Comparison of myocardial perfusion estimates from dynamic contrast-enhanced magnetic resonance imaging with four quantitative analysis methods.

Dynamic contrast-enhanced MRI has been used to quantify myocardial perfusion in recent years. Published results have varied widely, possibly depending on the method used to analyze the dynamic perfusion data. Here, four quantitative analysis methods (two-compartment modeling, Fermi function modeling, model-independent analysis, and Patlak plot analysis) were implemented and compared for quantifying myocardial perfusion. Dynamic contrast-enhanced MRI data were acquired in 20 human subjects at rest with low-dose (0.019 +/- 0.005 mmol/kg) bolus injections of gadolinium. Fourteen of these subjects were also imaged at adenosine stress (0.021 +/- 0.005 mmol/kg). Aggregate rest perfusion estimates were not significantly different between all four analysis methods. At stress, perfusion estimates were not significantly different between two-compartment modeling, model-independent analysis, and Patlak plot analysis. Stress estimates from the Fermi model were significantly higher (approximately 20%) than the other three methods. Myocardial perfusion reserve values were not significantly different between all four methods. Model-independent analysis resulted in the lowest model curve-fit errors. When more than just the first pass of data was analyzed, perfusion estimates from two-compartment modeling and model-independent analysis did not change significantly, unlike results from Fermi function modeling.

Quantification of myocardial perfusion using CMR with a radial data acquisition: comparison with a dual-bolus method.

BACKGROUND: Quantitative estimates of myocardial perfusion generally require accurate measurement of the arterial input function (AIF). The saturation of signal intensity in the blood that occurs with most doses of contrast agent makes obtaining an accurate AIF challenging. This work seeks to evaluate the performance of a method that uses a radial k-space perfusion sequence and multiple saturation recovery times (SRT) to quantify myocardial perfusion with cardiovascular magnetic resonance (CMR). METHODS: Perfusion CMR was performed at 3 Tesla with a saturation recovery radial turboFLASH sequence with 72 rays. Fourteen subjects were given a low dose (0.004 mmol/kg) of dilute (1/5 concentration) contrast agent (Gd-BOPTA) and then a higher non-dilute dose of the same volume (0.02 mmol/kg). AIFs were calculated from the blood signal in three sub-images with differing effective saturation recovery times. The full and sub-images were reconstructed iteratively with a total variation constraint. The images from the full 72 ray data were processed to obtain six tissue enhancement curves in two slices of the left ventricle in each subject. A 2-compartment model was used to determine absolute flows RESULTS: The proposed multi-SRT method resulted in AIFs that were similar to those obtained with the dual-bolus method. Myocardial blood flow (MBF) estimates from the dual-bolus and the multi-SRT methods were related by MBFmulti-SRT = 0.85MBFdual-bolus + 0.18 (r = 0.91). CONCLUSIONS: The multi-SRT method, which uses a radial k-space perfusion sequence, can be used to obtain an accurate AIF and thus quantify myocardial perfusion for doses of contrast agent that result in a relatively saturated AIF.

Estimating myocardial perfusion from dynamic contrast-enhanced CMR with a model-independent deconvolution method.

BACKGROUND: Model-independent analysis with B-spline regularization has been used to quantify myocardial blood flow (perfusion) in dynamic contrast-enhanced cardiovascular magnetic resonance (CMR) studies. However, the model-independent approach has not been extensively evaluated to determine how the contrast-to-noise ratio between blood and tissue enhancement affects estimates of myocardial perfusion and the degree to which the regularization is dependent on the noise in the measured enhancement data. We investigated these questions with a model-independent analysis method that uses iterative minimization and a temporal smoothness regularizer. Perfusion estimates using this method were compared to results from dynamic 13N-ammonia PET. RESULTS: An iterative model-independent analysis method was developed and tested to estimate regional and pixelwise myocardial perfusion in five normal subjects imaged with a saturation recovery turboFLASH sequence at 3 T CMR. Estimates of myocardial perfusion using model-independent analysis are dependent on the choice of the regularization weight parameter, which increases nonlinearly to handle large decreases in the contrast-to-noise ratio of the measured tissue enhancement data. Quantitative perfusion estimates in five subjects imaged with 3 T CMR were 1.1 +/- 0.8 ml/min/g at rest and 3.1 +/- 1.7 ml/min/g at adenosine stress. The perfusion estimates correlated with dynamic 13N-ammonia PET (y = 0.90x + 0.24, r = 0.85) and were similar to results from other validated CMR studies. CONCLUSION: This work shows that a model-independent analysis method that uses iterative minimization and temporal regularization can be used to quantify myocardial perfusion with dynamic contrast-enhanced perfusion CMR. Results from this method are robust to choices in the regularization weight parameter over relatively large ranges in the contrast-to-noise ratio of the tissue enhancement data.

Quantitative myocardial distribution volume from dynamic contrast-enhanced MRI.

