Improving synthetic CT accuracy by combining the benefits of multiple normalized preprocesses.

ABSTRACT:

Purpose

To investigate the effect of different normalization preprocesses in deep learning on the accuracy of different tissues in synthetic computed tomography (sCT) and to combine their advantages to improve the accuracy of all tissues.

Methods

The cycle-consistent adversarial network (CycleGAN) model was used to generate sCT images from megavolt cone-beam CT (MVCBCT) images. In this study, 2639 head MVCBCT and CT image pairs from 203 patients were collected as a training set, and 249 image pairs from 29 patients were collected as a test set. We normalized the voxel values in images to 0 to 1 or -1 to 1, using two linear and five nonlinear normalization preprocessing methods to obtain seven data sets and compared the accuracy of different tissues in different sCT obtained

SUBMITTER: Cao Z 

PROVIDER: S-EPMC10402686 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature