenssen, Springer Berlin/Heidelberg, 2009, p. 400-409Conference paper (Refereed)
    Abstract [en]

    Level set methods are a popular way to solve the image segmentation problem in computer image analysis. A contour is implicitly represented by the zero level of a signed distance function, and evolved according to a motion equation in order to minimize a cost function. This function defines the objective of the segmentation problem and also includes regularization constraints. Gradient descent search is the de facto method used to solve this optimization problem. Basic gradient descent methods, however, are sensitive for local optima and often display slow convergence. Traditionally, the cost functions have been modified to avoid these problems. In this work, we instead propose using a modified gradient descent search based on resilient propagation (Rprop), a method commonly used in the machine learning community. Our results show faster convergence and less sensitivity to local optima, compared to traditional gradient descent.

  • 4.
    Andersson, Thord
    et al.
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Läthén, Gunnar
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Lenz, Reiner
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Borga, Magnus
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Modified Gradient Search for Level Set Based Image Segmentation2013In: IEEE Transactions on Image Processing, ISSN 1057-7149, E-ISSN 1941-0042, Vol. 22, no 2, p. 621-630Article in journal (Refereed)
    Abstract [en]

    Level set methods are a popular way to solve the image segmentation problem. The solution contour is found by solving an optimization problem where a cost functional is minimized. Gradient descent methods are often used to solve this optimization problem since they are very easy to implement and applicable to general nonconvex functionals. They are, however, sensitive to local minima and often display slow convergence. Traditionally, cost functionals have been modified to avoid these problems. In this paper, we instead propose using two modified gradient descent methods, one using a momentum term and one based on resilient propagation. These methods are commonly used in the machine learning community. In a series of 2-D/3-D-experiments using real and synthetic data with ground truth, the modifications are shown to reduce the sensitivity for local optima and to increase the convergence rate. The parameter sensitivity is also investigated. The proposed methods are very simple modifications of the basic method, and are directly compatible with any type of level set implementation. Downloadable reference code with examples is available online.

  • 5.
    Andersson, Thord
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Romu, Thobias
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Karlsson, Anette
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Norén, Bengt
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping.
    Forsgren, Mikael
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Smedby, Örjan
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping.
    Kechagias, Stergios
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Gastroentorology.
    Almer, Sven
    Linköping University, Department of Clinical and Experimental Medicine, Division of Neuro and Inflammation Science. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Gastroentorology.
    Lundberg, Peter
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Consistent intensity inhomogeneity correction in water–fat MRI2015In: Journal of Magnetic Resonance Imaging, ISSN 1053-1807, E-ISSN 1522-2586, Vol. 42, no 2, p. 468-476Article in journal (Refereed)
    Abstract [en]

    PURPOSE:

    To quantitatively and qualitatively evaluate the water-signal performance of the consistent intensity inhomogeneity correction (CIIC) method to correct for intensity inhomogeneities METHODS: Water-fat volumes were acquired using 1.5 Tesla (T) and 3.0T symmetrically sampled 2-point Dixon three-dimensional MRI. Two datasets: (i) 10 muscle tissue regions of interest (ROIs) from 10 subjects acquired with both 1.5T and 3.0T whole-body MRI. (ii) Seven liver tissue ROIs from 36 patients imaged using 1.5T MRI at six time points after Gd-EOB-DTPA injection. The performance of CIIC was evaluated quantitatively by analyzing its impact on the dispersion and bias of the water image ROI intensities, and qualitatively using side-by-side image comparisons.

    RESULTS:

    CIIC significantly ( P1.5T≤2.3×10-4,P3.0T≤1.0×10-6) decreased the nonphysiological intensity variance while preserving the average intensity levels. The side-by-side comparisons showed improved intensity consistency ( Pint⁡≤10-6) while not introducing artifacts ( Part=0.024) nor changed appearances ( Papp≤10-6).

    CONCLUSION:

    CIIC improves the spatiotemporal intensity consistency in regions of a homogenous tissue type. J. Magn. Reson. Imaging 2014.

  • 6.
    Andersson, Thord
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Romu, Thobias
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Norén, Bengt
    Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Forsgren, Mikael
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Smedby, Örjan
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Almer, Sven
    Linköping University, Department of Clinical and Experimental Medicine, Gastroenterology and Hepatology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Endocrinology.
    Lundberg, Peter
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Borga, Magnus
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Dahlqvist Leinhard, Olof
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences.
    Self-calibrated DCE MRI using Multi Scale Adaptive Normalized Averaging (MANA)2012In: Proceedings of the annual meeting of the International Society for Magnetic Resonance in Medicine (ISMRM 2012), 2012, 2012Conference paper (Other academic)
  • 7.
    Borga, Magnus
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization, CMIV.
    Center for medical image science and visualization (CMIV) a unique cross-disciplinary environment for medical image processing research2005In: Nordic Baltic Conference on Medical Engineering and Medical Physics,2005, 2005, p. 192-Conference paper (Refereed)
  • 8.
    Borga, Magnus
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Hierarchical Reinforcement Learning1993In: ICANN'93 eds S. Gielen and B. Kappen: Amsterdam, 1993Conference paper (Refereed)
    Abstract [en]

    A hierarchical representation of the input-output transition function in a learning system is suggested. The choice of either representing the knowledge in a learning system as a discrete set of input-output pairs or as a continuous input-output transition function is discussed. The conclusion that both representations could be efficient, but at different levels of abstraction is made. The difference between strategies and actions is defined. An algorithm for using adaptive critic methods in a two-level reinforcement learning system is presented. Simulations of a one dimensional hierarchical reinforcement learning system is presented.

  • 9.
    Borga, Magnus
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Learning Multidimensional Signal Processing1998Doctoral thesis, monograph (Other academic)
    Abstract [en]

    The subject of this dissertation is to show how learning can be used for multidimensional signal processing, in particular computer vision. Learning is a wide concept, but it can generally be defined as a system’s change of behaviour in order to improve its performance in some sense.

    Learning systems can be divided into three classes: supervised learning, reinforcement learning and unsupervised learning. Supervised learning requires a set of training data with correct answers and can be seen as a kind of function approximation. A reinforcement learning system does not require a set of answers. It learns by maximizing a scalar feedback signal indicating the system’s performance. Unsupervised learning can be seen as a way of finding a good representation of the input signals according to a given criterion.

    In learning and signal processing, the choice of signal representation is a central issue. For high-dimensional signals, dimensionality reduction is often necessary. It is then important not to discard useful information. For this reason, learning methods based on maximizing mutual information are particularly interesting.

