Feasibility of Data-Driven, Model-Free Quantitative MRI Protocol Design: Application to Brain and Prostate Diffusion-Relaxation Imaging

Purpose: We investigate the feasibility of data-driven, model-free quantitative MRI (qMRI) protocol design on in vivo brain and prostate diffusion-relaxation imaging (DRI).Methods: We select subsets of measurements within lengthy pilot scans, without identifying tissue parameters for which to optimi...

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Autores principales:	Francesco Grussu, Stefano B. Blumberg, Marco Battiston, Lebina S. Kakkar, Hongxiang Lin, Andrada Ianuş, Torben Schneider, Saurabh Singh, Roger Bourne, Shonit Punwani, David Atkinson, Claudia A. M. Gandini Wheeler-Kingshott, Eleftheria Panagiotaki, Thomy Mertzanidou, Daniel C. Alexander
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	quantitative MRI (qMRI) protocol design artificial neural network (ANN) diffusion-relaxation brain prostate Physics QC1-999
Acceso en línea:	https://doaj.org/article/dde3530e76cb45218a05c5fe9fae8da8
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spelling	oai:doaj.org-article:dde3530e76cb45218a05c5fe9fae8da82021-11-15T05:54:36ZFeasibility of Data-Driven, Model-Free Quantitative MRI Protocol Design: Application to Brain and Prostate Diffusion-Relaxation Imaging2296-424X10.3389/fphy.2021.752208https://doaj.org/article/dde3530e76cb45218a05c5fe9fae8da82021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fphy.2021.752208/fullhttps://doaj.org/toc/2296-424XPurpose: We investigate the feasibility of data-driven, model-free quantitative MRI (qMRI) protocol design on in vivo brain and prostate diffusion-relaxation imaging (DRI).Methods: We select subsets of measurements within lengthy pilot scans, without identifying tissue parameters for which to optimise for. We use the “select and retrieve via direct upsampling” (SARDU-Net) algorithm, made of a selector, identifying measurement subsets, and a predictor, estimating fully-sampled signals from the subsets. We implement both using artificial neural networks, which are trained jointly end-to-end. We deploy the algorithm on brain (32 diffusion-/T1-weightings) and prostate (16 diffusion-/T2-weightings) DRI scans acquired on three healthy volunteers on two separate 3T Philips systems each. We used SARDU-Net to identify sub-protocols of fixed size, assessing reproducibility and testing sub-protocols for their potential to inform multi-contrast analyses via the T1-weighted spherical mean diffusion tensor (T1-SMDT, brain) and hybrid multi-dimensional MRI (HM-MRI, prostate) models, for which sub-protocol selection was not optimised explicitly.Results: In both brain and prostate, SARDU-Net identifies sub-protocols that maximise information content in a reproducible manner across training instantiations using a small number of pilot scans. The sub-protocols support T1-SMDT and HM-MRI multi-contrast modelling for which they were not optimised explicitly, providing signal quality-of-fit in the top 5% against extensive sub-protocol comparisons.Conclusions: Identifying economical but informative qMRI protocols from subsets of rich pilot scans is feasible and potentially useful in acquisition-time-sensitive applications in which there is not a qMRI model of choice. SARDU-Net is demonstrated to be a robust algorithm for data-driven, model-free protocol design.Francesco GrussuFrancesco GrussuFrancesco GrussuStefano B. BlumbergMarco BattistonLebina S. KakkarHongxiang LinAndrada IanuşTorben SchneiderTorben SchneiderSaurabh SinghRoger BourneShonit PunwaniDavid AtkinsonClaudia A. M. Gandini Wheeler-KingshottClaudia A. M. Gandini Wheeler-KingshottClaudia A. M. Gandini Wheeler-KingshottEleftheria PanagiotakiThomy MertzanidouDaniel C. AlexanderFrontiers Media S.A.articlequantitative MRI (qMRI)protocol designartificial neural network (ANN)diffusion-relaxationbrainprostatePhysicsQC1-999ENFrontiers in Physics, Vol 9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	quantitative MRI (qMRI) protocol design artificial neural network (ANN) diffusion-relaxation brain prostate Physics QC1-999
