Fast Parametric Registration and Machine Learning Analysis of Whole-Body MRI Volumes for Age-Related Changes

Abstract: Using an extensive dataset provided by the UK Biobank, this project intended to develop methods for registering whole body MRI volumes and analyzing the changes in the body due to ageing. The registration method is developed using the pTV image registration module, which employs a fast registration approach based on parametric total-variation to align volumes to the same local coordinate frames of the reference, for point-wise anatomical region correspondence. The performance was evaluated using RMS error and Jacobian determinant measures. The changes in liver fat as the body aged were studied, and it was found that there was a weak correlation between age and liver fat. Based on variations of the liver fat over time and other features, machine learning was utilized to classify the status of Type II Diabetes. Results are discussed in terms of the correctness of the image registration method,and the changes in the average liver fat of the participants. Recall was used as the model metric for the classifier owing to the minimization of type II error.