1.
Sjöholm, Therese
(författare)
Cancer imaging and image analysis methods in whole-body MRI and PET/MRI
2023
Doktorsavhandling (övrigt vetenskapligt/konstnärligt) abstract
Diagnostic medical imaging techniques such as magnetic resonance imaging (MRI) and positron emission tomography (PET) can provide structural and functional assessments of the whole body. This has great value for potentially systemic diseases such as cancer. To take advantage of the enormous amount of data provided by current imaging systems, improvements in whole-body imaging protocols and advancements in image analysis methods are however needed. This thesis aims to develop advanced imaging and image analysis methods for the purpose of tumour characterisation in MRI and combined PET/MRI whole-body image datasets. Early prediction of progression free survival (PFS) and overall survival (OS) in patients with relapsed/refractory (r/r) large B-cell lymphoma (LBCL) undergoing chimeric antigen receptor (CAR) T-cell therapy was assessed using whole-body PET/MRI pre- and post-therapy. Reference standard manual segmentations of tumours and non-malignant lymphoid tissue were used, and an extended set of semi-quantitative and quantitative PET/MRI metrics was extracted. Predictive PET/MRI metrics included the metabolic tumour volume (MTV), tumour apparent diffusion coefficient (ADC) and 18F-fluorodeoxyglucose (FDG) uptake in non-malignant bone marrow. To enable automated image analysis, deformable image registration was used to create multiparametric normal atlases of healthy volunteers examined with whole-body FDG PET, diffusion weighted imaging (DWI) MRI and water-fat MRI. To improve the geometric accuracy of DWI in the normal atlas, the reverse polarity gradient (RPG) distortion correction method was evaluated. RPG increased the geometrical alignment between DWI and structural images acquired in the same scan session, with little effect on healthy tissue ADC. It was further shown that healthy tissue assessments in atlas space was possible, with the normal atlas employed to study voxel-wise correlations between ADC and age across the whole body, confirming results from a manual segmentation approach. As proof of concept, a probabilistic atlas based approach was successfully used for segmentation of suspected malignant disease in FDG PET data and detection of liver fat infiltration in fat fraction (FF) MRI data. Lastly, using a cohort of r/r LBCL patients, statistical deviations between patient and normal atlas DWI data included as input in a deep learning based model, improved its performance for automated tumour segmentation.
2.
Tarai, Sambit, et al.
(författare)
Improved automated tumor segmentation in whole-body 3D scans using multi-directional 2D projection-based priors
2024
Ingår i: Heliyon. - : Elsevier. - 2405-8440. ; 10:4
Tidskriftsartikel (refereegranskat) abstract
Early cancer detection, guided by whole-body imaging, is important for the overall survival and well-being of the patients. While various computer-assisted systems have been developed to expedite and enhance cancer diagnostics and longitudinal monitoring, the detection and segmentation of tumors, especially from whole-body scans, remain challenging. To address this, we propose a novel end -to-end automated framework that first generates a tumor probability distribution map (TPDM), incorporating prior information about the tumor characteristics (e.g. size, shape, location). Subsequently, the TPDM is integrated with a state-of-the-art 3D segmentation network along with the original PET/CT or PET/MR images. This aims to produce more meaningful tumor segmentation masks compared to using the baseline 3D segmentation network alone. The proposed method was evaluated on three independent cohorts (autoPET, CAR-T, cHL) of images containing different cancer forms, obtained with different imaging modalities, and acquisition parameters and lesions annotated by different experts. The evaluation demonstrated the superiority of our proposed method over the baseline model by significant margins in terms of Dice coefficient, and lesion-wise sensitivity and precision. Many of the extremely small tumor lesions (i.e. the most difficult to segment) were missed by the baseline model but detected by the proposed model without additional false positives, resulting in clinically more relevant assessments. On average, an improvement of 0.0251 (autoPET), 0.144 (CAR-T), and 0.0528 (cHL) in overall Dice was observed. In conclusion, the proposed TPDM-based approach can be integrated with any state-of-the-art 3D UNET with potentially more accurate and robust segmentation results.
3.
Andersson, Jonathan, et al.
