Anatomical evaluation of deep-learning synthetic computed tomography images generated from male pelvis cone-beam computed tomography.

ABSTRACT:

Background and purpose

To improve cone-beam computed tomography (CBCT), deep-learning (DL)-models are being explored to generate synthetic CTs (sCT). The sCT evaluation is mainly focused on image quality and CT number accuracy. However, correct representation of daily anatomy of the CBCT is also important for sCTs in adaptive radiotherapy. The aim of this study was to emphasize the importance of anatomical correctness by quantitatively assessing sCT scans generated from CBCT scans using different paired and unpaired dl-models.

Materials and methods

Planning CTs (pCT) and CBCTs of 56 prostate cancer patients were included to generate sCTs. Three different dl-models, Dual-UNet, Single-UNet and Cycle-consistent Generative Adversarial Network (CycleGAN), were evaluated on image

SUBMITTER: de Hond YJM 

PROVIDER: S-EPMC10037090 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature