Affiliations: Center for Advanced Radiotherapy Technologies,
Department of Radiation Oncology, University of California San Diego, La Jolla, CA, USA | Department of Mathematics, University of California
Los Angeles, Los Angeles, CA, USA
Note: [] Corresponding author: Steve B. Jiang, PhD, Center for Advanced
Radiotherapy Technologies, Department of Radiation Oncology, University of
California San Diego, La Jolla, CA 92037-0843, USA. Tel.: +1 858 822 5129; Fax:
+1 858 822 6078; E-mail: sbjiang@ucsd.edu
Abstract: X-ray imaging dose from serial Cone-beam CT (CBCT) scans raises a
clinical concern in most image guided radiation therapy procedures. The goal of
this paper 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 develop a GPU-friendly version of a forward-backward splitting algorithm to
solve this problem. A multi-grid technique is also employed. We test our CBCT
reconstruction algorithm on a digital phantom and a head-and-neck patient case.
The performance under low mAs is also validated using physical phantoms. It is
found that 40 x-ray projections are sufficient to reconstruct CBCT images with
satisfactory quality for clinical purposes. Phantom experiments indicate that
CBCT images can be successfully reconstructed under 0.1 mAs/projection.
Comparing with the widely used head-and-neck scanning protocol of about 360
projections with 0.4 mAs/projection, an overall 36 times dose reduction has
been achieved. The reconstruction time is about 130 sec on an NVIDIA Tesla
C1060 GPU card, which is estimated ∼ 100 times faster
than similar regularized iterative reconstruction approaches.
