|
|
|
|
| LEADER |
01000caa a22002652 4500 |
| 001 |
NLM34116111X |
| 003 |
DE-627 |
| 005 |
20240918231839.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
231226s2022 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1002/mrc.5289
|2 doi
|
| 028 |
5 |
2 |
|a pubmed24n1538.xml
|
| 035 |
|
|
|a (DE-627)NLM34116111X
|
| 035 |
|
|
|a (NLM)35593385
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Rozowski, Michael
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a Input layer regularization for magnetic resonance relaxometry biexponential parameter estimation
|
| 264 |
|
1 |
|c 2022
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ƒaComputermedien
|b c
|2 rdamedia
|
| 338 |
|
|
|a ƒa Online-Ressource
|b cr
|2 rdacarrier
|
| 500 |
|
|
|a Date Completed 14.10.2022
|
| 500 |
|
|
|a Date Revised 18.09.2024
|
| 500 |
|
|
|a published: Print-Electronic
|
| 500 |
|
|
|a Citation Status MEDLINE
|
| 520 |
|
|
|a © 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.
|
| 520 |
|
|
|a 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
|
| 650 |
|
4 |
|a Journal Article
|
| 650 |
|
4 |
|a Research Support, U.S. Gov't, Non-P.H.S.
|
| 650 |
|
4 |
|a Research Support, N.I.H., Intramural
|
| 650 |
|
4 |
|a MRI
|
| 650 |
|
4 |
|a biexponentials
|
| 650 |
|
4 |
|a deep learning
|
| 650 |
|
4 |
|a neural network
|
| 650 |
|
4 |
|a parameter estimation
|
| 650 |
|
4 |
|a regularization
|
| 650 |
|
4 |
|a relaxometry
|
| 700 |
1 |
|
|a Palumbo, Jonathan
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Bisen, Jay
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Bi, Chuan
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Bouhrara, Mustapha
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Czaja, Wojciech
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Spencer, Richard G
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t Magnetic resonance in chemistry : MRC
|d 1985
|g 60(2022), 11 vom: 17. Nov., Seite 1076-1086
|w (DE-627)NLM098179667
|x 1097-458X
|7 nnns
|
| 773 |
1 |
8 |
|g volume:60
|g year:2022
|g number:11
|g day:17
|g month:11
|g pages:1076-1086
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1002/mrc.5289
|3 Volltext
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_350
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|d 60
|j 2022
|e 11
|b 17
|c 11
|h 1076-1086
|