# Visual enhancement of Cone-beam CT by use of CycleGAN

## TL;DR

This paper introduces a CycleGAN-based method to enhance CBCT images by translating them into high-quality, fan-beam CT-like images, improving soft-tissue contrast and reducing artifacts without requiring paired training data.

## Contribution

The study presents a novel unpaired deep learning approach using CycleGAN to improve CBCT image quality, enabling better clinical applications without needing aligned training datasets.

## Key findings

- Significant improvement in image quality and artifact reduction in translated CBCT images.
- Preservation of anatomical structures in the enhanced images.
- Enhanced suitability of CBCT for adaptive radiation therapy.

## Abstract

Cone-beam computed tomography (CBCT) offers advantages over conventional fan-beam CT in that it requires a shorter time and less exposure to obtain images. CBCT has found a wide variety of applications in patient positioning for image-guided radiation therapy, extracting radiomic information for designing patient-specific treatment, and computing fractional dose distributions for adaptive radiation therapy. However, CBCT images suffer from low soft-tissue contrast, noise, and artifacts compared to conventional fan-beam CT images. Therefore, it is essential to improve the image quality of CBCT. In this paper, we propose a synthetic approach to translate CBCT images with deep neural networks. Our method requires only unpaired and unaligned CBCT images and planning fan-beam CT (PlanCT) images for training. Once trained, 3D reconstructed CBCT images can be directly translated to high-quality PlanCT-like images. We demonstrate the effectiveness of our method with images obtained from 24 prostate patients, and we provide a statistical and visual comparison. The image quality of the translated images shows substantial improvement in voxel values, spatial uniformity, and artifact suppression compared to those of the original CBCT. The anatomical structures of the original CBCT images were also well preserved in the translated images. Our method enables more accurate adaptive radiation therapy, and opens up new applications for CBCT that hinge on high-quality images.