Program Information
3D Fluoroscopic Image Generation From Patient-Specific 4DCBCT-Based Motion Models Derived From Clinical Patient Images
S Dhou1*, D Ionascu2 , W Cai3 , M Hurwitz4 , C Williams5 , J Lewis6 , (1) Brigham and Women's Hospital / Harvard Medical School, Boston, MA (2) William Beaumont Hospital, Royal Oak, MI, (3) Brigham and Women's Hospital / Harvard Medical School, Boston, MA (4) Brigham and Women's Hospital / Harvard Medical School, Boston, MA, (5) Brigham and Women's Hospital / Harvard Medical School, Boston, MA (6) University of California at Los Angeles, Los Angeles, CA
Presentations
SU-C-209-2 (Sunday, July 31, 2016) 1:00 PM - 1:55 PM Room: 209
Purpose: We develop a method to generate time varying volumetric images (3D fluoroscopic images) using patient-specific motion models derived from four-dimensional cone-beam CT (4DCBCT).
Methods: Motion models are derived by selecting one 4DCBCT phase as a reference image, and registering the remaining images to it. Principal component analysis (PCA) is performed on the resultant displacement vector fields (DVFs) to create a reduced set of PCA eigenvectors that capture the majority of respiratory motion. 3D fluoroscopic images are generated by optimizing the weights of the PCA eigenvectors iteratively through comparison of measured cone-beam projections and simulated projections generated from the motion model. This method was applied to images from five lung-cancer patients. The spatial accuracy of this method is evaluated by comparing landmark positions in the 3D fluoroscopic images to manually defined ground truth positions in the patient cone-beam projections.
Results: 4DCBCT motion models were shown to accurately generate 3D fluoroscopic images when the patient cone-beam projections contained clearly visible structures moving with respiration (e.g., the diaphragm). When no moving anatomical structure was clearly visible in the projections, the 3D fluoroscopic images generated did not capture breathing deformations, and reverted to the reference image. For the subset of 3D fluoroscopic images generated from projections with visibly moving anatomy, the average tumor localization error and the 95th percentile were 1.6 mm and 3.1 mm respectively.
Conclusion: This study showed that 4DCBCT-based 3D fluoroscopic images can accurately capture respiratory deformations in a patient dataset, so long as the cone-beam projections used contain visible structures that move with respiration. For clinical implementation of 3D fluoroscopic imaging for treatment verification, an imaging field of view (FOV) that contains visible structures moving with respiration should be selected. If no other appropriate structures are visible, the images should include the diaphragm.
Funding Support, Disclosures, and Conflict of Interest: This project was supported, in part, through a Master Research Agreement with Varian Medical Systems, Inc, Palo Alto, CA.
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