The overall objective of this project is to dramatically improve the capability of fetal MRI for diagnosis, analysis, and prognosis of high-risk pregnancies. Accurate analysis obtained by fetal MRI is crucial in evaluating the highly variable aetiology and poorly understood pathophysiology of fetal central nervous system developmental disorders which affect about one-half percent of pregnancies. Nevertheless, fetal MRI is limited to two- dimensional acquisitions by the small signal available from the small fetal brain, and by intermittent fetal motion that disrupts spatial encoding necessary for advanced three-dimensional volumetric MRI. In order to address this limitation and reveal the power of fetal MRI, we propose novel imaging and image processing technology pursuing four specific aims in this project;
Aim 1, which is pivotal to our overall objective, involves super- resolution reconstruction of three-dimensional high spatial resolution volumetric T2w images of the fetal brain.
Aim 2 involves the construction of a spatiotemporal fetal brain atlas.
Aim 3 involves the comparison of fetal brain biometry and evaluation using 2D MRI, 2D sonography and 3D MRI. Finally in Aim 4 improved assessment of ventriculomegaly is considered using 3D fetal MRI. Ventriculomegaly is the most frequently observed fetal brain abnormality affecting about 0.1-0.2 percent of fetuses.
Aims 1 and 2 involve the development of new technology for clinical use and Aims 3 and 4 involve both technical developments and hypothesis tests to see how much improvement is achieved in the evaluation, diagnosis, and analysis of fetal brain abnormalities using the developed technology as compared to the current practice.
This research challenges current fetal brain MRI practice, which is limited to expert evaluation of 2D slices. This research will develop 3D fetal brain MRI reconstruction, and quantitative 3D fetal biometry, and demonstrate the superiority of 3D fetal brain MRI for the in vivo evaluation of fetal brain developmental disorders. This will lead to more accurate diagnosis and prognosis, and improved patient outcomes.
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