4DFlowNet: Super-Resolution 4D Flow MRI Using Deep Learning and Computational Fluid Dynamics

4D flow magnetic resonance imaging (MRI) is an emerging imaging technique where spatiotemporal 3D blood velocity can be captured with full volumetric coverage in a single non-invasive examination. This enables qualitative and quantitative analysis of hemodynamic flow parameters of the heart and great vessels. An increase in the image resolution would provide more accuracy and allow better assessment of the blood flow, especially for patients with abnormal flows. However, this must be balanced with increasing imaging time. The recent success of deep learning in generating super resolution images shows promise for implementation in medical images. We utilized computational fluid dynamics simulations to generate fluid flow simulations and represent them as synthetic 4D flow MRI data. We built our training dataset to mimic actual 4D flow MRI data with its corresponding noise distribution. Our novel 4DFlowNet network was trained on this synthetic 4D flow data and was capable in producing noise-free super resolution 4D flow phase images with upsample factor of 2. We also tested the 4DFlowNet in actual 4D flow MR images of a phantom and normal volunteer data, and demonstrated comparable results with the actual flow rate measurements giving an absolute relative error of 0.6–5.8% and 1.1–3.8% in the phantom data and normal volunteer data, respectively

On the numerical treatment of viscous and convective effects in relative pressure reconstruction methods

The mechanism of many cardiovascular diseases can be understood by studying the pressure distribution in blood vessels. Direct pressure measurements, however, require invasive probing and provide only single‐point data. Alternatively, relative pressure fields can be reconstructed from imaging‐based velocity measurements by considering viscous and inertial forces. Both contributions can be potential troublemakers in pressure reconstruction: the former due to its higher‐order derivatives, and the latter because of the quadratic nonlinearity in the convective acceleration. Viscous and convective terms can be treated in various forms, which, although equivalent for ideal measurements, can perform differently in practice. In fact, multiple versions are often used in literature, with no apparent consensus on the more suitable variants. In this context, the present work investigates the performance of different versions of relative pressure estimators. For viscous effects, in particular, two new modified estimators are presented to circumvent second‐order differentiation without requiring surface integrals. In‐silico and in‐vitro data in the typical regime of cerebrovascular flows are considered, allowing a systematic noise sensitivity study. Convective terms are shown to be the main source of error, even for flows with pronounced viscous component. Moreover, the conservation (often integrated) form of convection exhibits higher noise sensitivity than the standard convective description, in all three families of estimators considered here. For the classical pressure Poisson estimator, the present modified version of the viscous term tends to yield better accuracy than the (recently introduced) integrated form, although this effect is in most cases negligible when compared to convection‐related errors

Investigation of Flow Disturbances and Multi-Directional Wall Shear Stress in the Stenosed Carotid Artery Bifurcation Using Particle Image Velocimetry

Hemodynamics and shear forces are associated with pathological changes in the vascular wall and its function, resulting in the focal development of atherosclerosis. Flow complexities that develop in the presence of established plaques create environments favourable to thrombosis formation and potentially plaque rupture leading to stroke. The carotid artery bifurcation is a common site of atherosclerosis development. Recently, the multi-directional nature of shear stress acting on the endothelial layer has been highlighted as a risk factor for atherogenesis, emphasizing the need for accurate measurements of shear stress magnitude as well direction. In the absence of comprehensive patient specific datasets numerical simulations of hemodynamics are limited by modeling assumptions. The objective of this thesis was to investigate the relative contributions of various factors - including geometry, rheology, pulsatility, and compliance – towards the development of disturbed flow and multi-directional wall shear stress (WSS) parameters related to the development of atherosclerosis An experimental stereoscopic particle image velocimetry (PIV) system was used to measure instantaneous full-field velocity in idealized asymmetrically stenosed carotid artery bifurcation models, enabling the extraction of bulk flow features and turbulence intensity (TI). The velocity data was combined with wall location information segmented from micro computed tomography (CT) to obtain phase-averaged maps of WSS magnitude and direction. A comparison between Newtonian and non-Newtonian blood-analogue fluids demonstrated that the conventional Newtonian viscosity assumption underestimates WSS magnitude while overestimating TI. Studies incorporating varying waveform pulsatility demonstrated that the levels of TI and oscillatory shear index (OSI) depend on the waveform amplitude in addition to the degree of vessel constriction. Local compliance resulted in a dampening of disturbed flow due to volumetric capacity of the upstream vessel, however wall tracking had a negligible effect on WSS prediction. While the degree of stenosis severity was found to have a dominant effect on local hemodynamics, comparable relative differences in metrics of flow and WSS disturbances were found due to viscosity model, waveform pulsatility and local vessel compliance

The Development of a Patient-Specific, Open Source Computational Fluid Dynamics Tool to Comprehensively and Innovatively Study Coarctation of the Aorta in a Limited Resource Clinical Context

