Fast Low-dose CT Image Processing Using Improved parallelized Nonlocal Means Filtering

Although effectively reducing the radiation exposure to patients, low dose CT (LDCT) images are often significantly degraded by severely increased mottled noise/artifacts, which can lead to lowered diagnostic accuracy in clinic. The nonlocal means (NLM) filtering can effectively remove mottled noise/artifacts by utilizing large-scale patch similarity information in LDCT images. But the NLM filtering application in LDCT imaging is also accompanied with high computation cost as a large searching window is often required to include much neighboring information for noise/artifact suppression. To accelerate the NLM filtering and improve its clinical feasibility, we propose in this paper an improved GPU-based parallelization approach. In addition to the straight pixel-wise parallelization, the improved parallelization approach exploits the high I/O speed of GPU shared memory. Quantitative experiment demonstrates that significant acceleration is achieved with respect to the traditional pixel-wise parallelization.