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Validation of MR multitasking myocardial perfusion reserve measurements against simultaneous 13N-ammonia PET
Anthony G Christodoulou1, Damini Dey1,2, Behzad Sharif1, Richard Tang1, Wafa Tawackoli1,3,4, Rohan Dharmakumar1,2,5, Piotr J Slomka1,5, Daniel S Berman1,5, and Debiao Li1,2

1Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States, 2Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States, 3Department of Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, United States, 4Board of Governors Regenerative Medicine Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States, 5Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States

Synopsis

Measurements from myocardial perfusion MRI have previously been compared against separate PET measurements. However, MR quantification is complicated by signal nonlinearity (leading to a dual-bolus paradigm) and ECG misfires; furthermore, physiological variation in between separate PET and MR assessments are a confounding factor in validation. This work leverages the recent advent of multimodal PET-MR systems to perform a preliminary validation of quantitative MPR measurements from MR multitasking—a new framework allowing single-bolus, non-ECG perfusion quantification—against simultaneous 13N-ammonia PET-MR measurements in pigs. Excellent agreement was found between modalities (no bias, p=0.66; intraclass correlation coefficient=0.95).

Introduction

Quantitative myocardial perfusion MRI is evolving as a promising tool for the diagnosis and stratification of patients with suspected coronary artery disease1. However, quantification is currently complicated by the nonlinear response of signal intensity to contrast agent concentration—leading to a dual-bolus2 paradigm—as well as potential ECG misfires. MR multitasking3, 4, a new framework for quantitative MR perfusion, addresses both ECG dependence and signal nonlinearity by acquiring continuously and resolving the other image dynamics (or “tasks”) which arise in addition to dynamic contrast enhancement. Resolving cardiac motion removes ECG dependence and allows analysis at any cardiac phase. Resolving T1 recovery accounts for signal nonlinearity, producing time-resolved ΔR1 measurements directly proportional to contrast agent concentration and permitting single-bolus perfusion quantification.

$$$\quad$$$In the past, myocardial perfusion reserve (MPR) measurements from MR methods have been validated against separate measurements from PET5-8, the noninvasive standard for quantitative perfusion. However, physiological variations arising from such asynchronous assessments have been shown to be a confounding factor in measurement comparison9. The recent advent of multimodal PET-MR systems10 now provides an excellent opportunity for simultaneous measurements. This work presents a preliminary validation of quantitative MPR measurements from single-bolus MR multitasking against simultaneous 13N-ammonia PET-MR measurements in pigs.

Methods

All images were acquired on a 3 T Siemens Biograph mMR PET-MR scanner. Myocardial perfusion was assessed first during vasodilator stress (300–320 μg/kg/min adenosine, administered for 6 min) and again later at rest. PET and MRI perfusion assessments were performed simultaneously, with the PET tracer (~4 mCi 13N-ammonia) injected 1 min before the MRI contrast agent (0.05 mmol/kg gadobutrol). Figure 1 illustrates this protocol, which was run in two farm pigs a total of four times (all on different days).

$$$\quad$$$PET images were collected in 3D list mode for 10 min. PET attenuation correction was performed using two-point Dixon MR images11 (Figure 2). Sixteen dynamic PET images (twelve 10-s, two 30-s, one 1-min, and one 6-min frame) were reconstructed using attenuation-weighted ordered-subsets expectation maximization with 3 iterations and 14 subsets, with high-definition resolution recovery option and 5-mm Gaussian postfiltering12. Flow was quantified from the first 2 min of images using a two-compartment model13, and the last 8 min were used to identify left ventricular contours, all in QPET14 (a clinically validated PET software).

$$$\quad$$$MR images were collected in a mid-ventricular short-axis slice during a 45-s breath-hold, using multitasking to perform single-bolus, non–ECG-gated continuous FLASH acquisition throughout repeated 300 ms saturation recovery periods4. Additional measurement parameters were flip angle = 10°, TE = 1.6 ms, TR = 3.6 ms, spatial resolution = 1.7 mm × 1.7 mm, and slice thickness = 8 mm. Time-resolved T1 mapping produced ΔR1 curves proportional to contrast agent concentration; systolic flow was quantified from these ΔR1 curves using Fermi deconvolution15,16 in MATLAB.

$$$\quad$$$For each modality, MPR was calculated as the ratio of stress flow to rest flow. MPR was compared between modalities in the six AHA mid-ventricular myocardial segments using a paired t-test and the intraclass correlation coefficient (ICC).

Results

Example rest and stress images from PET and MRI from one session are shown in Figure 3. Figure 4 shows a scatter plot and Bland–Altman plot comparing the n = (6 segments)*(4 sessions) = 24 measurements of MPR. During one session, the subject had no stress response to adenosine, resulting in MPR measurements below 1; this was observed in both modalities. No statistically significant bias was observed between modalities (p = 0.66), and ICC = 0.95.

Discussion

Measurements showed excellent agreement with no bias between modalities. ICC values are higher than have been seen in asynchronous PET-MR MPR comparisons5-8, which may be due to improved accuracy of T1-based multitasking perfusion measurements, lack of confounding physiological changes between asynchronous PET and MR scans, and/or increased between-sample variance from a wide range of stress responses. In this preliminary study, only a single mid-ventricular slice was collected during MR scanning, limiting the spatial coverage of the comparison. Follow-up work should include expansion of the MR method to multislice or 3D, evaluation in more subjects, and evaluation in subjects with coronary stenoses.

Conclusion

We have demonstrated that perfusion assessments using simultaneous 13N-ammonia PET and single-bolus MR multitasking in pigs exhibited excellent agreement between modalities. The results demonstrate the feasibility of validating MR measurements against concurrent PET measurements and exhibit promising accuracy and precision of measurements using MR multitasking.

Acknowledgements

This work was supported by NIH 1R01HL124649.

References

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Figures

Figure 1. Experimental protocol featuring simultaneous acquisition of PET and MR perfusion images. Each PET scan lasted 10 min, with the first 2 min used to quantify perfusion and the last 8 min used for left ventricular contouring. Each MR scan lasted 45 seconds and started 1 min after the start of the PET scan. Perfusion was assessed first under vasodilator stress and later at rest.

Figure 2. Fusion of PET images and MR images used for attenuation correction.

Figure 3. Example images from both modalities (left: PET, right: MRI) from the same stress period.

Figure 4. (a) Scatter plot of MPR measurements from both modalities, with line of identity (solid) and regression line (dashed). (b) Bland–Altman plot of MPR measurements with mean difference (solid) and limits of agreement (dashed).

Proc. Intl. Soc. Mag. Reson. Med. 26 (2018)
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