An electromagnetic reverse method of coil sensitivity mapping for parallel MRI – Theoretical framework

Jin, Jin; F, Liu; Weber, Ewald; Liu, Yu; Crozier, Stuart

doi:10.1016/j.jmr.2010.08.009

Cited by 28 publications

(28 citation statements)

References 32 publications

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“…The C matrices can be estimated by various approaches, [18][19][20] though previous CSS work has shown that a simple procedure is sufficient for proof-of-concept implementation. 14 Two sets of fast gradient-echo images were acquired with the same pulse sequence parameters (TE, 1.3 milliseconds; TR, 34 milliseconds; flip angle, 5°; field of view, 30 cm; 64 ϫ 64 acquisition matrix; section thickness, 5 mm): 1 set with the body coil and 1 set with the multichannel head coil receiver.…”

Section: Cssmrs Implementation and Spectral Analysismentioning

confidence: 99%

Constrained Source Space MR Spectroscopy: Multiple Voxels, No Gradient Readout

Landheer¹,

Sahgal²,

Das³

et al. 2015

AJNR Am J Neuroradiol

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Section: Cssmrs Implementation and Spectral Analysismentioning

confidence: 99%

Constrained Source Space MR Spectroscopy: Multiple Voxels, No Gradient Readout

Landheer¹,

Sahgal²,

Das³

et al. 2015

AJNR Am J Neuroradiol

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“…To prepare sensitivity profiles for registration, a multi-level singular value removal algorithm was developed. Similar to methods employed in [35], the map was first divided into reliable and unreliable regions. The unreliable regions consist of singular values of the magnitude map, sensitivity voids and phase discontinuities [36].…”

Section: In Vivo Sensitivity Mapping and Singular Value Correctionmentioning

confidence: 99%

“…The unreliable regions consist of singular values of the magnitude map, sensitivity voids and phase discontinuities [36]. An interpolation/extrapolation procedure [35], based on polynomial fitting, was then performed to correct the sensitivity profiles in the unreliable regions.…”

Section: In Vivo Sensitivity Mapping and Singular Value Correctionmentioning

confidence: 99%

“…The Method of Moments (MOM) [13,18,[47][48][49][50], used for calculating the EM field, is efficient for homogeneous loads, but it is not feasible to calculate the heterogeneous dielectric loads due to the complexity of calculating the Green function [51]. The Finite Element Method (FEM) [52], discretising heterogeneous subjects into tetrahedral or hexahedral elements, is capable of representing complex heterogeneous subjects smoothly and providing a more accurate solution.…”

Section: The Library Datamentioning

confidence: 99%

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In vivo sensitivity estimation and imaging acceleration with rotating RF coil arrays at 7 Tesla

Jin

Zuo

et al. 2015

Journal of Magnetic Resonance

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Using a new rotating SENSitivity Encoding (rotating-SENSE) algorithm, we have successfully demonstrated that the rotating radiofrequency coil array (RRFCA) is capable of achieving a significant reduction in scan time and a uniform image reconstruction for a homogeneous phantom at 7 Tesla. However, at 7 Tesla the in vivo with the same number of coil elements, the RRFCA was able to reconstruct good quality images at a high reduction factor. It is hoped that the proposed sensitivity estimation algorithm and the acceleration ability of the RRFCA will be particularly useful for ultra high field MRI.

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“…However, this increases the scan time considerably, which contradicts the main objective of parallel imaging. Moreover, since the reference lines provide low-resolution images, they are susceptible to the Gibbs phenomenon [9]. The fine details in the reconstructed images may also be lost due to low resolution.…”

Section: Introductionmentioning

confidence: 99%

MRI image enhancement using Biot--Savart law at 3 tesla

Esin¹,

Alpaslan²

2017

Turk J Elec Eng & Comp Sci

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Abstract:Coil sensitivity is considered as the most valuable data in the parallel reconstruction of magnetic resonance imaging (MRI). In this study, a novel coil sensitivity map extraction method is introduced for spatially fixed phased array coils. The proposed technique uses the Biot-Savart law with coil internal shape information and low-resolution phase image data to form sensitivity maps. The performance of this method is tested in a parallel image reconstruction task, using the sensitivity-encoding (SENSE) technique. Under the quasi-static assumption and using the duality principle, we computed the sensitivity maps of a phased-array head coil and reconstructed full FOV images. The experiments show that the resulting image quality is higher in terms of sharpness, artifact level, homogeneity, etc. than existing methods that use sensitivity maps calculated only from low-resolution image data. Moreover, several simulations were conducted using an electromagnetic simulation software tool to theoretically prove the success of the proposed technique.Our simulation results indicate that the success of the method depends heavily on the size of the coil elements. A new method for calculation of sensitivity maps is briefly introduced, which increases image quality and homogeneity while reducing the artifacts.

show abstract

An electromagnetic reverse method of coil sensitivity mapping for parallel MRI – Theoretical framework

Cited by 28 publications

References 32 publications

Constrained Source Space MR Spectroscopy: Multiple Voxels, No Gradient Readout

Constrained Source Space MR Spectroscopy: Multiple Voxels, No Gradient Readout

In vivo sensitivity estimation and imaging acceleration with rotating RF coil arrays at 7 Tesla

MRI image enhancement using Biot--Savart law at 3 tesla

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