Patient-specific design of the right ventricle to pulmonary artery conduit via computational analysis

Cardiovascular prostheses are routinely used in surgical procedures to address congenital malformations, for example establishing a pathway from the right ventricle to the pulmonary arteries (RV-PA) in pulmonary atresia and truncus arteriosus. Currently available options are fixed size and have limited durability. Hence, multiple re-operations are required to match the patients’ growth and address structural deterioration of the conduit. Moreover, the pre-set shape of these implants increases the complexity of operation to accommodate patient specific anatomy. The goal of the research group is to address these limitations by 3D printing geometrically customised implants with growth capacity. In this study, patient-specific geometrical models of the heart were constructed by segmenting MRI data of patients using Mimics inPrint 2.0. Computational Fluid Dynamics (CFD) analysis was performed, using ANSYS CFX, to design customised geometries with better haemodynamic performance. CFD simulations showed that customisation of a replacement RV-PA conduit can improve its performance. For instance, mechanical energy dissipation and wall shear stress can be significantly reduced. Finite Element modelling also allowed prediction of the suitable thickness of a synthetic material to replicate the behaviour of pulmonary artery wall under arterial pressures. Hence, eliminating costly and time-consuming experiments based on trial-and-error. In conclusion, it is shown that patient-specific design is feasible, and these designs are likely to improve the flow dynamics of the RV-PA connection. Modelling also provides information for optimisation of biomaterial. In time, 3D printing a customised implant may simplify replacement procedures and potentially reduce the number of operations required over a life time, bringing substantial improvements in quality of life to the patient

Acceleration of Subtractive Non-contrast-enhanced Magnetic Resonance Angiography

Although contrast-enhanced magnetic resonance angiography (CE-MRA) is widely established as a clinical examination for the diagnosis of human vascular diseases, non-contrast-enhanced MRA (NCE-MRA) techniques have drawn increasing attention in recent years. NCE-MRA is based on the intrinsic physical properties of blood and does not require the injection of any exogenous contrast agents. Subtractive NCE-MRA is a class of techniques that acquires two image sets with different vascular signal intensity, which are later subtracted to generate angiograms. The long acquisition time is an important drawback of NCE-MRA techniques, which not only limits the clinical acceptance of these techniques but also renders them sensitive to artefacts from patient motion. Another problem for subtractive NCE-MRA is the unwanted residual background signal caused by different static background signal levels on the two raw image sets. This thesis aims at improving subtractive NCE-MRA techniques by addressing both these limitations, with a particular focus on three-dimensional (3D) femoral artery fresh blood imaging (FBI). The structure of the thesis is as follows: Chapter 1 describes the anatomy and physiology of the vascular system, including the characteristics of arteries and veins, and the MR properties and flow characteristics of blood. These characteristics are the foundation of NCE-MRA technique development. Chapter 2 introduces commonly used diagnostic angiographic methods, particularly CE-MRA and NCE-MRA. Current NCE-MRA techniques are reviewed and categorised into different types. Their principles, implementations and limitations are summarised. Chapter 3 describes imaging acceleration theories including compressed sensing (CS), parallel imaging (PI) and partial Fourier (PF). The Split Bregman algorithm is described as an efficient CS reconstruction method. The SPIRiT reconstruction for PI and homodyne detection for PF are also introduced and combined with Split Bregman to form the basis of the reconstruction strategy for undersampled MR datasets. Four image quality metrics are presented for evaluating the quality of reconstructed images. In Chapter 4, an intensity correction method is proposed to improve background suppression for subtractive NCE-MRA techniques. Residual signals of background tissues are removed by performing a weighted subtraction, in which the weighting factor is obtained by a robust regression method. Image sparsity can also be increased and thereby potentially benefit CS reconstruction in the following chapters. Chapter 5 investigates the optimal k-space sampling patterns for the 3D accelerated femoral artery FBI sequence. A variable density Poisson-disk with a fully sampled centre region and missing partial Fourier fractions is employed for k-space undersampling in the ky-kz plane. Several key parameters in sampling pattern design, such as partial Fourier sampling ratios, fully sampled centre region size and density decay factor, are evaluated and optimised. Chapter 6 introduces several reconstruction strategies for accelerated subtractive NCE-MRA. A new reconstruction method, k-space subtraction with phase and intensity correction (KSPIC), is developed. By performing subtraction in k-space, KSPIC can exploit the sparsity of subtracted angiogram data and potentially improve the reconstruction performance. A phase correction procedure is used to restore the polarity of negative signals caused by subtraction. The intensity correction method proposed in Chapter 4 is also incorporated in KSPIC as it improves background suppression and thereby sparsity. The highly accelerated technique can be used not only to reduce the acquisition time, but also to enable imaging with increased resolution with no time penalty. A time-efficient high-resolution FBI technique is proposed in Chapter 7. By employing KSPIC and modifying the flow-compensation/spoiled gradients, the image matrix size can be increased from 256×256 to up to 512×512 without prolonging the acquisition time. Chapter 8 summarises the overall achievements and limitations of this thesis, as well as outlines potential future research directions.Cambridge Trust China Scholarship Council Addenbrooke’s Charitable Trust National Institute of Health Research, Cambridge Biomedical Research Cente

