GPU accelerated dynamic respiratory motion model correction for MRI-guided cardiac interventions. (November 2016)

• Record Type: Journal Article
• Authors: Xu, Robert; Wright, Graham A.
• Is Part Of: Computer methods and programs in biomedicine. Volume 136(2016)
• Page Start: 31
• Page End: 43
• Publication Date: 2016-11
• Subjects: Electrophysiology -- Cardiac interventions -- Magnetic resonance imaging -- Respiratory motion modeling -- GPU acceleration; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2016.08.003
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: The highlight of this study is that a GPU accelerated dynamic motion model framework is described. The proposed method is able to correct for respiratory motion in realtime, and be adaptive to changes in breathing pattern. This methodology can be used to improve the accuracy of MRI guided cardiovascular interventions. Abstract: Background and Objectives: The use of pre-procedural magnetic resonance (MR) roadmap images for interventional guidance has limited anatomical accuracy due to intra-procedural respiratory motion of the heart. Therefore, the objective of this study is to explore the use of a rapidly updated dynamic motion model to correct for respiratory motion induced errors during MRI-guided cardiac interventions. The motivation for the proposed technique is to improve the accuracy of MRI guidance by taking advantage of the anatomical context provided by the high resolution prior images and the respiratory motion information present in a series of realtime MR images. Methods: We implemented a GPU accelerated image registration algorithm to derive the respiratory motion information and used the resulting transformation parameters to update an adaptive motion model once every heart cycle. In the subsequent heart cycle, the dynamic motion model could be used to predict the respiratory motion and provide a motion estimate to realign the prior volume with the realtime MR image. This iterative update and prediction process is then continuously repeated. Results: The GPU accelerated image registration algorithm could be completed in an average of 176.9 ± 14.0 ms, which is 139× faster than a CPU implementation. Thus, it was feasible to update the dynamic model once every heart cycle. The proposed dynamic model was also able to improve the registration accuracy from 86.0 ± 7.5% to 93.0 ± 3.3% in case of variable breathing patterns, as evaluated by the dice similarity coefficient of the left ventricular border overlap between the prior and realtime images. Conclusions: The feasibility of a dynamic motion correction framework was demonstrated. The resulting improvements may lead to more accurate MRI-guided cardiac interventions in the future.