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Optimizing human pulmonary perfusion measurement using an in silico model of arterial spin labeling magnetic resonance imaging
Arterial spin labeling (ASL) magnetic resonance imaging (MRI) is an imaging methodology that uses blood as an endogenous contrast agent to quantify flow. One limitation of this method of capillary blood quantification when applied in the lung is the contribution of signals from non‐capillary blood....
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6565801/ https://www.ncbi.nlm.nih.gov/pubmed/31197965 http://dx.doi.org/10.14814/phy2.14077 |
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author | Addo, Daniel A. Kang, Wendy Prisk, Gordon Kim Tawhai, Merryn H. Burrowes, Kelly Suzzane |
author_facet | Addo, Daniel A. Kang, Wendy Prisk, Gordon Kim Tawhai, Merryn H. Burrowes, Kelly Suzzane |
author_sort | Addo, Daniel A. |
collection | PubMed |
description | Arterial spin labeling (ASL) magnetic resonance imaging (MRI) is an imaging methodology that uses blood as an endogenous contrast agent to quantify flow. One limitation of this method of capillary blood quantification when applied in the lung is the contribution of signals from non‐capillary blood. Intensity thresholding is one approach that has been proposed for minimizing the non‐capillary blood signal. This method has been tested in previous in silico modeling studies; however, it has only been tested under a restricted set of physiological conditions (supine posture and a cardiac output of 5 L/min). This study presents an in silico approach that extends previous intensity thresholding analysis to estimate the optimal “per‐slice” intensity threshold value using the individual components of the simulated ASL signal (signal arising independently from capillary blood as well as pulmonary arterial and pulmonary venous blood). The aim of this study was to assess whether the threshold value should vary with slice location, posture, or cardiac output. We applied an in silico modeling approach to predict the blood flow distribution and the corresponding ASL quantification of pulmonary perfusion in multiple sagittal imaging slices. There was a significant increase in ASL signal and heterogeneity (COV = 0.90 to COV = 1.65) of ASL signals when slice location changed from lateral to medial. Heterogeneity of the ASL signal within a slice was significantly lower (P = 0.03) in prone (COV = 1.08) compared to in the supine posture (COV = 1.17). Increasing stroke volume resulted in an increase in ASL signal and conversely an increase in heart rate resulted in a decrease in ASL signal. However, when cardiac output was increased via an increase in both stroke volume and heart rate, ASL signal remained relatively constant. Despite these differences, we conclude that a threshold value of 35% provides optimal removal of large vessel signal independent of slice location, posture, and cardiac output. |
format | Online Article Text |
id | pubmed-6565801 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-65658012019-06-20 Optimizing human pulmonary perfusion measurement using an in silico model of arterial spin labeling magnetic resonance imaging Addo, Daniel A. Kang, Wendy Prisk, Gordon Kim Tawhai, Merryn H. Burrowes, Kelly Suzzane Physiol Rep Original Research Arterial spin labeling (ASL) magnetic resonance imaging (MRI) is an imaging methodology that uses blood as an endogenous contrast agent to quantify flow. One limitation of this method of capillary blood quantification when applied in the lung is the contribution of signals from non‐capillary blood. Intensity thresholding is one approach that has been proposed for minimizing the non‐capillary blood signal. This method has been tested in previous in silico modeling studies; however, it has only been tested under a restricted set of physiological conditions (supine posture and a cardiac output of 5 L/min). This study presents an in silico approach that extends previous intensity thresholding analysis to estimate the optimal “per‐slice” intensity threshold value using the individual components of the simulated ASL signal (signal arising independently from capillary blood as well as pulmonary arterial and pulmonary venous blood). The aim of this study was to assess whether the threshold value should vary with slice location, posture, or cardiac output. We applied an in silico modeling approach to predict the blood flow distribution and the corresponding ASL quantification of pulmonary perfusion in multiple sagittal imaging slices. There was a significant increase in ASL signal and heterogeneity (COV = 0.90 to COV = 1.65) of ASL signals when slice location changed from lateral to medial. Heterogeneity of the ASL signal within a slice was significantly lower (P = 0.03) in prone (COV = 1.08) compared to in the supine posture (COV = 1.17). Increasing stroke volume resulted in an increase in ASL signal and conversely an increase in heart rate resulted in a decrease in ASL signal. However, when cardiac output was increased via an increase in both stroke volume and heart rate, ASL signal remained relatively constant. Despite these differences, we conclude that a threshold value of 35% provides optimal removal of large vessel signal independent of slice location, posture, and cardiac output. John Wiley and Sons Inc. 2019-06-13 /pmc/articles/PMC6565801/ /pubmed/31197965 http://dx.doi.org/10.14814/phy2.14077 Text en © 2019 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Research Addo, Daniel A. Kang, Wendy Prisk, Gordon Kim Tawhai, Merryn H. Burrowes, Kelly Suzzane Optimizing human pulmonary perfusion measurement using an in silico model of arterial spin labeling magnetic resonance imaging |
title | Optimizing human pulmonary perfusion measurement using an in silico model of arterial spin labeling magnetic resonance imaging |
title_full | Optimizing human pulmonary perfusion measurement using an in silico model of arterial spin labeling magnetic resonance imaging |
title_fullStr | Optimizing human pulmonary perfusion measurement using an in silico model of arterial spin labeling magnetic resonance imaging |
title_full_unstemmed | Optimizing human pulmonary perfusion measurement using an in silico model of arterial spin labeling magnetic resonance imaging |
title_short | Optimizing human pulmonary perfusion measurement using an in silico model of arterial spin labeling magnetic resonance imaging |
title_sort | optimizing human pulmonary perfusion measurement using an in silico model of arterial spin labeling magnetic resonance imaging |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6565801/ https://www.ncbi.nlm.nih.gov/pubmed/31197965 http://dx.doi.org/10.14814/phy2.14077 |
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