ArticleMagnetic resonance in medicine2019
Flexible and efficient optimization of quantitative sequences using automatic differentiation of Bloch simulations.
Article in Magnetic resonance in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Diffusion MRI experimental design optimization for microstructure imaging.Communications biology · 2026Article
- RF phase modulation improves quantitative transient state sequences under constrained conditions.Magma (New York, N.Y.) · 2026Article
- "MR Fingerprinting for Imaging Brain Hemodynamics and Oxygenation".Journal of magnetic resonance imaging : JMRI · 2025Review
- Gpu-accelerated JEMRIS for extensive MRI simulations.Magma (New York, N.Y.) · 2025Article
- Improved Spiral Projection MR Fingerprinting via Memory-Efficient Synergic Optimization of 3D Spiral Trajectory, Image Reconstruction and Parameter Estimation (SOTIP).IEEE transactions on medical imaging · 2025Article
- Three-dimensional high-isotropic-resolution MR fingerprinting optimized for 0.55 T.Magnetic resonance in medicine · 2025Article
- Incorporating Spatial and Spectral Saturation Modules Into MR Fingerprinting.NMR in biomedicine · 2025Article
- Cramér-Rao Bound Optimized Subspace Reconstruction in Quantitative MRI.IEEE transactions on bio-medical engineering · 2025Article
- 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
- High-resolution myelin-water fraction and quantitative relaxation mapping using 3D ViSTa-MR fingerprinting.Magnetic resonance in medicine · 2024Article
- DTI-MR fingerprinting for rapid high-resolution whole-brain TMagnetic resonance in medicine · 2024Article
- Emerging Trends in Magnetic Resonance Fingerprinting for Quantitative Biomedical Imaging Applications: A Review.Bioengineering (Basel, Switzerland) · 2024Review
- High-resolution myelin-water fraction and quantitative relaxation mapping using 3D ViSTa-MR fingerprinting.ArXiv · 2023Article
- Article
- Global deep learning optimization of chemical exchange saturation transfer magnetic resonance fingerprinting acquisition schedule.NMR in biomedicine · 2023Article
- Latent signal models: Learning compact representations of signal evolution for improved time-resolved, multi-contrast MRI.Magnetic resonance in medicine · 2023Article
- Optimized three-dimensional ultrashort echo time: Magnetic resonance fingerprinting for myelin tissue fraction mapping.Human brain mapping · 2023Article
- Efficient performance analysis and optimization of transient-state sequences for multiparametric magnetic resonance imaging.NMR in biomedicine · 2023Article
- CEST MR fingerprinting (CEST-MRF) for brain tumor quantification using EPI readout and deep learning reconstruction.Magnetic resonance in medicine · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
purposeTo investigate a computationally efficient method for optimizing the Cramér-Rao Lower Bound (CRLB) of quantitative sequences without using approximations or an analytical expression of the signal.
methodsAutomatic differentiation was applied to Bloch simulations and used to optimize several quantitative sequences without the need for approximations or an analytical expression. The results were validated with in vivo measurements and comparisons to prior art. Multi-echo spin echo and DESPO
resultsThe sequence parameters obtained for multi-echo spin echo and DESPO
conclusionsAutomatic differentiation can be used to optimize the CRLB of quantitative sequences without using approximations or analytical expressions. For MRF, the runtime is computationally efficient and can be used to investigate confounding factors as well as MRF sequences with a greater number of repetitions.
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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.