    A properly chosen data representation allows local linear models to be used in learning systems. Such models have the advantage of having a small number of parameters and can for this reason be estimated by using relatively few samples. An interesting method that can be used to estimate local linear models is canonical correlation analysis (CCA). CCA is strongly related to mutual information. The relation between CCA and three other linear methods is discussed. These methods are principal component analysis (PCA), partial least squares (PLS) and multivariate linear regression (MLR). An iterative method for CCA, PCA, PLS and MLR, in particular low-rank versions of these methods, is presented.

    A novel method for learning filters for multidimensional signal processing using CCA is presented. By showing the system signals in pairs, the filters can be adapted to detect certain features and to be invariant to others. A new method for local orientation estimation has been developed using this principle. This method is significantly less sensitive to noise than previously used methods.

    Finally, a novel stereo algorithm is presented. This algorithm uses CCA and phase analysis to detect the disparity in stereo images. The algorithm adapts filters in each local neighbourhood of the image in a way which maximizes the correlation between the filtered images. The adapted filters are then analysed to find the disparity. This is done by a simple phase analysis of the scalar product of the filters. The algorithm can even handle cases where the images have different scales. The algorithm can also handle depth discontinuities and give multiple depth estimates for semi-transparent images.

  • 10.
    Borga, Magnus
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Reinforcement Learning Using Local Adaptive Models1995Licentiate thesis, monograph (Other academic)
    Abstract [en]

    In this thesis, the theory of reinforcement learning is described and its relation to learning in biological systems is discussed. Some basic issues in reinforcement learning, the credit assignment problem and perceptual aliasing, are considered. The methods of temporal difference are described. Three important design issues are discussed: information representation and system architecture, rules for improving the behaviour and rules for the reward mechanisms. The use of local adaptive models in reinforcement learning is suggested and exemplified by some experiments. This idea is behind all the work presented in this thesis. A method for learning to predict the reward called the prediction matrix memory is presented. This structure is similar to the correlation matrix memory but differs in that it is not only able to generate responses to given stimuli but also to predict the rewards in reinforcement learning. The prediction matrix memory uses the channel representation, which is also described. A dynamic binary tree structure that uses the prediction matrix memories as local adaptive models is presented. The theory of canonical correlation is described and its relation to the generalized eigenproblem is discussed. It is argued that the directions of canonical correlations can be used as linear models in the input and output spaces respectively in order to represent input and output signals that are maximally correlated. It is also argued that this is a better representation in a response generating system than, for example, principal component analysis since the energy of the signals has nothing to do with their importance for the response generation. An iterative method for finding the canonical correlations is presented. Finally, the possibility of using the canonical correlation for response generation in a reinforcement learning system is indicated.

  • 11.
    Borga, Magnus
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Andersson, Mats
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Knutsson, Hans
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Generation of representations for supervised learning - A velocity estimation example2001In: SCIA 2001,2001, 2001Conference paper (Refereed)
  • 12.
    Borga, Magnus
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Andersson, Thord
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Dahlqvist Leinhard, Olof
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Semi-Supervised Learning of Anatomical Manifolds for Atlas-Based Segmentation of Medical Images2016In: Proceedings of the 23rd International Conference on Pattern Recognition (ICPR), IEEE Computer Society, 2016, p. 3146-3149Conference paper (Refereed)
    Abstract [en]

    This paper presents a novel method for atlas-based segmentation of medical images. The method uses semi- supervised learning of a graph describing a manifold of anatom- ical variations of whole-body images, where unlabelled data are used to find a path with small deformations from the labelled atlas to the target image. The method is evaluated on 36 whole-body magnetic resonance images with manually segmented livers as ground truth. Significant improvement (p < 0.001) was obtained compared to direct atlas-based registration. 

  • 13.
    Borga, Magnus
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Carlsson, Tomas
    n/a.
    A Survey of Current Techniques for Reinforcement Learning1992Report (Other academic)
    Abstract [en]

    This survey considers response generating systems that improve their behaviour using reinforcement learning. The difference between unsupervised learning, supervised learning, and reinforcement learning is described. Two general problems concerning learning systems are presented; the credit assignment problem and the problem of perceptual aliasing. Notations and some general issues concerning reinforcement learning systems are presented. Reinforcement learning systems are further divided into two main classes; memory mapping and projective mapping systems. Each of these classes is described and some examples are presented. Some other approaches are mentioned that do not fit into the two main classes. Finally some issues not covered by the surveyed articles are discussed, and some comments on the subject are made.

  • 14.
    Borga, Magnus
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Dahlqvist, Leinhard Olof
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Lundberg, Peter
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Improvement in Magnetic Resonance Imaging Relating to Correction of Chemical Shift Artifact and Intensity Inhomogeneity2011Patent (Other (popular science, discussion, etc.))
    Abstract [en]

    Present invention discloses systems and methods for improvement of magnetic resonance images. Correction of a chemical shift artefact in an image acquired from a magnetic resonance imaging system is obtained by a system and a method involving iterative - compensation for the misregistration effect in an image domain. Correction of an intensity inhomogeneity in such images is obtained by a system and a method involving locating voxels corresponding to pure adipose tissue and estimating correction field from these points.

  • 15.
    Borga, Magnus
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Friman, Ola
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Lundberg, Peter
    Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    A canonical correlation approach to exploratory data analysis in fMRI2002Conference paper (Other academic)
    Abstract [en]

    A computationally efficient data-driven method for exploratory analysis of functional MRI data is presented. The basic idea is to reveal underlying components in the fMRI data that have maximum autocorrelation. The tool for accomplishing this task is Canonical Correlation Analysis. The proposed method is more robust and much more computationally efficient than independent component analysis, which previously has been applied in fMRI.

  • 16.
    Borga, Magnus
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Friman, Ola
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Lundberg, Peter
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Blind Source Separation of Functional MRI Data2002Conference paper (Other academic)
  • 17.
    Borga, Magnus
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    A Binary Competition Tree for Reinforcement Learning1994Report (Other academic)
    Abstract [en]

    A robust, general and computationally simple reinforcement learning system is presented. It uses a channel representation which is robust and continuous. The accumulated knowledge is represented as a reward prediction function in the outer product space of the input- and output channel vectors. Each computational unit generates an output simply by a vector-matrix multiplication and the response can therefore be calculated fast. The response and a prediction of the reward are calculated simultaneously by the same system, which makes TD-methods easy to implement if needed. Several units can cooperate to solve more complicated problems. A dynamic tree structure of linear units is grown in order to divide the knowledge space into a sufficiently number of regions in which the reward function can be properly described. The tree continuously tests split- and prune criteria in order to adapt its size to the complexity of the problem.

