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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....

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Autores principales: Addo, Daniel A., Kang, Wendy, Prisk, Gordon Kim, Tawhai, Merryn H., Burrowes, Kelly Suzzane
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2019
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.
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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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