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Cone-beam CT (CBCT) has been widely used in image guided radiation therapy(IGRT) to acquire updated volumetric anatomical information before treatmentfractions for accurate patient alignment purpose. However, the excessive x-rayimaging dose from serial CBCT scans raises a clinical concern in most IGRTprocedures. The excessive imaging dose can be effectively reduced by reducingthe number of x-ray projections and/or lowering mAs levels in a CBCT scan. Thegoal of this work is to develop a fast GPU-based algorithm to reconstruct highquality CBCT images from undersampled and noisy projection data so as to lowerthe imaging dose. The CBCT is reconstructed by minimizing an energy functionalconsisting of a data fidelity term and a total variation regularization term.We developed a GPU-friendly version of the forward-backward splitting algorithmto solve this model. A multi-grid technique is also employed. We test our CBCTreconstruction algorithm on a digital NCAT phantom and a head-and-neck patientcase. The performance under low mAs is also validated using a physical Catphanphantom and a head-and-neck Rando phantom. It is found that 40 x-rayprojections are sufficient to reconstruct CBCT images with satisfactory qualityfor IGRT patient alignment purpose. Phantom experiments indicated that CBCTimages can be successfully reconstructed with our algorithm under as low as 0.1mAs/projection level. Comparing with currently widely used full-fanhead-and-neck scanning protocol of about 360 projections with 0.4mAs/projection, it is estimated that an overall 36 times dose reduction hasbeen achieved with our algorithm. Moreover, the reconstruction time is about130 sec on an NVIDIA Tesla C1060 GPU card, which is estimated ~100 times fasterthan similar iterative reconstruction approaches.

GPU-based Fast Low-dose Cone Beam CT Reconstruction via Total Variation