GPU-based fast cone beam CT reconstruction from undersampled and noisy projection data via total variation

Abstract

Purpose: Cone-beam CT(CBCT) plays an important role in image guided radiation therapy(IGRT). However, the large radiationdose from serial CBCT scans in most IGRT procedures raises a clinical concern, especially for pediatric patients who are essentially excluded from receiving IGRT for this reason. The goal of this work is to develop a fast GPU-based algorithm to reconstructCBCT from undersampled and noisy projection data so as to lower the imagingdose. Methods: The CBCT is reconstructed by minimizing an energy functional consisting of a data fidelity term and a total variation regularization term. The authors developed a GPU-friendly version of the forward-backward splitting algorithm to solve this model. A multigrid technique is also employed. Results: It is found that 20–40 x-ray projections are sufficient to reconstructimages with satisfactory quality for IGRT. The reconstruction time ranges from 77 to 130 s on an NVIDIA Tesla C1060 (NVIDIA, Santa Clara, CA) GPU card, depending on the number of projections used, which is estimated about 100 times faster than similar iterative reconstruction approaches. Moreover, phantom studies indicate that the algorithm enables the CBCT to be reconstructed under a scanning protocol with as low as 0.1 mA s/projection. Comparing with currently widely used full-fan head and neck scanning protocol of ∼ 360 projections with 0.4 mA s/projection, it is estimated that an overall 36–72 times dose reduction has been achieved in our fast CBCTreconstruction algorithm. Conclusions: This work indicates that the developed GPU-based CBCTreconstruction algorithm is capable of lowering imagingdose considerably. The high computation efficiency in this algorithm makes the iterative CBCTreconstruction approach applicable in real clinical environments.