Understanding normal and abnormal patterns of brain development in fetuses and neonates is a key factor in early detection of developmental disorders. This project proposal seeks to develop and refine novel magnetic resonance image reconstruction and analysis methodology to allow, for the first time, the mapping of in-utero fetal brain development. One of the most common abnormalities detected by clinical imaging of the developing fetal brain is ventriculomegaly, which, despite the absence of other clinical or imaging findings, is associated with neurodevelopmental disabilities in infancy and childhood in up to 50% of cases. Although ultrasound allows diagnosis of the condition, it has not been able to distinguish those fetus that will have poor neurological outcome from those with normal outcome. Recent developments in fast magnetic resonance imaging have permitted the use of MRI to study the fetal anatomy and this technique is now being routinely used at a small number of sites around the world including UCSF. However, MR imaging of the fetal brain is still challenging because of imaging distortions caused by motion of the fetus within the mother and by artifacts caused by the surrounding maternal anatomy. Higher resolution or 3D acquisitions are not possible because of motion of the fetus during the acquisition time^ required. The current clinical 2D slice data individually provide limited resolution and contrast and, most importantly, often contain severe motion corruption between slices. This project is motivated by the observation that it is possible to apply computer vision and image processing techniques to correct relative motion between the multiple stacks of low resolution fetal slices, and create a single volumetric image with high isotropic 3D resolution and consistent geometry. Such higher resolution images provide structure that may be analyzed using computational morphometric techniques that can detect subtle focal differences in the pattern of tissue volume, location and surface folding. This project will combine such powerful techniques with extensive fetal and neonatal imaging experience at UCSF, allowing direct clinical application of the methodology to study morphologic aberrations associated with ventriculomegaly and to correlate these with clinical outcome. The ability to apply these computational techniques to in-utero data will provide an entirely new view of the developing brain, which promises to shed new light on early developmental problems both in fetuses and premature neonates.
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