AbstractMagnetic resonance imaging (MRI) is a versatile imaging modality that is widely used to assess the morphology, function and tissue characteristics of the human heart. However, evaluation of the fetal heart relies principally on ultrasound, as fetal cardiac MRI is limited by the challenges associated with imaging a small, rapidly beating heart that is subject to various regular and spontaneous movements. If these challenges can be overcome, MRI has the potential to provide multi-planar and volumetric images that may serve as an adjunct to ultrasound for screening of structural cardiac anomalies.
This thesis explores the strengths and limitations of MRI with the aim of develop-ing an acquisition and reconstruction strategy to visualise the fetal cardiovascular system in the presence of fetal and maternal motion. Methods are presented that use rapid dynamic MRI to resolve the beating fetal heart in utero and retrospec-tive image-based techniques to achieve cardiac synchronisation, motion correction, outlier rejection and cine reconstruction. When combined, these techniques form a motion-tolerant framework for fetal cardiac MRI.
The framework is first established for two-dimensional cine reconstruction from single-slice dynamic balanced steady state free precession MRI. The framework is then extended to three-dimensions using volumetric reconstruction techniques for scat-tered multi-planar images. Finally, the potential for simultaneous reconstruction of a three-dimensional cine and fully-encoded velocity mapping is explored in a prelimi-nary study using the velocity information encoded in the phase of MRI data.
The proposed frameworks were tested on human fetal subjects, including many with congenital heart disease, imaged at 1.5T, and validated using in vivo and simulation data. The results show this is a promising framework for comprehensive fetal cardiac MRI, overcoming the key challenge of motion to enable further detail in studies of the fetal heart and great vessels.
|Date of Award||2019|
|Supervisor||Jo Hajnal (Supervisor) & Shaihan Malik (Supervisor)|