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  • 1.
    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.

  • 2.
    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)
  • 3.
    Eklund, Anders
    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.
    Friman, Ola
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). 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.
    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.
    Comparing fMRI Activity Maps from GLM and CCA at the Same Significance Level by Fast Random Permutation Tests on the GPU2011Conference paper (Other academic)
    Abstract [en]

    Parametric statistical methods are traditionally employed in functional magnetic resonance imaging (fMRI) for identifying areas in the brain that are active with a certain degree of statistical significance. These parametric methods, however, have two major drawbacks. First, it isassumed that the observed data are Gaussian distributed and independent; assumptions that generally are not valid for fMRI data. Second, the statistical test distribution can be derived theoretically only for very simple linear detection statistics. In this work it is shown how the computational power of the Graphics Processing Unit (GPU) can be used to speedup non-parametric tests, such as random permutation tests. With random permutation tests it is possible to calculate significance thresholds for any test statistics. As an example, fMRI activity maps from the General Linear Model (GLM) and Canonical Correlation Analysis (CCA) are compared at the same significance level.

  • 4.
    Friman, Ola
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Adaptive analysis of functional MRI data2003Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Functional Magnetic Resonance Imaging (fMRI) is a recently developed neuroimaging technique with capacity to map neural activity with high spatial precision. To locate active brain areas, the method utilizes local blood oxygenation changes which are reflected as small intensity changes in a special type of MR images. The ability to non-invasively map brain functions provides new opportunities to unravel the mysteries and advance the understanding of the human brain, as well as to perform pre-surgical examinations in order to optimize surgical interventions.

    This dissertation introduces new approaches for the analysis of fMRI data. The detection of active brain areas is a challenging problem due to high noise levels and artifacts present in the data. A fundamental tool in the developed methods is Canonical Correlation Analysis (CCA). CCA is used in two novel ways. First as a method with the ability to fully exploit the spatia-temporal nature of fMRI data for detecting active brain areas. Established analysis approaches mainly focus on the temporal dimension of the data and they are for this reason commonly referred to as being mass-univariate. The new CCA detection method encompasses and generalizes the traditional mass-univariate methods and can in this terminology be viewed as a mass-multivariate approach. The concept of spatial basis functions is introduced as a spatial counterpart of the temporal basis functions already in use in fMRI analysis. The spatial basis functions implicitly perform an adaptive spatial filtering of the fMRI images, which significantly improves detection performance. It is also shown how prior information can be incorporated into the analysis by imposing constraints on the temporal and spatial models and a constrained version of CCA is devised to this end. A general Principal Component Analysis technique for generating and constraining temporal and spatial subspace models is proposed to be used in combination with the constrained CCA analysis approach.

    The second use of CCA is found in a novel so-called exploratory analysis method which extracts interesting and representative structures in fMRI data. Functional MRI data sets are large, and exploratory analysis methods are useful for probing the data for unexpected components. It is also shown how drift and trend models adapted to the fMRI data set at hand can be constructed with this new exploratory CCA technique. Compared to traditionally employed drift models, such adaptive drift models better account for the temporal autocorrelation in the data.

    List of papers
    1. Detection of neural activity in functional MRI using canonical correlation analysis
    Open this publication in new window or tab >>Detection of neural activity in functional MRI using canonical correlation analysis
    Show others...
    2001 (English)In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 45, no 2, p. 323-330Article in journal (Refereed) Published
    Abstract [en]

    A novel method for detecting neural activity in functional magnetic resonance imaging (fMRI) data is introduced. It is based on canonical correlation analysis (CCA), which is a multivariate extension of the univariate correlation analysis widely used in fMRI. To detect homogeneous regions of activity, the method combines a subspace modeling of the hemodynamic response and the use of spatial relationships. The spatial correlation that undoubtedly exists in fMR images is completely ignored when univariate methods such as as t-tests, F-tests, and ordinary correlation analysis are used. Such methods are for this reason very sensitive to noise, leading to difficulties in detecting activation and significant contributions of false activations. In addition, the proposed CCA method also makes it possible to detect activated brain regions based not only on thresholding a correlation coefficient, but also on physiological parameters such as temporal shape and delay of the hemodynamic response. Excellent performance on real fMRI data is demonstrated.

    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:liu:diva-26699 (URN)10.1002/1522-2594(200102)45:2<323::AID-MRM1041>3.0.CO;2-# (DOI)11289 (Local ID)11289 (Archive number)11289 (OAI)
    Available from: 2009-10-08 Created: 2009-10-08 Last updated: 2017-12-13
    2. Detection of neural activity in fMRI using maximum correlation modeling
    Open this publication in new window or tab >>Detection of neural activity in fMRI using maximum correlation modeling
    2002 (English)In: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 15, no 2, p. 386-395Article in journal (Refereed) Published
    Abstract [en]

    A technique for detecting neural activity in functional MRI data is introduced. It is based on a novel framework termed maximum correlation modeling. The method employs a spatial filtering approach that adapts to the local activity patterns, which results in an improved detection sensitivity combined with good specificity. A spatially varying hemodynamic response is simultaneously modelled by a sum of two gamma functions. Comparisons to traditional analysis methods are made using both synthetic and real data. The results indicate that the maximum correlation modeling approach is a strong alternative for analyzing fMRI data.

    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:liu:diva-27016 (URN)10.1006/nimg.2001.0972 (DOI)11658 (Local ID)11658 (Archive number)11658 (OAI)
    Available from: 2009-10-08 Created: 2009-10-08 Last updated: 2017-12-13
    3. Exploratory fMRI analysis by autocorrelation maximization
    Open this publication in new window or tab >>Exploratory fMRI analysis by autocorrelation maximization
    2002 (English)In: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 16, no 2, p. 454-464Article in journal (Refereed) Published
    Abstract [en]

    A novel and computationally efficient 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 relation to Principal Component Analysis and Independent Component Analysis is discussed and the performance of the methods is compared using both simulated and real data.

    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:liu:diva-24549 (URN)10.1006/nimg.2002.1067 (DOI)6709 (Local ID)6709 (Archive number)6709 (OAI)
    Available from: 2009-10-07 Created: 2009-10-07 Last updated: 2017-12-13
    4. Adaptive analysis of fMRI data
    Open this publication in new window or tab >>Adaptive analysis of fMRI data
    2003 (English)In: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 19, no 3, p. 837-845Article in journal (Refereed) Published
    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.

    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-46573 (URN)10.1016/S1053-8119(03)00077-6 (DOI)
    Available from: 2009-10-11 Created: 2009-10-11 Last updated: 2017-12-13
    5. Detection and detrending in fMRI data analysis
    Open this publication in new window or tab >>Detection and detrending in fMRI data analysis
    2004 (English)In: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 22, no 2, p. 645-655Article in journal (Refereed) Published
    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.

    Keywords
    Detrending, fMRI analysis, Voxel
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-45479 (URN)10.1016/j.neuroimage.2004.01.033 (DOI)
    Available from: 2009-10-11 Created: 2009-10-11 Last updated: 2017-12-13
  • 5.
    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.

  • 6.
    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.

  • 7.
    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.

  • 8.
    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)
  • 9.
    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.

  • 10.
    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, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Detection of neural activity in fMRI using maximum correlation modeling2002In: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 15, no 2, p. 386-395Article in journal (Refereed)
    Abstract [en]

    A technique for detecting neural activity in functional MRI data is introduced. It is based on a novel framework termed maximum correlation modeling. The method employs a spatial filtering approach that adapts to the local activity patterns, which results in an improved detection sensitivity combined with good specificity. A spatially varying hemodynamic response is simultaneously modelled by a sum of two gamma functions. Comparisons to traditional analysis methods are made using both synthetic and real data. The results indicate that the maximum correlation modeling approach is a strong alternative for analyzing fMRI data.

