ArticleMagnetic resonance in medicine2022
An end-to-end AI-based framework for automated discovery of rapid CEST/MT MRI acquisition protocols and molecular parameter quantification (AutoCEST).
Article in Magnetic resonance in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- An Interpretable Deep-Learning Approach for Efficient CEST Parameter Quantification: Importance-Ranked Saturation Transfer MRI Protocol.Magnetic resonance in medicine · 2026Article
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- Dynamic Transitions for Fast Joint Acquisition and Reconstruction of CEST-Magnetic resonance in medicine · 2026Article
- Optimization of Pulsed Saturation Transfer MR Fingerprinting (ST MRF) Acquisition Using the Cramér-Rao Bound and Sequential Quadratic Programming.Magnetic resonance in medicine · 2026Article
- Physics-informed optimization of saturation-transfer MRI protocols using non-differentiable Bloch models.Physics in medicine and biology · 2026Article
- Chemical exchange saturation transfer MRI for neurodegenerative diseases: An update on clinical and preclinical studies.Neural regeneration research · 2026Article
- MixBranchNet: a task-adaptive network for glioma segmentation and genotype prediction by exploiting spatial-spectral correlations in CEST MRI.Frontiers in oncology · 2026Article
- Quantitative multi-metabolite imaging of Parkinson's disease using AI boosted molecular MRI.Npj imaging · 2025Article
- Multi-contrast generation and quantitative MRI using a transformer-based framework with RF excitation embeddings.Communications biology · 2025Article
- Chemical Exchange Saturation Transfer Imaging in Neuroinflammation: Methods, Challenges, and Recommendations.International journal of molecular sciences · 2025Review
- Quantitative molecular imaging using deep magnetic resonance fingerprinting.Nature protocols · 2025Review
- Saturation transfer MR fingerprinting for magnetization transfer contrast and chemical exchange saturation transfer quantification.Magnetic resonance in medicine · 2025Article
- Ungated, plug-and-play preclinical cardiac CEST-MRI using radial FLASH with segmented saturation.Magnetic resonance in medicine · 2025Article
- Toward quantitative CEST imaging of glutamate in the mouse brain using a multi-pool exchange model calibrated byMagnetic resonance in medicine · 2025Article
- Advancing In Vivo Molecular Bioimaging With Optimal Frequency Offset Selection and Deep Learning Reconstruction for CEST MRI.IEEE access : practical innovations, open solutions · 2025Article
- Article
- Artificial intelligence for neuro MRI acquisition: a review.Magma (New York, N.Y.) · 2024Review
- Deep learning for accelerated and robust MRI reconstruction.Magma (New York, N.Y.) · 2024Review
- Emerging Trends in Magnetic Resonance Fingerprinting for Quantitative Biomedical Imaging Applications: A Review.Bioengineering (Basel, Switzerland) · 2024Review
- Synthesis of MR fingerprinting information from magnitude-only MR imaging data using a parallelized, multi network U-Net convolutional neural network.Frontiers in radiology · 2024Article
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5 authors.
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Abstract
purposeTo develop an automated machine-learning-based method for the discovery of rapid and quantitative chemical exchange saturation transfer (CEST) MR fingerprinting acquisition and reconstruction protocols.
methodsAn MR physics-governed AI system was trained to generate optimized acquisition schedules and the corresponding quantitative reconstruction neural network. The system (termed AutoCEST) is composed of a CEST saturation block, a spin dynamics module, and a deep reconstruction network, all differentiable and jointly connected. The method was validated using a variety of chemical exchange phantoms and in vivo mouse brains at 9.4T.
resultsThe acquisition times for AutoCEST optimized schedules ranged from 35 to 71 s, with a quantitative image reconstruction time of only 29 ms. The resulting exchangeable proton concentration maps for the phantoms were in good agreement with the known solute concentrations for AutoCEST sequences (mean absolute error = 2.42 mM; Pearson's
conclusionAutoCEST can automatically generate optimized CEST/MT acquisition protocols that can be rapidly reconstructed into quantitative exchange parameter maps.
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