  Cone-beam CT (CBCT) has been widely used in image guided radiation therapy
(IGRT) to acquire updated volumetric anatomical information before treatment
fractions for accurate patient alignment purpose. However, the excessive x-ray
imaging dose from serial CBCT scans raises a clinical concern in most IGRT
procedures. The excessive imaging dose can be effectively reduced by reducing
the number of x-ray projections and/or lowering mAs levels in a CBCT scan. The
goal of this work is 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. 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. We test our CBCT
reconstruction algorithm on a digital NCAT phantom and a head-and-neck patient
case. The performance under low mAs is also validated using a physical Catphan
phantom and a head-and-neck Rando phantom. It is found that 40 x-ray
projections are sufficient to reconstruct CBCT images with satisfactory quality
for IGRT patient alignment purpose. Phantom experiments indicated that CBCT
images can be successfully reconstructed with our algorithm under as low as 0.1
mAs/projection level. Comparing with currently widely used full-fan
head-and-neck scanning protocol of about 360 projections with 0.4
mAs/projection, it is estimated that an overall 36 times dose reduction has
been achieved with our algorithm. Moreover, the reconstruction time is about
130 sec on an NVIDIA Tesla C1060 GPU card, which is estimated ~100 times faster
than similar iterative reconstruction approaches.