  • 18.
    Borga, Magnus
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Knutsson, Hans
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    A canonical correlation approach to blind source separation2001Report (Other academic)
  • 19.
    Borga, Magnus
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    An Adaptive Stereo Algorithm Based on Canonial Correlation Analysis1998Conference paper (Refereed)
    Abstract [en]

    This paper presents a novel algorithm that uses CCA and phase analysis to detect the disparity in stereo images. The algorithm adapts filters in each local neighbourhood of the image in a way which maximizes the correlation between the filtered images. The adapted filters are then analysed to find the disparity. This is done by a simple phase analysis of the scalar product of the filters. The algorithm can even handle cases where the images have different scales. The algorithm can also handle depth discontinuities and give multiple depth estimates for semitransparent images.

  • 20.
    Borga, Magnus
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    An Adaptive Stereo Algorithm Based on Canonical Correlation Analysis1998In: Proceedings of the Second IEEE International Conference on Intelligent Processing Systems: Gold Coast, Austalia, 1998, p. 177-182Conference paper (Refereed)
    Abstract [en]

    This paper presents a novel algorithm that uses CCA and phase analysis to detect the disparity in stereo images. The algorithm adapts filters in each local neighbourhood of the image in a way which maximizes the correlation between the filtered images. The adapted filters are then analyzed to find the disparity. This is done by a simple phase analysis of the scalar product of the filters. The algorithm can even handle cases where the images have different scales. The algorithm can also handle depth discontinuities and give multiple depth estimates for semi-transparent images.

  • 21.
    Borga, Magnus
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    An Adaptive Stereo Algorithm Based on Canonical Correlation Analysis1998Report (Other academic)
  • 22.
    Borga, Magnus
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Knutsson, Hans
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Canonical correlation analysis in early version processing2001In: European Symposium on Artificial neural Networks ESANN,2001, 2001Conference paper (Refereed)
  • 23.
    Borga, Magnus
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Estimating Multiple Depths in Semi-transparent Stereo Images1999In: Proceedings of the 11th Scandinavian Conference on Image Analysis, 1999Conference paper (Refereed)
    Abstract [en]

    A stereo algorithm that can estimate multiple depths in semi-transparent images is presented. The algorithm is based on a combination of phase analysis and canonical correlation analysis. The algorithm adapts filters in each local neighbourhood of the image in a way which maximizes the correlation between the filtered images. The adapted filters are then analysed to find the disparity. This is done by a simple phase analysis of the scalar product of the filters. For images with different but constant depths, a simple reconstruction procedure is suggested.

  • 24.
    Borga, Magnus
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Finding Efficient Nonlinear Visual Operators using Canonical Correlation Analysis2000In: Proceedings of the SSAB Symposium on Image Analysis: Halmstad, Linköping: Linköpings universitet , 2000, p. 13-16Conference paper (Refereed)
    Abstract [en]

    This paper presents a general strategy for designing efficient visual operators. The approach is highly task oriented and what constitutes the relevant information is defined by a set of examples. The examples are pairs of images displaying a strong dependence in the chosen feature but are otherwise independent. Particularly important concepts in the work are mutual information and canonical correlation. Visual operators learned from examples are presented, e.g. local shift invariant orientation operators and image content invariant disparity operators. Interesting similarities to biological vision functions are observed.

  • 25.
    Borga, Magnus
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Landelius, Tomas
    n/a.
    Learning Canonical Correlations1997In: SCIA10: Lappeenranta, Finland, 1997Conference paper (Refereed)
  • 26.
    Borga, Magnus
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Landelius, Tomas
    n/a.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    A Unified Approach to PCA, PLS, MLR and CCA1997Report (Other academic)
    Abstract [en]

    This paper presents a novel algorithm for analysis of stochastic processes. The algorithm can be used to find the required solutions in the cases of principal component analysis (PCA), partial least squares (PLS), canonical correlation analysis (CCA) or multiple linear regression (MLR). The algorithm is iterative and sequential in its structure and uses on-line stochastic approximation to reach an equilibrium point. A quotient between two quadratic forms is used as an energy function and it is shown that the equilibrium points constitute solutions to the generalized eigenproblem.

  • 27.
    Borga, Magnus
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Malmgren, Helge
    Dept of Philosophy Göteborgs universitet.
    Knutsson, Hans
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Feature selective edge detektion2000In: International Conference on Pattern Recognition,2000, IEEE , 2000, p. 229-232 vol.1Conference paper (Refereed)
    Abstract [en]

    We present a method that finds edges between certain image features, e.g. gray-levels, and disregards edges between other features. The method uses a channel representation of the features and performs normalized convolution using the channel values as certainties. This means that areas with certain features can be disregarded by the edge filter. The method provides an important tool for finding tissue specific edges in medical images, as demonstrated by an MR-image example

  • 28.
    Borga, Magnus
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Malmgren, Helge
    n/a.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    FSED - Feature Selective Edge Detection2000In: ICPR15: Barcelona, Spain, IEEE , 2000, Vol. 1, p. 229-232 vol.1Conference paper (Refereed)
    Abstract [en]

    We present a novel method that finds edges between certain image features, e.g. gray-levels, and disregards edges between other features. The method uses a channel representation of the features and performs normalized convolution using the channel values as certainties. This means that areas with certain features can be disregarded by the edge filter. The method provides an important new tool for finding tissue specific edges in medical images, as demonstrated by an MR-image example.

  • 29.
    Borga, Magnus
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Rydell, Joakim
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Signal and Anatomical Constraints in Adaptive Filtering of fMRI Data2007In: Biomedical Imaging: From Nano to Macro, 2007. ISBI 2007: From Nano to Macro, IEEE , 2007, p. 432-435Conference paper (Refereed)
    Abstract [en]

    An adaptive filtering method for fMRI data is presented. The method is related to bilateral filtering, but with a range filter that takes into account local similarities in signal as well as in anatomy. Performance is demonstrated on simulated and real data. It is shown that using both these similarity constraints give better performance than if only one of them is used, and clearly better than standard low-pass filtering.

