| 1. |
- Andersson, Thord, et al.
(författare)
-
Consistent intensity inhomogeneity correction in water-fat MRI
- 2015
-
Ingår i: Journal of Magnetic Resonance Imaging. - : Wiley-Blackwell. - 1053-1807 .- 1522-2586. ; 42:2
-
Tidskriftsartikel (refereegranskat)abstract
- PURPOSE: To quantitatively and qualitatively evaluate the water-signal performance of the consistent intensity inhomogeneity correction (CIIC) method to correct for intensity inhomogeneitiesMETHODS: Water-fat volumes were acquired using 1.5 Tesla (T) and 3.0T symmetrically sampled 2-point Dixon three-dimensional MRI. Two datasets: (i) 10 muscle tissue regions of interest (ROIs) from 10 subjects acquired with both 1.5T and 3.0T whole-body MRI. (ii) Seven liver tissue ROIs from 36 patients imaged using 1.5T MRI at six time points after Gd-EOB-DTPA injection. The performance of CIIC was evaluated quantitatively by analyzing its impact on the dispersion and bias of the water image ROI intensities, and qualitatively using side-by-side image comparisons.RESULTS: CIIC significantly ( P1.5T≤2.3×10-4,P3.0T≤1.0×10-6) decreased the nonphysiological intensity variance while preserving the average intensity levels. The side-by-side comparisons showed improved intensity consistency ( Pint≤10-6) while not introducing artifacts ( Part=0.024) nor changed appearances ( Papp≤10-6).CONCLUSION: CIIC improves the spatiotemporal intensity consistency in regions of a homogenous tissue type.
|
|
| 2. |
- Andersson, Thord, 1972-, et al.
(författare)
-
Geodesic registration for interactive atlas-based segmentation using learned multi-scale anatomical manifolds
- 2018
-
Ingår i: Pattern Recognition Letters. - : Elsevier. - 0167-8655 .- 1872-7344. ; 112, s. 340-345
-
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.
|
|
| 3. |
- Andersson, Thord, et al.
(författare)
-
Modified Gradient Search for Level Set Based Image Segmentation
- 2013
-
Ingår i: IEEE Transactions on Image Processing. - : IEEE Signal Processing Society. - 1057-7149 .- 1941-0042. ; 22:2, s. 621-630
-
Tidskriftsartikel (refereegranskat)abstract
- Level set methods are a popular way to solve the image segmentation problem. The solution contour is found by solving an optimization problem where a cost functional is minimized. Gradient descent methods are often used to solve this optimization problem since they are very easy to implement and applicable to general nonconvex functionals. They are, however, sensitive to local minima and often display slow convergence. Traditionally, cost functionals have been modified to avoid these problems. In this paper, we instead propose using two modified gradient descent methods, one using a momentum term and one based on resilient propagation. These methods are commonly used in the machine learning community. In a series of 2-D/3-D-experiments using real and synthetic data with ground truth, the modifications are shown to reduce the sensitivity for local optima and to increase the convergence rate. The parameter sensitivity is also investigated. The proposed methods are very simple modifications of the basic method, and are directly compatible with any type of level set implementation. Downloadable reference code with examples is available online.
|
|
| 4. |
|
|
| 5. |
- Borga, Magnus, 1965-, et al.
(författare)
-
Advanced body composition assessment: From body mass index to body composition profiling
- 2018
-
Ingår i: Journal of Investigative Medicine. - : BMJ Publishing Group Ltd. - 1081-5589 .- 1708-8267. ; 66:5, s. 887-895
-
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.
|
|
| 6. |
- Borga, Magnus, et al.
(författare)
-
Brown adipose tissue in humans: detection and functional analysis using PET (positron emission tomography), MRI (magnetic resonance imaging), and DECT (dual energy computed tomography).
