Feasibility of real-time motion tracking using cine MRI during MR-guided radiation therapy for abdominal targets.

Purpose: Real-time high soft-tissue contrast magnetic resonance imaging (MRI) from the MR-Linac offers the best opportunity for accurate motion tracking during radiation therapy delivery via high-frequency two-dimensional (2D) cine imaging. This work investigates the efficacy of real-time organ motion tracking based on the registration of MRI acquired on MR-Linac.

Methods: Algorithms based on image intensity were developed to determine the three-dimensional (3D) translation of abdominal targets. 2D and 3D abdominal MRIs were acquired for 10 healthy volunteers using a high-field MR-Linac. For each volunteer, 3D respiration-gated T2 and 2D T2/T1-weighted cine in sagittal, coronal, and axial planes with a planar temporal resolution of 0.6 for 60 s was captured. Datasets were also collected on MR-compatible physical and virtual four-dimensional (4D) motion phantoms. Target contours for the liver and pancreas from the 3D T2 were populated to the cine and assumed as the ground-truth motion. We performed image registration using a research software to track the target 3D motion. Standard deviations of the error (SDE) between the ground-truth and tracking were analyzed.

Results: Algorithms using a research software were demonstrated to be capable of tracking arbitrary targets in the abdomen at 5 Hz with an overall accuracy of 0.6 mm in phantom studies and 2.1 mm in volunteers. However, this value is subject to patient-specific considerations, namely motion amplitude. Calculation times of < 50 ms provide a pathway of real-time motion tracking integration. A major challenge in using 2D cine MRI to track the target is handling the full 3D motion of the target.

Conclusions: Feasibility to track organ motion using intensity-based registration of MRIs was demonstrated for abdominal targets. Tracking accuracy of about 2 mm was achieved for the motion of the liver and pancreatic head for typical patient motion. Further development is ongoing to improve the tracking algorithm for large and complex motions.