Double‐gated myocardial ASL perfusion imaging is robust to heart rate variation

Do, Hung P; Yoon, Andrew; Fong, Michael W.; Saremi, Farhood; Barr, Mark L.; Nayak, Krishna S.

doi:10.1002/mrm.26282

Cited by 20 publications

(33 citation statements)

References 35 publications

(57 reference statements)

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“…Global MP for diastolic SS‐EPI (1.67 ± 0.72 mL/g/min) and systolic SS‐EPI (1.50 ± 0.36 mL/g/min) were found to be statistically equivalent to diastolic bSSFP (1RR: 1.59 ± 0.80 mL/g/min) based on TOST with P ‐values of 0.022 and 0.031, respectively. MP values for diastolic SS‐EPI, systolic SS‐EPI and diastolic bSSFP were comparable to previous ASL‐CMR studies with diastolic 1RR bSSFP, but were slightly higher than previous positron emission tomography and first pass CMR studies. CMR first pass studies have shown that resting MP in systole and diastole should be equivalent.…”

Section: Resultssupporting

confidence: 78%

“…This underestimation is a significant limitation of the FAIR technique. This effect was not detected in the data, likely because variation in MP observed between subjects is >30%, which is large but consistent with previous ASL‐CMR studies . Larger sample sizes will be required to detect differences on the order of <30% with existing ASL‐CMR techniques.…”

Section: Discussionsupporting

confidence: 76%

“…Double gated flow alternating inversion recovery (DG‐FAIR) ASL was performed in SAX slices, shown in Figure . Adiabatic hyperbolic‐secant pulses were used for labeling.…”

Section: Methodsmentioning

confidence: 99%

“…MP was calculated using the interpolated signal difference

()(, normalΔ M)

between control and label T 1 curves as described by Poncelet et al and Do et al, in units of mL‐blood/g‐tissue/min. T1 curves for control and label images were fitted using established 3 parameter model for T1 mapping .…”

Section: Methodsmentioning

confidence: 99%

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Single‐shot EPI for ASL‐CMR

Javed

Nayak

2020

Magnetic Resonance in Med

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Purpose: To evaluate single-shot echo planar imaging (SS-EPI), as an alternative to snapshot balanced steady state free precession (bSSFP) imaging, for arterialspin-labeled cardiac MR (ASL-CMR). This study presents a practical implementation SS-EPI tailored to the needs of ASL-CMR at 3T and demonstrates sequential multi-slice ASL with no increase in scan time. Methods: Reduced field of view SS-EPI was performed using a 2DRF pulse. A spinecho was used with crushers optimized to maximize blood suppression and minimize myocardial signal loss, based on experiments in 4 healthy volunteers. SS-EPI was evaluated against the widely used bSSFP reference method in single-slice ASL-CMR in 4 healthy volunteers, during both systole and diastole. Sequential multi-slice ASL-CMR with SS-EPI was demonstrated during diastole (3 slices: basal, mid, and apical short-axis) and during systole (2 slices: mid and apical short-axis), in 3 volunteers. Results: Global myocardial perfusion for diastolic SS-EPI (1.66 ± 0.73 mL/g/min) and systolic SS-EPI (1.50 ± 0.36 mL/g/min) were found to be statistically equivalent (2 one-sided test with a difference of 0.4 mL/g/min) to diastolic bSSFP (duration of 1 cardiac cycle, 1.60 ± 0.80 mL/g/min) with P-values of 0.022 and 0.031, respectively.Global myocardial perfusion for sequential multi-slice experiments was 1.64 ± 0.47, 1.34 ± 0.29, and 1.88 ± 0.58 for basal, mid, and apical SAX slices during diastole and was 1.61 ± 0.35, and 1.66 ± 0.49 for mid and apical slice during systole. These values are comparable to published ASL-CMR and positron emission tomography studies. Conclusion: SS-EPI is a promising alternative to bSSFP imaging for ASL-CMR and can potentially improve the spatial coverage of ASL-CMR by 3-fold during diastole and 2-fold during systole, without increasing scan time.

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Section: Resultssupporting

confidence: 78%

Section: Discussionsupporting

confidence: 76%

“…Double gated flow alternating inversion recovery (DG‐FAIR) ASL was performed in SAX slices, shown in Figure . Adiabatic hyperbolic‐secant pulses were used for labeling.…”

Section: Methodsmentioning

confidence: 99%

“…MP was calculated using the interpolated signal difference

()(, normalΔ M)

Section: Methodsmentioning

confidence: 99%

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Single‐shot EPI for ASL‐CMR

Javed

Nayak

2020

Magnetic Resonance in Med

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“…Trained networks were tested on 144 unseen ASL images acquired at rest and during adenosine stress from 6 heart transplant recipients. Training, validation, and testing data were drawn from previously published studies (31)(32)(33), where manual segmentation and quantitative reginal MBF from all subjects were readily available.…”

Section: Network Architecturementioning

confidence: 99%

Accuracy, uncertainty, and adaptability of automatic myocardial ASL segmentation using deep CNN

