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  • 251.
    Nordberg, Klas
    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.
    Granlund, Gösta
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    On the Equivariance of the Orientation and the Tensor Field Representation1993Report (Other academic)
    Abstract [en]

    The tensor representation has proven a successful tool as a mean to describe local multi-dimensional orientation. In this respect, the tensor representation is a map from the local orientation to a second order tensor. This paper investigates how variations of the orientation are mapped to variation of the tensor, thereby giving an explicit equivariance relation. The results may be used in order to design tensor based algorithms for extraction of image features defined in terms of local variations of the orientation, e.g. multi-dimensional curvature or circular symmetries. It is assumed that the variation of the local orientation can be described in terms of an orthogonal transformation group. Under this assumption a corresponding orthogonal transformation group, acting on the tensor, is constructed. Several correspondences between the two groups are demonstrated.

  • 252.
    Nordberg, Klas
    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.
    Granlund, Gösta
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Signal Representation using Operators1992In: Proceedings of EUSIPCO--92, 1992Conference paper (Refereed)
  • 253.
    Norell, Björn
    et al.
    Philips Digital Mammography Sweden AB.
    Burdakov, Oleg
    Linköping University, Department of Mathematics, Optimization . Linköping University, The Institute of Technology.
    Andersson, Mats
    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.
    Approximate spectral factorization for design of efficient sub-filter sequences2011Report (Other academic)
    Abstract [en]

    A well-known approach to the design of computationally efficient filters is to use spectral factorization, i.e. a decomposition of a filter into a sequence of sub-filters. Due to the sparsity of the sub-filters, the typical processing speedup factor is within the range 1-10 in 2D, and for 3D it achieves10-100. The design of such decompositions consists in choosing the proper number of sub-filters, their individual types and sparsity. We focus here on finding optimal values of coefficients for given sequences of sparse sub-filters. It is a non-convex large scale optimization problem. The existing approaches are characterized by a lack of robustness - a very slow convergence with no guarantee of success. They are typically based on generating random initial points for further refinement with the use of local search methods. To deal with the multi-extremal nature of the original problem, we introduce a new constrained optimization problem. Its solution is then used as an initial point in the original problem for further refinement. Our approach is applicable to designing multi-dimensional filters. Its efficiency and robustness is illustrated by designing sub-filter sequences for 2D low-pass, band-pass and high-pass filters of approximately the same quality as with the use of a standard approach, but with the overall design speedup factor of several hundred.

  • 254.
    Ohlsson, Henrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Roll, Jacob
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Brun, Anders
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Knutsson, Hans
    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, Mats
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Ljung, Lennart
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Direct Weight Optimization Applied to Discontinuous Functions2008In: 47th IEEE Conference on Decision and Control, 2008. CDC 2008, IEEE , 2008, p. 117-122Conference paper (Refereed)
    Abstract [en]

    The Direct Weight Optimization (DWO) approach is a nonparametric estimation approach that has appeared in recent years within the field of nonlinear system identification. In previous work, all function classes for which DWO has been studied have included only continuous functions. However, in many applications it would be desirable also to be able to handle discontinuous functions. Inspired by the bilateral filter method from image processing, such an extension of the DWO framework is proposed for the smoothing problem. Examples show that the properties of the new approach regarding the handling of discontinuities are similar to the bilateral filter, while at the same time DWO offers a greater flexibility with respect to different function classes handled.

  • 255.
    Ohlsson, Henrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Rydell, Joakim
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Brun, Anders
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Roll, Jacob
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Ynnerman, Anders
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Enabling Bio-Feedback using Real-Time fMRI2008In: 47th IEEE Conference on Decision and Control, 2008, CDC 2008, IEEE , 2008, p. 3336-3341Conference paper (Refereed)
    Abstract [en]

    Despite the enormous complexity of the human mind, fMRI techniques are able to partially observe the state of a brain in action. In this paper we describe an experimental setup for real-time fMRI in a bio-feedback loop. One of the main challenges in the project is to reach a detection speed, accuracy and spatial resolution necessary to attain sufficient bandwidth of communication to close the bio-feedback loop. To this end we have banked on our previous work on real-time filtering for fMRI and system identification, which has been tailored for use in the experiment setup. In the experiments presented the system is trained to estimate where a person in the MRI scanner is looking from signals derived from the visual cortex only. We have been able to demonstrate that the user can induce an action and perform simple tasks with her mind sensed using real-time fMRI. The technique may have several clinical applications, for instance to allow paralyzed and "locked in" people to communicate with the outside world. In the meanwhile, the need for improved fMRI performance and brain state detection poses a challenge to the signal processing community. We also expect that the setup will serve as an invaluable tool for neuro science research in general.

  • 256.
    Petersson, Christer U.
    et al.
    n/a.
    Edholm, Paul
    n/a.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Knutsson, Hans E.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Ectomography. A New Radiographic Reconstruction Method: II. Computer Simulated Experiments1980In: IEEE Transactions on Biomedical Engineering, ISSN 0018-9294, E-ISSN 1558-2531, Vol. BME--27, no 11, p. 649-655Article in journal (Refereed)
    Abstract [en]

    In a special radiographic process, ectomography, an image of a slice is produced by simple summation of a set of specially filtered component images, of which each represents one of at least 60 different projections of the object. After being digitized, they are stored, filtered, and summed in a computer. Images representing any slice of any thickness in the object may be produced from the same set of component images. All details within the slice are pictured correctly while details outside are almost completely eliminated.

