Accurate and robust sparse-view angle CT image reconstruction using deep learning and prior image constrained compressed sensing (DL-PICCS).

ABSTRACT:

Background

Sparse-view CT image reconstruction problems encountered in dynamic CT acquisitions are technically challenging. Recently, many deep learning strategies have been proposed to reconstruct CT images from sparse-view angle acquisitions showing promising results. However, two fundamental problems with these deep learning reconstruction methods remain to be addressed: (1) limited reconstruction accuracy for individual patients and (2) limited generalizability for patient statistical cohorts.

Purpose

The purpose of this work is to address the previously mentioned challenges in current deep learning methods.

Methods

A method that combines a deep learning strategy with prior image constrained compressed sensing (PICCS) was developed to address these two problems. I

SUBMITTER: Zhang C 

PROVIDER: S-EPMC9580024 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature