Sparsity-based Sinogram Denoising for low-dose Computed Tomography
Résumé
We propose a sinogram restoration method which consists of a patch-wise non-linear processing, based on a sparsity prior in terms of a learned dictionary. An off-line learning process uses a statistical model of the sinogram noise and minimizes an error measure in the image domain over the training set. The error measure is designed to preserve low-contrast edges for visibility of soft tissues. Our numerical study shows that the algorithm improves on the performance of the standard Filtered Back-Projection algorithm and effectively allows to halve the radiation dose for the same image quality.
Mots clés
computed tomography
filtered backprojection algorithm
image quality
off-line learning
patch-wise nonlinear processing
radiation dose
sinogram restoration method
soft tissues
sparsity-based sinogram denoising
statistical model
biological effects of radiation
computerised tomography
image restoration
learning (artificial intelligence)
statistical analysis