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GPU-Based 4D Cone-Beam CT Reconstruction Using Adaptive Meshing Method

Z Zhong

Z Zhong1*, X Gu1 , P Iyengar1 , W Mao1 , X Guo2 , J Wang1 , (1) UT Southwestern Medical Center, Dallas, TX, (2) University of Texas at Dallas, Richardson, TX


SU-D-207-4 (Sunday, July 12, 2015) 2:05 PM - 3:00 PM Room: 207

Due to the limited number of projections at each phase, the image quality of a four-dimensional cone-beam CT (4D-CBCT) is often degraded, which decreases the accuracy of subsequent motion modeling. One of the promising methods is the simultaneous motion estimation and image reconstruction (SMEIR) approach. The objective of this work is to enhance the computational speed of the SMEIR algorithm using adaptive feature-based tetrahedral meshing and GPU-based parallelization.

The first step is to generate the tetrahedral mesh based on the features of a reference phase 4D-CBCT, so that the deformation can be well captured and accurately diffused from the mesh vertices to voxels of the image volume. After the mesh generation, the updated motion model and other phases of 4D-CBCT can be obtained by matching the 4D-CBCT projection images at each phase with the corresponding forward projections of the deformed reference phase of 4D-CBCT. The entire process of this 4D-CBCT reconstruction method is implemented on GPU, resulting in significantly increasing the computational efficiency due to its tremendous parallel computing ability.

A 4D XCAT digital phantom was used to test the proposed mesh-based image reconstruction algorithm. The image result shows both bone structures and inside of the lung are well-preserved and the tumor position can be well captured. Compared to the previous voxel-based CPU implementation of SMEIR, the proposed method is about 157 times faster for reconstructing a 10-phase 4D-CBCT with dimension 256x256x150.

The GPU-based parallel 4D CBCT reconstruction method uses the feature-based mesh for estimating motion model and demonstrates equivalent image result with previous voxel-based SMEIR approach, with significantly improved computational speed.

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