Abstract No. 49 Motion Compensation in 3D MRI-US Fusion Using Fast Deformable Registration: A Feasibility Study for Real-Time Intervention

Abstract

The heterogeneity of hepatocellular carcinoma may be better seen with magnetic resonance imaging (MRI) compared with CT due to its high soft tissue contrast, providing precise tumor targets during therapy, while ultrasound (US) remains as the imaging modality for real-time guidance. Image fusion methods existing in clinical workflows involve rigid registration only and fail to compensate for liver motion in US. In this work, we present a hybrid deformable fusion method to align pre-interventional 3D MRI and interventional 3D US in real-time. Multimodal pre-interventional MRI (pMRI) and US (pUS) volumes were obtained from 3 human volunteers using a simultaneous MRI-US acquisition system, with an MR-compatible, hands-free US probe. pMRI and pUS volumes were aligned using conventional deformable registration, as it is not time critical. Deep learning (DL)–based registration was used for real-time fusion of pUS to iUS, and consecutive iUS volumes in near real time. The predicted DL deformation fields were used to deform the pMRI to match each US volume. US data with respiration were collected at a temporal resolution of 4.2 volumes/sec. 1600 US volumes from one volunteer was used for DL training. The hybrid deformable registration method was evaluated for pMRI and 20 US volume alignments for each volunteer. Mean Euclidean distance error between expert placed landmarks and predicted positions of landmarks after image alignment were computed. Table 1 shows the mean landmark error (LE) and computation times for the hybrid (HDR) and conventional deformable registration (CDR) method aligning pMRI and 20 US volumes. Feasibility of a multimodal hybrid deformable registration method with clinically acceptable registration accuracy and low latency was shown. The method for motion compensation may improve tumor targeting in interventional procedures including liver ablation.