(författare)
MRI estimates of brown adipose tissue in children - Associations to adiposity, osteocalcin, and thigh muscle volume
2019
Ingår i: Magnetic Resonance Imaging. - : Elsevier BV. - 0730-725X .- 1873-5894. ; 58, s. 135-142
Tidskriftsartikel (refereegranskat) abstract
Context Brown adipose tissue is of metabolic interest. The tissue is however poorly explored in children. Methods: Sixty-three 7-year old subjects from the Swedish birth-cohort Halland Health and Growth Study were recruited. Care was taken to include both normal weight and overweight children, but the subjects were otherwise healthy. Only children born full term were included. Water-fat separated whole-body MRI scans, anthropometric measurements, and measurements of fasting glucose and levels of energy homeostasis related hormones, including the insulin-sensitizer osteocalcin, were performed. The fat fraction (FF) and effective transverse relaxation time (T-2(star)) of suspected brown adipose tissue in the cervical-supraclavicular-axillary fat depot (sBAT) and the FFs of abdominal visceral (VAT) and subcutaneous adipose tissue (SAT) were measured. Volumes of sBAT, abdominal VAT and SAT, and thigh muscle volumes were measured. Results: The FF in the sBAT depot was lower than in VAT and SAT for all children. In linear correlations including sex and age as explanatory variables, sBAT FF correlated positively with all measures of adiposity (p < 0.01), except for VAT FF and weight, positively with sBAT T-2* (p = 0.036), and negatively with osteocalcin (p = 0.017). When adding measures of adiposity as explanatory variables, sBAT FF also correlated negatively with thigh muscle volume (p < 0.01). Conclusions: Whole-body water-fat MRI of children allows for measurements of sBAT. The FF of sBAT was lower than that of VAT and SAT, indicating presence of BAT. Future studies could confirm whether the observed correlations corresponds to a hormonally active BAT.
4.
Kuzniar, Marek, et al.
(författare)
Feasibility of Assessing Inflammation in Asymptomatic Abdominal Aortic Aneurysms With Integrated 18F-Fluorodeoxyglucose Positron Emission Tomography/Magnetic Resonance Imaging
2020
Ingår i: European Journal of Vascular and Endovascular Surgery. - : Elsevier. - 1078-5884 .- 1532-2165. ; 59:3, s. 464-471
Tidskriftsartikel (refereegranskat) abstract
OBJECTIVE: This study aimed to evaluate the feasibility of 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) combined with contrast enhanced magnetic resonance imaging (MRI) to identify inflammation in asymptomatic abdominal aortic aneurysms (AAA).METHODS: FDG PET/MRI was performed on 15 patients with asymptomatic infrarenal AAAs >45 mm diameter. Prevalence of FDG uptake and MRI findings of inflammatory changes (oedema, wall thickening, and late gadolinium enhancement [LGE]) in the aortic wall were investigated at three levels: suprarenal aorta; non-aneurysmal aortic neck; and AAA.RESULTS: The median diameter of the AAAs was 54 mm (range 47-65 mm) and the median expansion rate in the last 12 months was 3 mm (range 1-13 mm). The standard uptake value (SUV) of FDG in the aneurysmal wall (SUVmax 2.5) was higher than the blood pool (SUVmax 1.0; p < .001). The maximum target to background ratio was higher in the suprarenal aorta (mean ± SD; 3.1 ± 0.6) and aortic neck (2.7 ± 0.5) than in the aneurysmal aorta (2.5 ± 0.5; p < .001). Thirty-six FDG hotspots were observed in the aneurysmal wall of 13 patients. Wall thickening and LGE were identified in eight patients. The number of FDG hotspots correlated with recent AAA growth (r = 0.62, p = .01). The recent aneurysm expansion rate was higher in aneurysms with LGE than in those without (7 mm vs. 2 mm; p = .03). MRI inflammatory changes were observed in nine of 36 hot spots (25%) and in three of 13 patients with focal FDG uptake.CONCLUSION: Fully integrated FDG PET/MRI can be used to study inflammation in asymptomatic AAAs. Heterogenous uptake of FDG in the aneurysmal wall indicates increased glucose metabolism, suggesting an ongoing inflammation. However, these FDG hotspots rarely correspond to MRI findings of inflammation, raising the question of which type of cellular activity is present in these areas. The presence of LGE and FDG hotspots both correlated to recent aneurysm growth, and their usefulness as clinical markers of aneurysm growth warrant additional investigation.
5.
