| 1. |
- Ahmad, Nouman, et al.
(författare)
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Voxel-wise body composition analysis using image registration of a three-slice CT imaging protocol : methodology and proof-of-concept studies
- 2024
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Ingår i: Biomedical engineering online. - : Springer Nature. - 1475-925X. ; 23:1
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Tidskriftsartikel (refereegranskat)abstract
- Background Computed tomography (CT) is an imaging modality commonly used for studies of internal body structures and very useful for detailed studies of body composition. The aim of this study was to develop and evaluate a fully automatic image registration framework for inter-subject CT slice registration. The aim was also to use the results, in a set of proof-of-concept studies, for voxel-wise statistical body composition analysis (Imiomics) of correlations between imaging and non-imaging data.Methods The current study utilized three single-slice CT images of the liver, abdomen, and thigh from two large cohort studies, SCAPIS and IGT. The image registration method developed and evaluated used both CT images together with image-derived tissue and organ segmentation masks. To evaluate the performance of the registration method, a set of baseline 3-single-slice CT images (from 2780 subjects including 8285 slices) from the SCAPIS and IGT cohorts were registered. Vector magnitude and intensity magnitude error indicating inverse consistency were used for evaluation. Image registration results were further used for voxel-wise analysis of associations between the CT images (as represented by tissue volume from Hounsfield unit and Jacobian determinant) and various explicit measurements of various tissues, fat depots, and organs collected in both cohort studies.Results Our findings demonstrated that the key organs and anatomical structures were registered appropriately. The evaluation parameters of inverse consistency, such as vector magnitude and intensity magnitude error, were on average less than 3 mm and 50 Hounsfield units. The registration followed by Imiomics analysis enabled the examination of associations between various explicit measurements (liver, spleen, abdominal muscle, visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), thigh SAT, intermuscular adipose tissue (IMAT), and thigh muscle) and the voxel-wise image information.Conclusion The developed and evaluated framework allows accurate image registrations of the collected three single-slice CT images and enables detailed voxel-wise studies of associations between body composition and associated diseases and risk factors.
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| 2. |
- Andersson, Jonathan
(författare)
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Water–fat separation in magnetic resonance imaging and its application in studies of brown adipose tissue
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Virtually all the magnetic resonance imaging (MRI) signal of a human originates from water and fat molecules. By utilizing the property chemical shift the signal can be separated, creating water- and fat-only images. From these images it is possible to calculate quantitative fat fraction (FF) images, where the value of each voxel is equal to the percentage of its signal originating from fat. In papers I and II methods for water–fat signal separation are presented and evaluated.The method in paper I utilizes a graph-cut to separate the signal and was designed to perform well even for a low signal-to-noise ratio (SNR). The method was shown to perform as well as previous methods at high SNRs, and better at low SNRs.The method presented in paper II uses convolutional neural networks to perform the signal separation. The method was shown to perform similarly to a previous method using a graph-cut when provided non-undersampled input data. Furthermore, the method was shown to be able to separate the signal using undersampled data. This may allow for accelerated MRI scans in the future.Brown adipose tissue (BAT) is a thermogenic organ with the main purpose of expending chemical energy to prevent the body temperature from falling too low. Its energy expending capability makes it a potential target for treating overweight/obesity and metabolic dysfunctions, such as type 2 diabetes. The most well-established way of estimating the metabolic potential of BAT is through measuring glucose uptake using 18F-fludeoxyglucose (18F-FDG) positron emission tomography (PET) during cooling. This technique exposes subjects to potentially harmful ionizing radiation, and alternative methods are desired. One alternative method is measuring the BAT FF using MRI.In paper III the BAT FF in 7-year olds was shown to be negatively associated with blood serum levels of the bone-specific protein osteocalcin and, after correction for adiposity, thigh muscle volume. This may have implications for how BAT interacts with both bone and muscle tissue.In paper IV the glucose uptake of BAT during cooling of adult humans was measured using 18F-FDG PET. Additionally, their BAT FF was measured using MRI, and their skin temperature during cooling near a major BAT depot was measured using infrared thermography (IRT). It was found that both the BAT FF and the temperature measured using IRT correlated with the BAT glucose uptake, meaning these measurements could be potential alternatives to 18F-FDG PET in future studies of BAT.
