4D Non-rigid registration of renal dynamic contrast enhanced MRI data

Abstract

This thesis presents a near-automatic non-rigid registration algorithm requiring minimal user interaction for renal dynamic contrast enhanced (DCE) MR images. The 12 patients? dataset (24 kidney volumes) to be registered were acquired on a 1.5T scanner of size 256 x 256 x 40 (voxel resolution of 1.66mm x 1.66mm x 2.5mm) with the number of static volumes in each dataset varying from 31 to 41. A multi-level registration algorithm is proposed to first account for initial large translational errors, followed by compensating for local deformations of the kidney. A graph-cut optimization technique integrating local gradient information into an energy function solves the initial problem of 3D translational registration. A motion/noise free pseudo ground-truth dataset is then estimated from the whole time sequence of each kidney dataset obtained after translational registration. Finally, the demons algorithm is used to register each 3-D volume (as floating image) to its corresponding estimated volume (as reference image) at each time frame. Experimental results on patient data demonstrate that the proposed algorithm is able to: (1) perform initial translational registration accurately with an error of up to 5 voxels; (2) correctly estimate the pseudo ground-truth dataset, and (3) achieve non-rigid registration of 4D time-series of renal DCE MRI data.