Program Information
Improved Synthetic 4D-MRI Using Linear Polynomial Fitting Model
J Yang1,2*, F Yin1, H Wang2 , Z Chang1, B Czito1, M Bashir3,M Palta1, J Cai1, (1) Department of Radiation Oncology, Duke University Medical Center, Durham, NC, (2) School of Information Science and Engineering, Shandong University, Jinan, Shandong, China, (3) Department of Radiology, Duke University Medical Center, Durham, NC
Presentations
MO-G-18C-4 Monday 4:30PM - 6:00PM Room: 18CPurpose: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 4D-MRI, original synthetic images and improved synthetic images. These were analyzed in the superior-inferior (SI), anterior-posterior (AP), and medial-lateral (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 4D-MRI caused by registration errors using the linear polynomial fitting model without much loss of respiratory motion information.
Funding Support, Disclosures, and Conflict of Interest: NIH (1R21CA165384-01A1), Golfers Against Cancer (GAC) Foundation, The China Scholarship Council (CSC)
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