An empirical method for reducing variability and complexity of myocardial perfusion quantification by dual bolus cardiac MRI.

PURPOSE: Myocardial perfusion can be quantified using the "dual bolus" technique, which uses two separate contrast boluses to avoid signal nonlinearity in the blood pool. This technique relies on knowing the precise ratio of contrast concentrations between the two boluses. In this study, we investigated the variability found in these ratios, as well as the error it introduces, and developed a method for correction. METHODS: Five dogs received dual bolus myocardial perfusion MRI scans. Perfusion was calculated separately using assumed contrast dilution ratios and empirically determined contrast ratios. Perfusion was compared with reference standard fluorescent microspheres. The same technique was then applied to a cohort of six patients with no significant coronary artery stenosis by cardiac catheterization. RESULTS: Assumed contrast dilution ratios were 10:1 for all animal and patient scans. Empirically derived contrast ratios were significantly different for animal (8.51:1 ± 1.53:1, P < 0.001) and patient scans (7.32:1 ± 2.27:1, P < 0.01). Incorporating empirically derived ratios for animal scans improved correlation with microspheres from 0.84 to 0.90 (P < 0.05). CONCLUSION: Variability in dual bolus contrast concentration ratios is an important source of experimental error, especially outside of a carefully controlled laboratory setting. Empirically deriving the correct ratio is feasible and improves the accuracy of quantitative perfusion measurements. Magn Reson Med 77:2347-2355, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Absolute quantification of myocardial blood flow with constrained estimation of the arterial input function.

PURPOSE: To evaluate the performance of the constrained alternating minimization with model (CAMM) method for estimating the input function from the myocardial tissue curves. MATERIALS AND METHODS: Myocardial perfusion imaging was performed on seven canine models of coronary artery disease in 15 imaging sessions. In each session, stress was induced with intravenous infusion of adenosine and a variable occluder created coronary artery stenosis. A dual bolus protocol was used for each acquisition, and input functions were then estimated using the CAMM method with data acquired from the high dose scan following each imaging session. For each acquisition, myocardial blood flow was measured by injected microspheres. RESULTS: The dual bolus and CAMM-derived flows were not significantly different (P = 0.18), and the correlation between the two methods was high (r = 0.97). The correlation between the dual bolus and CAMM methods and microsphere measurements was lower than that for the two MR methods (r = 0.53; r = 0.43, respectively). CONCLUSION: The CAMM method presented here shows promise in estimating myocardial blood flow in patients with coronary artery disease at stress with a single injection and without any specialized acquisitions. Further work is needed to validate the approach in a clinical setting.

Left atrial flow velocity distribution and flow coherence using four-dimensional FLOW MRI: a pilot study investigating the impact of age and Pre- and Postintervention atrial fibrillation on atrial hemodynamics.

PURPOSE: To use four-dimensional (4D) flow MRI to characterize and quantify 3D blood flow in the left atria (LA) of patients with a history of atrial fibrillation (AF). MATERIALS AND METHODS: The 4D flow MRI was acquired in 19 volunteers (n = 9<30 years, n = 10>50 years) and 10 patients with AF (62 ± 9.6 years; n = 4 in persistent AF, n = 6 postintervention). The LA in each dataset was segmented, and intra-atrial blood flow velocity was quantified. Flow coherence was measured as the consistency of the net blood flow vector. RESULTS: Quantification of atrial flow revealed significant differences in atrial hemodynamics between age groups. Postintervention AF patients had a mean blood flow of 0.22 ± 0.04 m/s, which was not significantly different than age-matched volunteers (0.21 ± 0.03 m/s). Patients with persistent AF had a mean blood flow of 0.13 ± 0.01 m/s, lower than AF patients in sinus rhythm (0.22 ± 0.04 m/s, P = 0.005), or age-matched volunteers (0.21 ± 0.03 m/s, P < 0.001). Flow coherence was significantly impaired in patients in AF. CONCLUSION: Flow-sensitive MRI shows that patients with a history of AF had global hemodynamics in the LA similar to those of age-matched volunteers. Additional studies with larger cohorts of AF patients and correlation with outcome are needed to further investigate the potential of atrial 4D flow MRI to flow patterns indicative of stroke risk in AF.