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| 3. |
- Breznik, Eva
(författare)
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Image Processing and Analysis Methods for Biomedical Applications
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- With new technologies and developments medical images can be acquired more quickly and at a larger scale than ever before. However, increased amount of data induces an overhead in the human labour needed for its inspection and analysis. To support clinicians in decision making and enable swifter examinations, computerized methods can be utilized to automate the more time-consuming tasks. For such use, methods need be highly accurate, fast, reliable and interpretable. In this thesis we develop and improve methods for image segmentation, retrieval and statistical analysis, with applications in imaging-based diagnostic pipelines. Individual objects often need to first be extracted/segmented from the image before they can be analysed further. We propose methodological improvements for deep learning-based segmentation methods using distance maps, with the focus on fully-supervised 3D patch-based training and training on 2D slices under point supervision. We show that using a directly interpretable distance prior helps to improve segmentation accuracy and training stability.For histological data in particular, we propose and extensively evaluate a contrastive learning and bag of words-based pipeline for cross-modal image retrieval. The method is able to recover correct matches from the database across modalities and small transformations with improved accuracy compared to the competitors. In addition, we examine a number of methods for multiplicity correction on statistical analyses of correlation using medical images. Evaluation strategies are discussed and anatomy-observing extensions to the methods are developed as a way of directly decreasing the multiplicity issue in an interpretable manner, providing improvements in error control. The methods presented in this thesis were developed with clinical applications in mind and provide a strong base for further developments and future use in medical practice.
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| 6. |
- Ekström, Simon, 1991-, et al.
(författare)
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Fast graph-cut based optimization for practical dense deformable registration of volume images
- 2020
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Ingår i: Computerized Medical Imaging and Graphics. - : Elsevier. - 0895-6111 .- 1879-0771. ; 84
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Tidskriftsartikel (refereegranskat)abstract
- Deformable image registration is a fundamental problem in medical image analysis, with applications such as longitudinal studies, population modeling, and atlas-based image segmentation. Registration is often phrased as an optimization problem, i.e., finding a deformation field that is optimal according to a given objective function. Discrete, combinatorial, optimization techniques have successfully been employed to solve the resulting optimization problem. Specifically, optimization based on α-expansion with minimal graph cuts has been proposed as a powerful tool for image registration. The high computational cost of the graph-cut based optimization approach, however, limits the utility of this approach for registration of large volume images. Here, we propose to accelerate graph-cut based deformable registration by dividing the image into overlapping sub-regions and restricting the α-expansion moves to a single sub-region at a time. We demonstrate empirically that this approach can achieve a large reduction in computation time - from days to minutes - with only a small penalty in terms of solution quality. The reduction in computation time provided by the proposed method makes graph-cut based deformable registration viable for large volume images. Graph-cut based image registration has previously been shown to produce excellent results, but the high computational cost has hindered the adoption of the method for registration of large medical volume images. Our proposed method lifts this restriction, requiring only a small fraction of the computational cost to produce results of comparable quality.
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| 7. |
- Ekström, Simon, 1991-, et al.
(författare)
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Faster dense deformable image registration by utilizing both CPU and GPU
- 2021
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Ingår i: Journal of Medical Imaging. - 2329-4302 .- 2329-4310. ; 8:1
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: Image registration is an important aspect of medical image analysis and a key component in many analysis concepts. Applications include fusion of multimodal images, multi-atlas segmentation, and whole-body analysis. Deformable image registration is often computationally expensive, and the need for efficient registration methods is highlighted by the emergence of large-scale image databases, e.g., the UK Biobank, providing imaging from 100,000 participants. Approach: We present a heterogeneous computing approach, utilizing both the CPU and the graphics processing unit (GPU), to accelerate a previously proposed image registration method. The parallelizable task of computing the matching criterion is offloaded to the GPU, where it can be computed efficiently, while the more complex optimization task is performed on the CPU. To lessen the impact of data synchronization between the CPU and GPU, we propose a pipeline model, effectively overlapping computational tasks with data synchronization. The performance is evaluated on a brain labeling task and compared with a CPU implementation of the same method and the popular advanced normalization tools (ANTs) software. Results: The proposed method presents a speed-up by factors of 4 and 8 against the CPU implementation and the ANTs software, respectively. A significant improvement in labeling quality was also observed, with measured mean Dice overlaps of 0.712 and 0.701 for our method and ANTs, respectively. Conclusions: We showed that the proposed method compares favorably to the ANTs software yielding both a significant speed-up and an improvement in labeling quality. The registration method together with the proposed parallelization strategy is implemented as an open-source software package, deform.