  • 257.
    Pettersson, Johanna
    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).
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Volume morphing for segmentation of bone from 3D data2005In: Symposium on Image Analysis SSBA,2005, 2005, p. 89-92Conference paper (Other academic)
  • 258.
    Pettersson, Johanna
    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).
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Some issues on the segmentation of the femur in CT data2004In: Proceedings of the Swedish Symposium on Image Analysis (2004), 2004, p. 158-161Conference paper (Other academic)
    Abstract [en]

    This paper presents a recently started project which goal is to automatically generate patient specific models for visual and haptic simulation of hip fracture surgery. It includes a preliminary study of a computed tomography (CT) dataset of the pelvic region. The paper emphasizes some issues encountered when segmenting bones in this region, especially in the area around the proximal femur.

  • 259.
    Pettersson, Johanna
    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.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Automatic hip bone segmentation using non-rigid registration2006In: 18th International Conference on Pattern Recognition, 2006, ICPR 2006 (Volume:3 ), IEEE Computer Society, 2006, p. 946-949Conference paper (Refereed)
    Abstract [en]

    This paper presents a method for automatic segmentation of bone from volumetric computed tomography (CT) data. Due to osteoporosis, which degenerates the bone density and hence decreases the intensity of the bone in the CT dataset, it is not possible to use conventional thresholding techniques to handle the segmentation. Furthermore we want to use prior knowledge about shapes and relations of the bones in the area of interest to be able to e.g. separate adjoining bones from each other. The method we suggest is the morphon algorithm in Knutsson and Andersson (2005). This is a non-rigid registration technique where an 2D or 3D image is iteratively deformed to match the corresponding structure in a target image. The method uses difference in local quadrature phase and certainty measures to estimate the deformations

  • 260.
    Pettersson, Johanna
    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).
    Knutsson, Hans
    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).
    Generation of patient specific bone models from volume data using morphons2005In: IFMBE Proceedings: NBC'05 13th Nordic Baltic Conference Biomedical Engineering and Medical Physics / [ed] Ronnie Lundström, Britt Andersson, Helena Grip, Umeå: IFMBE , 2005, p. 199-200Conference paper (Refereed)
    Abstract [en]

    The use of simulator systems for surgical planning and training is growing as the systems become more advanced. One important feature of these systems is the possibility to work on real patient data. This paper presents a method for generating patient-specific models of the femoral bone and the pelvis to be used in a hip surgery simulator. The bones are segmented from volumetric CT data using the Morphon method [3], where a prototype pattern is iteratively morphed to fit the corresponding structure in the input data. 

  • 261.
    Pettersson, Johanna
    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.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Non-rigid registration for automatic fracture segmentation2006In: Image Processing, 2006, Atlanta: IEEE , 2006, p. 1185-1188Conference paper (Refereed)
    Abstract [en]

    Automatic segmentation of anatomical structures is often performed using model-based non-rigid registration methods. These algorithms work well when the images do not contain any large deviations from the normal anatomy. We have previously used such a method to generate patient specific models of hip bones for surgery simulation. The method that was used, the morphon method, registers two-or three-dimensional images using a multi-resolution deformation scheme. A prototype image is iteratively registered to a target image using quadrature filter phase difference to estimate the local displacement. The morphon method has in this work been extended to deal with automatic segmentation of fractured bones. Two features have been added. First, the method is modified such that multiple prototypes (in this case two) can be used. Second, normalised convolution is utilized for the displacement estimation, to guide the registration of the second prototype, based on the result of the registration of the first one

  • 262.
    Pettersson, Johanna
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    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).
    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).
    Normalised convolution and morphons for non-rigid registration2006In: SSBA Symposium on Image Analysis,2006, 2006, p. 61-65Conference paper (Other academic)
  • 263.
    Pettersson, Johanna
    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.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Segmentation and registration with the Morphon method, four different applications2007In: Proceedings of the {SSBA} Symposium on Image Analysis,2007, 2007Conference paper (Other academic)
    Abstract [en]

    The Morphon method has shown to be a useful non-rigid registration method since it was first presented in 2005. This paper demonstrates how the method has been adapted for four different applications; hip fracture segmentation from CT data, hippocampus segmentation from MR data and registration the prostate and the head-neck region from CT data for radiotherapy planning.

  • 264.
    Pettersson, Johanna
    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. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Malmgren, Helge
    Department of Philosophy, Göteborg University, Sweden.
    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).
    Automatic segmentation of CT and MR volume data using non-rigid morphon registrationManuscript (preprint) (Other academic)
    Abstract [en]

    Automatic segmentation of anatomical structures is often performed using model based non-rigid registration methods. The morphon algorithm is one such method. In this algorithm, two or three dimensional images are registered using a multi-resolution deformation scheme. A prototype image is iteratively registered to a target image, using local phase difference to estimate the displacement between the images. This method has been extended with normalised convolution, to guide the registration process using prior knowledge about the target image. By defining a certainty mask used in the normalised convolution one can either specify a region of interest in the target image, or, on the other hand, occlude regions in the image that should not affect the matching. Two different applications are presented, which each demonstrates the use of this method for automatic segmentation.