Constrained estimation of the arterial input function for myocardial perfusion cardiovascular magnetic resonance.

Accurate quantification of myocardial perfusion remains challenging due to saturation of the arterial input function at high contrast concentrations. A method for estimating the arterial input function directly from tissue curves in the myocardium that avoids these difficulties is presented. In this constrained alternating minimization with model (CAMM) algorithm, a portion of the left ventricular blood pool signal is also used to constrain the estimation process. Extensive computer simulations assessing the accuracy of kinetic parameter estimation were performed. In 5000 noise realizations, the use of the AIF given by the estimation method returned kinetic parameters with mean Ktrans error of -2% and mean kep error of 0.4%. Twenty in vivo resting perfusion datasets were also processed with this method, and pharmacokinetic parameter values derived from the blind AIF were compared with those derived from a dual-bolus measured AIF. For 17 of the 20 datasets, there were no statistically significant differences in Ktrans estimates, and in aggregate the kinetic parameters were not significantly different from the dual-bolus method. The cardiac constrained alternating minimization with model method presented here provides a promising approach to quantifying perfusion of myocardial tissue with a single injection of contrast agent and without a special pulse sequence though further work is needed to validate the approach in a clinical setting.

Toward local arterial input functions in dynamic contrast-enhanced MRI.

PURPOSE: To present a method for estimating the local arterial input function (AIF) within a dynamic contrast-enhanced MRI scan, based on the alternating minimization with model (AMM) method. MATERIALS AND METHODS: This method clusters a subset of data into representative curves, which are then input to the AMM algorithm to return a parameterized AIF and pharmacokinetic parameters. Computer simulations are used to investigate the accuracy with which the AMM is able to estimate the true AIF as a function of the input tissue curves. RESULTS: Simulations show that a power law relates uncertainty in kinetic parameters and SNR and heterogeneity of the input. Kinetic parameters calculated with the measured AIF are significantly different from those calculated with either a global (P < 0.005) or a local input function (P = 0.0). The use of local AIFs instead of measured AIFs yield mean lesion-averaged parameter changes: K(trans): +24% [+15%, +70%], k(ep): +13% [-36%, +300%]. Globally estimated input functions yield mean lesion-averaged changes: K(trans): +9% [-38%, +65%], k(ep): +13% [-100%, +400%]. The observed improvement in fit quality with local AIFs was found to be significant when additional free parameters were accounted for using the Akaike information criterion. CONCLUSION: Local AIFs result in significantly different kinetic parameter values. The statistically significant improvement in fit quality suggests that changes in parameter estimates using local AIFs reflect differences in underlying tissue physiology.

Model-based blind estimation of kinetic parameters in dynamic contrast enhanced (DCE)-MRI.

A method to simultaneously estimate the arterial input function (AIF) and pharmacokinetic model parameters from dynamic contrast-enhanced (DCE)-MRI data was developed. This algorithm uses a parameterized functional form to model the AIF and k-means clustering to classify tissue time-concentration measurements into a set of characteristic curves. An iterative blind estimation algorithm alternately estimated parameters for the input function and the pharmacokinetic model. Computer simulations were used to investigate the algorithm's sensitivity to noise and initial estimates. In 12 patients with sarcomas, pharmacokinetic parameter estimates were compared with "truth" obtained from model regression using a measured AIF. When arterial voxels were included in the blind estimation algorithm, the resulting AIF was similar to the measured input function. The "true" K(trans) values in tumor regions were not significantly different than the estimated values, 0.99 +/- 0.41 and 0.86 +/- 0.40 min(-1), respectively, P = 0.27. "True" k(ep) values also matched closely, 0.70 +/- 0.24 and 0.65 +/- 0.25 min(-1), P = 0.08. When only tissue curves free of significant vascular contribution are used (v(p) < 0.05), the resulting AIF showed substantial delay and dispersion consistent with a more local AIF such as has been observed in dynamic susceptibility contrast imaging in the brain.

A Comparison of Theory-Based and Experimentally Determined Myocardial Signal Intensity Correction Methods in First-Pass Perfusion Magnetic Resonance Imaging.

