Deep learning can accelerate and quantify simulated localized correlated spectroscopy

Abstract Nuclear magnetic resonance spectroscopy (MRS) allows for the determination of atomic structures and concentrations of different chemicals in a biochemical sample of interest. MRS is used in vivo clinically to aid in the diagnosis of several pathologies that affect metabolic pathways in the...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Zohaib Iqbal, Dan Nguyen, Michael Albert Thomas, Steve Jiang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/934dd7114c014eccb332bbe5d449288b
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:934dd7114c014eccb332bbe5d449288b
record_format	dspace
spelling	oai:doaj.org-article:934dd7114c014eccb332bbe5d449288b2021-12-02T17:32:57ZDeep learning can accelerate and quantify simulated localized correlated spectroscopy10.1038/s41598-021-88158-y2045-2322https://doaj.org/article/934dd7114c014eccb332bbe5d449288b2021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-88158-yhttps://doaj.org/toc/2045-2322Abstract Nuclear magnetic resonance spectroscopy (MRS) allows for the determination of atomic structures and concentrations of different chemicals in a biochemical sample of interest. MRS is used in vivo clinically to aid in the diagnosis of several pathologies that affect metabolic pathways in the body. Typically, this experiment produces a one dimensional (1D) 1H spectrum containing several peaks that are well associated with biochemicals, or metabolites. However, since many of these peaks overlap, distinguishing chemicals with similar atomic structures becomes much more challenging. One technique capable of overcoming this issue is the localized correlated spectroscopy (L-COSY) experiment, which acquires a second spectral dimension and spreads overlapping signal across this second dimension. Unfortunately, the acquisition of a two dimensional (2D) spectroscopy experiment is extremely time consuming. Furthermore, quantitation of a 2D spectrum is more complex. Recently, artificial intelligence has emerged in the field of medicine as a powerful force capable of diagnosing disease, aiding in treatment, and even predicting treatment outcome. In this study, we utilize deep learning to: (1) accelerate the L-COSY experiment and (2) quantify L-COSY spectra. All training and testing samples were produced using simulated metabolite spectra for chemicals found in the human body. We demonstrate that our deep learning model greatly outperforms compressed sensing based reconstruction of L-COSY spectra at higher acceleration factors. Specifically, at four-fold acceleration, our method has less than 5% normalized mean squared error, whereas compressed sensing yields 20% normalized mean squared error. We also show that at low SNR (25% noise compared to maximum signal), our deep learning model has less than 8% normalized mean squared error for quantitation of L-COSY spectra. These pilot simulation results appear promising and may help improve the efficiency and accuracy of L-COSY experiments in the future.Zohaib IqbalDan NguyenMichael Albert ThomasSteve JiangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Zohaib Iqbal Dan Nguyen Michael Albert Thomas Steve Jiang Deep learning can accelerate and quantify simulated localized correlated spectroscopy
description	Abstract Nuclear magnetic resonance spectroscopy (MRS) allows for the determination of atomic structures and concentrations of different chemicals in a biochemical sample of interest. MRS is used in vivo clinically to aid in the diagnosis of several pathologies that affect metabolic pathways in the body. Typically, this experiment produces a one dimensional (1D) 1H spectrum containing several peaks that are well associated with biochemicals, or metabolites. However, since many of these peaks overlap, distinguishing chemicals with similar atomic structures becomes much more challenging. One technique capable of overcoming this issue is the localized correlated spectroscopy (L-COSY) experiment, which acquires a second spectral dimension and spreads overlapping signal across this second dimension. Unfortunately, the acquisition of a two dimensional (2D) spectroscopy experiment is extremely time consuming. Furthermore, quantitation of a 2D spectrum is more complex. Recently, artificial intelligence has emerged in the field of medicine as a powerful force capable of diagnosing disease, aiding in treatment, and even predicting treatment outcome. In this study, we utilize deep learning to: (1) accelerate the L-COSY experiment and (2) quantify L-COSY spectra. All training and testing samples were produced using simulated metabolite spectra for chemicals found in the human body. We demonstrate that our deep learning model greatly outperforms compressed sensing based reconstruction of L-COSY spectra at higher acceleration factors. Specifically, at four-fold acceleration, our method has less than 5% normalized mean squared error, whereas compressed sensing yields 20% normalized mean squared error. We also show that at low SNR (25% noise compared to maximum signal), our deep learning model has less than 8% normalized mean squared error for quantitation of L-COSY spectra. These pilot simulation results appear promising and may help improve the efficiency and accuracy of L-COSY experiments in the future.
format	article
author	Zohaib Iqbal Dan Nguyen Michael Albert Thomas Steve Jiang
author_facet	Zohaib Iqbal Dan Nguyen Michael Albert Thomas Steve Jiang
author_sort	Zohaib Iqbal
title	Deep learning can accelerate and quantify simulated localized correlated spectroscopy
title_short	Deep learning can accelerate and quantify simulated localized correlated spectroscopy
title_full	Deep learning can accelerate and quantify simulated localized correlated spectroscopy
title_fullStr	Deep learning can accelerate and quantify simulated localized correlated spectroscopy
title_full_unstemmed	Deep learning can accelerate and quantify simulated localized correlated spectroscopy
title_sort	deep learning can accelerate and quantify simulated localized correlated spectroscopy
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/934dd7114c014eccb332bbe5d449288b
work_keys_str_mv	AT zohaibiqbal deeplearningcanaccelerateandquantifysimulatedlocalizedcorrelatedspectroscopy AT dannguyen deeplearningcanaccelerateandquantifysimulatedlocalizedcorrelatedspectroscopy AT michaelalbertthomas deeplearningcanaccelerateandquantifysimulatedlocalizedcorrelatedspectroscopy AT stevejiang deeplearningcanaccelerateandquantifysimulatedlocalizedcorrelatedspectroscopy
_version_	1718380135394050048

Deep learning can accelerate and quantify simulated localized correlated spectroscopy

Ejemplares similares