  • 11.
    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.
    Exploratory fMRI analysis by autocorrelation maximization2002In: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 16, no 2, p. 454-464Article in journal (Refereed)
    Abstract [en]

    A novel and computationally efficient 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 relation to Principal Component Analysis and Independent Component Analysis is discussed and the performance of the methods is compared using both simulated and real data.

  • 12.
    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, Peter
    Linköping University, Department of Medicine and Care, Radiation Physics. Linköping University, Faculty of Health Sciences. 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).
    Hierarchical temporal blind source separation of fMRI data2002In: Proceedings of the ISMRM Annual Meeting (ISMRM'02), 2002Conference paper (Refereed)
    Abstract [en]

    Blind Source Separation (BSS) of fMRI data can be done both temporally and spatially. Temporal BSS of fMRI data has one fundamental problem not encountered in the spatial BSS approach. There are thousands of observed timecourses in an fMRI data set while the number of samples of each timecourse typically is less than two hundred. This re lation makes the problem of recovering the underlying temporal sources ill-posed. This contribution eliminates this problem by introducing a hierarchical approach for performing temporal BSS of fMRI data.

  • 13.
    Friman, Ola
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Borga, Magnus
    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.
    Imaging Brain Function2002Conference paper (Other academic)
  • 14.
    Friman, Ola
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Borga, Magnus
    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.
    Tylén, U.
    Göteborgs universitet.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Emphysema Detection in CT Images2002Conference paper (Other academic)
    Abstract [en]

    This paper describes a fully automatic approach for detecting emphysema in CT im ages of the lungs. The method combines an image processing step, where potential emphysematous area s are extracted, and a neural network step trained to rec

  • 15.
    Friman, Ola
    et al.
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Cedefamn, Jonny
    Linköping University, Department of Neuroscience and Locomotion. Linköping University, Faculty of Health Sciences.
    Lundberg, Peter
    Östergötlands Läns Landsting, Centre of Surgery and Oncology, Department of Radiation Physics. Linköping University, Faculty of Health Sciences.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Detection of neural activity in functional MRI using canonical correlation analysis2001In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 45, no 2, p. 323-330Article in journal (Refereed)
    Abstract [en]

    A novel method for detecting neural activity in functional magnetic resonance imaging (fMRI) data is introduced. It is based on canonical correlation analysis (CCA), which is a multivariate extension of the univariate correlation analysis widely used in fMRI. To detect homogeneous regions of activity, the method combines a subspace modeling of the hemodynamic response and the use of spatial relationships. The spatial correlation that undoubtedly exists in fMR images is completely ignored when univariate methods such as as t-tests, F-tests, and ordinary correlation analysis are used. Such methods are for this reason very sensitive to noise, leading to difficulties in detecting activation and significant contributions of false activations. In addition, the proposed CCA method also makes it possible to detect activated brain regions based not only on thresholding a correlation coefficient, but also on physiological parameters such as temporal shape and delay of the hemodynamic response. Excellent performance on real fMRI data is demonstrated.

  • 16.
    Friman, Ola
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Swedish Defence Research Agency, Linköping, Sweden.
    Follo, Peter
    Swedish Defence Research Agency, Linköping, Sweden.
    Ahlberg, Jörgen
    Linköping University, Department of Electrical Engineering, Information Coding. Linköping University, The Institute of Technology. Termisk Systemteknik AB, Linköping, Sweden.
    Sjökvist, Stefan
    Termisk Systemteknik AB, Linköping, Sweden.
    Methods for Large-Scale Monitoring of District Heating Systems Using Airborne Thermography2014In: IEEE Transactions on Geoscience and Remote Sensing, ISSN 0196-2892, E-ISSN 1558-0644, Vol. 52, no 8, p. 5175-5182Article in journal (Refereed)
    Abstract [en]