The objective of this study was to investigate if dynamic contrast-enhanced magnetic resonance imaging (MRI) can be used to quantitate the distribution volume (v(e)) in regions of normal and infarcted myocardium. v(e) reflects the volume of the extracellular, extravascular space within the myocardial tissue. In regions of the heart where an infarct has occurred, the loss of viable cardiac cells results in an elevated v(e) compared to normal regions. A quantitative estimate of the magnitude and spatial distribution of v(e) is significant because it may provide information complementary to delayed enhancement MRI alone. Using a hybrid gradient echo-echoplanar imaging pulse sequence on a 1.5T MRI scanner, 12 normal subjects and four infarct patients were imaged dynamically, during the injection of a contrast agent, to measure the regional blood and tissue enhancement in the left ventricular (LV) myocardium. Seven of the normal subjects and all of the infarct patients were also imaged at steady-state contrast enhancement to estimate the steady-state ratio of contrast agent in the tissue and blood (Ct/Cb) - a validated measure of v(e). Normal and infarct regions of the LV were manually selected, and the blood and tissue enhancement curves were fit to a compartment model to estimate v(e). Also, the effect of the vascular blood signal on estimates of v(e) was evaluated using simulations and in the dynamic and steady-state studies. Aggregate estimates of v(e) were 23.6+/-6.3% in normal myocardium and 45.7+/-3.4% in regions of infarct. These results were not significantly different from the reference standards of Ct/Cb (22.9+/-6.8% and 42.6+/-6.3%, P=.073). From the dynamic contrast-enhanced studies, approximately 1 min of scan time was necessary to estimate v(e) in the normal myocardium to within 10% of the steady-state estimate. In regions of infarct, up to 3 min of dynamic data were required to estimate v(e) to within 10% of the steady-state v(e) value. By measuring the kinetics of blood and tissue enhancement in the myocardium during an extended dynamic contrast enhanced MRI study, v(e) may be estimated using compartment modeling.

The effect of obesity on regadenoson-induced myocardial hyperemia: a quantitative magnetic resonance imaging study.

The A2(A) receptor agonist, regadenoson, is increasingly used as a vasodilator during nuclear myocardial perfusion imaging. Regadenoson is administered as a single, fixed dose. Given the frequency of obesity in patients with symptoms of heart disease, it is important to know whether the fixed dose of regadenoson produces maximal coronary hyperemia in subjects of widely varying body size. Thirty subjects (12 female, 18 male, mean BMI 30.3 ± 6.5, range 19.6-46.6) were imaged on a 3T magnetic resonance scanner. Imaging with a saturation recovery radial turboFLASH sequence was done first at rest, then during adenosine infusion (140 µg/kg/min) and 30 min later with regadenoson (0.4 mg/5 ml bolus). A 5 cc/s injection of Gd-BOPTA was used for each perfusion sequence, with doses of 0.02, 0.03 and 0.03 mmol/kg, respectively. Analysis of the upslope of myocardial time-intensity curves and quantitative processing to obtain myocardial perfusion reserve (MPR) values were performed for each vasodilator. The tissue upslopes for adenosine and regadenoson matched closely (y = 1.1x + 0.03, r = 0.9). Mean MPR was 2.3 ± 0.6 for adenosine and 2.4 ± 0.9 for regadenoson (p = 0.14). There was good agreement between MPR measured with adenosine and regadenoson (y = 1.1x - 0.06, r = 0.7). The MPR values measured with both agents tended to be lower as BMI increased. There were no complications during administration of either agent. Regadenoson produced fewer side effects. Fixed dose regadenoson and weight adjusted adenosine produce similar measures of MPR in patients with a wide range of body sizes. Regadenoson is a potentially useful vasodilator for stress MRI studies.

Strain measurement in the left ventricle during systole with deformable image registration.

The objective of this study was to validate a deformable image registration technique, termed Hyperelastic Warping, for left ventricular strain measurement during systole using cine-gated, non-tagged MR images with strains measured from tagged MRI. The technique combines deformation from high resolution, non-tagged MR image data with a detailed computational model, including estimated myocardial material properties, fiber direction, and active fiber contraction, to provide a comprehensive description of myocardial contractile function. A normal volunteer (male, age 30) with no history of cardiac pathology was imaged with a 1.5 T Siemens Avanto clinical scanner using a TrueFISP imaging sequence and a 32-channel cardiac coil. Both tagged and non-tagged cine MR images were obtained. The Hyperelastic Warping solution was evolved using a series of non-tagged images in ten intermediate phases from end-diastole to end-systole. The solution may be considered as ten separate warping problems with multiple templates and targets. At each stage, an active contraction was initially applied to a finite element model, and then image-based warping penalty forces were utilized to generate the final registration. Warping results for circumferential strain (R(2)=0.75) and radial strain (R(2)=0.78) were strongly correlated with results obtained from tagged MR images analyzed with a Harmonic Phase (HARP) algorithm. Results for fiber stretch, LV twist, and transmural strain distributions were in good agreement with experimental values in the literature. In conclusion, Hyperelastic Warping provides a unique alternative for quantifying regional LV deformation during systole without the need for tags.