Improving abdomen tumor low-dose CT images using dictionary learning based patch processing and unsharp filtering.

International audienceReducing patient radiation dose, while maintaining a high-quality image, is a major challenge in Computed Tomography (CT). The purpose of this work is to improve abdomen tumor low-dose CT (LDCT) image quality by using a two-step strategy: a first patch-wise non linear processing is first applied to suppress the noise and artifacts, that is based on a sparsity prior in term of a learned dictionary, then an unsharp filtering aiming to enhance the contrast of tissues and compensate the contrast loss caused by the DL processing. Preliminary results show that the proposed method is effective in suppressing mottled noise as well as improving tumor detectability

IMPROVING ABDOMEN TUMOR LOW-DOSE CT IMAGES USING DICTIONARY LEARNING BASED PATCH PROCESSING AND UNSHARP FILTERING

Reducing patient radiation dose, while maintaining a highquality image, is a major challenge in Computed Tomography. The purpose of this work is to improve abdomen tumor low-dose CT (LDCT) image quality by using a two-step strategy: a first patch-wise non linear processing is first applied to remove the noise, that is based on a sparsity prior in term of a learned dictionary, then an unsharp filtering aims to enhance the contrast of tissues and compensate the contrast loss caused by the DL processing. Preliminary results show that the proposed method is effective in suppressing mottled noise as well as improving tumor detectability. Index Terms—Low-dose CT (LDCT), abdomen tumor, Gaussian kernel, preprocessing, learning dictionary 1