GPU-based Iterative Cone Beam CT Reconstruction Using Tight Frame Regularization

Xun Jia, Bin Dong, Yifei Lou, Steve B. Jiang
Center for Advanced Radiotherapy Technologies, University of California San Diego, La Jolla, CA 92037-0843, USA
arXiv:1008.2042 [physics.med-ph] (12 Aug 2010)


   title={GPU-based Iterative Cone Beam CT Reconstruction Using Tight Frame Regularization},

   author={Jia, X. and Dong, B. and Lou, Y. and Jiang, S.B.},

   journal={Arxiv preprint arXiv:1008.2042},



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X-ray imaging dose from serial cone-beam CT (CBCT) scans raises a clinical concern in most image guided radiation therapy procedures. It is the goal of this paper to develop a fast GPU-based algorithm to reconstruct high quality CBCT images from undersampled and noisy projection data so as to lower the imaging dose. For this purpose, we have developed an iterative tight frame (TF) based CBCT reconstruction algorithm. A condition that a real CBCT image has a sparse representation under a TF basis is imposed in the iteration process as regularization to the solution. To speed up the computation, a multi-grid method is employed. Our GPU implementation has achieved high computational efficiency and a CBCT image of resolution 512x512x70 can be reconstructed in about ~139 sec. We have tested our algorithm on a digital NCAT phantom and a physical Catphan phantom. It is found that our TF-based algorithm leads to much higher CBCT quality than those obtained from a conventional FDK algorithm in the context of undersampling and low mAs levels. Higer spatial resolution has also been achieved comparing to a recently developed TV-based iterative CBCT reconstruction algorithm. We have also quantitatively analyzed the reconstructed CBCT image quality in terms of modulation-transfer-function and contrast-to-noise ratio under various scanning conditions. The results confirm the high CBCT image quality obtained from our TF algorithm. Moreover, our algorithm has also been validated in a real clinical context using a head-and-neck patient case.
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