Optimizing human pulmonary perfusion measurement using an in silico model of arterial spin labeling magnetic resonance imaging
Abstract 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‐capillar...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-06-01
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| Series: | Physiological Reports |
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| Online Access: | https://doi.org/10.14814/phy2.14077 |
| _version_ | 1818066899062226944 |
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| author | Daniel A. Addo Wendy Kang Gordon Kim Prisk Merryn H. Tawhai Kelly Suzzane Burrowes |
| author_facet | Daniel A. Addo Wendy Kang Gordon Kim Prisk Merryn H. Tawhai Kelly Suzzane Burrowes |
| author_sort | Daniel A. Addo |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-10T15:15:07Z |
| format | Article |
| id | doaj.art-281d597b0010424fa3c415aa51123358 |
| institution | Directory Open Access Journal |
| issn | 2051-817X |
| language | English |
| last_indexed | 2024-12-10T15:15:07Z |
| publishDate | 2019-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Physiological Reports |
| spelling | doaj.art-281d597b0010424fa3c415aa511233582022-12-22T01:43:48ZengWileyPhysiological Reports2051-817X2019-06-01711n/an/a10.14814/phy2.14077Optimizing human pulmonary perfusion measurement using an in silico model of arterial spin labeling magnetic resonance imagingDaniel A. Addo0Wendy Kang1Gordon Kim Prisk2Merryn H. Tawhai3Kelly Suzzane Burrowes4Auckland Bioengineering Institute University of Auckland Auckland New ZealandAuckland Bioengineering Institute University of Auckland Auckland New ZealandDepartments of Medicine and Radiology University of California San Diego La Jolla CaliforniaAuckland Bioengineering Institute University of Auckland Auckland New ZealandAuckland Bioengineering Institute University of Auckland Auckland New ZealandAbstract 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.https://doi.org/10.14814/phy2.14077Arterial spin labelingmagnetic resonance imagingpulmonary blood flowregional pulmonary blood |
| spellingShingle | Daniel A. Addo Wendy Kang Gordon Kim Prisk Merryn H. Tawhai Kelly Suzzane Burrowes Optimizing human pulmonary perfusion measurement using an in silico model of arterial spin labeling magnetic resonance imaging Physiological Reports Arterial spin labeling magnetic resonance imaging pulmonary blood flow regional pulmonary blood |
| 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 | Arterial spin labeling magnetic resonance imaging pulmonary blood flow regional pulmonary blood |
| url | https://doi.org/10.14814/phy2.14077 |
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