Accelerating whole-heart 3D T2 mapping: Impact of undersampling strategies and reconstruction techniques.

<h4>Purpose</h4>We aim to determine an advantageous approach for the acceleration of high spatial resolution 3D cardiac T2 relaxometry data by comparing the performance of different undersampling patterns and reconstruction methods over a range of acceleration rates.<h4>Methods<...

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Autores principales:	Dan Zhu, Haiyan Ding, M Muz Zviman, Henry Halperin, Michael Schär, Daniel A Herzka
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b9db53a99edd4ce79742c0bd0ad5b55e
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spelling	oai:doaj.org-article:b9db53a99edd4ce79742c0bd0ad5b55e2021-12-02T20:08:21ZAccelerating whole-heart 3D T2 mapping: Impact of undersampling strategies and reconstruction techniques.1932-620310.1371/journal.pone.0252777https://doaj.org/article/b9db53a99edd4ce79742c0bd0ad5b55e2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0252777https://doaj.org/toc/1932-6203<h4>Purpose</h4>We aim to determine an advantageous approach for the acceleration of high spatial resolution 3D cardiac T2 relaxometry data by comparing the performance of different undersampling patterns and reconstruction methods over a range of acceleration rates.<h4>Methods</h4>Multi-volume 3D high-resolution cardiac images were acquired fully and undersampled retrospectively using 1) optimal CAIPIRINHA and 2) a variable density random (VDR) sampling. Data were reconstructed using 1) multi-volume sensitivity encoding (SENSE), 2) joint-sparsity SENSE and 3) model-based SENSE. Four metrics were calculated on 3 naïve swine and 8 normal human subjects over a whole left-ventricular region of interest: root-mean-square error (RMSE) of image signal intensity, RMSE of T2, the bias of mean T2, and standard deviation (SD) of T2. Fully sampled data and volume-by-volume SENSE with standard equally spaced undersampling were used as references. The Jaccard index calculated from one swine with acute myocardial infarction (MI) was used to demonstrate preservation of segmentation of edematous tissues with elevated T2.<h4>Results</h4>In naïve swine and normal human subjects, all methods had similar performance when the net reduction factor (Rnet) <2.5. VDR sampling with model-based SENSE showed the lowest RMSEs (10.5%-14.2%) and SDs (+1.7-2.4 ms) of T2 when Rnet>2.5, while VDR sampling with the joint-sparsity SENSE had the lowest bias of mean T2 (0.0-1.1ms) when Rnet>3. The RMSEs of parametric T2 values (9.2%-24.6%) were larger than for image signal intensities (5.2%-18.4%). In the swine with MI, VDR sampling with either joint-sparsity or model-based SENSE showed consistently higher Jaccard index for all Rnet (0.71-0.50) than volume-by-volume SENSE (0.68-0.30).<h4>Conclusions</h4>Retrospective exploration of undersampling and reconstruction in 3D whole-heart T2 parametric mapping revealed that maps were more sensitive to undersampling than images, presenting a more stringent limiting factor on Rnet. The combination of VDR sampling patterns with model-based or joint-sparsity SENSE reconstructions were more robust for Rnet>3.Dan ZhuHaiyan DingM Muz ZvimanHenry HalperinMichael SchärDaniel A HerzkaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 9, p e0252777 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Dan Zhu Haiyan Ding M Muz Zviman Henry Halperin Michael Schär Daniel A Herzka Accelerating whole-heart 3D T2 mapping: Impact of undersampling strategies and reconstruction techniques.
description	<h4>Purpose</h4>We aim to determine an advantageous approach for the acceleration of high spatial resolution 3D cardiac T2 relaxometry data by comparing the performance of different undersampling patterns and reconstruction methods over a range of acceleration rates.<h4>Methods</h4>Multi-volume 3D high-resolution cardiac images were acquired fully and undersampled retrospectively using 1) optimal CAIPIRINHA and 2) a variable density random (VDR) sampling. Data were reconstructed using 1) multi-volume sensitivity encoding (SENSE), 2) joint-sparsity SENSE and 3) model-based SENSE. Four metrics were calculated on 3 naïve swine and 8 normal human subjects over a whole left-ventricular region of interest: root-mean-square error (RMSE) of image signal intensity, RMSE of T2, the bias of mean T2, and standard deviation (SD) of T2. Fully sampled data and volume-by-volume SENSE with standard equally spaced undersampling were used as references. The Jaccard index calculated from one swine with acute myocardial infarction (MI) was used to demonstrate preservation of segmentation of edematous tissues with elevated T2.<h4>Results</h4>In naïve swine and normal human subjects, all methods had similar performance when the net reduction factor (Rnet) <2.5. VDR sampling with model-based SENSE showed the lowest RMSEs (10.5%-14.2%) and SDs (+1.7-2.4 ms) of T2 when Rnet>2.5, while VDR sampling with the joint-sparsity SENSE had the lowest bias of mean T2 (0.0-1.1ms) when Rnet>3. The RMSEs of parametric T2 values (9.2%-24.6%) were larger than for image signal intensities (5.2%-18.4%). In the swine with MI, VDR sampling with either joint-sparsity or model-based SENSE showed consistently higher Jaccard index for all Rnet (0.71-0.50) than volume-by-volume SENSE (0.68-0.30).<h4>Conclusions</h4>Retrospective exploration of undersampling and reconstruction in 3D whole-heart T2 parametric mapping revealed that maps were more sensitive to undersampling than images, presenting a more stringent limiting factor on Rnet. The combination of VDR sampling patterns with model-based or joint-sparsity SENSE reconstructions were more robust for Rnet>3.
format	article
author	Dan Zhu Haiyan Ding M Muz Zviman Henry Halperin Michael Schär Daniel A Herzka
author_facet	Dan Zhu Haiyan Ding M Muz Zviman Henry Halperin Michael Schär Daniel A Herzka
author_sort	Dan Zhu
title	Accelerating whole-heart 3D T2 mapping: Impact of undersampling strategies and reconstruction techniques.
title_short	Accelerating whole-heart 3D T2 mapping: Impact of undersampling strategies and reconstruction techniques.
title_full	Accelerating whole-heart 3D T2 mapping: Impact of undersampling strategies and reconstruction techniques.
title_fullStr	Accelerating whole-heart 3D T2 mapping: Impact of undersampling strategies and reconstruction techniques.
title_full_unstemmed	Accelerating whole-heart 3D T2 mapping: Impact of undersampling strategies and reconstruction techniques.
title_sort	accelerating whole-heart 3d t2 mapping: impact of undersampling strategies and reconstruction techniques.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/b9db53a99edd4ce79742c0bd0ad5b55e
work_keys_str_mv	AT danzhu acceleratingwholeheart3dt2mappingimpactofundersamplingstrategiesandreconstructiontechniques AT haiyanding acceleratingwholeheart3dt2mappingimpactofundersamplingstrategiesandreconstructiontechniques AT mmuzzviman acceleratingwholeheart3dt2mappingimpactofundersamplingstrategiesandreconstructiontechniques AT henryhalperin acceleratingwholeheart3dt2mappingimpactofundersamplingstrategiesandreconstructiontechniques AT michaelschar acceleratingwholeheart3dt2mappingimpactofundersamplingstrategiesandreconstructiontechniques AT danielaherzka acceleratingwholeheart3dt2mappingimpactofundersamplingstrategiesandreconstructiontechniques
_version_	1718375203861430272

Accelerating whole-heart 3D T2 mapping: Impact of undersampling strategies and reconstruction techniques.

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