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A hybrid deformable registration method to generate motion-compensated 3D virtual MRI for fusion with interventional real-time 3D ultrasound
Journal article   Peer reviewed

A hybrid deformable registration method to generate motion-compensated 3D virtual MRI for fusion with interventional real-time 3D ultrasound

Jhimli Mitra, Chitresh Bhushan, Soumya Ghose, David Mills, Aqsa Patel, Heather Chan, Matthew Tarasek, Thomas Foo, Shane Wells, Sydney Jupitz, …
International journal for computer assisted radiology and surgery, Vol.18(8), pp.1501-1509
08/01/2023
DOI: 10.1007/s11548-023-02833-1
PMCID: PMC12110320
PMID: 36648702
url
https://pmc.ncbi.nlm.nih.gov/articles/PMC12110320/pdf/nihms-2080795.pdfView
Open Access

Abstract

Purpose Ultrasound is often the preferred modality for image-guided therapy or treatment in organs such as liver due to real-time imaging capabilities. However, the reduced conspicuity of tumors in ultrasound images adversely impacts the precision and accuracy of treatment delivery. This problem is compounded by deformable motion due to breathing and other physiological activity. This creates the need for a fusion method to align interventional US with pre-interventional modalities that provide superior soft-tissue contrast (e.g., MRI) to accurately target a structure-of-interest and compensate for liver motion. Method In this work, we developed a hybrid deformable fusion method to align 3D pre-interventional MRI and 3D interventional US volumes to target the structures-of-interest in liver accurately in real-time. The deformable multimodal fusion method involved an offline alignment of a pre-interventionMRI with a pre-interventionUS volume using a traditional registration method, followed by real-time prediction of deformation using a trained deep-learning model between interventional US volumes across different respiratory states. This framework enables motion-compensated MRI-US image fusion in real-time for image-guided treatment. Results The proposed hybrid deformable registration method was evaluated on three healthy volunteers across the pre-intervention MRI and 20 US volume pairs in the free-breathing respiratory cycle. The mean Euclidean landmark distance of three homologous targets in all three volunteers was less than 3 mm for percutaneous liver procedures. Conclusions Preliminary results show that clinically acceptable registration accuracies for near real-time, deformable MRI-US fusion can be achieved by our proposed hybrid approach. The proposed combination of traditional and deep-learning deformable registration techniques is thus a promising approach formotion-compensated MRI-US fusion to improve targeting in image-guided liver interventions.
 Engineering Surgery Technology Engineering, Biomedical Life Sciences & Biomedicine Radiology, Nuclear Medicine & Medical Imaging Science & Technology

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