CBCT-to-CT synthesis with a single neural network for head-and-neck, lung and breast cancer adaptive radiotherapy

TL;DR

This study demonstrates that a single cycle-GAN neural network can efficiently generate synthetic CT images from CBCT scans across head-and-neck, lung, and breast cancer patients, enabling rapid and accurate dose calculations for adaptive radiotherapy.

Contribution

The paper introduces a unified cycle-GAN model capable of synthesizing CT images from CBCT scans for multiple cancer sites, simplifying and speeding up adaptive radiotherapy workflows.

Findings

Synthetic CTs generated in 10 seconds.

High image similarity across models trained on different sites.

Dose differences less than 0.5% in high-dose regions.

Abstract

Purpose: CBCT-based adaptive radiotherapy requires daily images for accurate dose calculations. This study investigates the feasibility of applying a single convolutional network to facilitate CBCT-to-CT synthesis for head-and-neck, lung, and breast cancer patients. Methods: Ninety-nine patients diagnosed with head-and-neck, lung or breast cancer undergoing radiotherapy with CBCT-based position verification were included in this study. CBCTs were registered to planning CTs according to clinical procedures. Three cycle-consistent generative adversarial networks (cycle-GANs) were trained in an unpaired manner on 15 patients per anatomical site generating synthetic-CTs (sCTs). Another network was trained with all the anatomical sites together. Performances of all four networks were compared and evaluated for image similarity against rescan CT (rCT). Clinical plans were recalculated on CT and sCT and analysed through voxel-based dose differences and {\gamma}-analysis. Results: A sCT was generated in 10 seconds. Image similarity was comparable between models trained on different anatomical sites and a single model for all sites. Mean dose differences < 0.5% were obtained in high-dose regions. Mean gamma (2%,2mm) pass-rates > 95% were achieved for all sites. Conclusions: Cycle-GAN reduced CBCT artefacts and increased HU similarity to CT, enabling sCT-based dose calculations. The speed of the network can facilitate on-line adaptive radiotherapy using a single network for head-and-neck, lung and breast cancer patients.