In this project, the inverse problem of determining regional pressure variations from measured blood velocity data in the contect of a cardiovascular setting has been approached. A common esimator, the pressure poisson estimator (PPE) has been implemented in a non-variational setting and evaluated for clinically relevant synthetic flow cases, over dynamically varying domains, mimicking or directly representing the intra-cardiac space: A synthetic dynamic domain benchmark problem and a patient specific model of the left ventricle. The results obtained show under ideal condition the capability of the approach to tackle complex domains successfully and to obtain regional pressure fields to a high degree of accuracy when compared to a locally provided state of the art estimator, the stokes estimator (STE). Under noise, results obtained suggest that divergence may occur with finer temporal resolution. Spatially convergence in a setting mimicking an image scenario is observed with minor exceptions though to stem from the specific composition of the flow field between discretizations. The implementation at hand avoids common problems in the non-variational approaches of this estimator stemming from domain complexity and leads to a simple application of the pure neumann boundary conditions required to compute the relative pressure field while avoiding the need to estimate boundary normals or use an embedded approach. The resulting linear system has desirable properties such as symmetry and compliance with the discrete compatibility condition by construction. / Syftet med följande projekt har varit att undersöka metoder för uppskattning av regionala tryckvariationer från uppmätta flödeshastigheter, med direkt tillämpning för förbättrad kardiovaskulär diagnostik. Mer specifikt har en tillgänglig gold-standardmetod; Pressure Poisson Estimatorn (PPE); implementerats i en icke-variationell miljö och utvärderats över en samling testfall med ökande komplexitet och med ökande relevans för det kliniska problemet med kardiovaskulär tryckmätning i det dynamiskt varierande hjärtutrymmet: ett syntetiskt referensproblem med varierande dynamisk rörelse, och en patientspecifik modell av vänster kammare. De erhållna resultaten visar att den icke-variationella implementeringen av PPE framgångsrikt kan hantera komplexa domäner och erhålla regionala tryckfält med hög noggrannhet. PPE-metoden påvisar också konkurrenskraftig noggrannhet i jamförelse med alternativa referensmetoder så som den s.k. Stokes-estimators (STE). Resultat visar också på tillfredställande beteende under realistiska signal-till-brus-förhallanden, likväl som spatiotemporell konvergens vid upplösningar som motsvarar vad som kan förväntas vid klinisk bildgivning. I summering visar våra resultat att vår implementering av PPE undviker vanliga problem i alterantiva icke-variationella implementeringar som annars kan uppkomma vid analys av komplexa flödesdomaner, och att en förenklad men likväl korrekt implementering av de rena Neumann-gränsvillkor som krävs för att beräkna det relativa tryckfältet kan uppnås utan behovet av att uppskatta icke-triviala gränsnormaler. Utöver detta påvisar det resulterande linjära systemet även önskvarda egenskaper såsom numerisk symmetri och överenstämmelse med diskreta kompatibilitetsvillkor.

The main function of the heart is to pump blood throughout the cardiovascular system by generating pressure differences created through volume changes. Although the main purpose of the heart and vessels is to lead the flowing blood throughout the body, clinical assessments of cardiac function are usually based on morphology, approximating the flow features by viewing the motion of the myocardium and vessels. Measurement of three-directional, three-dimensional and time-resolved velocity (4D Flow) data is feasible using magnetic resonance (MR). The focus of this thesis is the development and application of methods that facilitate the analysis of larger groups of data in order to increase our understanding of intracardiac flow patterns and take the 4D flow technique closer to the clinical setting. In the first studies underlying this thesis, a pathline based method for analysis of intra ventricular blood flow patterns has been implemented and applied. A pathline is integrated from the velocity data and shows the path an imaginary massless particle would take through the data volume. This method separates the end-diastolic volume (EDV) into four functional components, based on the position for each individual pathline at end-diastole (ED) and end-systole (ES). This approach enables tracking of the full EDV over one cardiac cycle and facilitates calculation of parameters such as e.g. volumes and kinetic energy (KE). Besides blood flow, pressure plays an important role in the cardiac dynamics. In order to study this parameter in the left ventricle, the relative pressure field was computed using the pressure Poisson equation. A comprehensive presentation of the pressure data was obtained dividing the LV blood pool into 17 pie-shaped segments based on a modification of the standard seventeen segment model. Further insight into intracardiac blood flow dynamics was obtained by studying the turbulent kinetic energy (TKE) in the LV. The methods were applied to data from a group of healthy subjects and patients with dilated cardiomyopathy (DCM). DCM is a pathological state where the cardiac function is impaired and the left ventricle or both ventricles are dilated. The validation study of the flow analysis method showed that a reliable user friendly tool for intra ventricular blood flow analysis was obtained. The application of this tool also showed that roughly one third of the blood that enters the LV, directly leaves the LV again in the same heart beat. The distribution of the four LV EDV components was altered in the DCM group as compared to the healthy group; the component that enters and leaves the LV during one cardiac cycle (Direct Flow) was significantly larger in the healthy subjects. Furthermore, when the kinetic energy was normalized by the volume for each component, at time of ED, the Direct Flow had the highest values in the healthy subjects. In the DCM group, however, the Retained Inflow and Delayed Ejection Flow had higher values. The relative pressure field showed to be highly heterogeneous, in the healthy heart. During diastole the predominate pressure differences in the LV occur along the long axis from base to apex. The distribution and variability of 3D pressure fields differ between early and late diastolic filling phases, but common to both phases is a relatively lower pressure in the outflow segment. In the normal LV, TKE values are low. The highest TKE values can be seen during early diastole and are regionally distributed near the basal LV regions. In contrast, in a heterogeneous group of DCM patients, total diastolic and late diastolic TKE values are higher than in normals, and increase with the LV volume. In conclusion, in this thesis, methods for analysis of multidirectional intra cardiac velocity data have been obtained. These methods allow assessment of data quality, intra cardiac blood flow patterns, relative pressure fields, and TKE. Using these methods, new insights have been obtained in intra cardiac blood flow dynamics in health and disease. The work underlying this thesis facilitates assessment of data from a larger population of healthy subjects and patients, thus bringing the 4D Flow MRI technique closer to the clinical setting.