  • 30.
    Borga, Magnus
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Thomas, E. Louise
    Department of Life Sciences Faculty of Science and Technology University of Westminster, London, United Kingdom.
    Romu, Thobias
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Rosander, Johannes
    Advanced MR Analytics AB, Linköping, Sweden.
    Fitzpatrick, Julie
    Department of Life Sciences Faculty of Science and Technology University of Westminster, London, United Kingdom.
    Dahlqvist Leinhard, Olof
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Bell, Jimmy D
    Department of Life Sciences Faculty of Science and Technology University of Westminster, London, United Kingdom.
    Validation of a Fast Method for Quantification of Intra-abdominal and Subcutaneous Adipose Tissue for Large Scale Human Studies2015In: NMR in Biomedicine, ISSN 1099-1492, Vol. 28, no 12, p. 1747-1753Article in journal (Refereed)
    Abstract [en]

    Central obesity is the hallmark of a number of non-inheritable disorders. The advent of imaging techniques such as magnetic resonance imaging (MRI) has allowed for a fast and accurate assessment of body fat content and distribution. However, image analysis continues to be one of the major obstacles for the use of MRI in large scale studies. In this study we assess the validity of the recently proposed fat-muscle-quantitation-system (AMRATM Profiler) for the quantification of intra-abdominal adipose tissue (IAAT) and abdominal subcutaneous adipose tissue (ASAT) from abdominal MR images.  Abdominal MR images were acquired from 23 volunteers with a broad range of BMIs and analysed using SliceOmatic, the current gold-standard, and the AMRATM Profiler based on a non-rigid image registration of a library of segmented atlases. The results show that there was a highly significant correlation between the fat volumes generated by both analysis methods, (Pearson correlation r = 0.97 p<0.001), with the AMRATM Profiler analysis being significantly faster (~3 mins) than the conventional SliceOmatic approach (~40 mins). There was also excellent agreement between the methods for the quantification of IAAT (AMRA 4.73 ± 1.99 vs SliceOmatic 4.73 ± 1.75 litres, p=0.97). For the AMRATM Profiler analysis, the intra-observer coefficient of variation was 1.6 % for IAAT and 1.1 % for ASAT, the inter-observer coefficient of variation was 1.4 % for IAAT and 1.2 % for ASAT, the intra-observer correlation was 0.998 for IAAT and 0.999 for ASAT, and the inter-observer correlation was 0.999 for both IAAT and ASAT. These results indicate that precise and accurate measures of body fat content and distribution can be obtained in a fast and reliable form by the AMRATM Profiler, opening up the possibility of large-scale human phenotypic studies.

  • 31.
    Borga, Magnus
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Virtanen, Kirsi A.
    Turku PET Centre, University of Turku, Finland.
    Romu, Thobias
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Dahlqvist Leinhard, Olof
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Persson, Anders
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Nuutila, Pirjo
    Turku PET Centre, University of Turku, Finland.
    Enerbäck, Sven
    Department of Biomedicine, University of Gothenburg, Sweden.
    Brown adipose tissue in humans: detection and functional analysis using PET (Positron Emission Tomography), MRI (Magnetic Resonance Imaging), and DECT (Dual Energy Computed Tomography)2014In: Methods in Enzymology: Methods of Adipose Tissue Biology / [ed] Ormond MacDougald, Elsevier, 2014, 1, p. 141-159Chapter in book (Other academic)
    Abstract [en]

    Research with the aim to translate findings of the beneficial effects induced by brown adipose tissue (BAT) on metabolism, as seen in various non-human experimental systems to also include human metabolism requires tools that accurately measure how BAT influences human metabolism. This review sets out to discuss such techniques, how they can be used, what they can measure and also some of their limitations. The focus is on detection and functional analysis of human BAT and how this can be facilitated by applying advanced imaging technology such as:  PET (Positron Emission Tomography), MRI (Magnetic Resonance Imaging), and DECT (Dual Energy Computed Tomography).

  • 32.
    Borga, Magnus
    et al.
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    West, Janne
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Bell, Jimmy
    Westminster University, London, UK.
    Harvey, Nicholas
    University of Southampton, IK.
    Romu, Thobias
    Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Heymsfield, Steven
    Pennington Biomedical Research Center, Baton Rouge, LA, US.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Advanced body composition assessment: From body mass index to body composition profilingIn: Journal of Investigative Medicine, ISSN 1081-5589, E-ISSN 1708-8267Article in journal (Refereed)
    Abstract [en]

    This paper gives a brief overview of common non-invasive techniques for body composition analysis and a more in-depth review of a body composition assessment method based on fat-referenced quantitative magnetic resonance imaging (MRI). Earlier published studies of this method are summarized, and a previously un-published validation study, based on 4.753 subjects from the UK Biobank imaging cohort, comparing the quantitative MRI method with dual-energy x-ray absorptiometry (DXA) is presented. For whole-body measurements of adipose tissue (AT) or fat and lean tissue (LT), DXA and quantitative MRI show excellent agreement with linear correlation of 0.99 and 0.97, and coefficient of variation (CV) of 4.5 % and 4.6 % for fat (computed from AT) and lean tissue respectively, but the agreement was found significantly lower for visceral adipose tissue, with a CV of more than 20 %. The additional ability of MRI to also measure muscle volumes, muscle AT infiltration and ectopic fat in combination with rapid scanning protocols and efficient image analysis tools make quantitative MRI a powerful tool for advanced body composition assessment. 

  • 33.
    Chellappa, Rama
    et al.
    Department of Electrical and Computer Engineering, University of Maryland, USA.
    Heyden, AndersLund University, Sweden.Laurendeau, DenisUniversité Laval, Canada.Felsberg, MichaelLinköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).Borga, MagnusLinköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Arts and Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Special issue on ICPR 2014 awarded papers2016Collection (editor) (Refereed)
    Abstract [en]

    We, the Guest Editors of this special issue of Pattern Recognition Letters are pleased to share these contributions with you. The papers included here are based on work from the 22nd International Conference on Pattern Recognition (IAPR) in Stockholm, Sweden, held August 24–28, 2014. The papers selected for this special issue were those winning one of the IAPR awards, as well as one paper by a former student of the winner of the KS Fu Prize, Prof. Jitendra Malik. Taken together, this body of work represents some of the finest research being conducted by the IAPR community worldwide, it builds on a rich legacy of accomplishment by the entire community, and it offers a view to the future, to where we are going as a scientific community.

    For each of the award-winning papers, the authors were asked to revise and extend their contributions to full journal length and to provide true added value vis-à-vis the original conference submission. In some cases, the authors elected to modify the titles slightly, and in some cases the list of authors has also been modified. The resulting manuscripts were sent out for full review by a different set of referees than those who reviewed the conference versions. The process, including required revisions, was in accordance with the standing editorial policy of Pattern Recognition Letters, resulting in the final versions accepted and appearing here. These are thoroughly vetted, high-caliber scientific contributions.

