Article

MRecover: A Conditional Generative Model for Recovering Motion- Corrupted MR images Using AI Generated Contrast

2026-06-18

Abstract excerpt

<title>Abstract</title> <p> Hippocampal subfield segmentation requires high-resolution T2w turbo spin echo (TSE) MRI, yet this sequence is susceptible to motion artifacts, leading to substantial data loss. We developed a conditional generative model ( <italic>MRecover</italic> ) that synthesizes routinely acquired T1w images to create TSE images with autoregressive slice conditioning for volumetric consistency...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: dbcbf8bf-3c49-5d6e-8596-afab2cbff080
DOI: 10.21203/rs.3.rs-9794116/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

MRecover: A Conditional Generative Model for Recovering Motion- Corrupted MR images Using AI Generated ContrastDOI 10.21203/rs.3.rs-9794116/v1Read

Denoising 7T Structural MRI with Conditional Generative Diffusion ModelsSemantic proximity Read

Brain MRI Technics Images Translation by Generative Adversarial NetworkSemantic proximity Read

High-fidelity fast volumetric brain MRI using synergistic wave-controlled aliasing in parallel imaging and a hybrid denoising generative adversarial networkSemantic proximity Read

Enhancing MR imaging driven Alzheimer’s disease classification performance using generative adversarial learningSemantic proximity Read

Comparative Evaluation of U-Net-Based Conditioned Diffusion Model and Cycle-GAN for Unpaired CT-MRI Brain Image Synthesis with XAI ValidationSemantic proximity Read

MRI2PET: Realistic PET Image Synthesis from MRI for Automated Inference of Brain Atrophy and Alzheimer’sSemantic proximity Read

Counterfactual MRI Generation with Denoising Diffusion Models for Interpretable Alzheimer’s Disease Effect DetectionSemantic proximity Read

Towards robust and generalizable super-resolution generative adversarial networks for magnetic resonance neuroimaging: a cross-population approachSemantic proximity Read

CT-to-MRI Synthesis with Dual CycleGAN: Advancing Radiotherapy Planning in Brain Glioblastoma MultiformSemantic proximity Read

Multi-contrast high-field quality image synthesis for portable low-field MRI using generative adversarial networks and paired dataSemantic proximity Read

Leveraging Deep Learning to Enhance MRI for Brain DisordersSemantic proximity Read

GenBrain: A Generative Foundation Model of Multimodal Brain ImagingSemantic proximity Read