  Four-dimensional computed tomography (4DCT) has been widely used in cancer
radiotherapy for accurate target delineation and motion measurement for tumors
in thorax and upper abdomen areas. However, 4DCT simulation is associated with
much higher imaging dose than conventional CT simulation, which is a major
concern in its clinical application. Conventionally, each phase of 4DCT is
reconstructed independently using the filtered backprojection (FBP) algorithm.
The basic idea of our new algorithm is that, by utilizing the common
information among different phases, the input information required to
reconstruct image of high quality, and thus the imaging dose, can be reduced.
We proposed a temporal non-local means (TNLM) method to explore the inter-phase
similarity. All phases of the 4DCT images are reconstructed simultaneously by
minimizing a cost function consisting of a data fidelity term and a TNLM
regularization term. We utilized a forward-backward splitting algorithm and a
Gauss-Jacobi iteration method to efficiently solve the minimization problem.
The algorithm was also implemented on graphics processing unit (GPU) to achieve
a high computational speed. Our reconstruction algorithm has been tested on a
digital NCAT thorax phantom in three low dose scenarios. Our new algorithm
generates visually much better CT images containing less image noise and
streaking artifacts compared with the standard FBP algorithm. Quantitative
analysis shows that much higher contrast-to-noise ratio and signal-to-noise
ratio can be obtained using our algorithm. The total reconstruction time for
all 10 phases of a slice ranges from 90 to 140 seconds on an NVIDIA Tesla C1060
GPU card.
