| 1. |
- Andersson, Thord, 1972-, et al.
(författare)
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Geodesic registration for interactive atlas-based segmentation using learned multi-scale anatomical manifolds
- 2018
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Ingår i: Pattern Recognition Letters. - : Elsevier. - 0167-8655 .- 1872-7344. ; 112, s. 340-345
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Tidskriftsartikel (refereegranskat)abstract
- Atlas-based segmentation is often used to segment medical image regions. For intensity-normalized data, the quality of these segmentations is highly dependent on the similarity between the atlas and the target under the used registration method. We propose a geodesic registration method for interactive atlas-based segmentation using empirical multi-scale anatomical manifolds. The method utilizes unlabeled images together with the labeled atlases to learn empirical anatomical manifolds. These manifolds are defined on distinct scales and regions and are used to propagate the labeling information from the atlases to the target along anatomical geodesics. The resulting competing segmentations from the different manifolds are then ranked according to an image-based similarity measure. We used image volumes acquired using magnetic resonance imaging from 36 subjects. The performance of the method was evaluated using a liver segmentation task. The result was then compared to the corresponding performance of direct segmentation using Dice Index statistics. The method shows a significant improvement in liver segmentation performance between the proposed method and direct segmentation. Furthermore, the standard deviation in performance decreased significantly. Using competing complementary manifolds defined over a hierarchy of region of interests gives an additional improvement in segmentation performance compared to the single manifold segmentation.
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| 2. |
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Artificial Neural Networks in Medicine and Biology
- 2000
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Samlingsverk (redaktörskap) (övrigt vetenskapligt/konstnärligt)abstract
- This book contains the proceedings of ANNIMAB-1, the first international conference on artificial neural networks in medicine and biology. Comprising a selection of papers from leading researchers in the field, it summarises the state-of-the-art, analyses the relationship between ANN techniques and other available methods and points to possible future biomedical and medical uses of ANNs.
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| 3. |
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| 4. |
- Borga, Magnus, 1965-, et al.
(författare)
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A canonical correlation approach to exploratory data analysis in fMRI
- 2002
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 5. |
- Borga, Magnus, 1965-, et al.
(författare)
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Advanced body composition assessment: From body mass index to body composition profiling
- 2018
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Ingår i: Journal of Investigative Medicine. - : BMJ Publishing Group Ltd. - 1081-5589 .- 1708-8267. ; 66:5, s. 887-895
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Forskningsöversikt (refereegranskat)abstract
- This paper gives a brief overview of common non-invasive techniques for body composition analysis and a more in-depth review of a body composition assessment method based on fat-referenced quantitative magnetic resonance imaging (MRI). Earlier published studies of this method are summarized, and a previously un-published validation study, based on 4.753 subjects from the UK Biobank imaging cohort, comparing the quantitative MRI method with dual-energy x-ray absorptiometry (DXA) is presented. For whole-body measurements of adipose tissue (AT) or fat and lean tissue (LT), DXA and quantitative MRI show excellent agreement with linear correlation of 0.99 and 0.97, and coefficient of variation (CV) of 4.5 % and 4.6 % for fat (computed from AT) and lean tissue respectively, but the agreement was found significantly lower for visceral adipose tissue, with a CV of more than 20 %. The additional ability of MRI to also measure muscle volumes, muscle AT infiltration and ectopic fat in combination with rapid scanning protocols and efficient image analysis tools make quantitative MRI a powerful tool for advanced body composition assessment.
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| 6. |
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| 7. |
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| 8. |
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| 9. |
- Borga, Magnus, 1965-
(författare)
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Learning Multidimensional Signal Processing
- 1998
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The subject of this dissertation is to show how learning can be used for multidimensional signal processing, in particular computer vision. Learning is a wide concept, but it can generally be defined as a system’s change of behaviour in order to improve its performance in some sense.Learning systems can be divided into three classes: supervised learning, reinforcement learning and unsupervised learning. Supervised learning requires a set of training data with correct answers and can be seen as a kind of function approximation. A reinforcement learning system does not require a set of answers. It learns by maximizing a scalar feedback signal indicating the system’s performance. Unsupervised learning can be seen as a way of finding a good representation of the input signals according to a given criterion.In learning and signal processing, the choice of signal representation is a central issue. For high-dimensional signals, dimensionality reduction is often necessary. It is then important not to discard useful information. For this reason, learning methods based on maximizing mutual information are particularly interesting.A properly chosen data representation allows local linear models to be used in learning systems. Such models have the advantage of having a small number of parameters and can for this reason be estimated by using relatively few samples. An interesting method that can be used to estimate local linear models is canonical correlation analysis (CCA). CCA is strongly related to mutual information. The relation between CCA and three other linear methods is discussed. These methods are principal component analysis (PCA), partial least squares (PLS) and multivariate linear regression (MLR). An iterative method for CCA, PCA, PLS and MLR, in particular low-rank versions of these methods, is presented.A novel method for learning filters for multidimensional signal processing using CCA is presented. By showing the system signals in pairs, the filters can be adapted to detect certain features and to be invariant to others. A new method for local orientation estimation has been developed using this principle. This method is significantly less sensitive to noise than previously used methods.Finally, a novel stereo algorithm is presented. This algorithm uses CCA and phase analysis to detect the disparity in stereo images. The algorithm adapts filters in each local neighbourhood of the image in a way which maximizes the correlation between the filtered images. The adapted filters are then analysed to find the disparity. This is done by a simple phase analysis of the scalar product of the filters. The algorithm can even handle cases where the images have different scales. The algorithm can also handle depth discontinuities and give multiple depth estimates for semi-transparent images.
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| 10. |
- Borga, Magnus, 1965-
(författare)
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MRI adipose tissue and muscle composition analysis : a review of automation techniques
- 2018
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Ingår i: British Journal of Radiology. - London, United Kingdom : British Institute of Radiology. - 0007-1285 .- 1748-880X. ; 91:1089
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Forskningsöversikt (refereegranskat)abstract
- MRI is becoming more frequently used in studies involving measurements of adipose tissue and volume and composition of skeletal muscles. The large amount of data generated by MRI calls for automated analysis methods. This review article presents a summary of automated and semi-automated techniques published between 2013 and 2017. Technical aspects and clinical applications for MRI-based adipose tissue and muscle composition analysis are discussed based on recently published studies. The conclusion is that very few clinical studies have used highly automated analysis methods, despite the rapidly increasing use of MRI for body composition analysis. Possible reasons for this are that the availability of highly automated methods has been limited for non-imaging experts, and also that there is a limited number of studies investigating the reproducibility of automated methods for MRI-based body composition analysis.
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| 11. |
- Borga, Magnus, 1965-, et al.
(författare)
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MRI-Based Body Composition Analysis
- 2022. - 1
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Ingår i: Basic Protocols in Foods and Nutrition. - New York, NY, United States : Springer Nature. - 9781071623442 ; , s. 307-334
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Bokkapitel (refereegranskat)abstract
- Magnetic resonance imaging (MRI) is considered being state-of-the-art technology for body composition analysis. Compared to other indirect techniques such as scales, calipers, bioimpedance, and dual-energy X-ray absorptiometry (DXA), MRI offers direct and precise measurements of the volumes of different tissue compartments and also enables quantification of diffuse fat infiltration in organs. Here, we describe a protocol for acquiring of fat–water-separated MRI data and the image postprocessing required for the quantification of several body composition biomarkers relevant for metabolic research. This protocol has successfully been used in several clinical studies and also in the large UK Biobank population study.
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| 12. |
- Friman, Ola, 1975-, et al.
(författare)
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A Correlation Framwork For Functional Mri Data Analysis.
