Technical Note: Cumulative Dose Modeling For Organ Motion Management In Mri-Guided Radiation Therapy

Purpose To develop a method for continuous online dose accumulation during irradiation in MRI-guided radiation therapy (MRgRT) and to demonstrate its application in evaluating the impact of internal organ motion on cumulative dose.Methods An intensity-modulated radiation therapy (IMRT) treatment plan is partitioned into its unique apertures. Dose for each planned aperture is calculated using Monte Carlo dose simulation on each phase of a four-dimensional computed tomography (4D-CT) dataset. Deformable image registration is then performed both (a) between each frame of a cine-MRI acquisition obtained during treatment and a reference frame, and (b) between each volume of the 4D-CT phases and a reference phase. These registrations are used to associate each cine image with a 4D-CT phase. Additionally, for each 4D-CT phase, the deformation vector field (DVF) is used to warp the pre-calculated dose volumes per aperture onto the reference CT dataset. To estimate the dose volume delivered during each frame of the cine-MRI acquisition, we retrieve the pre-calculated warped dose volume for the delivered aperture on the associated 4D-CT phase and adjust it by a rigid translation to account for baseline drift and instances where motion on the cine image exceeds the amplitude observed between 4D-CT phases.Results The proposed dose accumulation method is retrospectively applied to a liver cancer case previously treated on an MRgRT platform. Cumulative dose estimated for free-breathing and respiration-gated delivery is compared against dose calculated on static anatomy. In this sample case, the target minimum dose and D98 varied by as much as 5% and 7%, respectively.Conclusion We demonstrate a technique suitable for continuous online dose accumulation during MRgRT. In contrast to other approaches, dose is pre-calculated per aperture and phase and then retrieved based on a mapping scheme between cine MRI and 4D-CT datasets, aiming at reducing the computational burden for potential real-time applications.