| 1. |
- Eriksson, Jan W, et al.
(författare)
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Tissue-specific glucose partitioning and fat content in prediabetes and type 2 diabetes: whole-body PET/MRI during hyperinsulinemia
- 2021
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Ingår i: European journal of endocrinology. - : Bioscientifica. - 0804-4643 .- 1479-683X. ; 184:6, s. 879-899
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Tidskriftsartikel (refereegranskat)abstract
- Objective: To obtain direct quantifications of glucose turnover, volumes an d fat content of several tissues in the development of type 2 diabetes (T2D) using a novel integrated a pproach for whole-body imaging. Design and methods: Hyperinsulinemic-euglycemic clamps and simultaneous whole-body integrated [18F]FDG-PET/MRI with automated analyses were performed in control (n = 12), prediabetes (n = 16) and T2D (n = 13) subjects matched for age, sex and BMI. Results: Whole-body glucose uptake (Rd) was reduced by approximately 25% in T2D vs control subjects, and partitioning to brain was increased from 3.8% of total Rd in co ntrols to 7.1% in T2D. In liver, subcutaneous AT, thigh muscle, total tissue glucose metabolic rates (MRglu) and their % of total Rd were reduced in T2D compared to contr ol subjects. The prediabetes group had intermediate findings. Total MRglu in heart, visceral AT, gluteus and calf muscle was similar across groups. Whole-body insulin sensitivity asses sed as glucose infusion rate correlated with liver MR glu but inversely with brain MRglu. Liver fat content correlated with MRglu in brain but inversely with MRglu in other tissues. Calf muscle fat was inversely associated with MR glu only in the same muscle group. Conclusions: This integrated imaging approach provides detailed quantification of tissue-specific glucose metabolism. During T2D development, insulin-stimulated glucose disposal is impaired and increasingly shifted away from muscle, liver and fat toward the brain. Altered glucose handling in the brain and liver fat accumulation may aggravate insulin resistance in several organs. © 2021 BioScientifica Ltd.. All rights reserved.
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| 2. |
- Guglielmo, Priscilla, et al.
(författare)
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Validation of automated whole-body analysis of metabolic and morphological parameters from an integrated FDG-PET/MRI acquisition
- 2020
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- Automated quantification of tissue morphology and tracer uptake in PET/MR images could streamline the analysis compared to traditional manual methods. To validate a single atlas image segmentation approach for automated assessment of tissue volume, fat content (FF) and glucose uptake (GU) from whole-body [18F]FDG-PET/MR images. Twelve subjects underwent whole-body [18F]FDG-PET/MRI during hyperinsulinemic-euglycemic clamp. Automated analysis of tissue volumes, FF and GU were achieved using image registration to a single atlas image with reference segmentations of 18 volume of interests (VOIs). Manual segmentations by an experienced radiologist were used as reference. Quantification accuracy was assessed with Dice scores, group comparisons and correlations. VOI Dice scores ranged from 0.93 to 0.32. Muscles, brain, VAT and liver showed the highest scores. Pancreas, large and small intestines demonstrated lower segmentation accuracy and poor correlations. Estimated tissue volumes differed significantly in 8 cases. Tissue FFs were often slightly but significantly overestimated. Satisfactory agreements were observed in most tissue GUs. Automated tissue identification and characterization using a single atlas segmentation performs well compared to manual segmentation in most tissues and will be valuable in future studies. In certain tissues, alternative quantification methods or improvements to the current approach is needed.
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| 3. |
- Visvanathar, Robin, et al.
(författare)
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Exploration of whole-body PET/MRI and clinical variables in type 2 diabetes for data-driven hypothesis generation
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Aim To explore the feasibility of using the automated holistic image analysis approach Imiomics in voxel-level association screening with clinical variables for hypothesis-generation in whole-body [18F]fluorodeoxyglucose (FDG) PET/MR images. Material and methods Three experimental groups consisting of healthy individuals (n=12), individuals with prediabetes (n=16) and individuals with type 2 diabetes (n=13) were examined with simultaneous whole-body PET/MRI during hyperinsulinemic euglycemic clamp. The Imiomics-framework was utilised to create correlation maps between the clinical biomarkers and PET/MRI data. Results Multiple significant moderate-strong associations were detected, the inflammatory biomarkers (P-CRP, B-Leukocytes and B-Neutrophils) were positively associated with visceral adipose tissue (VAT) volume and inversely associated with skeletal muscle Ki. B-monocytes were positively associated with VAT volume, and negatively associated with gluteofemoral SAT volume. Furthermore, insulin sensitivity (M-value) was shown to be negatively associated with brain Ki. Of the plasma lipids, HDL was positively associated with Ki in the liver, VAT and skeletal muscle. Several additional confirmatory and distinct findings are reported. Conclusions An Imiomics-based data-driven exploratory approach allows for rapid and holistic analysis of the massive image datasets generated.