- 2001
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Ingår i: Proceedings of SCIA 2001. Bergen,2001. - 8299594006 ; , s. 3-9
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 13. |
- Friman, O., et al.
(författare)
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A General Method for Correction of Intensity Inhomogeniety in Two Point Dixon Imaging
- 2008
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Ingår i: Proceedings of the International Society for Magnetic Resonance in Medicine annual meeting (ISMRM'08). - : International Society for Magnetic Resonance in Medicine.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Two point Dixon imaging can be used for quantitative fat estimation. However, field inhomogeneities pose a problem that needs to be corrected for before quantitative measurements can be obtained. We present a general framework for field inhomogeneitiy correction by fitting a set of smooth 3D spatial basis functions to voxels with high fat content. By choosing the number of basis functions, the smoothness constraint of the field can be controlled. The method is evaluated by measuring the FWHM of the fat peak in histograms for different number of basis functions. It is also compared to a previous method with good results.
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| 14. |
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| 15. |
- Friman, Ola, 1975-, et al.
(författare)
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Detection of neural activity in functional MRI using canonical correlation analysis
- 2001
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Ingår i: Magnetic Resonance in Medicine. - 0740-3194 .- 1522-2594. ; 45:2, s. 323-330
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 16. |
- Friman, Ola, 1975-, et al.
(författare)
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Exploratory fMRI analysis by autocorrelation maximization
- 2002
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Ingår i: NeuroImage. - : Elsevier BV. - 1053-8119 .- 1095-9572. ; 16:2, s. 454-464
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 17. |
- Friman, Ola, 1975-, et al.
(författare)
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Hierarchical temporal blind source separation of fMRI data
- 2002
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Ingår i: Proceedings of the ISMRM Annual Meeting (ISMRM'02).
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 18. |
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| 19. |
- Friman, Ola, 1975-, et al.
(författare)
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Recognizing emphysema - A neural network approach
- 2002
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Ingår i: Pattern Recognition, 2002. Proceedings. 16th International Conference on (Volume:1). - : IEEE Computer Society. ; , s. 512-515
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 20. |
- Karlsson, Anette, 1986-
(författare)
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Quantitative Muscle Composition Analysis Using Magnetic Resonance Imaging
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Changes in muscle tissue composition, e.g. decrease in volume and/or increase of fat infiltration, are related to adverse health conditions such as sarcopenia, inflammation, muscular dystrophy, and chronic pain. However, the onset and progression of disease and the effect of potential intervention effects are not fully understood, partly due to insufficient measurement tools. For advanced knowledge regarding these diseases, an accurate and precise measurement tool for detecting changes in muscle composition is needed. The tool must be able to detect both local changes on specific muscles for investigating local injuries and generalized muscle composition changes on a whole-body level. Magnetic resonance imaging is an excellent tool due to its superior soft tissue contrast but is normally not quantitative, making it challenging to produce reproducible results. Furthermore, manual analysis of the vast amount of images produced is extremely time consuming and therefore expensive. The aim of this thesis was to develop and validate a new magnetic resonance imaging method for muscle volume quantification and fat infiltration estimation that would have the potential to be used in both large-scale studies and for analyzing small individual muscles.The method development was divided into four main steps: 1) Rapid acquisition and reconstruction of data with sufficient resolution and calibration giving quantitative images where the relative fat content of each voxel (related to pure fat voxels) is attainable; 2) Automated muscle tissue classification based on non-rigid multi-atlas segmentation followed by probability voting to acquire the region of interest for each muscle; 3) Quantification of muscle tissue volume