  Purpose: Cone-beam CT (CBCT) plays an important role in image guided
radiation therapy (IGRT). However, the large radiation dose 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
reconstruct CBCT from undersampled and noisy projection data so as to lower the
imaging dose. Methods: The CBCT is reconstructed by minimizing an energy
functional consisting of a data fidelity term and a total variation
regularization term. We developed a GPU-friendly version of the
forward-backward splitting algorithm to solve this model. A multi-grid
technique is also employed. Results: It is found that 20~40 x-ray projections
are sufficient to reconstruct images with satisfactory quality for IGRT. The
reconstruction time ranges from 77 to 130 sec on a NVIDIA Tesla C1060 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 our algorithm enables the CBCT to be reconstructed under
a scanning protocol with as low as 0.1 mAs/projection. Comparing with currently
widely used full-fan head and neck scanning protocol of ~360 projections with
0.4 mAs/projection, it is estimated that an overall 36~72 times dose reduction
has been achieved in our fast CBCT reconstruction algorithm. Conclusions: This
work indicates that the developed GPU-based CBCT reconstruction algorithm is
capable of lowering imaging dose considerably. The high computation efficiency
in this algorithm makes the iterative CBCT reconstruction approach applicable
in real clinical environments.
