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New H3D-MarNet framework enhances CT image quality for radiotherapy

By PulseAugur Editorial · [1 sources] ·

Researchers have developed H3D-MarNet, a novel two-stage framework designed to improve CT image quality for radiotherapy. The system first suppresses metal artifacts using wavelet-based denoising and then transforms kilo-voltage CT (kVCT) images to mega-voltage CT (MVCT) using a hybrid CNN and transformer architecture. This approach aims to enhance diagnostic accuracy and radiotherapy planning by preserving anatomical details and ensuring spatial coherence across slices, demonstrating significant improvements in PSNR and SSIM metrics. AI

IMPACT Improves medical imaging analysis for radiotherapy, potentially leading to more accurate cancer treatment planning.

RANK_REASON The cluster contains an academic paper detailing a new technical approach. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New H3D-MarNet framework enhances CT image quality for radiotherapy

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The cluster contains an academic paper detailing a new technical approach. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Christian Micheloni ·

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

    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 transformati…

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