Dedicated convolutional neural network for noise reduction in ultra-high-resolution photon-counting detector computed tomography.

ABSTRACT: Objective.To develop a convolutional neural network (CNN) noise reduction technique for ultra-high-resolution photon-counting detector computed tomography (UHR-PCD-CT) that can be efficiently implemented using only clinically available reconstructed images. The developed technique was demonstrated for skeletal survey, lung screening, and head angiography (CTA).Approach. There were 39 participants enrolled in this study, each received a UHR-PCD and an energy integrating detector (EID) CT scan. The developed CNN noise reduction technique uses image-based noise insertion and UHR-PCD-CT images to train a U-Net via supervised learning. For each application, 13 patient scans were reconstructed using filtered back projection (FBP) and iterative reconstruction (IR) and allocated into

SUBMITTER: Huber NR 

PROVIDER: S-EPMC9444982 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature