ARTInp: CBCT-to-CT Image Inpainting and Image Translation in Radiotherapy

TL;DR

ARTInp is a deep-learning framework that improves CBCT images by filling anatomical gaps and translating them into high-quality synthetic CT images, enhancing accuracy in adaptive radiotherapy workflows.

Contribution

It introduces a dual-network deep learning approach combining inpainting and translation for CBCT enhancement in radiotherapy.

Findings

Achieved high-quality synthetic CT generation from CBCT data.

Effectively filled anatomical gaps in CBCT volumes.

Demonstrated potential for improved treatment validation.

Abstract

A key step in Adaptive Radiation Therapy (ART) workflows is the evaluation of the patient's anatomy at treatment time to ensure the accuracy of the delivery. To this end, Cone Beam Computerized Tomography (CBCT) is widely used being cost-effective and easy to integrate into the treatment process. Nonetheless, CBCT images have lower resolution and more artifacts than CT scans, making them less reliable for precise treatment validation. Moreover, in complex treatments such as Total Marrow and Lymph Node Irradiation (TMLI), where full-body visualization of the patient is critical for accurate dose delivery, the CBCT images are often discontinuous, leaving gaps that could contain relevant anatomical information. To address these limitations, we propose ARTInp (Adaptive Radiation Therapy Inpainting), a novel deep-learning framework combining image inpainting and CBCT-to-CT translation. ARTInp employs a dual-network approach: a completion network that fills anatomical gaps in CBCT volumes and a custom Generative Adversarial Network (GAN) to generate high-quality synthetic CT (sCT) images. We trained ARTInp on a dataset of paired CBCT and CT images from the SynthRad 2023 challenge, and the performance achieved on a test set of 18 patients demonstrates its potential for enhancing CBCT-based workflows in radiotherapy.