  • 265.
    Pettersson, Johanna
    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.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Nordqvist, Per
    Melerit Medical AB .
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    A hip surgery simulator based on patient specific models generated by automatic segmentation2006In: Medicine Meets Virtual Reality 14: Accelerating Change in Healthcare: Next Medical Toolkit / [ed] James D Westwood; et al, Amsterdam, Nederländerna: IOS Press, 2006, p. 431-436Conference paper (Refereed)
    Abstract [en]

    The use of surgical simulator systems for education and preoperative planning is likely to increase in the future. A natural course of development of these systems is to incorporate patient specific anatomical models. This step requires some kind of segmentation process in which the different anatomical parts are extracted. Anatomical datasets are, however, usually very large and manual processing would be too demanding. Hence, automatic, or semi-automatic, methods to handle this step are required. The framework presented in this paper uses nonrigid registration, based on the morphon method, to automatically segment the hip anatomy and generate models for a hip surgery simulator system.

  • 266.
    Pettersson, Johanna
    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.
    Palmerius, Karljohan
    Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Wahlström, Ola
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Tillander, Bo
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    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.
    Simulation of Patient Specific Cervical Hip Fracture Surgery With a Volume Haptic Interface2008In: IEEE Transactions on Biomedical Engineering, ISSN 0018-9294, E-ISSN 1558-2531, Vol. 55, no 4, p. 1255-1265Article in journal (Refereed)
    Abstract [en]

    The interest for surgery simulator systems with anatomical models generated from authentic patient data is growing as these systems evolve.With access to volumetric patient data, e.g., from a computer tomography scan, haptic and visual feedback can be created directly from this dataset. This opens the door for patient specific simulations. Hip fracture surgery is one area where simulator systems is useful to train new surgeons and plan operations. To simulate the drilling procedure in this type of surgery, a repositioning of the fractured bone into correct position is first needed. This requires a segmentation process in which the bone segments are identified and the position of the dislocated part is determined. The segmentation must be automatic to cope with the large amount of data from the computer tomography scan. This work presents the first steps in the development of a hip fracture surgery simulation with patient specific models. Visual and haptic feedback is generated from the computer tomography data by simulating fluoroscopic images and the drilling process. We also present an automatic segmentation method to identify the fractured bone and determine the dislocation. This segmentation method is based on nonrigid registration with the Morphon method.

  • 267.
    Plumat, J.
    et al.
    Laboratoire de T´el´ecommunication et T´el´ed´ection (TELE), Universit´e Catholique de Louvain, Belgium.
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Janssens, G.
    Laboratoire de T´el´ecommunication et T´el´ed´ection (TELE), Universit´e Catholique de Louvain, Belgium.
    Orban de Xivry, J.
    Laboratoire de T´el´ecommunication et T´el´ed´ection (TELE), Universit´e Catholique de Louvain, Belgium.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Macq, B.
    Laboratoire de T´el´ecommunication et T´el´ed´ection (TELE), Universit´e Catholique de Louvain, Belgium.
    Image registration using Morphon algorithm: an ITK implementation2009In: Insight Journal, ISSN 2327-770XArticle in journal (Other academic)
    Abstract [en]

    Medical image registration is becoming a more and more useful component of a large number of applications. The presented method aims to enrich the ITK library. This method, called Morphon registration algorithm, computes a dense deformation field accepting inputs from different intensity contrasts. This article presents its implementation within the Insight Toolkit. In this paper, we provide a brief description of the algorithm, a presentation of the implementation, the justification of our modified classes and the results given by the algorithm. We demonstrate the algorithm in application of different images intesity constrasts and dimensions.

  • 268.
    Ragnehed, Mattias
    et al.
    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.
    Engström, Maria
    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, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Biomedical Instrumentation. Linköping University, The Institute of Technology.
    Axelsson Söderfeldt, Birgitta
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Clinical and Experimental Medicine, Neurology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Local Health Care Services in Central Östergötland, Department of Neurology.
    Lundberg, Peter
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre of Surgery and Oncology, Department of Radiation Physics. Östergötlands Läns Landsting, Centre for Medical Imaging, Department of Radiology in Linköping.
    Restricted Canonical Correlation Analysis in Functional MRI-Validation and a Novel Thresholding Technique2009In: Journal of Magnetic Resonance Imaging, ISSN 1053-1807, E-ISSN 1522-2586, Vol. 29, no 1, p. 146-154Article in journal (Refereed)
    Abstract [en]

    Purpose: To validate the performance of an analysis method for fMRI data based on restricted canonical correlation analysis (rCCA) and adaptive filtering, and to increase the usability of the method by introducing a new technique for significance estimation of rCCA maps.

    Materials and Methods: Activation data from a language task and also a resting state fMRI data were collected from eight volunteers. Data was analyzed using both the rCCA method and the General Linear Model (GLM). A modified Receiver Operating Characteristic (ROC) method was used to evaluate the performance of the different analysis methods. The area under a fraction of the ROC curve was used as a measure of performance. On resting state data the fraction of voxels above certain significance thresholds were used to evaluate the significance estimation method.