Deep convolutional neural networks (CNN’s) have achieved state-of-the-art accuraciesfor multi-class segmentation in biomedical image science. In this thesis, A 3D U-Net isused to segment 4D flow Magnetic Resonance Images that include the heart and its largevessels. The 4 dimensional flow MRI dataset has been segmented and validated using amulti-atlas based registration technique. This multi-atlas based technique resulted in highquality segmentations, with the disadvantage of long computation times typically requiredby three-dimensional registration techniques. The 3D U-Net framework learns to classifyvoxels by transforming the information about the segmentation into a latent feature spacein a contracting path and upsampling them to semantic segmentation in an expandingpath. A CNN trained using a sufficiently diverse set of volumes at different time intervalsof the diastole and systole should be able to handle more extreme morphological differencesbetween subjects. Evaluation of the results is based on metric for segmentation evaluationsuch as Dice coefficient. Uncertainty is estimated using a bayesian implementationof the 3D U-Net of similar architecture. / <p>The presentation was online over zoom due to covid19 restrictions.</p>

The data obtained from computational fluid dynamics (CFD) simulations of blood flow in the heart is plentiful, and processing this data takes time and the procedure for that is not straightforward. This project aims to develop a tool that can semi-automatically process CFD simulation data, which is based on 4D flow computed tomography (CT) data, with minimal user input. The tool should be able to time efficiently calculate flow parameters from the data, and automatically create overview images of the flow field while doing so, to aid the user's analysis process. The tool is coded using Python programming language, and the Python scripts are inputted to the application ParaView for processing of the simulation data.  The tool generates 3 chamber views of the heart by calculating three points from the given patient data, which represent the aortic and mitral valves, and the apex of the heart. A plane is generated that pass through these three points, and the heart is sliced along this plane to visualize 3 chambers of the heart. The camera position is also manipulated to optimize the 3 chamber view. The maximum outflow velocity over the cardiac cycle in the left atrial appendage (LAA) is determined by searching in a time range around the maximum outflow rate of the LAA in a cardiac cycle, and finding the highest velocity value that points away from the LAA in this range. The flow component analysis is calculated in the LAA and left ventricle (LV) by seeding particles in each at the start of the cardiac cycle, and tracking these particles forwards and backwards in time to determine where the particles end up and come from, respectively. By knowing these two aspects, the four different flow components of the blood can be determined in both the LAA and LV.  The tool can successfully create 3 chamber views of the heart model from three semi-automatically determined points, at a manipulated camera location. It can also calculate the maximum outflow velocity of the flow field over a cardiac cycle in the LAA, and perform a flow component analysis of the LAA and the LV by tracking particles forwards and backwards in time through a cardiac cycle. The maximum velocity calculation is relatively time efficient and produces results similar to those found manually, yet the output is dependent on the user-defined inputs and processing techniques, and varies between users. The flow component analysis is also time efficient, produces results for the LV that are comparable to pre-existing research, and produces results for the LAA that are comparable to the LVs' results. Although, the extraction process of the LAA sometimes includes part of the left atrium, which impacts the accuracy of the results. After processing each part, the tool creates a single file containing each part's main results for easier analysis of the patient data. In conclusion, the tool is capable of semi-automatically processing CFD simulation data which saves the user time, and it has thus met all the project aims

