(Wednesday, 8/1/2018) 1:45 PM - 3:45 PM
Room: Karl Dean Ballroom B1
Purpose: To develop a technique to estimate on-board VC-MRI using multi-slice undersampled cine images reconstructed using spatio-temporal k-space data, patient prior 4D MRI, motion modeling and free-form deformation for real-time 3D target verification of lung radiotherapy.
Methods: A previous method has been developed to generate on-board VC-MRI by deforming prior MRI images based on a motion model (MM) extracted from prior 4D-MRI and a single-slice on-board 2D-cine image. In this study, free-form deformation (FD) was introduced to estimate VC-MRI from multi-slice 2D cine images to correct for inaccuracies in the MM. The 2D-cine images were reconstructed using only 10% of total k-space based on a novel k-t SLR reconstruction method, which uses spatio-temporal low-rank decomposition in the k-space. The method was evaluated using XCAT simulation of lung cancer patients with various anatomical and respiratory changes from prior 4D MRI to onboard volume. The accuracy was evaluated using Volume Percent Difference VPD, Volume Dice Coefficient (VDC) and Center of Mass Shift (COMS) of the estimated tumor volume. Effects of Region of Interest (ROI) selection, 2D-cine slice orientation, slice number and slice location on the estimation accuracy were evaluated.
Results: VC-MRI estimated using 10 undersampled sagittal 2D cine MRIs achieved VPD/VDC/COMS of 9.77±3.71%/0.95±0.02/0.75±0.26mm among all scenarios based on estimation with MM and FD in the ROI. The FD optimization improved estimation significantly for scenarios with anatomical changes, such as tumor size change. Using ROI FD achieved better estimation than global FD. Changing the multi-slice orientation to axial, coronal, and axial/sagittal orthogonal reduced the accuracy of VC-MRI. VC-MRI estimation using slices sampled uniformly through the tumor achieved better accuracy than using slices sampled non-uniformly.
Conclusion: Preliminary studies showed that it is feasible to generate VC-MRI from FD and multi-slice undersampled 2D cine images reconstructed using spatio-temporal k-space data for real-time 3D target verification.
Funding Support, Disclosures, and Conflict of Interest: NIH Grant No: R01 CA-184173
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