GPU accelerated dynamic respiratory motion model correction for MRI-guided cardiac interventions.
OBJECTIVE
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.
Physical Sciences Platform and Schulich Research Centre, Sunnybrook Research Institute, Toronto, ON, M4N 3M5, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON, M5G 2M9, Canada. Electronic address: robert.xu@mail.utoronto.ca.
Robert
R
Xu
Physical Sciences Platform and Schulich Research Centre, Sunnybrook Research Institute, Toronto, ON, M4N 3M5, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON, M5G 2M9, Canada.
Graham A
GA
Wright
D006321:Heart / Q000502:physiology*; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008954:Models, Biological; D015656:Respiratory Mechanics
Cardiac interventions; Electrophysiology; GPU acceleration; Magnetic resonance imaging; Respiratory motion modeling