Accelerating T2* Mapping with Maximum Likelihood Estimation (MLE) and Parallel Imaging (PI)

Wajiha Bano; Mohammad Golbabaee; Arnold V. J. Benjamin; Ian  Marshall; Mike Davies

Accelerating T2* Mapping with Maximum Likelihood Estimation (MLE) and Parallel Imaging (PI)

Wajiha Bano, Mohammad Golbabaee, Arnold V. J. Benjamin, Ian Marshall, Mike Davies

University of Edinburgh

Research output: Contribution to conference › Paper › peer-review

Abstract

The utility of MR parametric mapping is limited due to the lengthy acquisition time. A Maximum Likelihood Estimation (MLE) and Parallel Imaging (PI) method is presented for MR parameteric mapping. The approach is based on a high Signal to Noise ratio (SNR) assumption such that the noise can be modelled as Gaussian and estimates the parameters that maximizes the signal from a multichannel coil. The method was tested on a multiecho gradient-echo T2* mapping experiment in a phantom and a human brain. Accurate T2* maps were reconstructed up to an acceleration factor
of 6 with a small error for phantom and human brain.

Original language	English
Publication status	Published - 1 Apr 2018
Event	Proceeding of the joint annual meeting ISMRM-ESMRMB - Duration: 1 Apr 2018 → …

Conference

Conference	Proceeding of the joint annual meeting ISMRM-ESMRMB
Period	1/04/18 → …

Access to Document

https://www.pure.ed.ac.uk/ws/portalfiles/portal/53949499/ISMRM_abstract_wajiha_.pdf

Cite this

@conference{285fc69e616748449298ca04228ac521,

title = "Accelerating T2* Mapping with Maximum Likelihood Estimation (MLE) and Parallel Imaging (PI)",

abstract = "The utility of MR parametric mapping is limited due to the lengthy acquisition time. A Maximum Likelihood Estimation (MLE) and Parallel Imaging (PI) method is presented for MR parameteric mapping. The approach is based on a high Signal to Noise ratio (SNR) assumption such that the noise can be modelled as Gaussian and estimates the parameters that maximizes the signal from a multichannel coil. The method was tested on a multiecho gradient-echo T2* mapping experiment in a phantom and a human brain. Accurate T2* maps were reconstructed up to an acceleration factorof 6 with a small error for phantom and human brain.",

author = "Wajiha Bano and Mohammad Golbabaee and Benjamin, {Arnold V. J.} and Ian Marshall and Mike Davies",

year = "2018",

month = apr,

day = "1",

language = "English",

note = "Proceeding of the joint annual meeting ISMRM-ESMRMB ; Conference date: 01-04-2018",

}

TY - CONF

T1 - Accelerating T2* Mapping with Maximum Likelihood Estimation (MLE) and Parallel Imaging (PI)

AU - Bano, Wajiha

AU - Golbabaee, Mohammad

AU - Benjamin, Arnold V. J.

AU - Marshall, Ian

AU - Davies, Mike

PY - 2018/4/1

Y1 - 2018/4/1

N2 - The utility of MR parametric mapping is limited due to the lengthy acquisition time. A Maximum Likelihood Estimation (MLE) and Parallel Imaging (PI) method is presented for MR parameteric mapping. The approach is based on a high Signal to Noise ratio (SNR) assumption such that the noise can be modelled as Gaussian and estimates the parameters that maximizes the signal from a multichannel coil. The method was tested on a multiecho gradient-echo T2* mapping experiment in a phantom and a human brain. Accurate T2* maps were reconstructed up to an acceleration factorof 6 with a small error for phantom and human brain.

AB - The utility of MR parametric mapping is limited due to the lengthy acquisition time. A Maximum Likelihood Estimation (MLE) and Parallel Imaging (PI) method is presented for MR parameteric mapping. The approach is based on a high Signal to Noise ratio (SNR) assumption such that the noise can be modelled as Gaussian and estimates the parameters that maximizes the signal from a multichannel coil. The method was tested on a multiecho gradient-echo T2* mapping experiment in a phantom and a human brain. Accurate T2* maps were reconstructed up to an acceleration factorof 6 with a small error for phantom and human brain.

M3 - Paper

T2 - Proceeding of the joint annual meeting ISMRM-ESMRMB

Y2 - 1 April 2018

ER -

Accelerating T2* Mapping with Maximum Likelihood Estimation (MLE) and Parallel Imaging (PI)

Abstract

Conference

Access to Document

Fingerprint

Cite this