ReMAR-DS: Recalibrated Feature Learning for Metal Artifact Reduction and CT Domain Transformation

TL;DR

This paper introduces ReMAR-DS, a deep learning framework that effectively reduces metal artifacts in CT images and transforms kVCT images into MVCT-like images, improving clinical imaging and reducing radiation exposure.

Contribution

ReMAR-DS is a novel deep learning model with feature recalibration that enhances artifact reduction and domain transformation in CT imaging, bridging the gap between kVCT and MVCT.

Findings

Significant reduction in artifacts demonstrated in qualitative evaluations.

Quantitative metrics show improved image quality and domain transformation accuracy.

Clinically validated for improved radiotherapy planning.

Abstract

Artifacts in kilo-Voltage CT (kVCT) imaging degrade image quality, impacting clinical decisions. We propose a deep learning framework for metal artifact reduction (MAR) and domain transformation from kVCT to Mega-Voltage CT (MVCT). The proposed framework, ReMAR-DS, utilizes an encoder-decoder architecture with enhanced feature recalibration, effectively reducing artifacts while preserving anatomical structures. This ensures that only relevant information is utilized in the reconstruction process. By infusing recalibrated features from the encoder block, the model focuses on relevant spatial regions (e.g., areas with artifacts) and highlights key features across channels (e.g., anatomical structures), leading to improved reconstruction of artifact-corrupted regions. Unlike traditional MAR methods, our approach bridges the gap between high-resolution kVCT and artifact-resistant MVCT, enhancing radiotherapy planning. It produces high-quality MVCT-like reconstructions, validated through qualitative and quantitative evaluations. Clinically, this enables oncologists to rely on kVCT alone, reducing repeated high-dose MVCT scans and lowering radiation exposure for cancer patients.