    District heating is a common way of providing heat to buildings in urban areas. The heat is carried by hot water or steam and distributed in a network of pipes from a central powerplant. It is of great interest to minimize energy losses due to bad pipe insulation or leakages in such district heating networks. As the pipes generally are placed underground, it may be difficult to establish the presence and location of losses and leakages. Toward this end, this work presents methods for large-scale monitoring and detection of leakages by means of remote sensing using thermal cameras, so-called airborne thermography. The methods rely on the fact that underground losses in district heating systems lead to increased surface temperatures. The main contribution of this work is methods for automatic analysis of aerial thermal images to localize leaking district heating pipes. Results and experiences from large-scale leakage detection in several cities in Sweden and Norway are presented.

  • 17.
    Friman, Ola
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    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.
    Borga, Magnus
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    Cedefamn, Jonny
    Knutsson, Hans
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    Increased detection sensitivity in fMRI by adaptive filtering.2001In: Proceedings iSMRM and ESMRM meeting 2001, Glasgow,2001, 2001, p. 1209-1209Conference paper (Refereed)
  • 18.
    Friman, Ola
    et al.
    Swedish Defence Research Agency, Linköping, Sweden.
    Tolt, Gustav
    Swedish Defence Research Agency, Linköping, Sweden.
    Ahlberg, Jörgen
    Termisk Systemteknik, Linköping, Sweden.
    Illumination and shadow compensation of hyperspectral images using a digital surface model and non-linear least squares estimation2011In: Proc. SPIE 8180, Image and Signal Processing for Remote Sensing XVII / [ed] Lorenzo Bruzzone, SPIE - International Society for Optical Engineering, 2011, p. Art.nr 8180-26-Conference paper (Refereed)
    Abstract [en]

    Object detection and material classification are two central tasks in electro-optical remote sensing and hyperspectral imaging applications. These are challenging problems as the measured spectra in hyperspectral images from satellite or airborne platforms vary significantly depending on the light conditions at the imaged surface, e.g., shadow versus non-shadow. In this work, a Digital Surface Model (DSM) is used to estimate different components of the incident light. These light components are subsequently used to predict what a measured spectrum would look like under different light conditions. The derived method is evaluated using an urban hyperspectral data set with 24 bands in the wavelength range 381.9 nm to 1040.4 nm and a DSM created from LIDAR 3D data acquired simultaneously with the hyperspectral data

  • 19.
    Lundberg, Peter
    et al.
    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.
    Friman, Ola
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Carlsson, J
    Borga, M
    Söderfeldt, B
    Knutsson, H
    Detection of neural activity in functional MRI using canonical correlation analysis.2001Conference paper (Other academic)
    Abstract [en]

    A novel method for detecting neural activity in functional magnetic resonance imaging (fMRI) data is introduced. It is based on canonical correlation analysis (CCA), which is a multivariate extension of the univariate correlation analysis widely used in fMRI. To detect homogeneous regions of activity, the method combines a subspace modeling of the hemodynamic response and the use of spatial relationships. The spatial correlation that undoubtedly exists in fMR images is completely ignored when univariate methods such as as t-tests, F-tests, and ordinary correlation analysis are used. Such methods are for this reason very sensitive to noise, leading to difficulties in detecting activation and significant contributions of false activations. In addition, the proposed CCA method also makes it possible to detect activated brain regions based not only on thresholding a correlation coefficient, but also on physiological parameters such as temporal shape and delay of the hemodynamic response. Excellent performance on real fMRI data is demonstrated. Magn Reson Med 45:323-330, 2001

  • 20.
    Ringaby, Erik
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Forssén, Per-Erik
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Friman, Ola
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Sick IVP AB, Linköping, Sweden.
    Olsvik Opsahl, Thomas
    Norwegian Defence Research Establishment.
    Vegard Haavardsholm, Trym
    Norwegian Defence Research Establishment.
    Kåsen, Ingebjørg
    Norwegian Defence Research Establishment.
    Anisotropic Scattered Data Interpolation for Pushbroom Image Rectification2014In: IEEE Transactions on Image Processing, ISSN 1057-7149, E-ISSN 1941-0042, Vol. 23, no 5, p. 2302-2314Article in journal (Refereed)
    Abstract [en]

