Efficient Low-Dose CT Denoising by Locally-Consistent Non-Local Means (LC-NLM)
Michael Green, Edith Michelle Marom, Nahum Kiryati, Eli Konen, Arnaldo Mayer · Lecture notes in computer science · 2016
The never-ending quest for lower radiation exposure is a major challenge to the image quality of advanced CT scans. Post-processing algorithms have been recently proposed to improve low-dose CT denoising after image reconstruction. In this work, a novel algorithm, termed the locally-consistent non-local means (LC-NLM), is proposed for this challenging task. By using a database of high-SNR CT patches to filter noisy pixels while locally enforcing spatial consistency, the proposed algorithm achieves both powerful denoising and preservation of fine image details. The LC-NLM is compared both quantitatively and qualitatively, for synthetic and real noise, to state-of-the-art published algorithms. The highest structural similarity index (SSIM) were achieved by LC-NLM in 8 out of 10 denoised chest CT volumes. Also, the visual appearance of the denoised images was clearly better for the proposed algorithm. The favorable comparison results, together with the computational efficiency of LC-NLM makes it a promising tool for low-dose CT denoising. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.