    Results: The rCCA method scored significantly higher on the area under the ROC curve than the GLM. The fraction of activated voxels determined by thresholding according to the introduced significance estimation technique showed good agreement with the thresholds selected.

    Conclusion: The rCCA method is an effective analysis tool for fMRI data and its usability is increased with the introduced significance estimation method.

  • 269.
    Ragnehed, Mattias
    et al.
    Linköping University, Department of Medicine and Care, Radiology. Linköping University, Faculty of Health Sciences.
    Engström, Maria
    Linköping University, Department of Medicine and Care, Center for Medical Image Science and Visualization. Linköping University, Faculty of Health Sciences.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Health Sciences.
    Lundberg, Peter
    Linköping University, Department of Medicine and Care, Radiation Physics. Linköping University, Faculty of Health Sciences.
    Söderfeldt, Birgitta
    Linköping University, Department of Neuroscience and Locomotion. Linköping University, Faculty of Health Sciences.
    Performance of canonical correlation analysis in language tests by functional MRIManuscript (preprint) (Other academic)
    Abstract [en]

    In this paper a new method based on constraitwd canonical correlation analysis (CCA) for the analysis of fMRI data is evaluated. In particular the method benefits from a powerful way of choosing temporal basis functions in additiou to an adaptive spatial filtering scheme. A modified receiver operating characteristic (ROC) method was used to quantify the results and to compare it with traditionally used statistics in an objective way. The evaluation was performed using real fMRI data form a language test. It was shown that the CCA based method offers a significant gain in detection power.

  • 270.
    Ragnehed, Mattias
    et al.
    Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences.
    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, Faculty of Health Sciences. Linköping University, Department of Medical and Health Sciences, Radiology.
    Söderfeldt, Birgitta
    Linköping University, Department of Neuroscience and Locomotion, Neurology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Local Health Care Services in Central Östergötland, Department of Neurology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Comparing CCA and SPM992003Conference paper (Refereed)
  • 271.
    Rodríguez-Vila, Borja
    et al.
    Bioengineering and Telemedicine Group, Universidad Politécnica de Madrid Spain.
    Pettersson, Johanna
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    García-Vicente, Feliciano
    Medical Physics, Radiotherapy Department, University Hospital La Princesa Spain.
    Gómez, Enrique J.
    Bioengineering and Telemedicine Group, Universidad Politécnica de Madrid Spain.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    3D deformable registration for monitoring radiotherapy treatment in prostate cancer2007In: Image Analysis: 15th Scandinavian Conference, SCIA 2007, Aalborg, Denmark, June 10-14, 2007, Berlin/Heidelberg, Germany: Springer Berlin/Heidelberg, 2007, p. 750-759Conference paper (Refereed)
    Abstract [en]

    Two deformable registration methods, the Demons and the Morphon algorithms, have been used for registration of CT datasets to evaluate their usability in radiotherapy planning for prostate cancer. These methods were chosen because they can perform deformable registration in a fully automated way. The experiments show that for intrapatient registration both of the methods give useful results, although some differences exist in the way they deform the template. The Morphon method has, however, some advantageous compared to the Demons method. It is invariant to the image intensity and it does not distort the deformed data. The conclusion is therefore to recommend the Morphon method as a registration tool for this application. A more flexible regularization model is needed, though, in order to be able to catch the full range of deformations required to match the datasets.

  • 272.
    Rudner, Mary
    et al.
    Linköping University, Department of Neuroscience and Locomotion. Linköping University, Faculty of Health Sciences.
    Cedefamn, Jonny
    Friman, Ola
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
    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.
    Söderfeldt, Birgitta
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Neuroscience and Locomotion. Östergötlands Läns Landsting, Local Health Care Services in Central Östergötland, Department of Neurology.
    Are levels of language processing reflected in neural activation? - An fMRI study.2001In: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 13, no 6Article in journal (Refereed)
  • 273.
    Rydell, Joakim
    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.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Correlation controlled bilateral filtering of fMRI data2005In: Proceedings of the International Society for Magnetic Resonance in MEdicine Annual Meeting (ISMRM) 2005, 2005Conference paper (Refereed)
    Abstract [en]

    A novel filtering method for analysis of fMRI data is presented. The method is based on weighted averaging of neighboring voxels whose time-series are, in a sense, similar. A comparison between the new method and other filtering strategies is also presented, and the novel method is shown to have superior ability to discriminate between active and inactive voxels.

  • 274.
    Rydell, Joakim
    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.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Robust correelation analysis with an application to functional MRI2007In: SSBA Symposium on Image Analysis,2007, 2007Conference paper (Other academic)
  • 275.
    Rydell, Joakim
    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.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Robust Correlation Analysis with an Application to Functional MRI2008In: Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE, IEEE conference proceedings, 2008, p. 453-456Conference paper (Refereed)
    Abstract [en]

    Correlation is often used to measure the similarity between signals and is an important tool in signal and image processing. In some applications it is common that signals are corrupted by local bursts of noise. This adversely affects the performance of signal recognition algorithms. This paper presents a novel correlation estimator, which is robust to locally corrupted signals. The estimator is generalized to multivariate correlation analysis (general linear model, GLM, and canonical correlation analysis, CCA). Synthetic functional MRI data is used to demonstrate the estimator, and its robustness is shown to increase the performance of signal detection.