    This article deals with fast and accurate visualization of pushbroom image data from airborne and spaceborne platforms. A pushbroom sensor acquires images in a line-scanning fashion, and this results in scattered input data that needs to be resampled onto a uniform grid for geometrically correct visualization. To this end, we model the anisotropic spatial dependence structure caused by the acquisition process. Several methods for scattered data interpolation are then adapted to handle the induced anisotropic metric and compared for the pushbroom image rectification problem. A trick that exploits the semi-ordered line structure of pushbroom data to improve the computational complexity several orders of magnitude is also presented.

  • 21.
    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)
  • 22.
    Tylén, Ulf
    et al.
    Dept Radiology Göteborgs universitet.
    Friman, Ola
    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.
    Angelhed, Jan-Erik
    Dept Radiology Göteborgs universitet.
    An improved algorithm for computerized detection and quantification of pulmonary emphysema at high resolution computed tomography (HRCT)2001In: SPIE01,2001, 2001, p. 254-262Conference paper (Refereed)
    Abstract [en]

    Emphysema is characterized by destruction of lung tissue with development of small or large holes within the lung. These areas will have Hounsfield values (HU) approaching -1000. It is possible to detect and quantificate such areas using simple density mask technique. The edge enhancement reconstruction algorithm, gravity and motion of the heart and vessels during scanning causes artefacts, however. The purpose of our work was to construct an algorithm that detects such image artefacts and corrects them. The first step is to apply inverse filtering to the image removing much of the effect of the edge enhancement reconstruction algorithm. The next step implies computation of the antero-posterior density gradient caused by gravity and correction for that. Motion artefacts are in a third step corrected for by use of normalized averaging, thresholding and region growing. Twenty healthy volunteers were investigated, 10 with slight emphysema and 10 without. Using simple density mask technique it was not possible to separate persons with disease from those without. Our algorithm improved separation of the two groups considerably. Our algorithm needs further refinement, but may form a basis for further development of methods for computerized diagnosis and quantification of emphysema by HRCT.

  • 23. Vikgren, J
    et al.
    Friman, Ola
    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.
    Boijsen, M
    Gustavsson, S
    Jansson, AE
    Bake, B
    Tylen, U
    Detection of mild emphysema by computed tomography density measurements2005In: Acta Radiologica, ISSN 0284-1851, E-ISSN 1600-0455, Vol. 46, no 3, p. 237-245Article in journal (Refereed)
    Abstract [en]

    Purpose: To assess the ability of a conventional density mask method to detect mild emphysema by high- resolution computed tomography ( HRCT), to analyze factors influencing quantification of mild emphysema, and to validate a new algorithm for detection of mild emphysema. Material and Methods: Fifty- five healthy male smokers and 34 never- smokers, 61 - 62 years of age, were examined. Emphysema was evaluated visually, by the conventional density mask method, and by a new algorithm compensating for the effects of gravity and artifacts due to motion and the reconstruction algorithm. Effects of the reconstruction algorithm, slice thickness, and various threshold levels on the outcome of the density mask area were evaluated. Results: Forty- nine percent of the smokers had mild emphysema. The density mask area was higher the thinner the slice irrespective of the reconstruction algorithm and threshold level. The sharp algorithm resulted in increased density mask area. The new reconstruction algorithm could discriminate between smokers with and those without mild emphysema, whereas the density mask method could not. The diagnostic ability of the new algorithm was dependent on lung level. At about 90% specificity, sensitivity was 65 - 100% in the apical levels, but low in the rest of the lung. Conclusion: The conventional density mask method is inadequate for detecting mild emphysema, while the new algorithm improves the diagnostic ability but is nevertheless still imperfect.

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