Optimized Parallelization for Nonlocal Means Based Low Dose CT Image Processing

Abstract EN

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 also requires high computation cost because intensive patch similarity calculation within a large searching window is often required to be used to include enough structure-similarity information for noise/artifact suppression.

To improve its clinical feasibility, in this study we further optimize the parallelization of NLM filtering by avoiding the repeated computation with the row-wise intensity calculation and the symmetry weight calculation.

The shared memory with fast I/O speed is also used in row-wise intensity calculation for the proposed method.

Quantitative experiment demonstrates that significant acceleration can be achieved with respect to the traditional straight pixel-wise parallelization.