    It has been our honor to serve as Guest Editors for this special issue. We would like to thank the Editors of Pattern Recognition Letters for allowing us this opportunity. We are especially grateful to Dr. Gabriella Sanniti di Baja for her enthusiasm, support, and her willingness to keep prodding us along to bring the special issue through to completion. We would also like to thank all of those who reviewed the papers, both originally for the conference and subsequently for the journal, and those who served on the ICPR awards and KS Fu Prize committees.

    Finally, we express our heartfelt gratitude to all of the authors for taking the time to prepare these versions for our collective enlightenment, sharing their knowledge, innovation, and discoveries with the rest of us.

  • 34.
    Cros, Olivier
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Borga, Magnus
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Pauwels, Elin
    University of Ghent, Belgium.
    Dirckx, Joris J. J.
    University of Antwerp, Belgium.
    Gaihede, Michael
    Aalborg University Hospital, Denmark.
    Micro-channels in the mastoid anatomy. Indications of a separate blood supply of the air cell system mucosa by micro-CT scanning2013In: Hearing Research, ISSN 0378-5955, E-ISSN 1878-5891, Vol. 301, p. 60-65Article in journal (Refereed)
    Abstract [en]

    The mastoid air cell system has traditionally been considered to have a passive role in gas exchange and pressure regulation of the middle ear possibly with some acoustic function. However, more evidence has focused on the mucosa of the mastoid, which may play a more active role in regulation of middle ear pressure.

    In this study we have applied micro-CT scanning on a series of three human temporal bones. This approach greatly enhances the resolution (40–60 μm), so that we have discovered anatomical details, which has not been reported earlier. Thus, qualitative analysis using volume rendering has demonstrated notable micro-channels connecting the surface of the compact bone directly to the mastoid air cells as well as forming a network of connections between the air cells. Quantitative analysis on 2D slices was employed to determine the average diameter of these micro-channels (158 μm; range = 40–440 μm) as well as their density at a localized area (average = 75 cm−2; range = 64–97 cm−2).

    These channels are hypothesized to contain a separate vascular supply for the mastoid mucosa. However, future studies of the histological structure of the micro-channels are warranted to confirm the hypothesis. Studies on the mastoid mucosa and its blood supply may improve our knowledge of its physiological properties, which may have important implications for our understanding of the pressure regulation of the middle ear.

  • 35.
    Cros, Olivier
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV). Department of Otolaryngology, Head and Neck Surgery, Aalborg Hospital, Aarhus University Hospital, Denmark.
    Gaihede, Michael L.
    Department of Otolaryngology, Head and Neck Surgery, Aalborg Hospital, Aarhus University Hospital, Denmark.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Smedby, Örjan
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Mastoid structural properties determined by imaging analysis of high resolution CT-scanning2010In: Hearing Research, ISSN 0378-5955, E-ISSN 1878-5891, Vol. 263, no 1-2, p. 242-243Article in journal (Refereed)
    Abstract [en]

    Hypothesis: The structure of the mastoid air cells can be described by quantitative imaging analysis of high-resolution CT-scans, which may contribute to understand its function in normal and pathological ears. Background: Negative middle ear pressure is a common factor in middle ear diseases resulting from an imbalance between mastoid gas exchange and Eustachian tube function. While the Eustachian tube function has been the main focus of research, more recent studies indicate that the mastoid may play an active role in pressure regulation. The mastoid structure with numerous air cells reflects a large area to volume ratio (AV-ratio) adapted to efficient gas exchange. Imaging analysis applied to high resolution CT-scanning can describe quantitative measures, which may reveal important information about mastoid function and its role in healthy and diseased ears. Materials and methods: Quantitative analysis was performed on a series of unselected high resolution CT-scans (voxel size: 0.29 _ 0.29 _ 0.625 mm) from 36 ears in 24 patients. Area and volume were determined using Cavalieri’s method, i.e. by summing cross-sectional areas. The AV-ratio was computed for each scan. Results: Mean area was 69 cm2 (range: 23–134cm2), mean volume was 4 cm3 (range: 1.3–10.8 cm3), and mean AV-ratio was 16 cm-1 (range: 11.2–21.0 cm-1). The area correlated linearly to the volume by A = 17.2*V-0.2. Conclusion: The area and volume values corresponded with previous studies, and the additional AV-ratio reflected the functional properties of the mastoid in terms of capability for gas exchange. Due to a series of similarities between structure and function of the lungs and mastoid, it seems likely to propose a tree-structure of dividing mastoid cells. In respiratory research, analysis describing the dimensions of series of bronchi generations has been applied, and based on current results; our aim of future research is to establish similar details of mastoid tree-structure. Funding source: Various private Danish funds.

  • 36.
    Cros, Olivier
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Department of Otolaryngology, Head and Neck Surgery, Aalborg University Hospital, Denmark.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Pawels, Elin
    Centre for X-ray Tomography, Department of Physics and Astronomy, University of Ghent, Belgium.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Gaihede, Michael
    Department of Otolaryngology, Head and Neck Surgery, Aalborg University Hospital, Denmark / Department of Clinical Medicine, Aalborg University, Denmark.
    Determination of the mastoid surface area and volume based on micro-CT scanning of human temporal bone: Geometrical parameters dependence on scanning resolutions2016In: Hearing Research, ISSN 0378-5955, E-ISSN 1878-5891, Vol. 340, p. 127-134Article in journal (Other academic)
    Abstract [en]

    The mastoid air cell system (MACS) with its large complex of interconnected air cells reflects an enhanced surface area (SA) relative to its volume (V), which may indicate that the MACS is adapted to gas exchange and has a potential role in middle ear pressure regulation. Thus, these geometric parameters of the MACS have been studied by high resolution clinical CT scanning. However, the resolution of these scans is limited to a voxel size of around 0.6 mm in all dimensions, and so, the geometrical parameters are also limited. Small air cells may appear below the resolution and cannot be detected. Such air cells may contribute to a much higher SA than the V, and thus, also the SA/V ratio. More accurate parameters are important for analysis of the function of the MACS including physiological modeling.

    Our aim was to determine the SA, V, and SA/V ratio in MACS in human temporal bones at highest resolution by using micro-CT-scanning. Further, the influence of the resolution on these parameters was investigated by downsampling the data. Eight normally aerated temporal bones were scanned at the highest possible resolution (30-60 μm). The SA was determined using a triangular mesh fitted onto the segmented MACS. The V was determined by summing all the voxels containing air. Downsampling of the original data was applied four times by a factor of 2.

