ArticleIEEE transactions on medical imaging2018
Dictionary-Free MRI PERK: Parameter Estimation via Regression with Kernels.
Article in IEEE transactions on medical imaging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
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- Cramér-Rao Bound Optimized Subspace Reconstruction in Quantitative MRI.IEEE transactions on bio-medical engineering · 2025Article
- Constrained alternating minimization for parameter mapping (CAMP).Magnetic resonance imaging · 2024Article
- Rapid quantitative magnetization transfer imaging: Utilizing the hybrid state and the generalized Bloch model.Magnetic resonance in medicine · 2024Article
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- A Physics-Based Algorithm to Universally Standardize Routinely Obtained Clinical TAcademic radiology · 2024Article
- Unconstrained quantitative magnetization transfer imaging: Disentangling TImaging neuroscience (Cambridge, Mass.) · 2024Article
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- Cramér-Rao bound-informed training of neural networks for quantitative MRI.Magnetic resonance in medicine · 2022Article
- Rapid MR relaxometry using deep learning: An overview of current techniques and emerging trends.NMR in biomedicine · 2022Review
- Towards an efficient validation of dynamical whole-brain models.Scientific reports · 2022Article
- Magnetic resonance fingerprinting: from evolution to clinical applications.Journal of medical radiation sciences · 2020Review
- Myelin water fraction estimation using small-tip fast recovery MRI.Magnetic resonance in medicine · 2020Article
- Magnetization transfer in magnetic resonance fingerprinting.Magnetic resonance in medicine · 2020Article
- Optimizing MRF-ASL scan design for precise quantification of brain hemodynamics using neural network regression.Magnetic resonance in medicine · 2020Article
- Magnetic resonance fingerprinting review part 2: Technique and directions.Journal of magnetic resonance imaging : JMRI · 2020Review
- Dictionary-Free MRI PERK: Parameter Estimation via Regression with Kernels.IEEE transactions on medical imaging · 2018Article
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4 authors.
Funding
Abstract
This paper introduces a fast, general method for dictionary-free parameter estimation in quantitative magnetic resonance imaging (QMRI) parameter estimation via regression with kernels (PERK). PERK first uses prior distributions and the nonlinear MR signal model to simulate many parameter-measurement pairs. Inspired by machine learning, PERK then takes these parameter-measurement pairs as labeled training points and learns from them a nonlinear regression function using kernel functions and convex optimization. PERK admits a simple implementation as per-voxel nonlinear lifting of MRI measurements followed by linear minimum mean-squared error regression. We demonstrate PERK for $ {\textit {T}_{1}}, {\textit {T}_{2}}$ estimation, a well-studied application where it is simple to compare PERK estimates against dictionary-based grid search estimates and iterative optimization estimates. Numerical simulations as well as single-slice phantom and in vivo experiments demonstrate that PERK and other tested methods produce comparable $ {\textit {T}_{1}}, {\textit {T}_{2}}$ estimates in white and gray matter, but PERK is consistently at least $140\times $ faster. This acceleration factor may increase by several orders of magnitude for full-volume QMRI estimation problems involving more latent parameters per voxel.
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