Quantifying Cardiac Chamber Motion for Noninvasive Stereotactic Cardiac Radiosurgery Using Deformable Image Registration

Abstract

Noninvasive Stereotactic Cardiac Radiosurgery (NSCR) is a novel treatment for drug resistant ventricular tachycardia. One of the major issues with this new treatment style is uncertainty in the target location due to cardiac and respiratory motion. This motion uncertainty results in planning target volumes that are 3 to 4 times the original target volume. Quantification of this motion requires accurate segmentation of the heart chambers throughout cardiac and respiratory phases. Manual segmentation of the heart is incredibly time consuming and not feasible to be done for each patient. This work aimed to develop a semi-automated workflow that takes manual contours of the heart chambers in one phase of a breath-hold ECG cardiac 4DCT and warp these contours onto all other phases. Four chambers of the heart and the aorta were manually contoured on one phase of a patient's breath-hold ECG cardiac 4DCT. A single shot deep learning deformable image registration code called GroupRegNet was used to propagate the contours onto the additional phases. This code uses a global smoothness parameter that does not work well in cardiac cases. To account for the type of motion that is expected in the cardiac 4DCTs, a piecewise smoothing parameter that only enforces smoothness within each of the original contours was added to the code. To determine the accuracy of the registration, a second phase with significant motion from the contoured phase was also manually contoured. The Dice coefficient and surface-to-surface distance between the manual and propagated contours were calculated. The tables below show the improvement of the contour propagation accuracy, measured as the Dice coefficients and the mean surface-to-surface distance using the original global smoothing and piecewise-smoothing only within the original contours. These results show an improvement in registration accuracy due to using a piecewise smoothing factor instead of global smoothness. Along with improvements in registration accuracy, this work shows promise for this semi-automated methodology to be further improved with the hope of fully automating the process and eventually implemented into the workflow for NSCR treatment to quantify cardiac chamber motion more accurately and eventually reduce the margins during radiation treatment.