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Objectif : Déterminer la capacité de la séquence IRM 4D flow à mesurer la forme et le flot (débit, vélocité) de l’artère hépatique et de ses branches en trois dimensions. Méthodologie : Un fantôme de l’artère hépatique réaliste qui imite le flux sanguin et les mouvements respiratoires ainsi que 20 volontaires ont été imagés. La précision du 4D flow Cartésien avec navigateur et remplissage de l’espace-k selon la position respiratoire était déterminée in-vitro à quatre résolutions spatiales (0,5 à 1,0 mm isotropique) et fenêtres d’acceptation du navigateur (± 8 et ± 2 mm) avec un scanner IRM à 3T. Deux séquences centrées sur les branches hépatiques et gastroduodénales étaient évaluées in-vivo et comparés au contraste de phase 2D. Résultats : In vitro, l’augmentation de la résolution spatiale diminuait plus l’erreur qu’une fenêtre d’acceptation plus étroite (30.5 à -4.67% vs -6.64 à -4.67% pour le débit). In vivo, les artèreshépatiques et gastroduodénales étaient mieux visualisées avec la séquence de haute résolution (90 vs 71%). Malgré un accord interobservateur similaire (κ = 0.660 et 0.704), la séquence à plus haute résolution avait moins de variabilité pour l’aire, le débit, et la vélocité moyenne. Le 4D flow avait une meilleure cohérence interne entre l’afflux et l’efflux à la bifurcation de l’artère hépatique (1.03 ± 5.05% et 15.69 ± 6.14%) que le contraste de phase 2D (28.77 ± 21.01%). Conclusion : Le 4D flow à haute résolution peut évaluer l’anatomie et l’hémodynamie de l’artère hépatique avec une meilleure précision, visibilité, moindre variabilité et meilleure concordance interne. / Objectives: To assess the ability of four-dimensional (4D) flow, an MRI sequence that captures the form and flow of vessels in three dimensions, to measure hepatic arterial hemodynamics. Methods: A dynamic hepatic artery phantom and 20 consecutive volunteers were scanned. The accuracies of Cartesian 4D flow sequences with k-space reordering and navigator gating at four spatial resolutions (0.5- to 1-mm isotropic) and navigator acceptance windows (± 8 to ± 2 mm) were assessed in vitro at 3 T. Two sequences centered on gastroduodenal and hepatic artery branches were assessed in vivo for intra - and interobserver agreement and compared to 2D phase-contrast (0.5-mm in -plane). Results In vitro, higher spatial resolution led to a greater decrease in error than narrower navigator window (30.5 to −4.67% vs−6.64 to −4.67% for flow). In vivo, hepatic and gastroduodenal arteries were visualized more frequently with the higher resolution sequence (90 vs 71%). Despite similar interobserver agreement (κ = 0.660 and 0.704), the higher resolution sequence had lower variability for area, flow, and average velocity. 4D flow had lower differences between inflow and outflow at the hepatic artery bifurcation (11.03 ± 5.05% and 15.69 ± 6.14%) than 2D phase-contrast (28.77 ± 21.01%). Conclusion: High-resolution 4D flow can assess hepatic artery anatomy and hemodynamics with improved accuracy, greater vessel visibility, better interobserver reliability, and internal consistency.

No description available.

Magnetic resonance imaging (MRI), is a noninvasive medical imaging testing techniquewhich is used to produce detailed images of internal structure of the human body, includingbones, muscles, organs, and blood vessels. MRI scanners use large magnets and radiowaves to create images of the body. Cardiac MRI scan helps doctors to detect and monitorcardiac diseases like blood clots, artery blockages, and scar tissue etc. Cardiovasculardisease is a type of disease that affects the heart or the blood vessels.This thesis aims to explore the reconstruction of accelerated cardiovascular MRI datato reconstruct under-sampled MRI data acquired after applying accelerated techniques.The focus of this research is to study and implement deep learning techniques to overcomethe aliasing artifacts caused by accelerated imaging. The results of this study will becompared with fully sampled data acquired with traditional existing techniques such asParallel Imaging (PI) and Compressed Sensing (CS).The primary findings of this study show that the proposed deep learning network caneffectively reconstruct under-sampled cardiovascular MRI data acquired using acceleratedimaging techniques. Many experiments were performed to handle 4D Flow data with limitedmemory for training the network. The network’s performance was found to be comparableto the fully sampled data acquired using traditional imaging techniques such asPI and CS. It is also important to note that this study also aimed to investigate the generalizabilityof the proposed deep learning network, specifically FlowVN, when appliedto different datasets. To explore this aspect, two different models were employed: a pretrainedmodel using previous research data and configurations, and a model trained fromscratch using CMIV data with experiments performed to address limited memory issuesassociated with 4D Flow data.