MRI2MRI: A deep convolutional network that accurately transforms between brain MRI contrasts
Abstract
Different brain MRI contrasts represent different tissue properties and are sensitive to different artifacts. The relationship between different contrasts is therefore complex and nonlinear. We developed a deep convolutional network that learns the mapping between different MRI contrasts. Using a publicly available dataset, we demonstrate that this algorithm accurately transforms between T1- and T2-weighted images, proton density images, time-of-flight angiograms, and diffusion MRI images. We demonstrate that these transformed images can be used to improve spatial registration between MR images of different contrasts.
02 /Specification & Classification
Click any tag to browse matching papers- Citations
- 3
- Assay
- Research Focus
- Approach
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Harmonizing Inter-Site Differences in T1-Weighted Images Using CycleGAN
Masaaki Shimizu, Yuichi Yamashita, … Genichi Sugihara·bioRxiv·Apr 7, 2025
- Virtual Mouse Brain Histology from Multi-contrast MRI via Deep Learning
- Towards an informed choice of diffusion MRI image contrasts for cerebellar segmentation
Jon Haitz Legarreta, Lan Zhou, … Lauren J. O’Donnell·Human Brain MappingPeer-reviewed·Aug 1, 2025
- Applying Unet for extraction of vascular metrics from T1-weighted and T2-weighted MRI
Farnaz Orooji, Russell Butler·bioRxiv·Dec 18, 2022
- Probabilistic Multi-site MR Image Harmonization via Feature Preserving Conditional Generative Adversarial Networks
Saeed Moazami, Sepideh Rezvani, … Assad A. Oberai·Computerized Medical Imaging and GraphicsPeer-reviewed·May 20, 2026
- Transfer learning of deep neural network representations for fMRI decoding
Michele Svanera, Mattia Savardi, … Giancarlo Valente·Journal of Neuroscience MethodsPeer-reviewed·Oct 1, 2019