Deep-learning micro-CT perfusion quantification

This study investigates the use of a convolutional neural network to perform micro-CT perfusion quantification. The ability to quantify perfusion metrics such as blood flow, blood volume, and mean transit time provides valuable insights into tissue viability and function, aiding in diagnosis, treatment planning, and monitoring therapeutic responses. Preclinical micro-CT perfusion imaging holds significant promise for advancing our understanding of various physiological and pathological processes in small animal models. Various methods have been developed to quantify perfusion metrics from CT data; however, these methods have notable drawbacks, particularly their voxel-by-voxel nature which introduces significant noise and variability into the perfusion maps. In this work, we demonstrate a potential deep learning approach to perfusion quantification. The network input consisted of 20 timepoints along random geometric arrangements of typical time attenuation curves. The output of the network consisted of 4 parametric maps representing the numerical parameters of a gamma variate curve. This approach led to reduction in noise and accurate recreation of time attenuation curves.