  • 276.
    Rydell, Joakim
    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, Peter
    Linköping University, Department of Medicine and Care, Radiation Physics. Linköping University, The Institute of Technology. Östergötlands Läns Landsting, Centre of Surgery and Oncology, Department of Radiation Physics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Dimensionality and degrees of freedom in fMRI data analysis - a comparative study2004In: Biomedical Imaging: Nano to Macro, 2004. IEEE International Symposium on, IEEE , 2004, p. 988-991 vol.1Conference paper (Refereed)
    Abstract [en]

    Two- and three-dimensional isotropic and anisotropic spatial filters for adaptive fMRI data analysis are compared in terms of activation detection sensitivity and specificity. Evaluations using both real and artificial data are presented. It is shown that three-dimensional anisotropic filters provide superior activation detection performance.

  • 277.
    Rydell, Joakim
    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, Peter
    Linköping University, Department of Medicine and Care, Radiation Physics. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre of Surgery and Oncology, Department of Radiation Physics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Dimensionality and number of parameters in adaptive filtering of fMRI data2004In: Proceedings of the Swedish Symposium on Image Analysis (2004), 2004, p. 90-93Conference paper (Other academic)
  • 278.
    Rydell, Joakim
    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).
    Johansson, Andreas
    Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences.
    Leinard, Olof Dahlqvist
    Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Health Sciences.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Health Sciences.
    Farnebäck, Gunnar
    Linköping University, Department of Biomedical Engineering. 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, Radiation Physics. Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre of Surgery and Oncology, Department of Radiation Physics. 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, Faculty of Health Sciences.
    Three Dimensional Phase Sensitive Reconstruction for Water/Fat Separation in MR Imaging using Inverse Gradient2008In: Proceedings of the International Society for Magnetic Resonance in Medicine annual meeting (ISMRM'08), 2008, p. 1519-Conference paper (Other academic)
    Abstract [en]

    Three dimensional phase sensitive reconstruction on two point Dixon volumes has been implemented with use of the inverse gradient. The results has beencompared with the inverse gradient method in two dimensions as well as with the well established region growing method proposed by Ma. The inversegradient method in 3D is able to unwrap the phase field in uncertain regions where the region growing method and the inverse gradient method in 2D cometo a stop.

  • 279.
    Rydell, Joakim
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    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).
    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).
    A multidimensional similarity measure for bilateral adaptive filtering of fMRI data2006In: SSBA Symposium on Image Analysis,2006, 2006, p. 53-56Conference paper (Other academic)
  • 280.
    Rydell, Joakim
    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.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Adaptive filtering of fMRI data based on correlation and BOLD response similarity2006In: Acoustics, Speech and Signal Processing, 2006. ICASSP 2006. Vol. 2, IEEE conference proceedings, 2006, p. II-997-II-1000Conference paper (Refereed)
    Abstract [en]

    In analysis of fMRI data, it is common to average neighboring voxels in order to obtain robust estimates of the correlations between voxel time-series and the model of the signal expected to be present in activated regions. We have previously proposed a method where only voxels with similar correlation coefficients are averaged. In this paper we extend this idea, and present a novel method for analysis of fMRI data. In the proposed method, only voxels with similar correlation coefficients and similar time-series are averaged. The proposed method is compared to our previous method and to two well-known filtering strategies, and is shown to have superior ability to discriminate between active and inactive voxels

  • 281.
    Rydell, Joakim
    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).
    Knutsson, Hans
    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).
    Adaptive fMRI data filtering based in tissue and signal similarities2007In: Joint Annual Meeting ISMRM-ESMRMB,2007, 2007Conference paper (Other academic)
    Abstract [en]

    A novel method for analyzing fMRI data is presented. In order to detect activation with the highest possible accuracy, adaptive filtering is used to enahancethe signal to noise ratio. Using a method similar to bilateral filtering, signals from different voxels are averaged if the voxels belong to the same type oftissue and their signal variations over time are similar. The detection performance is evaluated on synthetic and real data, and it is shown that the twocriterions for averaging complement each other, providing very good detection of activation.

  • 282.
    Rydell, Joakim
    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).
    Knutsson, Hans
    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).
    Bilateral Filtering of fMRI Data2008In: IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, ISSN 1932-4553, Vol. 2, no 6, p. 891-896Article in journal (Refereed)
    Abstract [en]

    We present a class of adaptive filtering techniques of functional magnetic resonance imaging (fMRI) data related to bilateral filtering. This class of methods average activities in consistent regions rather than regions that maximize correlation with a BOLD model. Similarity measures based on signal similarity and anatomical similarity are discussed and compared experimentally to standard linear low pass filtering. It is demonstrated that adaptive filtering provides improved detection of activated regions.

  • 283.
    Rydell, Joakim
    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).
    Knutsson, Hans
    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).
    Correlation controlled adaptive filtering for FMRI data2005In: IFMBE Proceedings: NBC'05 13th Nordic Baltic Conference Biomedical Engineering and Medical Physics / [ed] Ronnie Lundström, Britt Andersson, Helena Grip, Umeå: IFMBE , 2005, p. 193-194Conference paper (Refereed)
    Abstract [en]

    In analysis of fMRI data, it is common to average neighboring voxels in order to obtain robust estimates of the correlations between voxel timeseries and the model of the signal expected to be present in activated regions. This paper presents a novel method for analysis of fMRI data, which extends this approach by averaging only neighboring voxels whose timeseries have similar correlation coefficients. A comparison between the new method and two other filtering strategies is also presented, and the novel method is shown to have superior ability to discriminate between active and inactive voxels.

