Fast Low-Dose CT Image Processing Using Improved Parallelized Nonlocal Means Filtering

Zhikun Zhuang (1) , Yang Chen (1, 2) , Huazhong Shu (2, 1) , Limin Luo (1, 2) , C. Toumoulin (3) , J.-L Coatrieux (3)

1 LIST - Laboratory of Image Science and Technology [Nanjing]
2 CRIBS - Centre de Recherche en Information Biomédicale sino-français
3 LTSI - Laboratoire Traitement du Signal et de l'Image

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 GPUbased 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

Keywords: Acceleration; Computed Tomography; computerised tomography; dosimetry; fast low-dose computerised tomography image processing; Filtering; filtering theory; GPU; GPU-based parallelization; GPU shared memory; graphics processing units; high I-O speed; image denoising; Image processing; improved parallelized nonlocal means filtering; Kernel; large-scale patch similarity information; large searching window; low-dose CT; lowered diagnostic accuracy; medical image processing; mottled noise-artifact removal; Noise; noise-artifact suppression; nonlocal means (NLM); radiation exposure; severely increased mottled noise-artifacts; straight pixel-wise parallelization

Domains: Life Sciences [q-bio]; Bioengineering

Zhikun Zhuang, Yang Chen, Huazhong Shu, Limin Luo, C. Toumoulin, et al.. Fast Low-Dose CT Image Processing Using Improved Parallelized Nonlocal Means Filtering. 2014 International Conference on Medical Biometrics, 2014, Shenzhen China. pp.147--150, ⟨10.1109/ICMB.2014.33⟩. ⟨hal-01260663⟩