Anatomically Guided Registration for Multimodal Images

Abstract

With an increase in full-body scans and longitudinal acquisitions to track disease progression, it becomes significant to find correspondence between multiple images. One example would be the monitoring size/location of tumors using PET images during chemotherapy to determine treatment progression. While there is a need to go beyond a single parametric transform to recover misalignments, pure deformable solutions become complex, time-consuming and unnecessary at times. Simple anatomically guided approach for whole body image registration offers enhanced alignment of large coverage inter-scan studies. In this experiment, we provide anatomy specific transformations to capture their independent motions. This solution is characterized by an automatic segmentation of regions in the image, followed by a custom registration and volume stitching. We have tested this algorithm on phantom images as well as clinical longitudinal datasets. We were successful in proving that decoupling transformations improves the overall registration quality.