    The mean SA was 194 cm2, the mean V was 9 cm3, and the mean SA/V amounted to 22 cm-1. Decreasing the resolution resulted in a non-linear decrement of SA and SA/V, whereas V was mainly independent of the resolution.

    The current study found significantly higher SA and SA/V compared with previous studies using clinical CT scanning at lower resolutions. These findings indicate a separate role of the MACS compared with the tympanum, and the results are important for a more accurate modeling of the middle ear physiology.

  • 37.
    Dahlqvist Leinhard, Olof
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences. Linköping University, Faculty of Health Sciences.
    Johansson, Andreas
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Rydell, Joakim
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Kihlberg, Johan
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Health Sciences. Linköping University, Department of Medical and Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Smedby, Örjan
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Medical Imaging, Department of Radiology in Linköping.
    Nyström, Fredrik H.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences. Linköping University, Faculty of Health Sciences.
    Lundberg, Peter
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Medical Imaging, Department of Radiology in Linköping.
    Borga, Magnus
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Quantification of abdominal fat accumulation during hyperalimentation using MRI2009In: Proceedings of the ISMRM Annual Meeting (ISMRM'09), 2009, Berkeley, CA, USA: International Society for Magnetic Resonance in Medicine , 2009, p. 206-Conference paper (Other academic)
    Abstract [en]

    There is an increasing demand for imaging methods that can be used for automatic, accurate and quantitative determination of the amounts of abdominal fat. Such methods are important as they will allow the evaluation of some of the risk factors underlying the ’metabolic syndrome’. The metabolic syndrome is becoming common in large parts of the world, and it appears that a dominant risk factor for developing this syndrome is abdominal obesity. Subjects that are afflicted with the metabolic syndrome are exposed to a high risk for developing a large range of diseases such as type 2 diabetes, cardiac failure, and stroke. The aim of this work

  • 38.
    Dahlqvist Leinhard, Olof
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Linge, Jennifer
    Advanced MR Analytics AB, Linköping, Sweden.
    West, Janne
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Bell, Jimmy
    Westminster University, London, UK.
    Borga, Magnus
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering.
    Body Composition Profiling using MRI - Normative Data for Subjects with Cardiovascular Disease Extracted from the UK Biobank Imaging Cohort2016Conference paper (Other academic)
    Abstract [en]

    PURPOSE

    To describe the distribution of MRI-derived body composition measurements in subjects with cardiovascular disease (CVD) compared to subjects without any history of CVD.

    METHOD AND MATERIALS

    1864 males and 2036 females with an age range from 45 to 78 years from the UK Biobank imaging study were included in the study. Visceral adipose tissue volume normalized with height2 (VATi), total abdominal adipose tissue volume normalized with height2 (ATATi), total lean thigh muscle volume normalized with body weight (muscle ratio) and liver proton density fat fraction (PDFF) were measured with a 2-point Dixon imaging protocol covering neck to knee and a 10-point Dixon single slice protocol positioned within the liver using a 1.5T MR-scanner (Siemens, Germany). The MR-images were analyzed using AMRA® Profiler research (AMRA, Sweden). 213 subjects with history of cardiovascular events (angina, heart attack, or stroke) (event group) were age and gender matched to subjects with high blood pressure (HBP group), and subjects without CVD (controls).Kruskal-Wallis and Mann-Whitney U tests were used to test the observed differences for each measurement and group without correction for multiple comparisons.

    RESULTS

    VATi in the event group was 1.73 (1.13 - 2.32) l/m2 (median, 25%-75% percentile) compared to 1.68 (1.19 - 2.23) in the HBP group, and 1.30 (0.82-1.87) in the controls. ATATi in the event group was 4.31 (2.90-5.39) l/m2 compared to 4.05 (3.07-5.12) in the HBP group, and 3.48 (2.48-4.61) in the controls. Muscle ratio in the event group was 0.13 (0.12 - 0.15) l/kg as well as in the HBP group, compared to 0.14 (0.12 - 0.15) in the controls. Liver PDFF in the event group was 2.88 (1.77 - 7.72) % compared to 3.44 (2.04-6.18) in the HBP group, and 2.50 (1.58 - 5.15) in the controls. Kruskal-Wallis test showed significant differences for all variables and group comparisons (p<0.007). The post hoc test showed significant differences comparing the controls to both the event group and the HBP group. These were more significant for VATi and ATATi (p<10-4) than for muscle ratio and PDFF (p<0.03). No significant differences were detected between the event group and the HBP group.

    CONCLUSION

    Cardiovascular disease is strongly associated with high VATi, liver fat, and ATATi, and with low muscle ratio.

    CLINICAL RELEVANCE/APPLICATION

    The metabolic syndrome component in CVD can be effectively described using MRI-based body composition profiling.

  • 39.
    Dahlqvist Leinhard, Olof
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Linge, Jennifer
    Advanced MR Analytics AB, Linköping, Sweden.
    West, Janne
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Bell, Jimmy
    Westminster University, London, UK.
    Borga, Magnus
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Body Composition Profiling using MRI - Normative Data for Subjects with Diabetes Extracted from the UK Biobank Imaging Cohort2016Conference paper (Other academic)
    Abstract [en]

    PURPOSE

    To describe the distribution of MRI derived body composition measurements in subjects with diabetes mellitus (DM) compared to subjects without diabetes.

    METHOD AND MATERIALS

    3900 subjects (1864 males and 2036 females) from the UK Biobank imaging study were included in the study. The age range was 45 to 78 years. Visceral adipose tissue volume normalized with height2 (VATi), total abdominal adipose tissue volume normalized with height2 (ATATi), total lean thigh muscle volume normalized with body weight (muscle ratio) and liver proton density fat fraction (PDFF) were measured with a 6 minutes 2-point Dixon imaging protocol covering neck to knee and a 10-point Dixon single axial slice protocol positioned within the liver using a 1.5T MR-scanner (Siemens, Germany). The MR-images were analyzed using AMRA® Profiler research (AMRA, Sweden). 194 subjects with clinically diagnosed DM (DM group) were age and gender matched to subjects without DM (control group). For each variable and group, the median, 25%-percentile and 75%-percentile was calculated. Mann-Whitney U test was used to test the observed differences.

