Multiscale Registration of Real-Time and Prior MRI Data for Image-Guided Cardiac Interventions

Recently, there is a growing interest in using magnetic resonance imaging (MRI) to guide interventional procedures due to its excellent soft tissue contrast and lack of ionizing radiation compared to traditional radiographic guidance. One of these applications is the use of MRI to guide radio frequency ablation of anatomic substrates, within the left ventricle, responsible for ventricular tachycardia. However, different MRI acquisition schemes have significant tradeoffs between image quality and acquisition time. Guidance using high-quality preoperative 3-D MR images is limited in the case of cardiac interventions because the heart moves dynamically during the procedure. On the other hand, 2-D real-time MR images acquired during the intervention sacrifice image quality for shorter image acquisition time, leading to real-time positional updates of cardiac anatomy. Ideally, we wish to combine the advantages of live feedback from real-time images and accurate visualization of anatomical structures from preoperative images. Therefore, to improve the MRI guidance capabilities for cardiac interventions, we describe a novel multiscale rigid registration framework to correct for respiratory motion between the prior and real-time datasets. In the proposed approach, we use a weighted total variation flow algorithm to extract coarse-to-fine features from the input images and subsequently register the corresponding scales in a hierarchical manner. Registration experiments were performed with in vivo human imaging data, and the target registration error achieved was 1.51 mm. Thus, the feasibility of motion correction in an interventional setting has been demonstrated, which may lead to significant improvements in the guidance of cardiac interventions.