ArticleMagnetic resonance in medicine2023
CEST MR fingerprinting (CEST-MRF) for brain tumor quantification using EPI readout and deep learning reconstruction.
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 44 papers.
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44 citing papers in PubMed, 57 citations in OpenAlex.
- An Interpretable Deep-Learning Approach for Efficient CEST Parameter Quantification: Importance-Ranked Saturation Transfer MRI Protocol.Magnetic resonance in medicine · 2026Article
- Agreement and reproducibility of quantitative values depend on acquisition parameters in magnetic resonance fingerprinting.Radiological physics and technology · 2026Article
- Multi-molecular saturation-transfer MR fingerprinting for differentiating tumor progression from treatment-related change in post-treatment IDH-wildtype glioblastoma: a feasibility study.Journal of neuro-oncology · 2026Article
- Enhanced Quantitative Phosphocreatine MR Imaging of Skeletal Muscle Using a Global-Local Two-Branch Deep Learning Model.Magnetic resonance in medicine · 2026Article
- Deep-learning saturation transfer magnetic resonance fingerprinting (ST-MRF) in patients with Parkinson's disease.NeuroImage · 2026Article
- Dynamic Transitions for Fast Joint Acquisition and Reconstruction of CEST-Magnetic resonance in medicine · 2026Article
- Brain Tumor Imaging with Iopamidol CEST MRI: In Vivo Detection and Validation.Chemical & biomedical imaging · 2026Article
- Ultra-low fieldCommunications chemistry · 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
- 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
- CEST MRI data analysis using Kolmogorov-Arnold network (KAN) and Lorentzian-KAN (LKAN) models.Magnetic resonance in medicine · 2025Article
- Saturation transfer MR fingerprinting for magnetization transfer contrast and chemical exchange saturation transfer quantification.Magnetic resonance in medicine · 2025Article
- Article
- Quantitative multislice and jointly optimized rapid CEST for in vivo whole-brain imaging.Magnetic resonance in medicine · 2025Article
- Harnessing Dynamic Supramolecular Interactions for Lanthanide Detection via Computational Pattern Recognition of Magnetic Resonance Fingerprints.Journal of the American Chemical Society · 2025Article
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
Abstract
purposeTo develop a clinical CEST MR fingerprinting (CEST-MRF) method for brain tumor quantification using EPI acquisition and deep learning reconstruction.
methodsA CEST-MRF pulse sequence originally designed for animal imaging was modified to conform to hardware limits on clinical scanners while keeping scan time under 2 min. Quantitative MRF reconstruction was performed using a deep reconstruction network (DRONE) to yield the water relaxation and chemical exchange parameters. The feasibility of the six parameter DRONE reconstruction was tested in simulations using a digital brain phantom. A healthy subject was scanned with the CEST-MRF sequence, conventional MRF and CEST sequences for comparison. Reproducibility was assessed via test-retest experiments and the concordance correlation coefficient calculated for white matter and gray matter. The clinical utility of CEST-MRF was demonstrated on four patients with brain metastases in comparison to standard clinical imaging sequences. Tumors were segmented into edema, solid core, and necrotic core regions and the CEST-MRF values compared to the contra-lateral side.
resultsDRONE reconstruction of the digital phantom yielded a normalized RMS error of ≤7% for all parameters. The CEST-MRF parameters were in good agreement with those from conventional MRF and CEST sequences and previous studies. The mean concordance correlation coefficient for all six parameters was 0.98 ± 0.01 in white matter and 0.98 ± 0.02 in gray matter. The CEST-MRF values in nearly all tumor regions were significantly different (P = 0.05) from each other and the contra-lateral side.
conclusionCombination of EPI readout and deep learning reconstruction enabled fast, accurate and reproducible CEST-MRF in brain tumors.
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