Improved parallel magnetic resonance imaging reconstruction with multiple variable density sampling

Abstract Generalized auto-calibrating partially parallel acquisitions (GRAPPA) and other parallel Magnetic Resonance Imaging (pMRI) methods restore the unacquired data in k-space by linearly calculating the undersampled data around the missing points. In order to obtain the weight of the linear calc...

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Autores principales:	Jinhua Sheng, Yuchen Shi, Qiao Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d66648dc8f36427ca117bcefc8b50120
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spelling	oai:doaj.org-article:d66648dc8f36427ca117bcefc8b501202021-12-02T17:15:33ZImproved parallel magnetic resonance imaging reconstruction with multiple variable density sampling10.1038/s41598-021-88567-z2045-2322https://doaj.org/article/d66648dc8f36427ca117bcefc8b501202021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-88567-zhttps://doaj.org/toc/2045-2322Abstract Generalized auto-calibrating partially parallel acquisitions (GRAPPA) and other parallel Magnetic Resonance Imaging (pMRI) methods restore the unacquired data in k-space by linearly calculating the undersampled data around the missing points. In order to obtain the weight of the linear calculation, a small number of auto-calibration signal (ACS) lines need to be sampled at the center of the k-space. Therefore, the sampling pattern used in this type of method is to full sample data in the middle area and undersample in the outer k-space with nominal reduction factors. In this paper, we propose a novel reconstruction method with a multiple variable density sampling (MVDS) that is different from traditional sampling patterns. Our method can significantly improve the image quality using multiple reduction factors with fewer ACS lines. Specifically, the traditional sampling pattern only uses a single reduction factor to uniformly undersample data in the region outside the ACS, but we use multiple reduction factors. When sampling the k-space data, we keep the ACS lines unchanged, use a smaller reduction factor for undersampling data near the ACS lines and a larger reduction factor for the outermost part of k-space. The error is lower after reconstruction of this region by undersampled data with a smaller reduction factor. The experimental results show that with the same amount of data sampled, using NL-GRAPPA to reconstruct the k-space data sampled by our method can result in lower noise and fewer artifacts than traditional methods. In particular, our method is extremely effective when the number of ACS lines is small.Jinhua ShengYuchen ShiQiao ZhangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jinhua Sheng Yuchen Shi Qiao Zhang Improved parallel magnetic resonance imaging reconstruction with multiple variable density sampling
description	Abstract Generalized auto-calibrating partially parallel acquisitions (GRAPPA) and other parallel Magnetic Resonance Imaging (pMRI) methods restore the unacquired data in k-space by linearly calculating the undersampled data around the missing points. In order to obtain the weight of the linear calculation, a small number of auto-calibration signal (ACS) lines need to be sampled at the center of the k-space. Therefore, the sampling pattern used in this type of method is to full sample data in the middle area and undersample in the outer k-space with nominal reduction factors. In this paper, we propose a novel reconstruction method with a multiple variable density sampling (MVDS) that is different from traditional sampling patterns. Our method can significantly improve the image quality using multiple reduction factors with fewer ACS lines. Specifically, the traditional sampling pattern only uses a single reduction factor to uniformly undersample data in the region outside the ACS, but we use multiple reduction factors. When sampling the k-space data, we keep the ACS lines unchanged, use a smaller reduction factor for undersampling data near the ACS lines and a larger reduction factor for the outermost part of k-space. The error is lower after reconstruction of this region by undersampled data with a smaller reduction factor. The experimental results show that with the same amount of data sampled, using NL-GRAPPA to reconstruct the k-space data sampled by our method can result in lower noise and fewer artifacts than traditional methods. In particular, our method is extremely effective when the number of ACS lines is small.
format	article
author	Jinhua Sheng Yuchen Shi Qiao Zhang
author_facet	Jinhua Sheng Yuchen Shi Qiao Zhang
author_sort	Jinhua Sheng
title	Improved parallel magnetic resonance imaging reconstruction with multiple variable density sampling
title_short	Improved parallel magnetic resonance imaging reconstruction with multiple variable density sampling
title_full	Improved parallel magnetic resonance imaging reconstruction with multiple variable density sampling
title_fullStr	Improved parallel magnetic resonance imaging reconstruction with multiple variable density sampling
title_full_unstemmed	Improved parallel magnetic resonance imaging reconstruction with multiple variable density sampling
title_sort	improved parallel magnetic resonance imaging reconstruction with multiple variable density sampling
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/d66648dc8f36427ca117bcefc8b50120
work_keys_str_mv	AT jinhuasheng improvedparallelmagneticresonanceimagingreconstructionwithmultiplevariabledensitysampling AT yuchenshi improvedparallelmagneticresonanceimagingreconstructionwithmultiplevariabledensitysampling AT qiaozhang improvedparallelmagneticresonanceimagingreconstructionwithmultiplevariabledensitysampling
_version_	1718381290145710080

Improved parallel magnetic resonance imaging reconstruction with multiple variable density sampling

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