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

• URL: http://arxiv.org/abs/2506.19531v1
• Date: Tue, 24 Jun 2025 11:34:35 GMT
• Title: ReMAR-DS: Recalibrated Feature Learning for Metal Artifact Reduction and CT Domain Transformation
• Authors: Mubashara Rehman, Niki Martinel, Michele Avanzo, Riccardo Spizzo, Christian Micheloni,
• Abstract summary: Artifacts in kilo-Voltage CT (kVCT) imaging degrade image quality, impacting clinical decisions.<n>We propose a deep learning framework for metal artifact reduction (MAR) and domain transformation from kVCT to Mega-Voltage CT (MVCT)<n>The proposed framework, ReMAR-DS, utilizes an encoder-decoder architecture with enhanced feature recalibration, effectively reducing artifacts while preserving anatomical structures.
• Score: 12.205008231517326
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• RSFR: A Coarse-to-Fine Reconstruction Framework for Diffusion Tensor Cardiac MRI with Semantic-Aware Refinement [10.620138922340258]
  We introduce RSFR (Reconstruction, Fusion & Refinement), a novel framework for cardiac diffusion-weighted image reconstruction.<n> RSFR employs a coarse-to-fine strategy, leveraging zero-shot semantic priors via the Segment Anything Model and a robust Vision Mamba-based reconstruction backbone.<n>Our framework integrates semantic features effectively to mitigate artefacts and enhance fidelity, achieving state-of-the-art reconstruction quality and accurate DT parameter estimation under high under rates.
  arXiv  Detail & Related papers  (2025-04-25T17:41:14Z)
• Motion Artifact Removal in Pixel-Frequency Domain via Alternate Masks and Diffusion Model [58.694932010573346]
  Motion artifacts present in magnetic resonance imaging (MRI) can seriously interfere with clinical diagnosis.<n>We propose a novel unsupervised purification method which leverages pixel-frequency information of noisy MRI images to guide a pre-trained diffusion model to recover clean MRI images.
  arXiv  Detail & Related papers  (2024-12-10T15:25:18Z)
• MAR-DTN: Metal Artifact Reduction using Domain Transformation Network for Radiotherapy Planning [10.515417851330877]
  In patients with head and neck cancer, quality of standard CT scans generated using kilo-Voltage (kVCT) tube potentials is severely degraded by streak artifacts. Some radiotherapy devices offer the possibility of acquiring Mega-Voltage CT (MVCT) for daily patient setup verification. We propose a deep learning-based approach capable of generating artifact-free MVCT images from acquired kVCT images.
  arXiv  Detail & Related papers  (2024-09-23T16:04:00Z)
• TC-KANRecon: High-Quality and Accelerated MRI Reconstruction via Adaptive KAN Mechanisms and Intelligent Feature Scaling [8.301957310590712]
  This study presents an innovative conditional guided diffusion model, named as TC-KANRecon.<n>It incorporates the Multi-Free U-KAN (MF-UKAN) module and a dynamic clipping strategy.<n> Experimental results demonstrate that the proposed method outperforms other MRI reconstruction methods in both qualitative and quantitative evaluations.
  arXiv  Detail & Related papers  (2024-08-11T06:31:56Z)
• CoCPF: Coordinate-based Continuous Projection Field for Ill-Posed Inverse Problem in Imaging [78.734927709231]
  Sparse-view computed tomography (SVCT) reconstruction aims to acquire CT images based on sparsely-sampled measurements. Due to ill-posedness, implicit neural representation (INR) techniques may leave considerable holes'' (i.e., unmodeled spaces) in their fields, leading to sub-optimal results. We propose the Coordinate-based Continuous Projection Field (CoCPF), which aims to build hole-free representation fields for SVCT reconstruction.
  arXiv  Detail & Related papers  (2024-06-21T08:38:30Z)
• K-Space-Aware Cross-Modality Score for Synthesized Neuroimage Quality Assessment [71.27193056354741]
  The problem of how to assess cross-modality medical image synthesis has been largely unexplored. We propose a new metric K-CROSS to spur progress on this challenging problem. K-CROSS uses a pre-trained multi-modality segmentation network to predict the lesion location.
  arXiv  Detail & Related papers  (2023-07-10T01:26:48Z)
• Cross-domain Denoising for Low-dose Multi-frame Spiral Computed Tomography [20.463308418655526]
  X-ray exposure raises concerns about potential health risks such as cancer. The desire for lower radiation doses has driven researchers to improve reconstruction quality. This paper proposes a two-stage method for the commercially available multi-slice spiral CT scanners.
  arXiv  Detail & Related papers  (2023-04-21T09:30:22Z)
• Geometry-Aware Attenuation Learning for Sparse-View CBCT Reconstruction [53.93674177236367]
  Cone Beam Computed Tomography (CBCT) plays a vital role in clinical imaging. Traditional methods typically require hundreds of 2D X-ray projections to reconstruct a high-quality 3D CBCT image. This has led to a growing interest in sparse-view CBCT reconstruction to reduce radiation doses. We introduce a novel geometry-aware encoder-decoder framework to solve this problem.
  arXiv  Detail & Related papers  (2023-03-26T14:38:42Z)
• Orientation-Shared Convolution Representation for CT Metal Artifact Learning [63.67718355820655]
  During X-ray computed tomography (CT) scanning, metallic implants carrying with patients often lead to adverse artifacts. Existing deep-learning-based methods have gained promising reconstruction performance. We propose an orientation-shared convolution representation strategy to adapt the physical prior structures of artifacts.
  arXiv  Detail & Related papers  (2022-12-26T13:56:12Z)
• Multi-scale reconstruction of undersampled spectral-spatial OCT data for coronary imaging using deep learning [1.8359410255568984]
  Intravascular optical coherence tomography (IV OCT) has been considered as an optimal imagining system for the diagnosis and treatment of coronary artery disease (CAD) There is a trade-off between high spatial resolution and fast scanning rate for coronary imaging. We propose a viable spectral-spatial acquisition method that down-scales the sampling process in both spectral and spatial domain.
  arXiv  Detail & Related papers  (2022-04-25T16:37:25Z)
• Multi-Channel Convolutional Analysis Operator Learning for Dual-Energy CT Reconstruction [108.06731611196291]
  We develop a multi-channel convolutional analysis operator learning (MCAOL) method to exploit common spatial features within attenuation images at different energies. We propose an optimization method which jointly reconstructs the attenuation images at low and high energies with a mixed norm regularization on the sparse features.
  arXiv  Detail & Related papers  (2022-03-10T14:22:54Z)
• Reference-based Magnetic Resonance Image Reconstruction Using Texture Transforme [86.6394254676369]
  We propose a novel Texture Transformer Module (TTM) for accelerated MRI reconstruction. We formulate the under-sampled data and reference data as queries and keys in a transformer. The proposed TTM can be stacked on prior MRI reconstruction approaches to further improve their performance.
  arXiv  Detail & Related papers  (2021-11-18T03:06:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.