Suppressing Multi-Channel Ultra-Low-Field MRI Measurement Noise Using Data Consistency and Image Sparsity

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Suppressing Multi-Channel Ultra-Low-Field MRI Measurement Noise Using Data Consistency and Image Sparsity. / Lin, Fa-Hsuan; Vesanen, Panu T.; Hsu, Yi-Cheng; Nieminen, Jaakko O.; Zevenhoven, Koos C.J.; Dabek, Juhani; Parkkonen, Lauri T.; Simola, Juha; Ahonen, Antti I.; Ilmoniemi, Risto J.

In: PloS one, Vol. 8, No. 4, e61652, 2013, p. 1-6.

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@article{cdccad53691a41f38a92deb604996917,
title = "Suppressing Multi-Channel Ultra-Low-Field MRI Measurement Noise Using Data Consistency and Image Sparsity",
abstract = "Ultra-low-field (ULF) MRI (B0 = 10–100 µT) typically suffers from a low signal-to-noise ratio (SNR). While SNR can be improved by pre-polarization and signal detection using highly sensitive superconducting quantum interference device (SQUID) sensors, we propose to use the inter-dependency of the k-space data from highly parallel detection with up to tens of sensors readily available in the ULF MRI in order to suppress the noise. Furthermore, the prior information that an image can be sparsely represented can be integrated with this data consistency constraint to further improve the SNR. Simulations and experimental data using 47 SQUID sensors demonstrate the effectiveness of this data consistency constraint and sparsity prior in ULF-MRI reconstruction.",
author = "Fa-Hsuan Lin and Vesanen, {Panu T.} and Yi-Cheng Hsu and Nieminen, {Jaakko O.} and Zevenhoven, {Koos C.J.} and Juhani Dabek and Parkkonen, {Lauri T.} and Juha Simola and Ahonen, {Antti I.} and Ilmoniemi, {Risto J.}",
year = "2013",
doi = "10.1371/journal.pone.0061652",
language = "English",
volume = "8",
pages = "1--6",
journal = "PloS one",
issn = "1932-6203",
number = "4",

}

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TY - JOUR

T1 - Suppressing Multi-Channel Ultra-Low-Field MRI Measurement Noise Using Data Consistency and Image Sparsity

AU - Lin, Fa-Hsuan

AU - Vesanen, Panu T.

AU - Hsu, Yi-Cheng

AU - Nieminen, Jaakko O.

AU - Zevenhoven, Koos C.J.

AU - Dabek, Juhani

AU - Parkkonen, Lauri T.

AU - Simola, Juha

AU - Ahonen, Antti I.

AU - Ilmoniemi, Risto J.

PY - 2013

Y1 - 2013

N2 - Ultra-low-field (ULF) MRI (B0 = 10–100 µT) typically suffers from a low signal-to-noise ratio (SNR). While SNR can be improved by pre-polarization and signal detection using highly sensitive superconducting quantum interference device (SQUID) sensors, we propose to use the inter-dependency of the k-space data from highly parallel detection with up to tens of sensors readily available in the ULF MRI in order to suppress the noise. Furthermore, the prior information that an image can be sparsely represented can be integrated with this data consistency constraint to further improve the SNR. Simulations and experimental data using 47 SQUID sensors demonstrate the effectiveness of this data consistency constraint and sparsity prior in ULF-MRI reconstruction.

AB - Ultra-low-field (ULF) MRI (B0 = 10–100 µT) typically suffers from a low signal-to-noise ratio (SNR). While SNR can be improved by pre-polarization and signal detection using highly sensitive superconducting quantum interference device (SQUID) sensors, we propose to use the inter-dependency of the k-space data from highly parallel detection with up to tens of sensors readily available in the ULF MRI in order to suppress the noise. Furthermore, the prior information that an image can be sparsely represented can be integrated with this data consistency constraint to further improve the SNR. Simulations and experimental data using 47 SQUID sensors demonstrate the effectiveness of this data consistency constraint and sparsity prior in ULF-MRI reconstruction.

U2 - 10.1371/journal.pone.0061652

DO - 10.1371/journal.pone.0061652

M3 - Article

VL - 8

SP - 1

EP - 6

JO - PloS one

JF - PloS one

SN - 1932-6203

IS - 4

M1 - e61652

ER -

ID: 856340