and fat infiltration from the classification step and the local fat signal; 4) Evaluation of the potential of the method in clinical studies.In Paper I, a method for automatic muscle volume quantification of both whole-body and regional muscles, i.e. involving steps 1–3, is presented. The automated method showed good agreement compared to manual segmentation. It was robust to an 8-fold resolution difference using two different scanner field strengths. Papers II and III evaluated the clinical relevance and the need for developing methods with high-resolution images to answer the research questions regarding the effect of a whiplash trauma on the multifidus muscles. This involved steps 1–4. The method enabled acquisition of high-resolution data to distinguish the small multifidus muscles (Paper II). The paper also showed a higher fat infiltration in the multifidus muscles in individuals with severe self-reported disability compared to individuals with milder symptoms and to healthy controls. Furthermore, the local fat infiltration was also related to widespread muscle fat infiltration (Paper III). However, the difference in widespread muscle fat infiltration could not alone distinguish between the three different groups. Paper IV showed the robustness of fat infiltration estimation when changing flip angle, and thereby the T1 weighting, of the acquired images (steps 1–3). The higher flip angle also provided better noise characteristics. Therefore, this quantitative method can be used with higher flip angle, and thus a potentially better anatomical contrast, without losing accuracy or precision.To conclude, this thesis presents a method that quantifies muscle volume and estimates fat infiltration robustly and reproducibly. The versatility of the method allows for both high-resolution images of small muscles and rapid acquisition of whole-body data. The method can be a useful tool in clinical studies regarding small individual muscles. Furthermore, the combination of being quantitative and automatic means that the method has potential to be used in longitudinal, multi-center, and large-scale studies for advanced understanding of muscular diseases.
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| 21. |
- Karlsson, Anette, 1986-, et al.
(författare)
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The effect on precision and T1 bias comparing two flip angles when estimating muscle fat infiltration using fat-referenced chemical shift-encoded imaging
- 2021
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Ingår i: NMR in Biomedicine. - : John Wiley & Sons. - 0952-3480 .- 1099-1492. ; 34:11
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Tidskriftsartikel (refereegranskat)abstract
- Investigation of the effect on accuracy and precision of different parameter settings is important for quantitative Magnetic Resonance Imaging. The purpose of this study was to investigate T1-bias and precision for muscle fat infiltration (MFI) using fat-referenced chemical shift magnetic resonance imaging at 5° and 10° flip angle. This [MB1] experimental study was done on forty postmenopausal women using 3T MRI test and retest images using 4-point 3D spoiled gradient multi-echo acquisition including real and imaginary images for reconstruction acquired at Flip angles 5° and 10°. Post-processing included T2* correction and fat-referenced calibration of the fat signal. The mean MFI was calculated in six different automatically segmented muscle regions using both the fat-referenced fat signal and the fat fraction calculated from the fat and water image pair for each acquisition. The variance of the difference between mean MFI from test and retest was used as measure of precision. The SNR characteristics were analyzed by measuring difference of the full width half maximum of the fat signal distribution using Student’s t-test.There was no difference in the mean fat-referenced MFI at different flip angles with the fat-referenced technique, which was the case using the fat fraction. No significant difference in the precision was found in any of the muscles analyzed. However, the full width half maximum of the fat signal distribution was significantly lower at 10° flip angle compared to 5°. Fat-referenced MFI is insensitive to T1 bias in chemical shift magnetic resonance imaging enabling usage of a higher and more SNR effective flip angle. The lower full-width-at half-maximum in fat-referenced MFI at 10° indicates that high flip angle acquisition is advantageous although no significant differences in precision was observed comparing 5° and 10°.
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| 22. |
- Karlsson, Anette, 1986-, et al.
(författare)
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The relation between local and distal muscle fat infiltration in chronic whiplash using magnetic resonance imaging.