    RESULTS

    VATi in the DM group was 2.13 (1.43-2.62) l/m2 (median, 25% - 75% percentile) compared to 1.32 (0.86 - 1.79) l/m2 in the control group. ATATi in the DM group was 4.94 (3.86-6.19) l/m2 compared to 3.40 (2.56 - 4.70) l/m2 in the control group. Muscle ratio in the DM group was 0.13 (0.11 - 0.14) l/kg compared to 0.14 (0.12 - 0.15) l/kg in the control group. Liver PDFF in the DM group was 7.23 (2.68 - 13.26) % compared to 2.49 (1.53 - 4.73) % in the control group. Mann-Whitney U test detected significant differences between the DM group and the control group for all variables (p<10-5).

    CONCLUSION

    DM is strongly associated with high visceral fat, liver fat, and total abdominal fat, and low muscle ratio.

    CLINICAL RELEVANCE/APPLICATION

    Body composition profiling shows high potential to provide direct biomarkers to improve characterization and early diagnosis of DM.

  • 40.
    Dahlqvist Leinhard, Olof
    et al.
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences.
    Romu, Thobias
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Karlsson, Anette
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Borga, Magnus
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    High resolution isotropic whole-­‐body symmetrically sampled two point Dixon acquisition imaging at 3T2012In: ISMRM workshop on Fat-­‐Water Separation: Insights, Applications & Progress in MRI, 2012Conference paper (Other academic)
  • 41.
    Dahlqvist Leinhard, Olof
    et al.
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences.
    Romu, Thobias
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Kihlberg, Johan
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences.
    Gjellan, Solveig
    Linköping University, Department of Medical and Health Sciences, Internal Medicine. Linköping University, Faculty of Health Sciences.
    Zanjani, Sepehr
    Linköping University, Department of Medical and Health Sciences, Internal Medicine. Linköping University, Faculty of Health Sciences.
    Smedby, Örjan
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Diagnostics, Department of Radiology in Linköping.
    Nyström, Fredrik
    Linköping University, Department of Medical and Health Sciences, Internal Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Centre, Department of Endocrinology.
    Borga, Magnus
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Validation of whole-­‐body adipose tissue quantification using air displacement plethysmometry2012In: ISMRM workshop on Fat-­‐Water Separation: Insights, Applications & Progress in MRI, 2012Conference paper (Other academic)
  • 42.
    Eckerström, C.
    et al.
    Institute of Neuroscience and Physiology, Göteborg University, Sweden.
    Olsson, E.
    Department of Philosophy, Göteborg University, Sweden, Institute of Biomedicine, Göteborg University, Sweden.
    Borga, Magnus
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Ekholm, S.
    Department of Radiology, University of Rochester Medical Center, United States.
    Ribbelin, S.
    Department of Radiology, Göteborg University, Sweden.
    Rolstad, S.
    Institute of Neuroscience and Physiology, Göteborg University, Sweden.
    Starck, G.
    Department of Radiology, Göteborg University, Sweden, Department of Radiation Physics, Göteborg University, Sweden.
    Edman, A.
    Edman, Å., Institute of Neuroscience and Physiology, Göteborg University, Sweden.
    Wallin, A.
    Institute of Neuroscience and Physiology, Göteborg University, Sweden.
    Malmgren, H.
    Department of Philosophy, Göteborg University, Sweden.
    Small baseline volume of left hippocampus is associated with subsequent conversion of MCI into dementia: The Göteborg MCI study2008In: Journal of the Neurological Sciences, ISSN 0022-510X, E-ISSN 1878-5883, Vol. 272, no 1-2, p. 48-59Article in journal (Refereed)
    Abstract [en]

    Background: Earlier studies have reported that hippocampal atrophy can to some extent predict which patients with mild cognitive impairment (MCI) will subsequently convert to dementia, and that converters have an enhanced rate of hippocampal volume loss. Objective: To further validate the hypothesis that hippocampal atrophy predicts conversion from MCI to dementia, to relate baseline hippocampal volume to different forms of dementia, and to investigate the role of hippocampal side differences and rate of volume loss over time. Patients: The subjects (N = 68) include patients with MCI at baseline and progression to dementia at the two-year follow-up (N = 21), stable MCI patients (N = 21), and controls (N = 26). Among the progressing patients, 13 were diagnosed as having AD. Methods: The Göteborg MCI study is a clinically based longitudinal study with biannual clinical assessments. Hippocampal volumetry was performed manually on the MRI investigations at baseline and at the two-year follow-up. Results: Hippocampal volumetry could predict conversion to dementia in both the AD and the non-AD subgroup of converters. Left hippocampal volume in particular discriminated between converting and stable MCI. Cut off points for individual discrimination were shown to be potentially useful. The converting MCI group had a significantly higher rate of hippocampal volume loss as compared to the stable MCI group. Conclusions: In MCI patients, hippocampal volumetry at baseline gives prognostic information about possible development of AD and non-AD dementia. Contrary to earlier studies, we found that left hippocampal volume has the best predictive power. Reliable predictions appear to be possible in many individual cases. © 2008 Elsevier B.V. All rights reserved.

  • 43.
    Erlingsson, Styrbjörn
    et al.
    Linköping University, Department of Medical and Health Sciences, Internal Medicine. Linköping University, Faculty of Health Sciences.
    Herard, Sebastian
    Linköping University, Department of Medical and Health Sciences, Internal Medicine. Linköping University, Faculty of Health Sciences.
    Dahlqvist Leinhard, Olof
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences.
    Lindström, Torbjörn
    Linköping University, Department of Medical and Health Sciences, Internal Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Medicine, Department of Endocrinology and Gastroenterology UHL.
    Länne, Toste
    Linköping University, Department of Medical and Health Sciences, Physiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Thoracic and Vascular Surgery.
    Borga, Magnus
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Nyström, Fredrik
    Linköping University, Department of Medical and Health Sciences, Internal Medicine. Linköping University, Faculty of Health Sciences.
    Men develop more intraabdominal obesity and signs of the metabolic syndrome after hyperalimentation than women2009In: Metabolism: Clinical and Experimental, ISSN 0026-0495, E-ISSN 1532-8600, Vol. 58, no 7, p. 995-1001Article in journal (Refereed)
    Abstract [en]

