Automatic and reproducible positioning of phase-contrast MRI for the quantification of global cerebral blood flow

Peiying Liu, Hanzhang Lu, Francesca M. Filbey, Amy E. Pinkham, Carrie J. McAdams, Bryon Adinoff, Vamsi Daliparthi, Yan Cao

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

Phase-Contrast MRI (PC-MRI) is a noninvasive technique to measure blood flow. In particular, global but highly quantitative cerebral blood flow (CBF) measurement using PC-MRI complements several other CBF mapping methods such as arterial spin labeling and dynamic susceptibility contrast MRI by providing a calibration factor. The ability to estimate blood supply in physiological units also lays a foundation for assessment of brain metabolic rate. However, a major obstacle before wider applications of this method is that the slice positioning of the scan, ideally placed perpendicular to the feeding arteries, requires considerable expertise and can present a burden to the operator. In the present work, we proposed that the majority of PC-MRI scans can be positioned using an automatic algorithm, leaving only a small fraction of arteries requiring manual positioning. We implemented and evaluated an algorithm for this purpose based on feature extraction of a survey angiogram, which is of minimal operator dependence. In a comparative test-retest study with 7 subjects, the blood flow measurement using this algorithm showed an inter-session coefficient of variation (CoV) of 4:07±3:03%. The Bland-Altman method showed that the automatic method differs from the manual method by between -8% and 11%, for 95% of the CBF measurements. This is comparable to the variance in CBF measurement using manually-positioned PC MRI alone. In a further application of this algorithm to 157 consecutive subjects from typical clinical cohorts, the algorithm provided successful positioning in 89.7% of the arteries. In 79.6% of the subjects, all four arteries could be planned using the algorithm. Chi-square tests of independence showed that the success rate was not dependent on the age or gender, but the patients showed a trend of lower success rate (p = 0.14) compared to healthy controls. In conclusion, this automatic positioning algorithm could improve the application of PC-MRI in CBF quantification.

Original languageEnglish (US)
Article numbere95721
JournalPLoS One
Volume9
Issue number5
DOIs
StatePublished - May 2 2014

Fingerprint

Cerebrovascular Circulation
Magnetic resonance imaging
blood flow
Blood
Flow measurement
arteries
Arteries
operator regions
methodology
Chi-Square Distribution
application methods
Calibration
Angiography
complement
Labeling
calibration
Feature extraction
Brain
Magnetic Resonance Imaging
brain

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)

Cite this

Automatic and reproducible positioning of phase-contrast MRI for the quantification of global cerebral blood flow. / Liu, Peiying; Lu, Hanzhang; Filbey, Francesca M.; Pinkham, Amy E.; McAdams, Carrie J.; Adinoff, Bryon; Daliparthi, Vamsi; Cao, Yan.

In: PLoS One, Vol. 9, No. 5, e95721, 02.05.2014.

Research output: Contribution to journalArticle

Liu, Peiying ; Lu, Hanzhang ; Filbey, Francesca M. ; Pinkham, Amy E. ; McAdams, Carrie J. ; Adinoff, Bryon ; Daliparthi, Vamsi ; Cao, Yan. / Automatic and reproducible positioning of phase-contrast MRI for the quantification of global cerebral blood flow. In: PLoS One. 2014 ; Vol. 9, No. 5.
@article{133c0ccb4b2644cf953edeea209480bc,
title = "Automatic and reproducible positioning of phase-contrast MRI for the quantification of global cerebral blood flow",
abstract = "Phase-Contrast MRI (PC-MRI) is a noninvasive technique to measure blood flow. In particular, global but highly quantitative cerebral blood flow (CBF) measurement using PC-MRI complements several other CBF mapping methods such as arterial spin labeling and dynamic susceptibility contrast MRI by providing a calibration factor. The ability to estimate blood supply in physiological units also lays a foundation for assessment of brain metabolic rate. However, a major obstacle before wider applications of this method is that the slice positioning of the scan, ideally placed perpendicular to the feeding arteries, requires considerable expertise and can present a burden to the operator. In the present work, we proposed that the majority of PC-MRI scans can be positioned using an automatic algorithm, leaving only a small fraction of arteries requiring manual positioning. We implemented and evaluated an algorithm for this purpose based on feature extraction of a survey angiogram, which is of minimal operator dependence. In a comparative test-retest study with 7 subjects, the blood flow measurement using this algorithm showed an inter-session coefficient of variation (CoV) of 4:07±3:03{\%}. The Bland-Altman method showed that the automatic method differs from the manual method by between -8{\%} and 11{\%}, for 95{\%} of the CBF measurements. This is comparable to the variance in CBF measurement using manually-positioned PC MRI alone. In a further application of this algorithm to 157 consecutive subjects from typical clinical cohorts, the algorithm provided successful positioning in 89.7{\%} of the arteries. In 79.6{\%} of the subjects, all four arteries could be planned using the algorithm. Chi-square tests of independence showed that the success rate was not dependent on the age or gender, but the patients showed a trend of lower success rate (p = 0.14) compared to healthy controls. In conclusion, this automatic positioning algorithm could improve the application of PC-MRI in CBF quantification.",
author = "Peiying Liu and Hanzhang Lu and Filbey, {Francesca M.} and Pinkham, {Amy E.} and McAdams, {Carrie J.} and Bryon Adinoff and Vamsi Daliparthi and Yan Cao",
year = "2014",
month = "5",
day = "2",
doi = "10.1371/journal.pone.0095721",
language = "English (US)",
volume = "9",
journal = "PLoS One",
issn = "1932-6203",
publisher = "Public Library of Science",
number = "5",