Ahmad, Nouman, et al.
(författare)
Automatic segmentation of large-scale CT image datasets for detailed body composition analysis
2023
Ingår i: BMC BIOINFORMATICS. - : BioMed Central (BMC). - 1471-2105. ; 24:1
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.
6.
Johansson, Emil, et al.
(författare)
Whole-Body Imaging of Tissue-specific Insulin Sensitivity and Body Composition by Using an Integrated PET/MR System: A Feasibility Study
2018
Ingår i: Radiology. - : Radiological Society of North America (RSNA). - 0033-8419 .- 1527-1315. ; 286:1, s. 271-278
Tidskriftsartikel (refereegranskat) abstract
Purpose: To develop, evaluate, and demonstrate the feasibility of a whole-body protocol for simultaneous assessment of tissue-specific insulin-mediated fluorine 18 (F-18) fluorodeoxyglucose (FDG) influx rates, tissue depots, and whole-body insulin sensitivity (referred to as the M value). Materials and Methods: An integrated positron emission tomography (PET)/magnetic resonance (MR) imaging system combined with hyperinsulinemic euglycemic clamp (HEC) was used. Dynamic whole-body PET imaging was used to determine the insulin-mediated F-18-FDG tissue influx rate (K-i) in the whole-body region by using the Patlak method. M value was determined with the HEC method at PET imaging. Tissue depots were quantified by using water-fat separated MR imaging and manual segmentations. Feasibility of the imaging protocol was demonstrated by using five healthy control participants and five patients with type 2 diabetes. Associations between M value and K-i were studied in multiple tissues by using the Pearson correlation. Results: Positive correlations were found between M value and K-i in multiple tissues: the gluteus muscle (r = 0.875; P = .001), thigh muscle (r = 0.903; P < .001), calf muscle (r = 0.825; P = .003), and abdominal visceral adipose tissue (r = 0.820; P = .004). A negative correlation was found in the brain (r = 0.798; P = .006). The MR imaging-based method for quantification of tissue depots was feasible for determining adipose tissue volumes and fat fractions. Conclusion: This PET/MR imaging protocol may be feasible for simultaneous assessment of tissue-specific insulin-mediated F-18-FDG influx rates, tissue depots, and M value. (C) RSNA, 2017
7.
Ahmad, Nouman, et al.
(författare)
Voxel-wise body composition analysis using image registration of a three-slice CT imaging protocol : methodology and proof-of-concept studies
2024
Ingår i: Biomedical engineering online. - : Springer Nature. - 1475-925X. ; 23:1
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.
8.
Omran, Meis, et al.
(författare)
Whole-Body MRI Surveillance : Baseline Findings in the Swedish Multicentre Hereditary TP53-Related Cancer Syndrome Study (SWEP53)
2022
Ingår i: Cancers. - : MDPI. - 2072-6694. ; 14:2
Tidskriftsartikel (refereegranskat) abstract
A surveillance strategy of the heritable TP53-related cancer syndrome (hTP53rc), commonly referred to as the Li–Fraumeni syndrome (LFS), is studied in a prospective observational nationwide multi-centre study in Sweden (SWEP53). The aim of this sub-study is to evaluate whole-body MRI (WB-MRI) regarding the rate of malignant, indeterminate, and benign imaging findings and the associated further workup generated by the baseline examination. Individuals with hTP53rc were enrolled in a surveillance program including annual whole-body MRI (WB-MRI), brain-MRI, and in female carriers, dedicated breast MRI. A total of 68 adults ≥18 years old have been enrolled to date. Of these, 61 fulfilled the inclusion criteria for the baseline MRI scan. In total, 42 showed a normal scan, while 19 (31%) needed further workup, of whom three individuals (3/19 = 16%) were diagnosed with asymptomatic malignant tumours (thyroid cancer, disseminated upper GI cancer, and liver metastasis from a previous breast cancer). Forty-three participants were women, of whom 21 had performed risk-reducing mastectomy prior to inclusion. The remaining were monitored with breast MRI, and no breast tumours were detected on baseline MRI. WB-MRI has the potential to identify asymptomatic tumours in individuals with hTP53rc syndrome. The challenge is to adequately and efficiently investigate all indeterminate findings. Thus, a multidisciplinary team should be considered in surveillance programs for individuals with hTP53rc syndrome.