  • 284.
    Rydell, Joakim
    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).
    Knutsson, Hans
    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).
    fMRI Data analysis using correlation controlled adaptive filtering2005In: Symposium on Image Analysis SSBA,2005, 2005, p. 85-88Conference paper (Other academic)
  • 285.
    Rydell, Joakim
    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).
    Knutsson, Hans
    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).
    On Rotational Invariance in Adaptive Spatial Filtering of fMRI Data2006In: NeuroImage, ISSN 1053-8119, Vol. 30, no 1, p. 144-150Article in journal (Refereed)
    Abstract [en]

    Canonical correlation analysis (CCA) has previously been shown to work well for detecting neural activity in fMRI data. The reason is that CCA enables simultaneous temporal modeling and adaptive spatial filtering of the data. This article introduces a novel method for adaptive anisotropic filtering using the CCA framework and compares it to a previously proposed method. Isotropic adaptive filtering, which is only able to form isotropic filters of different sizes, is also presented and evaluated. It is shown that a new feature of the proposed method is invariance to the orientation of activated regions, and that the detection performance is superior to both that of the previous method and to isotropic filtering.

  • 286.
    Rydell, Joakim
    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.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Rotational invariance in adaptive fMRI data analysis2006In: Image Processing, 2006, IEEE , 2006, p. 2841-2844Conference paper (Refereed)
    Abstract [en]

    It has previously been shown that canonical correlation analysis (CCA) works well for detecting neural activity in fMRI data. This is due to the ability of CCA to perform simultaneous temporal modeling and adaptive spatial filtering of the data. In this paper, we demonstrate that our previously proposed method for CCA-based fMRI data analysis does not provide rotationally invariant detection of activated regions. We propose a modification of the previous method and show that it resolves the rotational invariance issue, thereby further improving the analysis method

  • 287.
    Rydell, Joakim
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    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).
    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).
    Rotationally invariant adaptive filtering of fMRI data2006In: SSBA Symposium on Image Analysis,2006, 2006, p. 77-80Conference paper (Other academic)
  • 288.
    Rydell, Joakim
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    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).
    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).
    Tissue-selective adaptive filtering of fMRI data2006In: ESMRMB,2006, 2006Conference paper (Refereed)
  • 289.
    Rydell, Joakim
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    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).
    Johansson, Andreas
    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.
    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).
    MRI Phase Unwrapping with Application to Water/Fat Separation2008In: Proceedings of the SSBA Symposium on Image Analysis,2008, 2008, p. 27-30Conference paper (Other academic)
  • 290.
    Rydell, Joakim
    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.
    Knutsson, Hans
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Pettersson, Johanna
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Johansson, Andreas
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Farnebäck, Gunnar
    Linköping University, Department of Biomedical Engineering. 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.
    Lundberg, Peter
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medicine and Care, Radiation Physics. Linköping University, Department of Medicine and Care, Medical Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Medical Imaging, Department of Radiology in Linköping. Östergötlands Läns Landsting, Centre of Surgery and Oncology, Department of Radiation Physics.
    Nyström, Fredrik
    Linköping University, Department of Medical and Health Sciences, Internal Medicine. Linköping University, Faculty of Health Sciences.
    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.
    Phase Sensitive Reconstruction for Water/Fat Separation in MR Imaging Using Inverse Gradient2007In: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007. 10th International Conference, Brisbane, Australia, October 29 - November 2, 2007, Proceedings, Part I / [ed] Nicholas Ayache, Sebastien Ourselin and Anthony Maeder, Springer Berlin/Heidelberg, 2007, p. 210-218Conference paper (Refereed)
    Abstract [en]

    This paper presents a novel method for phase unwrapping for phase sensitive reconstruction in MR imaging. The unwrapped phase is obtained by integrating the phase gradient by solving a Poisson equation. An efficient solver, which has been made publicly available, is used to solve the equation. The proposed method is demonstrated on a fat quantification MRI task that is a part of a prospective study of fat accumulation. The method is compared to a phase unwrapping method based on region growing. Results indicate that the proposed method provides more robust unwrapping. Unlike region growing methods, the proposed method is also straight-forward to implement in 3D.

  • 291.
    Shakya, Snehlata
    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).
    Batool, Nazre
    School of Technology and Health, KTH Royal Institute of Technology, Stockholm, Sweden.
    Özarslan, Evren
    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).
    Knutsson, Hans
    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).
    Multi-Fiber Reconstruction Using Probabilistic Mixture Models for Diffusion MRI Examinations of the Brain2017In: Modeling, Analysis, and Visualization of Anisotropy / [ed] Thomas Schultz, Evren Özarslan, Ingrid Hotz, Springer, 2017, p. 283-308Chapter in book (Other academic)
    Abstract [en]

    In the field of MRI brain image analysis, Diffusion tensor imaging (DTI) provides a description of the diffusion of water through tissue and makes it possible to trace fiber connectivity in the brain, yielding a map of how the brain is wired. DTI employs a second order diffusion tensor model based on the assumption of Gaussian diffusion. The Gaussian assumption, however, limits the use ofDTI in solving intra-voxel fiber heterogeneity as the diffusion can be non-Gaussian in several biological tissues including human brain. Several approaches to modeling the non-Gaussian diffusion and intra-voxel fiber heterogeneity reconstruction have been proposed in the last decades. Among such approaches are the multi-compartmental probabilistic mixture models. These models include the discrete or continuous mixtures of probability distributions such as Gaussian, Wishart or von Mises-Fisher distributions. Given the diffusion weighted MRI data, the problem of resolving multiple fibers within a single voxel boils down to estimating the parameters of such models.