}

TY - JOUR

T1 - Automatic and reproducible positioning of phase-contrast MRI for the quantification of global cerebral blood flow

AU - Liu, Peiying

AU - Lu, Hanzhang

AU - Filbey, Francesca M.

AU - Pinkham, Amy E.

AU - McAdams, Carrie J.

AU - Adinoff, Bryon

AU - Daliparthi, Vamsi

AU - Cao, Yan

PY - 2014/5/2

Y1 - 2014/5/2

N2 - Phase-Contrast MRI (PC-MRI) is a noninvasive technique to measure blood flow. In particular, global but highly quantitative cerebral blood flow (CBF) measurement using PC-MRI complements several other CBF mapping methods such as arterial spin labeling and dynamic susceptibility contrast MRI by providing a calibration factor. The ability to estimate blood supply in physiological units also lays a foundation for assessment of brain metabolic rate. However, a major obstacle before wider applications of this method is that the slice positioning of the scan, ideally placed perpendicular to the feeding arteries, requires considerable expertise and can present a burden to the operator. In the present work, we proposed that the majority of PC-MRI scans can be positioned using an automatic algorithm, leaving only a small fraction of arteries requiring manual positioning. We implemented and evaluated an algorithm for this purpose based on feature extraction of a survey angiogram, which is of minimal operator dependence. In a comparative test-retest study with 7 subjects, the blood flow measurement using this algorithm showed an inter-session coefficient of variation (CoV) of 4:07±3:03%. The Bland-Altman method showed that the automatic method differs from the manual method by between -8% and 11%, for 95% of the CBF measurements. This is comparable to the variance in CBF measurement using manually-positioned PC MRI alone. In a further application of this algorithm to 157 consecutive subjects from typical clinical cohorts, the algorithm provided successful positioning in 89.7% of the arteries. In 79.6% of the subjects, all four arteries could be planned using the algorithm. Chi-square tests of independence showed that the success rate was not dependent on the age or gender, but the patients showed a trend of lower success rate (p = 0.14) compared to healthy controls. In conclusion, this automatic positioning algorithm could improve the application of PC-MRI in CBF quantification.

AB - Phase-Contrast MRI (PC-MRI) is a noninvasive technique to measure blood flow. In particular, global but highly quantitative cerebral blood flow (CBF) measurement using PC-MRI complements several other CBF mapping methods such as arterial spin labeling and dynamic susceptibility contrast MRI by providing a calibration factor. The ability to estimate blood supply in physiological units also lays a foundation for assessment of brain metabolic rate. However, a major obstacle before wider applications of this method is that the slice positioning of the scan, ideally placed perpendicular to the feeding arteries, requires considerable expertise and can present a burden to the operator. In the present work, we proposed that the majority of PC-MRI scans can be positioned using an automatic algorithm, leaving only a small fraction of arteries requiring manual positioning. We implemented and evaluated an algorithm for this purpose based on feature extraction of a survey angiogram, which is of minimal operator dependence. In a comparative test-retest study with 7 subjects, the blood flow measurement using this algorithm showed an inter-session coefficient of variation (CoV) of 4:07±3:03%. The Bland-Altman method showed that the automatic method differs from the manual method by between -8% and 11%, for 95% of the CBF measurements. This is comparable to the variance in CBF measurement using manually-positioned PC MRI alone. In a further application of this algorithm to 157 consecutive subjects from typical clinical cohorts, the algorithm provided successful positioning in 89.7% of the arteries. In 79.6% of the subjects, all four arteries could be planned using the algorithm. Chi-square tests of independence showed that the success rate was not dependent on the age or gender, but the patients showed a trend of lower success rate (p = 0.14) compared to healthy controls. In conclusion, this automatic positioning algorithm could improve the application of PC-MRI in CBF quantification.

UR - http://www.scopus.com/inward/record.url?scp=84900409934&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84900409934&partnerID=8YFLogxK

U2 - 10.1371/journal.pone.0095721

DO - 10.1371/journal.pone.0095721

M3 - Article

VL - 9

JO - PLoS One

JF - PLoS One

SN - 1932-6203

IS - 5

M1 - e95721

ER -