Joint reconstruction framework of compressed sensing and nonlinear parallel imaging for dynamic cardiac magnetic resonance imaging

Abstract Compressed Sensing (CS) and parallel imaging are two promising techniques that accelerate the MRI acquisition process. Combining these two techniques is of great interest due to the complementary information used in each. In this study, we proposed a novel reconstruction framework that effe...

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Autores principales:	Zhanqi Hu, Cailei Zhao, Xia Zhao, Lingyu Kong, Jun Yang, Xiaoyan Wang, Jianxiang Liao, Yihang Zhou
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Dynamic cardiac MRI Compressed sensing Parallel imaging Nonlinear GRAPPA Medical technology R855-855.5
Acceso en línea:	https://doaj.org/article/10e7da0d9c2747a6aab1de706c4f471f
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spelling	oai:doaj.org-article:10e7da0d9c2747a6aab1de706c4f471f2021-12-05T12:21:25ZJoint reconstruction framework of compressed sensing and nonlinear parallel imaging for dynamic cardiac magnetic resonance imaging10.1186/s12880-021-00685-21471-2342https://doaj.org/article/10e7da0d9c2747a6aab1de706c4f471f2021-12-01T00:00:00Zhttps://doi.org/10.1186/s12880-021-00685-2https://doaj.org/toc/1471-2342Abstract Compressed Sensing (CS) and parallel imaging are two promising techniques that accelerate the MRI acquisition process. Combining these two techniques is of great interest due to the complementary information used in each. In this study, we proposed a novel reconstruction framework that effectively combined compressed sensing and nonlinear parallel imaging technique for dynamic cardiac imaging. Specifically, the proposed method decouples the reconstruction process into two sequential steps: In the first step, a series of aliased dynamic images were reconstructed from the highly undersampled k-space data using compressed sensing; In the second step, nonlinear parallel imaging technique, i.e. nonlinear GRAPPA, was utilized to reconstruct the original dynamic images from the reconstructed k-space data obtained from the first step. In addition, we also proposed a tailored k-space down-sampling scheme that satisfies both the incoherent undersampling requirement for CS and the structured undersampling requirement for nonlinear parallel imaging. The proposed method was validated using four in vivo experiments of dynamic cardiac cine MRI with retrospective undersampling. Experimental results showed that the proposed method is superior at reducing aliasing artifacts and preserving the spatial details and temporal variations, compared with the competing k-t FOCUSS and k-t FOCUSS with sensitivity encoding methods, with the same numbers of measurements.Zhanqi HuCailei ZhaoXia ZhaoLingyu KongJun YangXiaoyan WangJianxiang LiaoYihang ZhouBMCarticleDynamic cardiac MRICompressed sensingParallel imagingNonlinear GRAPPAMedical technologyR855-855.5ENBMC Medical Imaging, Vol 21, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Dynamic cardiac MRI Compressed sensing Parallel imaging Nonlinear GRAPPA Medical technology R855-855.5
spellingShingle	Dynamic cardiac MRI Compressed sensing Parallel imaging Nonlinear GRAPPA Medical technology R855-855.5 Zhanqi Hu Cailei Zhao Xia Zhao Lingyu Kong Jun Yang Xiaoyan Wang Jianxiang Liao Yihang Zhou Joint reconstruction framework of compressed sensing and nonlinear parallel imaging for dynamic cardiac magnetic resonance imaging
description	Abstract Compressed Sensing (CS) and parallel imaging are two promising techniques that accelerate the MRI acquisition process. Combining these two techniques is of great interest due to the complementary information used in each. In this study, we proposed a novel reconstruction framework that effectively combined compressed sensing and nonlinear parallel imaging technique for dynamic cardiac imaging. Specifically, the proposed method decouples the reconstruction process into two sequential steps: In the first step, a series of aliased dynamic images were reconstructed from the highly undersampled k-space data using compressed sensing; In the second step, nonlinear parallel imaging technique, i.e. nonlinear GRAPPA, was utilized to reconstruct the original dynamic images from the reconstructed k-space data obtained from the first step. In addition, we also proposed a tailored k-space down-sampling scheme that satisfies both the incoherent undersampling requirement for CS and the structured undersampling requirement for nonlinear parallel imaging. The proposed method was validated using four in vivo experiments of dynamic cardiac cine MRI with retrospective undersampling. Experimental results showed that the proposed method is superior at reducing aliasing artifacts and preserving the spatial details and temporal variations, compared with the competing k-t FOCUSS and k-t FOCUSS with sensitivity encoding methods, with the same numbers of measurements.
format	article
author	Zhanqi Hu Cailei Zhao Xia Zhao Lingyu Kong Jun Yang Xiaoyan Wang Jianxiang Liao Yihang Zhou
author_facet	Zhanqi Hu Cailei Zhao Xia Zhao Lingyu Kong Jun Yang Xiaoyan Wang Jianxiang Liao Yihang Zhou
author_sort	Zhanqi Hu
title	Joint reconstruction framework of compressed sensing and nonlinear parallel imaging for dynamic cardiac magnetic resonance imaging
title_short	Joint reconstruction framework of compressed sensing and nonlinear parallel imaging for dynamic cardiac magnetic resonance imaging
title_full	Joint reconstruction framework of compressed sensing and nonlinear parallel imaging for dynamic cardiac magnetic resonance imaging
title_fullStr	Joint reconstruction framework of compressed sensing and nonlinear parallel imaging for dynamic cardiac magnetic resonance imaging
title_full_unstemmed	Joint reconstruction framework of compressed sensing and nonlinear parallel imaging for dynamic cardiac magnetic resonance imaging
title_sort	joint reconstruction framework of compressed sensing and nonlinear parallel imaging for dynamic cardiac magnetic resonance imaging
publisher	BMC
publishDate	2021
url	https://doaj.org/article/10e7da0d9c2747a6aab1de706c4f471f
work_keys_str_mv	AT zhanqihu jointreconstructionframeworkofcompressedsensingandnonlinearparallelimagingfordynamiccardiacmagneticresonanceimaging AT caileizhao jointreconstructionframeworkofcompressedsensingandnonlinearparallelimagingfordynamiccardiacmagneticresonanceimaging AT xiazhao jointreconstructionframeworkofcompressedsensingandnonlinearparallelimagingfordynamiccardiacmagneticresonanceimaging AT lingyukong jointreconstructionframeworkofcompressedsensingandnonlinearparallelimagingfordynamiccardiacmagneticresonanceimaging AT junyang jointreconstructionframeworkofcompressedsensingandnonlinearparallelimagingfordynamiccardiacmagneticresonanceimaging AT xiaoyanwang jointreconstructionframeworkofcompressedsensingandnonlinearparallelimagingfordynamiccardiacmagneticresonanceimaging AT jianxiangliao jointreconstructionframeworkofcompressedsensingandnonlinearparallelimagingfordynamiccardiacmagneticresonanceimaging AT yihangzhou jointreconstructionframeworkofcompressedsensingandnonlinearparallelimagingfordynamiccardiacmagneticresonanceimaging
_version_	1718372000118865920

Joint reconstruction framework of compressed sensing and nonlinear parallel imaging for dynamic cardiac magnetic resonance imaging

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