    In this chapter, we focus on such multi-compartmental probabilistic mixture models. First we present a review including mathematical formulations of the most commonly applied mixture models. Then, we present a novel method based on the mixture of non-central Wishart distributions. A mixture model of central Wishart distributions has already been proposed earlier to resolve intra-voxel heterogeneity. However, we show with detailed experiments that our proposed model outperforms the previously proposed probabilistic models specifically for the challenging scenario when the separation angles between crossing fibers (two or three) are small. We compare our results with the recently proposed probabilistic models of mixture of central Wishart distributions and mixture of hyper-spherical von Mises-Fisher distributions. We validate our approach with several simulations including fiber orientations in two and three directions and with real data. Resistivity to noise is also demonstrated by increasing levels of Rician noise in simulated data. The experiments demonstrate the superior performance of our proposed model over the prior probabilistic mixture models.

  • 292.
    Sigfridsson, Andreas
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Wigström, Lars
    Linköping University, Department of Biomedical Engineering, Center for Medical Image Science and Visualization. Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    Kvitting, John-Peder Escobar
    Linköping University, Department of Biomedical Engineering, Center for Medical Image Science and Visualization. Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Thoracic and Vascular Surgery.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Improving Temporal Fidelity in k-t BLAST MRI Reconstruction2007In: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007: 10th International Conference, Brisbane, Australia, October 29 - November 2, 2007, Proceedings, Part II / [ed] Ayache, N; Ourdelin, S; Maeder, A, Springer Berlin/Heidelberg, 2007, p. 385-392Conference paper (Refereed)
    Abstract [en]

    Studies of myocardial motion using magnetic resonance imaging usually require multiple breath holds and several methods have been proposed in order to reduce the scan time. Rapid imaging using k-t BLAST has gained much attention with its high reduction factors and image quality. Temporal smoothing, however, may reduce the accuracy when assessing cardiac function. In the present work, a modified reconstruction filter is proposed, that preserves more of the high temporal frequencies. Artificial decimation of a fully sampled data set was used to evaluate the reconstruction filter. Compared to the conventional k-t BLAST reconstruction, the modified filter produced images with sharper temporal delineation of the myocardial walls.  Quantitative analysis by means of regional velocity estimation showed that the modified reconstruction filter produced more accurate velocity estimations.

  • 293.
    Sigfridsson, Andreas
    et al.
    Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Escobar Kvitting, John-Peder
    Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Wigström, Lars
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences.
    5D MRI - Cardiac and respiratory time-resolved volume imaging2004In: Proceedings of the annaual conference of the European Society for Magnetic Resonance in Medicine and Biology, 2004Conference paper (Refereed)
    Abstract [en]

    Respiratory motion is often a source of artifacts in cardiovascular imaging, but may also convey important physiological information. To improve our understanding

  • 294.
    Sigfridsson, Andreas
    et al.
    Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Escobar Kvitting, John-Peder
    Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences.
    Wigström, Lars
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences.
    Andersson, Mats
    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.
    Retrospective Respiratory Motion Compensation for Cardiac MRI2003Conference paper (Refereed)
    Abstract [en]

    Cardiac MRI is known to be degraded by respiratory motion. Short scans can be performed using breath-hold techniques, while coronary artery imaging commonly use navigator gated sequences, acquiring data in a known static respiration position.

  • 295.
    Sigfridsson, Andreas
    et al.
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Linköping University, Faculty of Health Sciences.
    Escobar Kvitting, John-Peder
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Thoracic and Vascular Surgery.
    Wigström, Lars
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    k-t2 BLAST: Exploiting spatiotemporal structure in simultaneous cardiac and respiratory resolved volume imaging2005Conference paper (Refereed)
    Abstract [en]

    Multidimensional imaging resolving both the cardiac and respiratory cycles simultaneously has the potential to describe important physiological interdependences between the heart and pulmonary processes. A fully five-dimensional acquisition with three spatial and two temporal dimensions is hampered, however, by the long acquisition time and low spatial resolution. A technique is proposed to reduce the scan time substantially by extending the k-t BLAST framework to two temporal dimensions. By sampling the k-t space sparsely in a lattice grid, the signal in the transform domain, x-f space, can be densely packed, exploiting the fact that large regions in the field of view have low temporal bandwidth. A volumetric online prospective triggering approach with full cardiac and respiratory cycle coverage was implemented. Retrospective temporal interpolation was used to refine the timing estimates for the center of k-space, which is sampled for all cardiac and respiratory time frames. This resulted in reduced reconstruction error compared with conventional k-t BLAST reconstruction. The k-t2 BLAST technique was evaluated by decimating a fully sampled five-dimensional data set, and feasibility was further demonstrated by performing sparsely sampled acquisitions. Compared to the fully sampled data, a fourfold improvement in spatial resolution was accomplished in approximately half the scan time.

