ArticleNMR in biomedicine2022
Learning-based optimization of acquisition schedule for magnetization transfer contrast MR fingerprinting.
Article in NMR in biomedicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 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
- Deep-learning saturation transfer magnetic resonance fingerprinting (ST-MRF) in patients with Parkinson's disease.NeuroImage · 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
- Multiparametric Saturation Transfer MR Fingerprinting Using Rosette-Accelerated Readout.NMR in biomedicine · 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
- Quantitative macromolecular proton fraction imaging using pulsed spin-lock.Magnetic resonance in medicine · 2025Article
- 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
- Quantitative multislice and jointly optimized rapid CEST for in vivo whole-brain imaging.Magnetic resonance in medicine · 2025Article
- Unraveling contributions to the Z-spectrum signal at 3.5 ppm of human brain tumors.Magnetic resonance in medicine · 2024Article
- Article
- Self-supervised learning for denoising of multidimensional MRI data.Magnetic resonance in medicine · 2024Article
- 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
- Dynamic and rapid deep synthesis of chemical exchange saturation transfer and semisolid magnetization transfer MRI signals.Scientific reports · 2023Article
- Bloch simulator-driven deep recurrent neural network for magnetization transfer contrast MR fingerprinting and CEST imaging.Magnetic resonance in medicine · 2023Article
- Global deep learning optimization of chemical exchange saturation transfer magnetic resonance fingerprinting acquisition schedule.NMR in biomedicine · 2023Article
- Only-train-once MR fingerprinting for BMagnetic resonance in medicine · 2023Article
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4 authors.
Funding
Abstract
Magnetization transfer contrast MR fingerprinting (MTC-MRF) is a novel quantitative imaging method that simultaneously quantifies free bulk water and semisolid macromolecule parameters using pseudo-randomized scan parameters. To improve acquisition efficiency and reconstruction accuracy, the optimization of MRF sequence design has been of recent interest in the MRF field, but has been challenging due to the large number of degrees of freedom to be optimized in the sequence. Herein, we propose a framework for learning-based optimization of the acquisition schedule (LOAS), which optimizes RF saturation-encoded MRF acquisitions with a minimal number of scan parameters for tissue parameter determination. In a supervised learning framework, scan parameters were subsequently updated to minimize a predefined loss function that can directly represent tissue quantification errors. We evaluated the performance of the proposed approach with a numerical phantom and in in vivo experiments. For validation, MRF images were synthesized using the tissue parameters estimated from a fully connected neural network framework and compared with references. Our results showed that LOAS outperformed existing indirect optimization methods with regard to quantification accuracy and acquisition efficiency. The proposed LOAS method could be a powerful optimization tool in the design of MRF pulse sequences.
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