Enabling the Assessment of Fetal Brain Development and Degeneration with Machine Learning

Project Summary Diffusion-weighted magnetic resonance imaging (dMRI) is the most promising tool for studying brain microstructure. However, the application of dMRI to the assessment of fetal brain in-utero is challenged by unpredictable motion, low signal-to-noise ratio, low spatial resolution, and imaging artifacts. While much effort has been spent on improving image acquisition and motion compensation techniques, data processing and analysis methods have remained largely unchanged. Existing biomarker estimation methods in fetal dMRI suffer from low accuracy and low reproducibility. Moreover, cross-subject and population studies require delineation of white matter (WM) tracts, which currently can only be performed via highly subjective and time-consuming manual segmentation. These shortcomings have significantly limited our ability to study the brain at this critical stage and to detect subtle changes in brain microstructure due to disorders. This proposed project will develop and validate a new generation of methods for analysis of fetal dMRI data. Unlike existing methods, which are based on biophysical models of the diffusion signal and mathematical model fitting, the new methods will rely on data-driven and machine learning techniques. Building on our pioneering works that have shown the potential of these methods, we will develop deep learning techniques for estimating microstructural biomarkers such as fractional anisotropy, neurite orientation dispersion, and fiber orientation distribution. The new methods will be based on two-stage transformer networks, which will be trained using dMRI data from preterm infants and fetuses. Moreover, we will develop methods that work with undersampled scans and provide a calibrated measure of estimation uncertainty. We will develop convolutional neural networks to segment WM tracts in the fetal brain based on the local fiber orientations. To address the noise in the input and target labels, we will build on our prior works on segmentation with noisy data and labels, shape-aware segmentation, and use of uncertainty to improve segmentation accuracy. The new technique will generate tracts automatically, with tracts that are indistinguishable from those created by the best human experts. We will evaluate the new methods using test-retest and bootstrapping methods and via assessment by experts in fetal brain microstructure and with histological knowledge of transient fetal fiber pathways. The new methods will enable assessment of fetal brain microstructure and the impact of neurodevelopmental disorders on tract-specific microstructure with a level of accuracy, detail, and reproducibility that is currently beyond reach. To definitively demonstrate the value and significance of the new methods, we will use them to assess the alterations in WM micro-structure due to congenital heart disease (CHD), which is the most common birth defect. In the process, we will produce the most comprehensive and detailed picture of the impact of CHD on the fetal brain microstructure ever attempted.