Input layer regularization for magnetic resonance relaxometry biexponential parameter estimation
© 2022 John Wiley & Sons Ltd. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.
| Veröffentlicht in: | Magnetic resonance in chemistry : MRC. - 1985. - 60(2022), 11 vom: 17. Nov., Seite 1076-1086 |
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| Weitere Verfasser: | , , , , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2022
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| Zugriff auf das übergeordnete Werk: | Magnetic resonance in chemistry : MRC |
| Schlagworte: | Journal Article Research Support, U.S. Gov't, Non-P.H.S. Research Support, N.I.H., Intramural MRI biexponentials deep learning neural network parameter estimation regularization relaxometry |
| Zusammenfassung: | © 2022 John Wiley & Sons Ltd. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA. Many methods have been developed for estimating the parameters of biexponential decay signals, which arise throughout magnetic resonance relaxometry (MRR) and the physical sciences. This is an intrinsically ill-posed problem so that estimates can depend strongly on noise and underlying parameter values. Regularization has proven to be a remarkably efficient procedure for providing more reliable solutions to ill-posed problems, while, more recently, neural networks have been used for parameter estimation. We re-address the problem of parameter estimation in biexponential models by introducing a novel form of neural network regularization which we call input layer regularization (ILR). Here, inputs to the neural network are composed of a biexponential decay signal augmented by signals constructed from parameters obtained from a regularized nonlinear least-squares estimate of the two decay time constants. We find that ILR results in a reduction in the error of time constant estimates on the order of 15%-50% or more, depending on the metric used and signal-to-noise level, with greater improvement seen for the time constant of the more rapidly decaying component. ILR is compatible with existing regularization techniques and should be applicable to a wide range of parameter estimation problems |
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| Beschreibung: | Date Completed 14.10.2022 Date Revised 18.09.2024 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 1097-458X |
| DOI: | 10.1002/mrc.5289 |