    We prospectively studied the effects of fast food-based hyperalimentation on insulin sensitivity and components of the metabolic syndrome and analyzed this with respect to sex. Twelve nonobese men and 6 nonobese women (26 +/- 6.6 years old), and an age-matched control group were recruited. Subjects in the intervention group aimed for 5% to 15% weight increase by doubling their regular caloric intake based on at least 2 fast food meals a day while also adopting a sedentary lifestyle for 4 weeks (andlt;5000 steps a day). Weight of Subjects in the intervention group increased from 67.6 +/- 9.1 to 74.0 +/- 11 kg (P andlt;.001), with no sex difference with regard to this or with respect to changes of total abdominal fat volumes or waist circumferences. Fasting insulin (men: before, 3.8 +/- 1.7 mu U/mL, after, 7.4 +/- 3.1 mu U/mL; P=.004; women: before, 4.9 +/- 2.3 mu U/mL; after, 5.9 +/- 2.8 mu U/mL; P =.17), systolic blood pressure (men: before, 117 +/- 13 mm Hg; after, 127 +/- 9.1 mm Hg; P =.002; women: before, 102 +/- 5.1 mm Hg; after, 98 +/- 5.4 mm Hg; P =.39), serum low-density lipoprotein cholesterol, and apolipoprotein B increased only in the men of the intervention group. The sex differences in the metabolic responses to the intervention were linked to a considerable difference in the fat accumulation pattern; 41.4% +/- 9.2% of the increase of the fat volume in the abdominal region was accumulated intraabdominally in men and 22.7 +/- 6.5% in women (P andlt;.0001). This Study thus showed that women are protected, compared with men, against developing intraabdominal obesity when adopting a standardized obesity-provoking lifestyle. Our findings suggest that it is not different lifestyles and/or behaviors that underlie the fact that men have a higher cardiovascular risk at the same level of percentage of body fat than women.

  • 44.
    Friman, O.
    et al.
    MeVis Research, Bremen, Germany.
    Dahlqvist Leinhard, Olof
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences.
    Lundberg, Peter
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre of Surgery and Oncology, Department of Radiation Physics.
    Borga, Magnus
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    A General Method for Correction of Intensity Inhomogeniety in Two Point Dixon Imaging2008In: Proceedings of the International Society for Magnetic Resonance in Medicine annual meeting (ISMRM'08), International Society for Magnetic Resonance in Medicine , 2008, article id 4637Conference paper (Other academic)
    Abstract [en]

    Two point Dixon imaging can be used for quantitative fat estimation. However, field inhomogeneities pose a problem that needs to be corrected for before quantitative measurements can be obtained. We present a general framework for field inhomogeneitiy correction by fitting a set of smooth 3D spatial basis functions to voxels with high fat content. By choosing the number of basis functions, the smoothness constraint of the field can be controlled. The method is evaluated by measuring the FWHM of the fat peak in histograms for different number of basis functions. It is also compared to a previous method with good results.

  • 45.
    Friman, O.
    et al.
    MeVis Research, Bremen, Germany.
    Dahlqvist Leinhard, Olof
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences.
    Lundberg, Peter
    Linköping University, Department of Medical and Health Sciences. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    A General Method for Correction of Intensity Inhomogenity in Two Point Dixon Imaging2009In: Proc. Intl. Soc. Mag. Reson. Med., 2009, p. 4637-Conference paper (Refereed)
  • 46.
    Friman, Ola
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Lundberg, Mikael
    Tylen, Ulf
    Department of Radioology, Göteborg University, Sweden.
    Knutsson, Hans
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Recognizing emphysema - A neural network approach2002In: Pattern Recognition, 2002. Proceedings. 16th International Conference on  (Volume:1) / [ed] R. Kasturi, D. Laurendeau, C. Suen, IEEE Computer Society, 2002, p. 512-515Conference paper (Refereed)
    Abstract [en]

    An accurate and fully automatic method for detecting and quantifying emphysema in CT-images is presented. The method is based on an image preprocessing step followed by a neural network classifier trained to separate true emphysema from artifacts. The proposed approach is shown to be superior to an established method when applied on real patient data.

  • 47.
    Friman, Ola
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Borga, Magnus
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Lundberg, Peter
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Radio Physics. Östergötlands Läns Landsting, Centre of Surgery and Oncology, Department of Radiation Physics.
    Knutsson, Hans
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    A Correlation Framwork For Functional Mri Data Analysis.2001In: Proceedings of SCIA 2001. Bergen,2001, 2001, p. 3-9Conference paper (Refereed)
    Abstract [en]

    A correlation framework for detecting brain activity in functional MRI data is presented. In this framework, a novel method based on canonical correlation analysis follows as a natural extension of established analysis methods. The new method shows very good detection performance. This is demonstrated by localizing brain areas which control finger movements and areas which are involved in numerical mental calculation.

  • 48.
    Friman, Ola
    et al.
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Lundberg, Peter
    Östergötlands Läns Landsting, Centre of Surgery and Oncology, Department of Radiation Physics. Linköping University, Faculty of Health Sciences.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Adaptive analysis of fMRI data2003In: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 19, no 3, p. 837-845Article in journal (Refereed)
    Abstract [en]

    This article introduces novel and fundamental improvements of fMRI data analysis. Central is a technique termed constrained canonical correlation analysis, which can be viewed as a natural extension and generalization of the popular general linear model method. The concept of spatial basis filters is presented and shown to be a very successful way of adaptively filtering the fMRI data. A general method for designing suitable hemodynamic response models is also proposed and incorporated into the constrained canonical correlation approach. Results that demonstrate how each of these parts significantly improves the detection of brain activity, with a computation time well within limits for practical use, are provided.

  • 49.
    Friman, Ola
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Borga, Magnus
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Lundberg, Peter
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Radiation Physics. Östergötlands Läns Landsting, Centre of Surgery and Oncology, Department of Radiation Physics.
    Knutsson, Hans
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Canonical correlation as a tool in functional MRI data analysis2001In: SSAB Symposium on Image Analysis,2001, 2001Conference paper (Other academic)
  • 50.
    Friman, Ola
    et al.
    Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Lundberg, Peter
    Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Detection and detrending in fMRI data analysis2004In: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 22, no 2, p. 645-655Article in journal (Refereed)
    Abstract [en]

    This article addresses the impact that colored noise, temporal filtering, and temporal detrending have on the fMRI analysis situation. Specifically, it is shown why the detection of event-related designs benefit more from pre-whitening than blocked designs in a colored noise structure. Both theoretical and empirical results are provided. Furthermore, a novel exploratory method for producing drift models that efficiently capture trends and drifts in the fMRI data is introduced. A comparison to currently employed detrending approaches is presented. It is shown that the novel exploratory model is able to remove a major part of the slowly varying drifts that are abundant in fMRI data. The value of such a model lies in its ability to remove drift components that otherwise would have contributed to a colored noise structure in the voxel time series.

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