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Main Authors: Baś, Klara, Lambert, Christian, Ashburner, John
Format: Preprint
Published: 2024
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Online Access:https://arxiv.org/abs/2410.17374
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author Baś, Klara
Lambert, Christian
Ashburner, John
author_facet Baś, Klara
Lambert, Christian
Ashburner, John
contents 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 (MT$_{sat}$), 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.
format Preprint
id arxiv_https___arxiv_org_abs_2410_17374
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Reconstructing MRI Parameters Using a Noncentral Chi Noise Model
Baś, Klara
Lambert, Christian
Ashburner, John
Signal Processing
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 (MT$_{sat}$), 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.
title Reconstructing MRI Parameters Using a Noncentral Chi Noise Model
topic Signal Processing
url https://arxiv.org/abs/2410.17374