# Adversarial Sparse-View CBCT Artifact Reduction

## TL;DR

This paper introduces an adversarial deep learning approach with a novel discriminator architecture to effectively reduce streak artifacts in sparse-view CBCT images, improving image quality while preserving anatomical details.

## Contribution

It presents a new adversarial training framework with a feature pyramid network discriminator and focus map for artifact reduction in sparse-view CBCT, outperforming existing methods.

## Key findings

- Significant artifact correction in clinical CBCT images reconstructed with 1/3 projections.
- Outperforms baseline methods quantitatively and qualitatively.
- Produces more perceptually realistic images while maintaining anatomical accuracy.

## Abstract

We present an effective post-processing method to reduce the artifacts from sparsely reconstructed cone-beam CT (CBCT) images. The proposed method is based on the state-of-the-art, image-to-image generative models with a perceptual loss as regulation. Unlike the traditional CT artifact-reduction approaches, our method is trained in an adversarial fashion that yields more perceptually realistic outputs while preserving the anatomical structures. To address the streak artifacts that are inherently local and appear across various scales, we further propose a novel discriminator architecture based on feature pyramid networks and a differentially modulated focus map to induce the adversarial training. Our experimental results show that the proposed method can greatly correct the cone-beam artifacts from clinical CBCT images reconstructed using 1/3 projections, and outperforms strong baseline methods both quantitatively and qualitatively.