Modelling Blood Flow and Metabolism in the Preclinical Neonatal Brain during and Following Hypoxic-Ischaemia

Hypoxia-ischaemia (HI) is a major cause of neonatal brain injury, often leading to long-term damage or death. In order to improve understanding and test new treatments, piglets are used as preclinical models for human neonates. We have extended an earlier computational model of piglet cerebral physiology for application to multimodal experimental data recorded during episodes of induced HI. The data include monitoring with near-infrared spectroscopy (NIRS) and magnetic resonance spectroscopy (MRS), and the model simulates the circulatory and metabolic processes that give rise to the measured signals. Model extensions include simulation of the carotid arterial occlusion used to induce HI, inclusion of cytoplasmic pH, and loss of metabolic function due to cell death. Model behaviour is compared to data from two piglets, one of which recovered following HI while the other did not. Behaviourally-important model parameters are identified via sensitivity analysis, and these are optimised to simulate the experimental data. For the non-recovering piglet, we investigate several state changes that might explain why some MRS and NIRS signals do not return to their baseline values following the HI insult. We discover that the model can explain this failure better when we include, among other factors such as mitochondrial uncoupling and poor cerebral blood flow restoration, the death of around 40% of the brain tissue. Copyright

Design and Fabrication of Cell-laden Gelatin Methacrylated Hydrogel Scaffold for Improving Biotransportation

One of the main goals of Tissue Engineering (TE), which has been developed rapidly over the recent years, is to re-create organs or tissues in vitro or in vivo with mimicked the anatomy and functions of body systems. Nowadays, replacing damaged tissues or organs has been a main focus in this field for addressing a significant shortage of donor tissues. Vascularisation plays a crucial role in supplying cells and tissue with essential oxygen and nutrients and removing waste products from the engineered tissue constructs. Any issue in nutrient perfusion and mass transport could significantly restrict construct development to dimensions smaller than clinically useful size, thus limiting the ability for in vivo integration. The main objectives of this study are to develop a novel framework for computational design using topology optimisation and microfabrication of 3D scaffolds using gelatin-based hydrogels (GelMa), allowing artificial vascularisation in vitro for testing if the framework is valid through the investigation into cellular viability inside the construct. In this thesis, computational models were first generated to simulate oxygen transport through solving the diffusion equation. The diffusion models are then used to optimise scaffold topology. By means of microfabrication technologies, hydrogel-based constructs were fabricated to prototype the sophisticated scaffolds. Cellular viability study was also performed to validate computational simulations and design. The results showed a higher cellular survival rate in optimally patterned constructs than the control. In summary, the work presented here is not only technically simple and cost-effective, but also establishes an effective approach to the design and fabrication of a vascularised biodegradable and scaffold-free constructs. The proposed methodology will be of considerable implication for engineering bulk tissue constructs which require sufficient ongoing vascularization in the future

Computational fluid dynamics modelling in cardiovascular medicine

This paper reviews the methods, benefits and challenges associated with the adoption and translation of computational fluid dynamics (CFD) modelling within cardiovascular medicine. CFD, a specialist area of mathematics and a branch of fluid mechanics, is used routinely in a diverse range of safety-critical engineering systems, which increasingly is being applied to the cardiovascular system. By facilitating rapid, economical, low-risk prototyping, CFD modelling has already revolutionised research and development of devices such as stents, valve prostheses, and ventricular assist devices. Combined with cardiovascular imaging, CFD simulation enables detailed characterisation of complex physiological pressure and flow fields and the computation of metrics which cannot be directly measured, for example, wall shear stress. CFD models are now being translated into clinical tools for physicians to use across the spectrum of coronary, valvular, congenital, myocardial and peripheral vascular diseases. CFD modelling is apposite for minimally-invasive patient assessment. Patient-specific (incorporating data unique to the individual) and multi-scale (combining models of different length-And time-scales) modelling enables individualised risk prediction and virtual treatment planning. This represents a significant departure from traditional dependence upon registry-based, populationaveraged data. Model integration is progressively moving towards 'digital patient' or 'virtual physiological human' representations. When combined with population-scale numerical models, these models have the potential to reduce the cost, time and risk associated with clinical trials. The adoption of CFD modelling signals a new era in cardiovascular medicine. While potentially highly beneficial, a number of academic and commercial groups are addressing the associated methodological, regulatory, education-And service-related challenges