{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Rozowski M"],"funding":["Intramural NIH HHS","NIA NIH HHS","Division of Mathematical Sciences","National Science Foundation of Sri Lanka","National Institute on Aging"],"pagination":["1076-1086"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10185331"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["60(11)"],"pubmed_abstract":["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."],"journal":["Magnetic resonance in chemistry : MRC"],"pubmed_title":["Input layer regularization for magnetic resonance relaxometry biexponential parameter estimation."],"pmcid":["PMC10185331"],"funding_grant_id":["DMS 1738003","1738003","Intramural Research Program","Z99 AG999999"],"pubmed_authors":["Palumbo J","Czaja W","Bisen J","Rozowski M","Bi C","Bouhrara M","Spencer RG"],"additional_accession":[]},"is_claimable":false,"name":"Input layer regularization for magnetic resonance relaxometry biexponential parameter estimation.","description":"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.","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Nov","modification":"2025-04-05T16:14:37.638Z","creation":"2025-04-05T16:14:37.638Z"},"accession":"S-EPMC10185331","cross_references":{"pubmed":["35593385"],"doi":["10.1002/mrc.5289"]}}