Purpose: To provide a patient-specific optimization of the 2D cine-imaging plane in MR-guided radiotherapy with respiratory gating, based on pre-treatment 4D information.
Methods: The sampling directions in MR imaging can be selected arbitrarily, thus leading to different imaging planes in 2D cine-MR. Our method performs a multi-threaded grid search of candidate planes for cine-MR imaging, whose orientation can be adjusted at user-defined angular steps and total range. The optimization maximizes the peak-to-peak motion amplitude over the tumor trajectory, as depicted in the treatment planning 4D-CT. The GTV contours delineated on the 4D-CT motion phases are processed to create the corresponding ITV. Candidate planes are centered in the center of mass of the ITV, whose cross-sectional area is assumed to be proportional to the motion amplitude sampled with cine-MR. The imaging plane orientation is defined by rotation angles about the principal direction of the ITV, which provide the grid search initialization. The algorithm has been tested on artificial images (ellipsoids and cuboids), where the ground truth motion trace allows determining the optimal plane unambiguously. Finally, the algorithm has been applied to 16 clinical 4D-CT datasets including lung, liver and pancreas tumors.
Results: The algorithm checked up to 4275 planes, with a computational time proportional to the angular range in the grid search procedure. Optimal planes in the artificial images were returned as expected. In clinical cases, motion in the optimal plane was larger (10.3 mm on average, range [4.1, 17.4] mm) compared to the clinically used sagittal orientation (8.5 mm on average, range [2.3, 13.8] mm).
Conclusion: An algorithm for the optimization of the cine-MR imaging plane based on pre-treatment 4D-CT images has been implemented and tested. The optimal plane consistently provides a larger motion amplitude compared to the sagittal plane, thus effectively minimizing effects due to out of plane motion.
Funding Support, Disclosures, and Conflict of Interest: The Department of Radiation Oncology of the University Hospital of LMU Munich has research agreements with Brainlab, Elekta and ViewRay