spellingShingle	quantitative MRI (qMRI) protocol design artificial neural network (ANN) diffusion-relaxation brain prostate Physics QC1-999 Francesco Grussu Francesco Grussu Francesco Grussu Stefano B. Blumberg Marco Battiston Lebina S. Kakkar Hongxiang Lin Andrada Ianuş Torben Schneider Torben Schneider Saurabh Singh Roger Bourne Shonit Punwani David Atkinson Claudia A. M. Gandini Wheeler-Kingshott Claudia A. M. Gandini Wheeler-Kingshott Claudia A. M. Gandini Wheeler-Kingshott Eleftheria Panagiotaki Thomy Mertzanidou Daniel C. Alexander Feasibility of Data-Driven, Model-Free Quantitative MRI Protocol Design: Application to Brain and Prostate Diffusion-Relaxation Imaging
description	Purpose: We investigate the feasibility of data-driven, model-free quantitative MRI (qMRI) protocol design on in vivo brain and prostate diffusion-relaxation imaging (DRI).Methods: We select subsets of measurements within lengthy pilot scans, without identifying tissue parameters for which to optimise for. We use the “select and retrieve via direct upsampling” (SARDU-Net) algorithm, made of a selector, identifying measurement subsets, and a predictor, estimating fully-sampled signals from the subsets. We implement both using artificial neural networks, which are trained jointly end-to-end. We deploy the algorithm on brain (32 diffusion-/T1-weightings) and prostate (16 diffusion-/T2-weightings) DRI scans acquired on three healthy volunteers on two separate 3T Philips systems each. We used SARDU-Net to identify sub-protocols of fixed size, assessing reproducibility and testing sub-protocols for their potential to inform multi-contrast analyses via the T1-weighted spherical mean diffusion tensor (T1-SMDT, brain) and hybrid multi-dimensional MRI (HM-MRI, prostate) models, for which sub-protocol selection was not optimised explicitly.Results: In both brain and prostate, SARDU-Net identifies sub-protocols that maximise information content in a reproducible manner across training instantiations using a small number of pilot scans. The sub-protocols support T1-SMDT and HM-MRI multi-contrast modelling for which they were not optimised explicitly, providing signal quality-of-fit in the top 5% against extensive sub-protocol comparisons.Conclusions: Identifying economical but informative qMRI protocols from subsets of rich pilot scans is feasible and potentially useful in acquisition-time-sensitive applications in which there is not a qMRI model of choice. SARDU-Net is demonstrated to be a robust algorithm for data-driven, model-free protocol design.
format	article
author	Francesco Grussu Francesco Grussu Francesco Grussu Stefano B. Blumberg Marco Battiston Lebina S. Kakkar Hongxiang Lin Andrada Ianuş Torben Schneider Torben Schneider Saurabh Singh Roger Bourne Shonit Punwani David Atkinson Claudia A. M. Gandini Wheeler-Kingshott Claudia A. M. Gandini Wheeler-Kingshott Claudia A. M. Gandini Wheeler-Kingshott Eleftheria Panagiotaki Thomy Mertzanidou Daniel C. Alexander
author_facet	Francesco Grussu Francesco Grussu Francesco Grussu Stefano B. Blumberg Marco Battiston Lebina S. Kakkar Hongxiang Lin Andrada Ianuş Torben Schneider Torben Schneider Saurabh Singh Roger Bourne Shonit Punwani David Atkinson Claudia A. M. Gandini Wheeler-Kingshott Claudia A. M. Gandini Wheeler-Kingshott Claudia A. M. Gandini Wheeler-Kingshott Eleftheria Panagiotaki Thomy Mertzanidou Daniel C. Alexander
author_sort	Francesco Grussu
title	Feasibility of Data-Driven, Model-Free Quantitative MRI Protocol Design: Application to Brain and Prostate Diffusion-Relaxation Imaging
title_short	Feasibility of Data-Driven, Model-Free Quantitative MRI Protocol Design: Application to Brain and Prostate Diffusion-Relaxation Imaging
title_full	Feasibility of Data-Driven, Model-Free Quantitative MRI Protocol Design: Application to Brain and Prostate Diffusion-Relaxation Imaging
title_fullStr	Feasibility of Data-Driven, Model-Free Quantitative MRI Protocol Design: Application to Brain and Prostate Diffusion-Relaxation Imaging
title_full_unstemmed	Feasibility of Data-Driven, Model-Free Quantitative MRI Protocol Design: Application to Brain and Prostate Diffusion-Relaxation Imaging
title_sort	feasibility of data-driven, model-free quantitative mri protocol design: application to brain and prostate diffusion-relaxation imaging
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/dde3530e76cb45218a05c5fe9fae8da8
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Feasibility of Data-Driven, Model-Free Quantitative MRI Protocol Design: Application to Brain and Prostate Diffusion-Relaxation Imaging

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