H3D-MarNet: Wavelet-Guided Dual-Path Learning for Metal Artifact Suppression and CT Modality Transformation for Radiotherapy Workflows

• URL: http://arxiv.org/abs/2605.12252v1
• Date: Tue, 12 May 2026 15:21:42 GMT
• Title: H3D-MarNet: Wavelet-Guided Dual-Path Learning for Metal Artifact Suppression and CT Modality Transformation for Radiotherapy Workflows
• Abstract summary: H3D-MarNet is a two-stage framework for artifact-aware CT domain transformation.<n>Wavelet-based preprocessing module suppresses metal-induced artifacts.<n>In second stage, Domain-TransNet performs kVCT-to-MVCT domain transformation.
• Score: 9.903943384975856
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Metal artifacts in computed tomography (CT) severely degrade image quality, compromising diagnostic accuracy and radiotherapy planning, especially in cancer patients with high-density implants. We propose H3D-MarNet, a two-stage framework for artifact-aware CT domain transformation from kilo-voltage CT (kVCT) to mega-voltage CT (MVCT). In the first stage, a wavelet-based preprocessing module suppresses metal-induced artifacts through frequency-aware denoising while preserving anatomical structures. In second stage, Domain-TransNet performs kVCT-to-MVCT domain transformation using a hybrid volumetric learning architecture. Domain-TransNet integrates a CNN-based encoder to capture fine-grained local anatomical details and a transformer-based encoder to model long-range volumetric dependencies. The complementary representations are fused through an attention-based feature fusion mechanism to ensure spatial and contextual coherence across slices. A multi-stage, attention-guided decoder, supported by deep supervision, progressively reconstructs artifact-suppressed MVCT volumes. Extensive experiments demonstrate that H3D-MarNet achieves 28.14 dB PSNR and 0.717 SSIM on artifact-affected slices from full dataset, indicating effective metal artifact suppression and anatomical preservation, highlighting its potential for reliable CT modality transformation in clinical radiotherapy workflows.

Related papers

• 3D Ultrasound-Derived Pseudo-CT Synthesis Using a Transformer-Augmented Residual Network for Real-Time Operator Guidance [0.0]
  Ultrasound (US) is non-ionizing and widely accessible; however, it is highly operator dependent and lacks quantitative tissue characterization.<n>This work presents a 3D ultrasound-derived pseudo-CT framework that generates CT-like anatomical reference volumes inferred from US.<n>The proposed method outperforms established baselines in structural fidelity and perceptual image quality.
  arXiv  Detail & Related papers  (2026-05-06T12:52:59Z)
• Learning from Compressed CT: Feature Attention Style Transfer and Structured Factorized Projections for Resource-Efficient Medical Image Analysis [0.18352113484137622]
  Feature Attention Style Transfer Transfer (FAST) is a novel framework that transfers from high-fidelity CT representations to a visual encoder operating on compressed inputs.<n>Structured Factorized Projection (SFP) is a parameter-efficient alternative to dense projection layers, reducing parameters by almost half.<n>Our contrastive learning pipeline, CT-Lite, integrates these components with a SigLIP-based objective alignment objective.
  arXiv  Detail & Related papers  (2026-05-01T06:35:53Z)
• PHASOR: Anatomy- and Phase-Consistent Volumetric Diffusion for CT Virtual Contrast Enhancement [60.81732730684265]
  This paper introduces PHASOR, a volumetric diffusion framework for high-fidelity virtual contrast enhancement (VCE)<n>By treating CT volumes as coherent sequences, we leverage a video diffusion model to enhance structural coherence and volumetric accuracy.<n>To ensure anatomy-phase consistent synthesis, we introduce two complementary modules. First, anatomy-routed mixture-of-experts (AR-MoE) anchors distinct enhancement patterns to anatomical semantics, with organ-specific memory to capture salient details.<n>Second, intensity-phase aware representation alignment (IP-REPA) highlights intricate contrast signals while mitigating the impact of imperfect spatial alignment.
  arXiv  Detail & Related papers  (2026-04-01T15:57:18Z)
• Flow Matching for Conditional MRI-CT and CBCT-CT Image Synthesis [0.0]
  Flow Matching framework is used to generate synthetic CT from MRI or CBCT images.<n>We train models for MRI sCT and CBCT sCT across three anatomical regions.<n>The results indicate that the method accurately reconstructs global anatomical structures.<n>Future work will explore patch-based training and latent-space flow models to improve resolution and local structural fidelity.
  arXiv  Detail & Related papers  (2025-10-06T14:07:03Z)
• Accelerating 3D Photoacoustic Computed Tomography with End-to-End Physics-Aware Neural Operators [74.65171736966131]
  Photoacoustic computed tomography (PACT) combines optical contrast with ultrasonic resolution, achieving deep-tissue imaging beyond the optical diffusion limit.<n>Current implementations require dense transducer arrays and prolonged acquisition times, limiting clinical translation.<n>We introduce Pano, an end-to-end physics-aware model that directly learns the inverse acoustic mapping from sensor measurements to volumetric reconstructions.
  arXiv  Detail & Related papers  (2025-09-11T23:12:55Z)
• 3D Wavelet Latent Diffusion Model for Whole-Body MR-to-CT Modality Translation [13.252652406393205]
  Existing MR-to-CT methods for whole-body imaging often suffer from poor spatial alignment between the generated CT and input MR images.<n>We present a novel 3D Wavelet Latent Diffusion Model (3D-WLDM) that addresses these limitations.<n>By incorporating a Wavelet Residual Module into the encoder-decoder architecture, we enhance the capture and reconstruction of fine-scale features across image and latent spaces.
  arXiv  Detail & Related papers  (2025-07-14T06:17:05Z)
• ReMAR-DS: Recalibrated Feature Learning for Metal Artifact Reduction and CT Domain Transformation [12.205008231517326]
  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.
  arXiv  Detail & Related papers  (2025-06-24T11:34:35Z)
• MARformer: An Efficient Metal Artifact Reduction Transformer for Dental CBCT Images [53.62335292022492]
  Metal teeth implants could bring annoying metal artifacts during the CBCT imaging process. We develop an efficient Transformer to perform metal artifacts reduction (MAR) from dental CBCT images. A Patch-wise Perceptive Feed Forward Network (P2FFN) is also proposed to perceive local image information for fine-grained restoration.
  arXiv  Detail & Related papers  (2023-11-16T06:02:03Z)
• 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)
• InDuDoNet+: A Model-Driven Interpretable Dual Domain Network for Metal Artifact Reduction in CT Images [53.4351366246531]
  We construct a novel interpretable dual domain network, termed InDuDoNet+, into which CT imaging process is finely embedded. We analyze the CT values among different tissues, and merge the prior observations into a prior network for our InDuDoNet+, which significantly improve its generalization performance.
  arXiv  Detail & Related papers  (2021-12-23T15:52:37Z)
• Revisiting 3D Context Modeling with Supervised Pre-training for Universal Lesion Detection in CT Slices [48.85784310158493]
  We propose a Modified Pseudo-3D Feature Pyramid Network (MP3D FPN) to efficiently extract 3D context enhanced 2D features for universal lesion detection in CT slices. With the novel pre-training method, the proposed MP3D FPN achieves state-of-the-art detection performance on the DeepLesion dataset. The proposed 3D pre-trained weights can potentially be used to boost the performance of other 3D medical image analysis tasks.
  arXiv  Detail & Related papers  (2020-12-16T07:11:16Z)

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.