- 2019
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Ingår i: PLOS ONE. - San Francisco, CA, United States : Public Library of Science. - 1932-6203. ; 14:12
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Tidskriftsartikel (refereegranskat)abstract
- The objective of this study was to investigate the relationship between fat infiltration in the cervical multifidi and fat infiltration measured in the lower extremities to move further into understanding the complex signs and symptoms arising from a whiplash trauma. Thirty-one individuals with chronic whiplash associated disorders, stratified into a mild/moderate group and a severe group, together with 31 age- and gender matched controls were enrolled in this study. Magnetic resonance imaging was used to acquire a 3D volume of the neck and of the whole-body. Cervical multifidi was used to represent muscles local to the whiplash trauma and all muscles below the hip joint, the lower extremities, were representing widespread muscles distal to the site of the trauma. The fat infiltration was determined by fat fraction in the segmented images. There was a linear correlation between local and distal muscle fat infiltration (p<0.001, r2 = 0.28). The correlation remained significant when adjusting for age and WAD group (p = 0.009) as well as when correcting for age, WAD group and BMI (p = 0.002). There was a correlation between local and distal muscle fat infiltration within the severe WAD group (p = 0.0016, r2 = 0.69) and in the healthy group (p = 0.022, r2 = 0.17) but not in the mild/moderate group (p = 0.29, r2 = 0.06). No significant differences (p = 0.11) in the lower extremities' MFI between the different groups were found. The absence of differences between the groups in terms of lower extremities' muscle fat infiltration indicates that, in this particular population, the whiplash trauma has a local effect on muscle fat infiltration rather than a generalized.
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| 23. |
- Knutsson, Hans, 1950-, et al.
(författare)
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Automated generation of representations in vision
- 2000
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Ingår i: International Conference on Pattern Recognition ICPR,2000. - Barcelona, Spain : IEEE. - 0769507506 ; , s. 59-66 vol.3
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Konferensbidrag (refereegranskat)abstract
- This paper presents a general strategy for automated generation of efficient representations in vision. The approach is highly task oriented and what constitutes the relevant information is defined by a set of examples. The examples are pairs of situations that are dependent through the chosen feature but are otherwise independent. Particularly important concepts in the work are mutual information and canonical correlation. How visual operators and representations can be generated from examples are presented for a number of features, e.g. local orientation, disparity and motion. Interesting similarities to biological vision functions are observed. The results clearly demonstrates the potential of combining advanced filtering techniques and learning strategies based on canonical correlation analysis (CCA).
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| 24. |
- Knutsson, Hans, 1950-, et al.
(författare)
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Learning Multidimensional Signal Processing
- 1998
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Ingår i: Proceedings of the 14th International Conference on Pattern Recognition, vol 2. - Linköping, Sweden : Linköping University, Department of Electrical Engineering. ; , s. 1416-1420
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- This paper presents our general strategy for designing learning machines as well as a number of particular designs. The search for methods allowing a sufficient level of adaptivity are based on two main principles: 1. Simple adaptive local models and 2. Adaptive model distribution. Particularly important concepts in our work is mutual information and canonical correlation. Examples are given on learning feature descriptors, modeling disparity, synthesis of a global 3-mode model and a setup for reinforcement learning of online video coder parameter control.
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| 25. |
- Knutsson, Hans, 1950-, et al.
(författare)
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Motion artifact reduction in MRI through generalized DFT
- 2004
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Ingår i: Biomedical Imaging: Nano to Macro, 2004. IEEE International Symposium on. - : IEEE. - 0780383885 ; , s. 896-899 vol.1
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Konferensbidrag (refereegranskat)abstract
- This paper presents a method that dramatically reduces artifacts caused by respiratory (and similar types of) patient motion in magnetic resonance imaging (MRI). The basis for the method is the observation that affine deformations of an object will correspond to a different but unique affine coordinate transform (plus phase shift) of the Fourier representation of the object. The resulting sample points will be irregularly distributed prohibiting the use of standard IFFT to reconstruct the object. The object can however be reconstructed through the use of a weighted regularized pseudo inverse. A standard pseudo inverse is, however, not possible due to excessive computational demands. For this reason a novel fast sequential pseudo inverse algorithm is also presented. Significantly improved results are obtained on both synthetic and clinical data.
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