Fast four-dimensional cone-beam computed tomography reconstruction using deformable convolutional networks.

ABSTRACT:

Background

Although four-dimensional cone-beam computed tomography (4D-CBCT) is valuable to provide onboard image guidance for radiotherapy of moving targets, it requires a long acquisition time to achieve sufficient image quality for target localization. To improve the utility, it is highly desirable to reduce the 4D-CBCT scanning time while maintaining high-quality images. Current motion-compensated methods are limited by slow speed and compensation errors due to the severe intraphase undersampling.

Purpose

In this work, we aim to propose an alternative feature-compensated method to realize the fast 4D-CBCT with high-quality images.

Methods

We proposed a feature-compensated deformable convolutional network (FeaCo-DCN) to perform interphase compensation in the latent

SUBMITTER: Jiang Z 

PROVIDER: S-EPMC9588592 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature