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Reconstructing MRI Parameters Using a Noncentral Chi Noise Model

Baś, K; Lambert, C; Ashburner, J; (2024) Reconstructing MRI Parameters Using a Noncentral Chi Noise Model. In: Medical Image Understanding and Analysis. (pp. pp. 174-184). Springer, Cham

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Abstract

Quantitative magnetic resonance imaging (qMRI) allows images to be compared across sites and time points, which is particularly important for assessing long-term conditions or for longitudinal studies. The multiparametric mapping (MPM) protocol is used to acquire images with conventional clinical contrasts, namely PD-, T1-, and MT-weighted volumes. Through multi-echo acquisition for each contrast and variations in flip angles between PD- and T1-weighted contrasts, parameter maps, such as proton density (PD), longitudinal relaxation rate (R1), apparent transverse relaxation rate (R2∗), and magnetization transfer saturation (MTsat), can be estimated. Various algorithms have been employed to estimate these parameters from the acquired volumes. This paper extends an existing maximum a posteriori approach, which uses joint total variation regularization, by transitioning from a Gaussian noise approximation to a more physically plausible model that assumes noncentral chi-distributed noise.

Type: Proceedings paper
Title: Reconstructing MRI Parameters Using a Noncentral Chi Noise Model
Event: MIUA 2024
ISBN-13: 9783031669576
DOI: 10.1007/978-3-031-66958-3_13
Publisher version: http://dx.doi.org/10.1007/978-3-031-66958-3_13
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: noise distribution, quantitative MRI, parameter map estimation
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Imaging Neuroscience
URI: https://discovery.ucl.ac.uk/id/eprint/10197254
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