- 2014
-
Ingår i: Methods in enzymology. - : Elsevier. - 1557-7988. ; 537, s. 141-59, s. 141-159
-
Tidskriftsartikel (refereegranskat)abstract
- If the beneficial effects of brown adipose tissue (BAT) on whole body metabolism, as observed in nonhuman experimental models, are to be translated to humans, tools that accurately measure how BAT influences human metabolism will be required. This chapter discusses such techniques, how they can be used, what they can measure and also some of their limitations. The focus is on detection and functional analysis of human BAT and how this can be facilitated by applying advanced imaging technology such as positron emission tomography, magnetic resonance imaging, and dual energy computed tomography.
|
|
| 7. |
- Borga, Magnus, 1965-
(författare)
-
MRI adipose tissue and muscle composition analysis : a review of automation techniques
- 2018
-
Ingår i: British Journal of Radiology. - London, United Kingdom : British Institute of Radiology. - 0007-1285 .- 1748-880X. ; 91:1089
-
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.
|
|
| 8. |
- Borga, Magnus, 1965-, et al.
(författare)
-
MRI-Based Body Composition Analysis
- 2022. - 1
-
Ingår i: Basic Protocols in Foods and Nutrition. - New York, NY, United States : Springer Nature. - 9781071623442 ; , s. 307-334
-
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.
|
|
| 9. |
- Borga, Magnus, et al.
(författare)
-
Semi-Supervised Learning of Anatomical Manifolds for Atlas-Based Segmentation of Medical Images
- 2016
-
Ingår i: Proceedings of the 23rd International Conference on Pattern Recognition (ICPR). - : IEEE Computer Society. - 9781509048472 - 9781509048489 ; , s. 3146-3149
-
Konferensbidrag (refereegranskat)abstract
- This paper presents a novel method for atlas-based segmentation of medical images. The method uses semi- supervised learning of a graph describing a manifold of anatom- ical variations of whole-body images, where unlabelled data are used to find a path with small deformations from the labelled atlas to the target image. The method is evaluated on 36 whole-body magnetic resonance images with manually segmented livers as ground truth. Significant improvement (p < 0.001) was obtained compared to direct atlas-based registration.
|
|
| 10. |
- Borga, Magnus, et al.
(författare)
-
Validation of a Fast Method for Quantification of Intra-abdominal and Subcutaneous Adipose Tissue for Large Scale Human Studies
- 2015
-
Ingår i: NMR in Biomedicine. - : John Wiley & Sons. - 1099-1492 .- 0952-3480. ; 28:12, s. 1747-1753
-
Tidskriftsartikel (refereegranskat)abstract
- Central obesity is the hallmark of a number of non-inheritable disorders. The advent of imaging techniques such as magnetic resonance imaging (MRI) has allowed for a fast and accurate assessment of body fat content and distribution. However, image analysis continues to be one of the major obstacles for the use of MRI in large scale studies. In this study we assess the validity of the recently proposed fat-muscle-quantitation-system (AMRATM Profiler) for the quantification of intra-abdominal adipose tissue (IAAT) and abdominal subcutaneous adipose tissue (ASAT) from abdominal MR images. Abdominal MR images were acquired from 23 volunteers with a broad range of BMIs and analysed using SliceOmatic, the current gold-standard, and the AMRATM Profiler based on a non-rigid image registration of a library of segmented atlases. The results show that there was a highly significant correlation between the fat volumes generated by both analysis methods, (Pearson correlation r = 0.97 p<0.001), with the AMRATM Profiler analysis being significantly faster (~3 mins) than the conventional SliceOmatic approach (~40 mins). There was also excellent agreement between the methods for the quantification of IAAT (AMRA 4.73 ± 1.99 vs SliceOmatic 4.73 ± 1.75 litres, p=0.97). For the AMRATM Profiler analysis, the intra-observer coefficient of variation was 1.6 % for IAAT and 1.1 % for ASAT, the inter-observer coefficient of variation was 1.4 % for IAAT and 1.2 % for ASAT, the intra-observer correlation was 0.998 for IAAT and 0.999 for ASAT, and the inter-observer correlation was 0.999 for both IAAT and ASAT. These results indicate that precise and accurate measures of body fat content and distribution can be obtained in a fast and reliable form by the AMRATM Profiler, opening up the possibility of large-scale human phenotypic studies.
|
|
