ArticleScientific reports2020
MRI Cross-Modality Image-to-Image Translation.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 1 of them a synthesis that pooled it.
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Who cites it
54 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- MRecover: A Conditional Generative Model for Recovering Motion- Corrupted MR images Using AI Generated Contrast.Research square · 2026Article
- From Structure to Function and Back Again: A GAN-Guided Diffusion Framework for Generating Clinically Meaningful Multimodal Neuroimaging Data.Research square · 2026Article
- Learning heritable multimodal brain representation via contrastive learning.Research square · 2026Article
- Generative deep learning for foundational video translation in ultrasound.Scientific reports · 2026Article
- AI-enabled RF data synthesis for breast ultrasound: efficacy in quantitative ultrasound tissue characterization.Scientific reports · 2026Article
- Enhancing generative networks for chest anomaly localization through automatic registration-based unpaired-to-pseudo-paired training data translation.Scientific reports · 2026Article
- Learning heritable multimodal brain representation via contrastive learning.bioRxiv : the preprint server for biology · 2026Article
- Dynamic Contrast-enhanced MRI for Evaluating Breast Cancer Chemotherapy Response Using Conditional Generative Adversarial Networks.Radiology. Imaging cancer · 2026Article
- CycleGAN models show consistent brain MRI synthesis across datasets supporting downstream tissue characterization in multiple sclerosis.Frontiers in neuroinformatics · 2026Article
- Probabilistic brain MR image transformation using generative models.Scientific reports · 2025Article
- Multimodal magnetic resonance imaging synthesis via cross-modal feature fusion and hierarchical feature extraction.Quantitative imaging in medicine and surgery · 2025Article
- 3D Latent Diffusion Model for MR-Only Radiotherapy: Accurate and Consistent Synthetic CT Generation.Diagnostics (Basel, Switzerland) · 2025Article
- Cross-modality 3D MRI synthesis via cycle-guided denoising diffusion probability model.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- High-resolution conditional MR image synthesis through the PACGAN framework.Scientific reports · 2025Article
- Research on concept generation design of automobile seats based on human-machine co-creation.Scientific reports · 2025Article
- All-in-one medical image-to-image translation.Cell reports methods · 2025Article
- Generation of high-resolution MPRAGE-like images from 3D head MRI localizer (AutoAlign Head) images using a deep learning-based model.Japanese journal of radiology · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present a cross-modality generation framework that learns to generate translated modalities from given modalities in MR images. Our proposed method performs Image Modality Translation (abbreviated as IMT) by means of a deep learning model that leverages conditional generative adversarial networks (cGANs). Our framework jointly exploits the low-level features (pixel-wise information) and high-level representations (e.g. brain tumors, brain structure like gray matter, etc.) between cross modalities which are important for resolving the challenging complexity in brain structures. Our framework can serve as an auxiliary method in medical use and has great application potential. Based on our proposed framework, we first propose a method for cross-modality registration by fusing the deformation fields to adopt the cross-modality information from translated modalities. Second, we propose an approach for MRI segmentation, translated multichannel segmentation (TMS), where given modalities, along with translated modalities, are segmented by fully convolutional networks (FCN) in a multichannel manner. Both of these two methods successfully adopt the cross-modality information to improve the performance without adding any extra data. Experiments demonstrate that our proposed framework advances the state-of-the-art on five brain MRI datasets. We also observe encouraging results in cross-modality registration and segmentation on some widely adopted brain datasets. Overall, our work can serve as an auxiliary method in medical use and be applied to various tasks in medical fields.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.