Guo

Yoon

et al. 2019

Magnetic Resonance in Med

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words maximum)Purpose: To apply deep CNN to the segmentation task in myocardial arterial spin labeled (ASL) perfusion imaging and to develop methods that measure uncertainty and that adapt the CNN model to a specific false positive vs. false negative tradeoff. Methods:The Monte Carlo dropout (MCD) U-Net was trained on data from 22 subjects and tested on data from 6 heart transplant recipients. Manual segmentation and regional myocardial blood flow (MBF) were available for comparison. We consider two global uncertainty measures, named "Dice Uncertainty" and "MCD Uncertainty", which were calculated with and without the use of manual segmentation, respectively. Tversky loss function with a hyperparameter β was used to adapt the model to a specific false positive vs. false negative tradeoff. Results:The MCD U-Net achieved Dice coefficient of 0.91 ± 0.04 on the test set. MBF measured using automatic segmentations was highly correlated to that measured using the manual segmentation (R 2 = 0.96). Dice Uncertainty and MCD Uncertainty were in good agreement (R 2 = 0.64). As β increased, the false positive rate systematically decreased and false negative rate systematically increased. Conclusion:We demonstrate the feasibility of deep CNN for automatic segmentation of myocardial ASL, with good accuracy. We also introduce two simple methods for assessing model uncertainty. Finally, we demonstrate the ability to adapt the CNN model to a specific false positive vs. false negative tradeoff. These findings are directly relevant to automatic segmentation in quantitative cardiac MRI and are broadly applicable to automatic segmentation problems in diagnostic imaging.Keywords: MRI, arterial spin labeling, automatic segmentation, deep convolutional neural network, false positive and false negative tradeoff, uncertainty measure, quality assessment, Bayesian, Monte Carlo Dropout

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Quantification of Myocardial Perfusion With Vasodilation Using Arterial Spin Labeling at 1.5T

Aramendía‐Vidaurreta

Echeverria‐Chasco

Vidorreta

et al. 2020

Magnetic Resonance Imaging

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Background Myocardial perfusion is evaluated in first‐pass MRI using a gadolinium‐based contrast agent, which limits its repeatability and restricts its use in patients with abnormal kidney function. Arterial spin labeling (ASL) is a promising technique for measuring myocardial perfusion without contrast injection. The ratio of stress to rest perfusion, termed myocardial perfusion reserve (MPR), is an indicator of the severity of stenosis in patients with coronary artery disease (CAD). Purpose To quantify perfusion increases with pharmacological vasodilation, explore MPR differences between segments with and without perfusion defects, and examine the correlations between quantitative ASL and semiquantitative first‐pass measurements. Study Type Prospective. Subjects Sixteen patients with suspected CAD: 10 classified as “healthy,” having normal perfusion on first‐pass and no enhancement on late gadolinium enhancement (LGE), and six as “nonhealthy,” having hypoperfused segments including ischemic and infarcted. Field Strength/Sequence Flow‐sensitive alternating inversion recovery (FAIR) rest–stress cardiac ASL with balanced steady‐state free precession (bSSFP), rest–stress first‐pass imaging using gradient‐echo and LGE using a phase‐sensitive inversion‐recovery bSSFP at 1.5T. Assessment For healthy subjects, rest–stress perfusion data were compared in global, coronary artery territory, and segment regions of interest (ROIs). A segmental MPR comparison was performed between normal segments from healthy subjects and abnormal segments from nonhealthy subjects. Correlations between ASL and first‐pass parameters were explored. Statistical Tests Wilcoxon‐signed‐rank test, nonparametric factorial analysis of variance (ANOVA), and Pearson's/Spearman's correlations. Results Perfusion increases were significant globally (P = 0.005), per coronary artery territory (P = 0.015), and per segment (P = 0.03 for all segments in ASL and first‐pass, except anteroseptal in ASL P = 0.04). MPR differences between normal and abnormal segments were significant (P = 0.0028: ASL, P = 0.033: first‐pass). ASL and first‐pass measurements were correlated (MPR: r = 0.64, P = 0.008 and perfusion: rho = 0.47, P = 0.007). Data Conclusion This study demonstrates the feasibility of ASL to detect hyperemia, the potential to differentiate segments with and without perfusion defects, and significant correlations between ASL and semiquantitative first‐pass. Level of Evidence 2 Technical Efficacy Stage 1

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Double‐gated myocardial ASL perfusion imaging is robust to heart rate variation

Abstract: Single-gated myocardial ASL suffers from reduced temporal SNR, while double-gated myocardial ASL provides consistent temporal SNR independent of HRV. Magn Reson Med 77:1975-1980, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Cited by 20 publications

References 35 publications

Single‐shot EPI for ASL‐CMR

Single‐shot EPI for ASL‐CMR

Accuracy, uncertainty, and adaptability of automatic myocardial ASL segmentation using deep CNN

Quantification of Myocardial Perfusion With Vasodilation Using Arterial Spin Labeling at 1.5T

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