Congenital heart disease (CHD) has a global prevalence of 8 per 1000 births [1] and coarctation of the aorta (CoA) is one of the most common defects with a prevalence of 7% of all cases. The occurrence of CHD in Africa is estimated to be significantly lower, which is attributed to a lack of data [2]. This emphasises the restricted human resources, as well as diagnostic and intervention capacity of specialists in Africa which leads to delayed treatment, presentation with established severity and, consequently, a worse prognosis. Computational Fluid Dynamics (CFD) is seen as the tool that will lead to a better understanding of the haemodynamic effects caused by the malformations related to CoA and provide insights into post-repair morbidity. In addition, the development of a computational tool is envisaged to improve the clinical capacity for diagnosis as well as provide a tool to conduct in silico repair planning. In a low and lower-middle income country healthcare facility, the supplementary data that CFD can provide can add diagnostic value, plan interventions to be more effective and efficient, as well as provide data that may improve postrepair patient management. The aim of this project is to develop a patient-specific, open source, computational fluid dynamics toolchain that is able to study the haemodynamics relating to CoA. In order to do so, a protocol for the collection of doppler echocardiography (echo) and CTA data is proposed. The method for processing the echo data and manually segmenting the CTA data is presented and evaluated. The open source, OpenFOAM code is used to simulate a patient-specific CoA case as well as two in silico designs of coarctation repairs based on expanding the coarctation from the original dataset. The CFD toolchain was developed such that patient data collected from the hospital could be processed to present key haemodynamic metrics such as velocities in the field at the coarctation zone, the pressure gradient across the coarctation and volumetric flow rates through each supra-aortic branch. These results are obtained for each case's geometry, and the trends and impacts that increasing the coarctation ratio has on each of the haemodynamic metrics is presented. The results show that the coarctation pressure gradient and maximum coarctation velocity decrease while perfusion of the lower limbs recovers with expanding coarctation ratio. Following an analysis of the results, it is evident that the pipeline is capable of running patient-specific CFD simulations and can present clinically relevant results. It is noted that this work is a proof of concept and so several steps are discussed that will improve the pipeline

Foetal echocardiographic segmentation

Congenital heart disease affects just under one percentage of all live births [1]. Those defects that manifest themselves as changes to the cardiac chamber volumes are the motivation for the research presented in this thesis. Blood volume measurements in vivo require delineation of the cardiac chambers and manual tracing of foetal cardiac chambers is very time consuming and operator dependent. This thesis presents a multi region based level set snake deformable model applied in both 2D and 3D which can automatically adapt to some extent towards ultrasound noise such as attenuation, speckle and partial occlusion artefacts. The algorithm presented is named Mumford Shah Sarti Collision Detection (MSSCD). The level set methods presented in this thesis have an optional shape prior term for constraining the segmentation by a template registered to the image in the presence of shadowing and heavy noise. When applied to real data in the absence of the template the MSSCD algorithm is initialised from seed primitives placed at the centre of each cardiac chamber. The voxel statistics inside the chamber is determined before evolution. The MSSCD stops at open boundaries between two chambers as the two approaching level set fronts meet. This has significance when determining volumes for all cardiac compartments since cardiac indices assume that each chamber is treated in isolation. Comparison of the segmentation results from the implemented snakes including a previous level set method in the foetal cardiac literature show that in both 2D and 3D on both real and synthetic data, the MSSCD formulation is better suited to these types of data. All the algorithms tested in this thesis are within 2mm error to manually traced segmentation of the foetal cardiac datasets. This corresponds to less than 10% of the length of a foetal heart. In addition to comparison with manual tracings all the amorphous deformable model segmentations in this thesis are validated using a physical phantom. The volume estimation of the phantom by the MSSCD segmentation is to within 13% of the physically determined volume

Rapid Segmentation Techniques for Cardiac and Neuroimage Analysis

Recent technological advances in medical imaging have allowed for the quick acquisition of highly resolved data to aid in diagnosis and characterization of diseases or to guide interventions. In order to to be integrated into a clinical work flow, accurate and robust methods of analysis must be developed which manage this increase in data. Recent improvements in in- expensive commercially available graphics hardware and General-Purpose Programming on Graphics Processing Units (GPGPU) have allowed for many large scale data analysis problems to be addressed in meaningful time and will continue to as parallel computing technology improves. In this thesis we propose methods to tackle two clinically relevant image segmentation problems: a user-guided segmentation of myocardial scar from Late-Enhancement Magnetic Resonance Images (LE-MRI) and a multi-atlas segmentation pipeline to automatically segment and partition brain tissue from multi-channel MRI. Both methods are based on recent advances in computer vision, in particular max-flow optimization that aims at solving the segmentation problem in continuous space. This allows for (approximately) globally optimal solvers to be employed in multi-region segmentation problems, without the particular drawbacks of their discrete counterparts, graph cuts, which typically present with metrication artefacts. Max-flow solvers are generally able to produce robust results, but are known for being computationally expensive, especially with large datasets, such as volume images. Additionally, we propose two new deformable registration methods based on Gauss-Newton optimization and smooth the resulting deformation fields via total-variation regularization to guarantee the problem is mathematically well-posed. We compare the performance of these two methods against four highly ranked and well-known deformable registration methods on four publicly available databases and are able to demonstrate a highly accurate performance with low run times. The best performing variant is subsequently used in a multi-atlas segmentation pipeline for the segmentation of brain tissue and facilitates fast run times for this computationally expensive approach. All proposed methods are implemented using GPGPU for a substantial increase in computational performance and so facilitate deployment into clinical work flows. We evaluate all proposed algorithms in terms of run times, accuracy, repeatability and errors arising from user interactions and we demonstrate that these methods are able to outperform established methods. The presented approaches demonstrate high performance in comparison with established methods in terms of accuracy and repeatability while largely reducing run times due to the employment of GPU hardware