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X-ray imaging dose from serial cone-beam CT (CBCT) scans raises a clinicalconcern in most image guided radiation therapy procedures. It is the goal ofthis paper to develop a fast GPU-based algorithm to reconstruct high qualityCBCT images from undersampled and noisy projection data so as to lower theimaging dose. For this purpose, we have developed an iterative tight frame (TF)based CBCT reconstruction algorithm. A condition that a real CBCT image has asparse representation under a TF basis is imposed in the iteration process asregularization to the solution. To speed up the computation, a multi-gridmethod is employed. Our GPU implementation has achieved high computationalefficiency and a CBCT image of resolution 512\times512\times70 can bereconstructed in ~5 min. We have tested our algorithm on a digital NCAT phantomand a physical Catphan phantom. It is found that our TF-based algorithm is ableto reconstrct CBCT in the context of undersampling and low mAs levels. We havealso quantitatively analyzed the reconstructed CBCT image quality in terms ofmodulation-transfer-function and contrast-to-noise ratio under various scanningconditions. The results confirm the high CBCT image quality obtained from ourTF algorithm. Moreover, our algorithm has also been validated in a realclinical context using a head-and-neck patient case. Comparisons of thedeveloped TF algorithm and the current state-of-the-art TV algorithm have alsobeen made in various cases studied in terms of reconstructed image quality andcomputation efficiency.

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