H3D-MarNet suppresses metal artifacts in kVCT via wavelet preprocessing and transforms to MVCT using a dual-path CNN-transformer architecture with attention fusion, reporting 28.14 dB PSNR and 0.717 SSIM on affected slices.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.CV 1years
2026 1verdicts
UNVERDICTED 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
H3D-MarNet: Wavelet-Guided Dual-Path Learning for Metal Artifact Suppression and CT Modality Transformation for Radiotherapy Workflows
H3D-MarNet suppresses metal artifacts in kVCT via wavelet preprocessing and transforms to MVCT using a dual-path CNN-transformer architecture with attention fusion, reporting 28.14 dB PSNR and 0.717 SSIM on affected slices.