BackgroundMyocardial arterial spin labeling (ASL) is a noninvasive MRI based technique that is capable of measuring myocardial blood flow (MBF) in humans. It suffers from poor sensitivity to MBF due to high physiological noise (PN). This study aims to determine if the sensitivity of myocardial ASL to MBF can be improved by reducing image acquisition time, via parallel imaging.MethodsMyocardial ASL scans were performed in 7 healthy subjects at rest using flow-sensitive alternating inversion recovery (FAIR) tagging and balanced steady state free precession (SSFP) imaging. Sensitivity encoding (SENSE) with a reduction factor of 2 was used to shorten each image acquisition from roughly 300 ms per heartbeat to roughly 150 ms per heartbeat. A paired Student’s t-test was performed to compare measurements of myocardial blood flow (MBF) and physiological noise (PN) from the reference and accelerated methods.ResultsThe measured PN (mean ± standard deviation) was 0.20 ± 0.08 ml/g/min for the reference method and 0.08 ± 0.05 ml/g/min for the accelerated method, corresponding to a 60% reduction. PN measured from the accelerated method was found to be significantly lower than that of the reference method (p = 0.0059). There was no significant difference between MBF measured from the accelerated and reference ASL methods (p = 0.7297).ConclusionsIn this study, significant PN reduction was achieved by shortening the acquisition window using parallel imaging with no significant impact on the measured MBF. This indicates an improvement in sensitivity to MBF and may also enable the imaging of subjects with higher heart rates and imaging during systole.

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.

2 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2017;46:413-420.

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

BackgroundFollowing acute myocardial infarction (AMI), microvascular integrity and function may be compromised as a result of microvascular obstruction (MVO) and vasodilator dysfunction. It has been observed that both infarcted and remote myocardial territories may exhibit impaired myocardial blood flow (MBF) patterns associated with an abnormal vasodilator response. Arterial spin labeled (ASL) CMR is a novel non-contrast technique that can quantitatively measure MBF. This study investigates the feasibility of ASL-CMR to assess MVO and vasodilator response in swine.MethodsThirty-one swine were included in this study. Resting ASL-CMR was performed on 24 healthy swine (baseline group). A subset of 13 swine from the baseline group underwent stress ASL-CMR to assess vasodilator response. Fifteen swine were subjected to a 90-min left anterior descending (LAD) coronary artery occlusion followed by reperfusion. Resting ASL-CMR was performed post-AMI at 1–2 days (N = 9, of which 6 were from the baseline group), 1–2 weeks (N = 8, of which 4 were from the day 1–2 group), and 4 weeks (N = 4, of which 2 were from the week 1–2 group). Resting first-pass CMR and late gadolinium enhancement (LGE) were performed post-AMI for reference.ResultsAt rest, regional MBF and physiological noise measured from ASL-CMR were 1.08 ± 0.62 and 0.15 ± 0.10 ml/g/min, respectively. Regional MBF increased to 1.47 ± 0.62 ml/g/min with dipyridamole vasodilation (P < 0.001). Significant reduction in MBF was found in the infarcted region 1–2 days, 1–2 weeks, and 4 weeks post-AMI compared to baseline (P < 0.03). This was consistent with perfusion deficit seen on first-pass CMR and with MVO seen on LGE. There were no significant differences between measured MBF in the remote regions pre and post-AMI (P > 0.60).ConclusionsASL-CMR can assess vasodilator response in healthy swine and detect significant reduction in regional MBF at rest following AMI. ASL-CMR is an alternative to gadolinium-based techniques for assessment of MVO and microvascular integrity within infarcted, as well as salvageable and remote myocardium. This has the potential to provide early indications of adverse remodeling processes post-ischemia.