ArticleMagnetic resonance in medicine2023
Direct synthesis of multi-contrast brain MR images from MR multitasking spatial factors using deep learning.
Article in Magnetic resonance in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 9 citations in OpenAlex.
- Semi-supervision for clinical contrast-weighted image synthesis from magnetic resonance fingerprinting.Magma (New York, N.Y.) · 2026Article
- All-in-One Cardiac MRI: Unified Non-Contrast-enhanced and Contrast-enhanced Imaging for a Continuous-Acquisition Examination.Radiology. Cardiothoracic imaging · 2025Article
- Accelerating 3D radial MPnRAGE using a self-supervised deep factor model.Magnetic resonance in medicine · 2025Article
- ACCELERATING QUANTITATIVE MRI USING SUBSPACE MULTISCALE ENERGY MODEL (SS-MUSE).Proceedings. IEEE International Symposium on Biomedical Imaging · 2025Article
- Cramér-Rao Bound Optimized Subspace Reconstruction in Quantitative MRI.IEEE transactions on bio-medical engineering · 2025Article
- 3D Echo Planar Time-resolved Imaging (3D-EPTI) for ultrafast multi-parametric quantitative MRI.NeuroImage · 2022Article
Corrections and comments
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Authors and funding
12 authors at 2 institutions in 1 country.
Funding
Abstract
purposeTo develop a deep learning method to synthesize conventional contrast-weighted images in the brain from MR multitasking spatial factors.
methodsEighteen subjects were imaged using a whole-brain quantitative T
resultsThe deep-learning synthetic images showed comparable contrasts of brain tissues with the reference images from true acquisitions and were substantially better than the Bloch-equation-based synthesis results. Averaging on the three contrasts, the deep learning synthesis achieved normalized root mean square error = 0.184 ± 0.075, peak SNR = 28.14 ± 2.51, and structural-similarity index = 0.918 ± 0.034, which were significantly better than Bloch-equation-based synthesis (p < 0.05). Radiologists' rating results show that compared with true acquisitions, deep learning synthesis had no notable quality degradation and was better than Bloch-equation-based synthesis.
conclusionA deep learning technique was developed to synthesize conventional weighted images from MR multitasking spatial factors in the brain, enabling the simultaneous acquisition of multiparametric quantitative maps and clinical contrast-weighted images in a single scan.
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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.