9.
Sousa, João M., et al.
(författare)
Evaluation of zero-echo-time attenuation correction for integrated PET/MR brain imaging-comparison to head atlas and 68Ge-transmission-based attenuation correction
2018
Ingår i: EJNMMI Physics. - : Springer Science and Business Media LLC. - 2197-7364. ; 5:20
Tidskriftsartikel (refereegranskat) abstract
Background: MRI does not offer a direct method to obtain attenuation correction maps as its predecessors (stand-alone PET and PET/CT), and bone visualisation is particularly challenging. Recently, zero-echo-time (ZTE) was suggested for MR-based attenuation correction (AC). The aim of this work was to evaluate ZTE- and atlas-AC by comparison to 68Ge-transmission scan-based AC.Nine patients underwent brain PET/MR and stand-alone PET scanning using the dopamine transporter ligand 11C-PE2I. For each of them, two AC maps were obtained from the MR images: an atlas-based, obtained from T1-weighted LAVA-FLEX imaging with cortical bone inserted using a CT-based atlas, and an AC map generated from proton-density-weighted ZTE images. Stand-alone PET 68Ge-transmission AC map was used as gold standard. PET images were reconstructed using the three AC methods and standardised uptake value (SUV) values for the striatal, limbic and cortical regions, as well as the cerebellum (VOIs) were compared. SUV ratio (SUVR) values normalised for the cerebellum were also assessed. Bias, precision and agreement were calculated; statistical significance was evaluated using Wilcoxon matched-pairs signed-rank test.Results: Both ZTE- and atlas-AC showed a similar bias of 6–8% in SUV values across the regions. Correlation coefficients with 68Ge-AC were consistently high for ZTE-AC (r 0.99 for all regions), whereas they were lower for atlas-AC, varying from 0.99 in the striatum to 0.88 in the posterior cortical regions. SUVR showed an overall bias of 2.9 and 0.5% for atlas-AC and ZTE-AC, respectively. Correlations with 68Ge-AC were higher for ZTE-AC, varying from 0.99 in the striatum to 0.96 in the limbic regions, compared to atlas-AC (0.99 striatum to 0.77 posterior cortex).Conclusions: Absolute SUV values showed less variability for ZTE-AC than for atlas-AC when compared to 68Ge-AC, but bias was similar for both methods. This bias is largely caused by higher linear attenuation coefficients in atlas- and ZTE-AC image compared to 68Ge-images. For SUVR, bias was lower when using ZTE-AC than for atlas-AC. ZTE-AC shows to be a more robust technique than atlas-AC in terms of both intra- and inter-patient variability.
10.
Ekström, Simon, 1991-
(författare)
Efficient GPU-based Image Registration : for Detailed Large-Scale Whole-body Analysis
2020
Doktorsavhandling (övrigt vetenskapligt/konstnärligt) abstract
Imaging has become an important aspect of medicine, enabling visualization of internals in a non-invasive manner. The rapid advancement and adoption of imaging techniques have led to a demand for tools able to take advantage of the information that is produced. Medical image analysis aims to extract relevant information from acquired images to aid diagnostics in healthcare and increase the understanding within medical research. The main subject of this thesis, image registration, is a widely used tool in image analysis that can be employed to find a spatial transformation aligning a set of images. One application, that is described in detail in this thesis, is the use of image registration for large-scale analysis of whole-body images through the utilization of the correspondences defined by the resulting transformations. To produce detailed results, the correspondences, i.e. transformations, need to be of high resolution and the quality of the result has a direct impact on the quality of the analysis. Also, this type of application aims to analyze large cohorts and the value of a registration method is not only weighted by its ability to produce an accurate result but also by its efficiency. This thesis presents two contributions on the subject; a new method for efficient image registration with the ability to produce dense deformable transformations, and the application of the presented method in large-scale analysis of a whole-body dataset acquired using an integrated positron emission tomography (PET) and magnetic resonance imaging (MRI) system. In this thesis, it is shown that efficient and detailed image registration can be performed by employing graph cuts and a heuristic where the optimization is performed on subregions of the image. The performance can be improved further by the efficient utilization of a graphics processing unit (GPU). It is also shown that the method can be employed to produce a model on health based on a PET-MRI dataset which can be utilized to automatically detect pathology in the imaging.
