MO-G-18C-04: Improved Synthetic 4D-MRI Using Linear Polynomial Fitting Model.

PURPOSE: To reduce deformable image registration error by fitting the displacement vector field (DVF) to smooth the motion trajectory of each pixel in synthetic 4D-MRI. METHODS: Five patients with cancers in the liver were enrolled in this study. For a 4D MR image data set, the DVF matrices relative to a specific reference phase were calculated using an in-house deformable image registration based on b-spline. The displacement trajectory of each voxel throughout the respiratory cycle was constituted by concatenating the corresponding displacement values from all DVF matrices. A linear polynomial fitting model was then used to fit the DVFs in three spatial and the temporal dimension, respectively. By warpping the source MR images using the remodeled DVFs, we synthesized MR images at selected phases. Tumor motion trajectories were derived from source 4DMRI, original synthetic images and improved synthetic images. These were analyzed in the superior-inferior (SI), anterior-posterior (AP), and mediallateral (ML) directions, respectively. Correlation coefficients (CC) and differences in motion amplitude (D) were calculated for comparison. RESULTS: For all patients, tumor motion trajectories were strongly correlated between source 4D-MRI images and improved synthetic 4D-MRI (mean CC 0.98±0.01). Differences in motion amplitude were small (mean D 0.46±0.14 mm) in all directions. Correlation between source 4D-MRI and original synthetic 4D-MRI was slightly less strong (mean CC 0.97±0.01) and motion amplitude differences were slightly larger (0.55±0.19 mm). CONCLUSION: The feasibility of synthesizing T2w 4D-MRI using remodeled DVFs has been investigated in this study. Preliminary results in oncologic patients demonstrated the potential of reducing inaccuracies in original synthetic 4DMRI caused by registration errors using the linear polynomial fitting model without much loss of respiratory motion information. NIH (1R21CA165384-01A1), Golfers Against Cancer (GAC) Foundation, The China Scholarship Council (CSC).

Duke Scholars

Author Zheng Chang Radiation Oncology

Author Mustafa Shadi Rifaat Bashir Radiology, Abdominal Imaging

Author Manisha Palta Radiation Oncology