Stress perfusion magnetic resonance imaging of the heart

Cardiac magnetic resonance imaging myocardial perfusion reserve index in heart transplant patients

OBJECTIVE

To compare the myocardial perfusion reserve index (MPRI) measured during stress cardiac magnetic resonance imaging (MRI) with regadenoson in patients with heart transplants versus in patients without heart transplants.

MATERIAL AND METHODS

We retrospectively compared 20 consecutive asymptomatic heart transplant patients without suspicion of microvascular disease who underwent stress cardiac MRI with regadenoson and coronary computed tomography angiography (CTA) to rule out cardiac allograft vasculopathy versus 16 patients without transplants who underwent clinically indicated stress cardiac MRI who were negative for ischemia and had no signs of structural heart disease. We estimated MPRI semiquantitatively after calculating the up-slope of the first-pass enhancement curve and dividing the value obtained during stress by the value obtained at rest. We compared MPRI in the two groups. Patients with positive findings for ischemia on stress cardiac MRI or significant coronary stenosis on coronary CTA were referred for conventional coronary angiography.

RESULTS

More than half the patients remained asymptomatic during the stress test. Stress cardiac MRI was positive for ischemia in two heart transplant patients; these findings were confirmed at coronary CTA and at conventional coronary angiography. Patients with transplants had lower end-diastolic volume index (59.3±15.2 ml/m² vs. 71.4±15.9 ml/m² in those without transplants, p=0.03), lower MPRI (1.35±0.19 vs. 1.6±0.28 in those without transplants, p=0.003), and a less pronounced hemodynamic response to regadenoson (mean increase in heart rate 13.1±5.4 bpm vs. 28.5±8.9 bpm in those without transplants, p <0.001).

CONCLUSION

Stress cardiac MRI with regadenoson is safe. In the absence of epicardial coronary artery disease, patients with heart transplants have lower MPRI than patients without transplants, suggesting microvascular disease. The hemodynamic response to regadenoson is less pronounced in patients with heart transplants than in patients without heart transplants.

Tools for cardiovascular magnetic resonance imaging

In less than fifteen years, as a non-invasive imaging option, cardiovascular MR has grown from a being a mere curiosity to becoming a widely used clinical tool for evaluating cardiovascular disease. Cardiovascular magnetic resonance imaging (CMRI) is now routinely used to study myocardial structure, cardiac function, macro vascular blood flow, myocardial perfusion, and myocardial viability. For someone entering the field of cardiac MR, this rapid pace of development in the field of CMRI might make it difficult to identify a cohesive starting point. In this brief review, we have attempted to summarize the key cardiovascular imaging techniques that have found widespread clinical acceptance. In particular, we describe the essential cardiac and respiratory gating techniques that form the backbone of all cardiovascular imaging methods. It is followed by four sections that discuss: (I) the gradient echo techniques that are used to assess ventricular function; (II) black-blood turbo spin echo (SE) methods used for morphologic assessment of the heart; (III) phase-contrast based techniques for the assessment of blood flow; and (IV) CMR methods for the assessment of myocardial ischemia and viability. In each section, we briefly summarize technical considerations relevant to the clinical use of these techniques, followed by practical information for its clinical implementation. In each of those four areas, CMRI is considered either as the benchmark imaging modality against which the diagnostic performance of other imaging modalities are compared against, or provides a complementary capability to existing imaging techniques. We have deliberately avoided including cutting-edge CMR imaging techniques practiced at few academic centers, and restricted our discussion to methods that are widely used and are likely to be available in a clinical setting. Our hope is that this review would propel an interested reader toward more comprehensive reviews in the literature.

Quantitative contrast-enhanced first-pass cardiac perfusion MRI at 3 tesla with accurate arterial input function and myocardial wall enhancement

PURPOSE

To develop, and validate in vivo, a robust quantitative first-pass perfusion cardiovascular MR (CMR) method with accurate arterial input function (AIF) and myocardial wall enhancement.

MATERIALS AND METHODS

A saturation-recovery (SR) pulse sequence was modified to sequentially acquire multiple slices after a single nonselective saturation pulse at 3 Tesla. In each heartbeat, an AIF image is acquired in the aortic root with a short time delay (TD) (50 ms), followed by the acquisition of myocardial images with longer TD values (∼150-400 ms). Longitudinal relaxation rates (R(1) = 1/T(1)) were calculated using an ideal saturation recovery equation based on the Bloch equation, and corresponding gadolinium contrast concentrations were calculated assuming fast water exchange condition. The proposed method was validated against a reference multi-point SR method by comparing their respective R(1) measurements in the blood and left ventricular myocardium, before and at multiple time-points following contrast injections, in 7 volunteers.

RESULTS

R(1) measurements with the proposed method and reference multi-point method were strongly correlated (r > 0.88, P < 10(-5)) and in good agreement (mean difference ±1.96 standard deviation 0.131 ± 0.317/0.018 ± 0.140 s(-1) for blood/myocardium, respectively).

CONCLUSION

The proposed quantitative first-pass perfusion CMR method measured accurate R(1) values for quantification of AIF and myocardial wall contrast agent concentrations in 3 cardiac short-axis slices, in a total acquisition time of 523 ms per heartbeat.

A model-constrained Monte Carlo method for blind arterial input function estimation in dynamic contrast-enhanced MRI: I. Simulations

Widespread adoption of quantitative pharmacokinetic modeling methods in conjunction with dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has led to increased recognition of the importance of obtaining accurate patient-specific arterial input function (AIF) measurements. Ideally, DCE-MRI studies use an AIF directly measured in an artery local to the tissue of interest, along with measured tissue concentration curves, to quantitatively determine pharmacokinetic parameters. However, the numerous technical and practical difficulties associated with AIF measurement have made the use of population-averaged AIF data a popular, if sub-optimal, alternative to AIF measurement. In this work, we present and characterize a new algorithm for determining the AIF solely from the measured tissue concentration curves. This Monte Carlo blind estimation (MCBE) algorithm estimates the AIF from the subsets of D concentration-time curves drawn from a larger pool of M candidate curves via nonlinear optimization, doing so for multiple (Q) subsets and statistically averaging these repeated estimates. The MCBE algorithm can be viewed as a generalization of previously published methods that employ clustering of concentration-time curves and only estimate the AIF once. Extensive computer simulations were performed over physiologically and experimentally realistic ranges of imaging and tissue parameters, and the impact of choosing different values of D and Q was investigated. We found the algorithm to be robust, computationally efficient and capable of accurately estimating the AIF even for relatively high noise levels, long sampling intervals and low diversity of tissue curves. With the incorporation of bootstrapping initialization, we further demonstrated the ability to blindly estimate AIFs that deviate substantially in shape from the population-averaged initial guess. Pharmacokinetic parameter estimates for K(trans), k(ep), v(p) and v(e) all showed relative biases and uncertainties of less than 10% for measurements having a temporal sampling rate of 4 s and a concentration measurement noise level of sigma = 0.04 mM. A companion paper discusses the application of the MCBE algorithm to DCE-MRI data acquired in eight patients with malignant brain tumors.