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| 4. |
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| 5. |
- Ahmad, Nouman, et al.
(författare)
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Automatic segmentation of large-scale CT image datasets for detailed body composition analysis
- 2023
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Ingår i: BMC BIOINFORMATICS. - : BioMed Central (BMC). - 1471-2105. ; 24:1
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundBody composition (BC) is an important factor in determining the risk of type 2-diabetes and cardiovascular disease. Computed tomography (CT) is a useful imaging technique for studying BC, however manual segmentation of CT images is time-consuming and subjective. The purpose of this study is to develop and evaluate fully automated segmentation techniques applicable to a 3-slice CT imaging protocol, consisting of single slices at the level of the liver, abdomen, and thigh, allowing detailed analysis of numerous tissues and organs.MethodsThe study used more than 4000 CT subjects acquired from the large-scale SCAPIS and IGT cohort to train and evaluate four convolutional neural network based architectures: ResUNET, UNET++, Ghost-UNET, and the proposed Ghost-UNET++. The segmentation techniques were developed and evaluated for automated segmentation of the liver, spleen, skeletal muscle, bone marrow, cortical bone, and various adipose tissue depots, including visceral (VAT), intraperitoneal (IPAT), retroperitoneal (RPAT), subcutaneous (SAT), deep (DSAT), and superficial SAT (SSAT), as well as intermuscular adipose tissue (IMAT). The models were trained and validated for each target using tenfold cross-validation and test sets.ResultsThe Dice scores on cross validation in SCAPIS were: ResUNET 0.964 (0.909-0.996), UNET++ 0.981 (0.927-0.996), Ghost-UNET 0.961 (0.904-0.991), and Ghost-UNET++ 0.968 (0.910-0.994). All four models showed relatively strong results, however UNET++ had the best performance overall. Ghost-UNET++ performed competitively compared to UNET++ and showed a more computationally efficient approach.ConclusionFully automated segmentation techniques can be successfully applied to a 3-slice CT imaging protocol to analyze multiple tissues and organs related to BC. The overall best performance was achieved by UNET++, against which Ghost-UNET++ showed competitive results based on a more computationally efficient approach. The use of fully automated segmentation methods can reduce analysis time and provide objective results in large-scale studies of BC.
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| 6. |
- 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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| 7. |
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| 8. |
- Asplund, Teo, et al.
(författare)
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A new approach to mathematical morphology on one dimensional sampled signals
- 2016
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Ingår i: Proceedings of the 23rd International Conference on Pattern Recognition ICPR 2016. - Piscataway, NJ : IEEE Communications Society. - 9781509048472 ; , s. 3904-3909
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Konferensbidrag (refereegranskat)abstract
- We present a new approach to approximate continuous-domain mathematical morphology operators. The approach is applicable to irregularly sampled signals. We define a dilation under this new approach, where samples are duplicated and shifted according to the flat, continuous structuring element. We define the erosion by adjunction, and the opening and closing by composition. These new operators will significantly increase precision in image measurements. Experiments show that these operators indeed approximate continuous-domain operators better than the standard operators on sampled one-dimensional signals, and that they may be applied to signals using structuring elements smaller than the distance between samples. We also show that we can apply the operators to scan lines of a two-dimensional image to filter horizontal and vertical linear structures.
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| 9. |
- Asplund, Teo, et al.
(författare)
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Adaptive Mathematical Morphology on Irregularly Sampled Signals in Two Dimensions
- 2020
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Ingår i: Mathematical Morphology : Theory and Applications. - : Walter de Gruyter. - 2353-3390. ; 4:1, s. 108-126
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Tidskriftsartikel (refereegranskat)abstract
- This paper proposes a way of better approximating continuous, two-dimensional morphologyin the discrete domain, by allowing for irregularly sampled input and output signals. We generalizeprevious work to allow for a greater variety of structuring elements, both flat and non-flat. Experimentallywe show improved results over regular, discrete morphology with respect to the approximation ofcontinuous morphology. It is also worth noting that the number of output samples can often be reducedwithout sacrificing the quality of the approximation, since the morphological operators usually generateoutput signals with many plateaus, which, intuitively do not need a large number of samples to be correctlyrepresented. Finally, the paper presents some results showing adaptive morphology on irregularlysampled signals.
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| 10. |
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