  • 296.
    Sigfridsson, Andreas
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Haraldsson, Henrik
    Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Ebbers, Tino
    Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Knutsson, Hans
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Sakuma, Hajima
    Mie University, Japan.
    SNR evaluation of 32 channel cardiac coils in DENSE MRI at 1.5 and 3T2010In: ISMRM 2010, International Society for Magnetic Resonance in Medicine ( ISMRM ) , 2010Conference paper (Other academic)
  • 297.
    Sigfridsson, Andreas
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    Haraldsson, Henrik
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    Ebbers, Tino
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    Knutsson, Hans
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Sakuma, Hajime
    Mie University, Tsu, Mie, Japan.
    In­vivo SNR in DENSE MRI: temporal and regional effects of field  strength, receiver coil sensitivity, and flip angle strategies2009In: Proceedings of the ISMRM Workshop on Cardiovascular Flow,  Function & Tissue Mechanics, 2009Conference paper (Other academic)
  • 298.
    Sigfridsson, Andreas
    et al.
    Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Haraldsson, Henrik
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences.
    Ebbers, Tino
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Physiology. Linköping University, Faculty of Health Sciences.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Health Sciences.
    Sakuma, Hajime
    Radiology, Mie University, Japan.
    In-vivo SNR in DENSE MRI: temporal and regional effects of field strength, receiver coil sensitivity, and flip angle strategies2011In: Magnetic Resonance Imaging, ISSN 0730-725X, E-ISSN 1873-5894, Vol. 29, no 2, p. 202-208Article in journal (Refereed)
    Abstract [en]

    Aim: The influences on the SNR of DENSE MRI of field strength, receiver coil sensitivity and choice of flip angle strategy have been previously investigated individually. In this study, all of these parameters have been investigated in the same setting, and a mutual comparison of their impact on SNR is presented.

    Materials and methods: Ten healthy volunteers were imaged in a 1.5T and a 3T MRI system, using standard 5 or 6 channel cardiac coils as well as 32 channel coils, with four different excitation patterns. Variation of spatial coil sensitivity was assessed by regional SNR analysis.

    Results: SNR ranging from 2.8 to 30.5 was found depending on the combination of excitation patterns, coil sensitivity and field strength. The SNR at 3T was 53 ± 26% higher than at 1.5T (p<0.001), whereas spatial differences of 59 ± 26% were found in the ventricle (p<0.001). 32 channel coils provided 52 ± 29% higher SNR compared to standard 5 or 6 channel coils (p<0.001). A fixed flip angle strategy provided an excess of 50% higher SNR in half of the imaged cardiac cycle compared to a sweeping flip angle strategy, and a single phase acquisition provided a six-fold increase of SNR compared to a cine acquisition.

    Conclusion: The effect of field strength and receiver coil sensitivity influences the SNR with the same order of magnitude, whereas flip angle strategy can have a larger effect on SNR. Thus, careful choice of imaging hardware in combination with adaptation of the acquisition protocol is crucial in order to realize sufficient SNR in DENSE MRI.

  • 299.
    Sigfridsson, Andreas
    et al.
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Haraldsson, Henrik
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Ebbers, Tino
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Knutsson, Hans
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Sakuma, Hajime
    Radiology, Mie University, Japan.
    Single Breath Hold Multiple Slice DENSE MRI2010In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 63, no 5, p. 1411-1414Article in journal (Refereed)
    Abstract [en]

    A method to acquire multiple displacement encoded slices within asingle breath hold is presented. Efficiency is improved overconventional Displacement ENcoding with Stimulated Echoes (DENSE) without compromising image quality by read-out of multiple slices inthe same cardiac cycle, thus utilizing the position encoded stimulatedecho available in the whole heart. The method was evaluated bycomparing strain values obtained using the proposed method to strainvalues obtained by conventional separate breath hold single-sliceDENSE acquisitions. Good agreement (Lagrangian E2 strainbias=0.000, 95% limits of agreement ±0.04,root-mean-square-difference 0.02 (9.4% of the mean end-systolic E2)) was found between the methods, indicating that the proposedmethod can replace a multiple breath hold acquisition. Eliminating theneed for multiple breath holds reduces the risk of changes in breathhold positions or heart rate, results in higher patient comfort andfacilitates inclusion of DENSE in a clinical routine protocol.

  • 300.
    Sigfridsson, Andreas
    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.
    Haraldsson, Henrik
    Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Ebbers, Tino
    Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Takase, S.
    Mie University, Tsu, Mie, Japan.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Sakuma, H.
    Mie University, Tsu, Mie, Japan.
    Flip Angle Strategies for Multiphase DENSE2009Conference paper (Refereed)
    Abstract [en]

    In multiphase DENSE MRI, available signal decreases during the cardiac cycle due to excitation and T1-relaxation. Because of this, a widely used method is to vary the flip angle to yield constant SNR during the cardiac cycle. Unfortunately, this may lead to significant sacrifices of SNR in the early cardiac phases.

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