A multimodal diffusion model trained on synthetic data enhances low-resolution EBSD and corrupted polarized light data, achieving near full-resolution performance with only 25% EBSD data.
Conversion between ct and mri images using diffusion and score-matching models
4 Pith papers cite this work, alongside 53 external citations. Polarity classification is still indexing.
4
Pith papers citing it
53
external citations · external index
citation-role summary
method 1
citation-polarity summary
years
2026 4verdicts
UNVERDICTED 4roles
method 1polarities
use method 1representative citing papers
Drifting models outperform diffusion, CNN, VAE, and GAN baselines in MRI-to-CT synthesis on two pelvis datasets with higher SSIM/PSNR, lower RMSE, and millisecond one-step inference.
Unpaired two-step DL model (DDIM + domain-adversarial U-Net) synthesizes PCCT material-basis maps from EID CT images.
Lightweight U-Net outperforms DDPM on T2w-to-MRI-SFF translation (r=0.975 vs 0.962, MAE=0.014 vs 0.019) with 208x faster inference on 230k paired images from NAKO.
citing papers explorer
-
Multimodal Diffusion to Mutually Enhance Polarized Light and Low Resolution EBSD Data
A multimodal diffusion model trained on synthetic data enhances low-resolution EBSD and corrupted polarized light data, achieving near full-resolution performance with only 25% EBSD data.
-
MRI-to-CT synthesis using drifting models
Drifting models outperform diffusion, CNN, VAE, and GAN baselines in MRI-to-CT synthesis on two pelvis datasets with higher SSIM/PSNR, lower RMSE, and millisecond one-step inference.
-
Unpaired Deep Learning Synthesis of Photon-Counting CT Material Basis Maps from Non-contrast Energy-Integrating Abdominal CT Images
Unpaired two-step DL model (DDIM + domain-adversarial U-Net) synthesizes PCCT material-basis maps from EID CT images.
-
Do We Really Need Diffusion? A Fast U-Net for Paired Medical Image Translation
Lightweight U-Net outperforms DDPM on T2w-to-MRI-SFF translation (r=0.975 vs 0.962, MAE=0.014 vs 0.019) with 208x faster inference on 230k paired images from NAKO.