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| 8. |
- Gifford, Aliya, et al.
(författare)
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Canine body composition quantification using 3 tesla fat–water MRI
- 2014
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Ingår i: Journal of Magnetic Resonance Imaging. - : Wiley. - 1053-1807 .- 1522-2586. ; 39:2, s. 485-491
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Tidskriftsartikel (refereegranskat)abstract
- PurposeTo test the hypothesis that a whole-body fat–water MRI (FWMRI) protocol acquired at 3 Tesla combined with semi-automated image analysis techniques enables precise volume and mass quantification of adipose, lean, and bone tissue depots that agree with static scale mass and scale mass changes in the context of a longitudinal study of large-breed dogs placed on an obesogenic high-fat, high-fructose diet.Materials and MethodsSix healthy adult male dogs were scanned twice, at weeks 0 (baseline) and 4, of the dietary regiment. FWMRI-derived volumes of adipose tissue (total, visceral, and subcutaneous), lean tissue, and cortical bone were quantified using a semi-automated approach. Volumes were converted to masses using published tissue densities.ResultsFWMRI-derived total mass corresponds with scale mass with a concordance correlation coefficient of 0.931 (95% confidence interval = [0.813, 0.975]), and slope and intercept values of 1.12 and −2.23 kg, respectively. Visceral, subcutaneous and total adipose tissue masses increased significantly from weeks 0 to 4, while neither cortical bone nor lean tissue masses changed significantly. This is evidenced by a mean percent change of 70.2% for visceral, 67.0% for subcutaneous, and 67.1% for total adipose tissue.ConclusionFWMRI can precisely quantify and map body composition with respect to adipose, lean, and bone tissue depots. The described approach provides a valuable tool to examine the role of distinct tissue depots in an established animal model of human metabolic disease.
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| 10. |
- Kullberg, Joel, et al.
(författare)
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Whole-body adipose tissue analysis: comparison of MRI, CT and dual energy X-ray absorptiometry.
- 2009
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Ingår i: The British journal of radiology. - : British Institute of Radiology. - 1748-880X .- 0007-1285. ; 82:974, s. 123-30
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Tidskriftsartikel (refereegranskat)abstract
- The aim of this study was to validate a recently proposed MRI-based T(1)-mapping method for analysis of whole-body adipose tissue (AT) using an established CT protocol as reference and to include results from dual energy X-ray absorptiometry (DEXA). 10 subjects, drawn from the Swedish Obese Subjects Sibling-pairs study, were examined using CT, MRI and DEXA. The CT analysis was based on 28 imaged slices. T(1) maps were calculated using contiguous MRI data from two different gradient echo sequences acquired using different flip angles. CT and MRI comparison was performed slice-wise and for the whole-body region. Fat weights were compared between all three modalities. Strong correlations (r > or = 0.977, p<0.0001) were found between MRI and CT whole-body and AT volumes. MRI visceral AT volume was underestimated by 0.79 +/- 0.75 l (p = 0.005), but total AT was not significantly different from that estimated by CT (MRI - CT = -0.61+/-1.17 l; p = 0.114). DEXA underestimated fat weights by 5.23 +/- 1.71 kg (p = 0.005) compared with CT. MRI underestimated whole-body volume by 2.03 +/- 1.61 l (p = 0.005) compared with CT. Weights estimated either by CT or by DEXA were not significantly different from weights measured using scales. In conclusion, strong correlations were found between whole-body AT results from CT, MRI-based T(1) mapping and DEXA. If the differences between the results from T(1)-mapping and CT-based analysis are accepted, the T(1)-mapping method allows fully automated post-processing of whole-body MRI data, allowing longitudinal whole-body studies that are also applicable for children and adolescents.
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