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Unbiased signal equation for quantitative magnetization transfer mapping in balanced steady‐state free precession MRI
PURPOSE: Quantitative magnetization transfer (qMT) imaging can be used to quantify the proportion of protons in a voxel attached to macromolecules. Here, we show that the original qMT balanced steady‐state free precession (bSSFP) model is biased due to over‐simplistic assumptions made in its derivat...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8951070/ https://www.ncbi.nlm.nih.gov/pubmed/34331470 http://dx.doi.org/10.1002/mrm.28940 |
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author | Bayer, Fritz M. Bock, Michael Jezzard, Peter Smith, Alex K. |
author_facet | Bayer, Fritz M. Bock, Michael Jezzard, Peter Smith, Alex K. |
author_sort | Bayer, Fritz M. |
collection | PubMed |
description | PURPOSE: Quantitative magnetization transfer (qMT) imaging can be used to quantify the proportion of protons in a voxel attached to macromolecules. Here, we show that the original qMT balanced steady‐state free precession (bSSFP) model is biased due to over‐simplistic assumptions made in its derivation. THEORY AND METHODS: We present an improved model for qMT bSSFP, which incorporates finite radiofrequency (RF) pulse effects as well as simultaneous exchange and relaxation. Furthermore, a correction relating to finite RF pulse effects for sinc‐shaped excitations is derived. The new model is compared to the original one in numerical simulations of the Bloch‐McConnell equations and in previously acquired in vivo data. RESULTS: Our numerical simulations show that the original signal equation is significantly biased in typical brain tissue structures (by 7%‐20%), whereas the new signal equation outperforms the original one with minimal bias (<1%). It is further shown that the bias of the original model strongly affects the acquired qMT parameters in human brain structures, with differences in the clinically relevant parameter of pool‐size‐ratio of up to 31%. Particularly high biases of the original signal equation are expected in an MS lesion within diseased brain tissue (due to a low T2/T1‐ratio), demanding a more accurate model for clinical applications. CONCLUSION: The improved model for qMT bSSFP is recommended for accurate qMT parameter mapping in healthy and diseased brain tissue structures. |
format | Online Article Text |
id | pubmed-8951070 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-89510702022-03-29 Unbiased signal equation for quantitative magnetization transfer mapping in balanced steady‐state free precession MRI Bayer, Fritz M. Bock, Michael Jezzard, Peter Smith, Alex K. Magn Reson Med Research Articles—Computer Processing and Modeling PURPOSE: Quantitative magnetization transfer (qMT) imaging can be used to quantify the proportion of protons in a voxel attached to macromolecules. Here, we show that the original qMT balanced steady‐state free precession (bSSFP) model is biased due to over‐simplistic assumptions made in its derivation. THEORY AND METHODS: We present an improved model for qMT bSSFP, which incorporates finite radiofrequency (RF) pulse effects as well as simultaneous exchange and relaxation. Furthermore, a correction relating to finite RF pulse effects for sinc‐shaped excitations is derived. The new model is compared to the original one in numerical simulations of the Bloch‐McConnell equations and in previously acquired in vivo data. RESULTS: Our numerical simulations show that the original signal equation is significantly biased in typical brain tissue structures (by 7%‐20%), whereas the new signal equation outperforms the original one with minimal bias (<1%). It is further shown that the bias of the original model strongly affects the acquired qMT parameters in human brain structures, with differences in the clinically relevant parameter of pool‐size‐ratio of up to 31%. Particularly high biases of the original signal equation are expected in an MS lesion within diseased brain tissue (due to a low T2/T1‐ratio), demanding a more accurate model for clinical applications. CONCLUSION: The improved model for qMT bSSFP is recommended for accurate qMT parameter mapping in healthy and diseased brain tissue structures. John Wiley and Sons Inc. 2021-07-31 2022-01 /pmc/articles/PMC8951070/ /pubmed/34331470 http://dx.doi.org/10.1002/mrm.28940 Text en © 2021 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Articles—Computer Processing and Modeling Bayer, Fritz M. Bock, Michael Jezzard, Peter Smith, Alex K. Unbiased signal equation for quantitative magnetization transfer mapping in balanced steady‐state free precession MRI |
title | Unbiased signal equation for quantitative magnetization transfer mapping in balanced steady‐state free precession MRI |
title_full | Unbiased signal equation for quantitative magnetization transfer mapping in balanced steady‐state free precession MRI |
title_fullStr | Unbiased signal equation for quantitative magnetization transfer mapping in balanced steady‐state free precession MRI |
title_full_unstemmed | Unbiased signal equation for quantitative magnetization transfer mapping in balanced steady‐state free precession MRI |
title_short | Unbiased signal equation for quantitative magnetization transfer mapping in balanced steady‐state free precession MRI |
title_sort | unbiased signal equation for quantitative magnetization transfer mapping in balanced steady‐state free precession mri |
topic | Research Articles—Computer Processing and Modeling |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8951070/ https://www.ncbi.nlm.nih.gov/pubmed/34331470 http://dx.doi.org/10.1002/mrm.28940 |
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