OBJECTIVES: To evaluate the impact of correcting myocardial signal saturation on the accuracy of absolute myocardial blood flow (MBF) measurements. MATERIALS AND METHODS: We performed 15 dual bolus first-pass perfusion studies in 7 dogs during global coronary vasodilation and variable degrees of coronary artery stenosis. We compared microsphere MBF to MBF calculated from uncorrected and corrected MRI signal. Four correction methods were tested, two theoretical methods (Th1 and Th2) and two empirical methods (Em1 and Em2). RESULTS: The correlations with microsphere MBF (n = 90 segments) were: uncorrected (y = 0.47x + 1.1, r = 0.70), Th1 (y = 0.53x + 1.0, r = 0.71), Th2 (y = 0.62x + 0.86, r = 0.73), Em1 (y = 0.82x + 0.86, r = 0.77), and Em2 (y = 0.72x + 0.84, r = 0.75). All corrected methods were not significantly different from microspheres, while uncorrected MBF values were significantly lower. For the top 50% of microsphere MBF values, flows were significantly underestimated by uncorrected SI (31%), Th1 (25%), and Th2 (19%), while Em1 (1%), and Em2 (9%) were similar to microsphere MBF. CONCLUSIONS: Myocardial signal saturation should be corrected prior to flow modeling to avoid underestimation of MBF by MR perfusion imaging.

A diffusion-compensated model for the analysis of DCE-MRI data: theory, simulations and experimental results.

Accurate quantification of pharmacokinetic parameters in dynamic contrast-enhanced (DCE) MRI may be affected by the passive diffusion of contrast agent (CA) within the tissue. By introducing an additional term into the standard Tofts-Kety (STK) model, we correct for the effects of CA diffusion. We first develop the theory describing a CA diffusion corrected STK model (DTK). The model is then tested in simulation with simple models of diffusion. The DTK model is also fit to 18 in vivo DCE-MRI acquisitions from murine models of cancer and results are compared to those from the STK model. The DTK model returned estimates with significantly lower error than the STK model (p ≪ 0.001). In poorly perfused (i.e., K(trans) ≤ 0.05 min(-1)) regions the STK model returned unphysical ve values, while the DTK model estimated ve with less than 7% error in noise-free simulations. Results in vivo data revealed similar trends. For voxels with low K(trans) values and late peak concentration times the STK model returned ve estimates >1.0 in 40% of the voxels as compared to only 16% for the DTK model. The DTK model presented here shows promise in estimating accurate kinetic parameters in the presence of passive contrast agent diffusion.

Reproducibility of static and dynamic (18)F-FDG, (18)F-FLT, and (18)F-FMISO MicroPET studies in a murine model of HER2+ breast cancer.

PURPOSE: The objective of this study is to determine the reproducibility of static 2-deoxy-2-[(18)F]fluoro-D-glucose ((18)F-FDG), 3'-deoxy-3'-[(18)F]fluorothymidine ((18)F-FLT), and [(18)F]-fluoromisonidazole ((18)F-FMISO) microPET measurements, as well as kinetic parameters returned from analyses of dynamic (18)F-FLT and (18)F-FMISO data. PROCEDURES: HER2+ xenografts were established in nude mice. Dynamic data were acquired for 60 min, followed by a repeat injection and second scan 6 h later. Reproducibility was assessed for the percent-injected dose per gram (%ID/g) for each radiotracer, and with kinetic parameters (K (1) -k (4) , K ( i )) for (18)F-FLT and (18)F-FMISO. RESULTS: The value needed to reflect a change in tumor physiology is given by the 95 % confidence interval (CI), which is ±14, ±5, and ±6 % for (18)F-FDG (n = 12), (18)F-FLT (n = 11), and (18)F-FMISO (n = 11) %ID/g, respectively. V ( d ) (=K (1) /k (2)), k (3), and K (FLT) are the most reproducible (18)F-FLT (n = 9) kinetic parameters, with 95 % CIs of ±18, ±10, and ±18 %, respectively. V ( d ) and K (FMISO) are the most reproducible (18)F-FMISO kinetic parameters (n = 7) with 95 % CIs of ±16 and ±14 %, respectively. CONCLUSIONS: Percent-injected dose per gram measurements are reproducible and appropriate for detecting treatment-induced changes. Kinetic parameters have larger threshold values, but are potentially sufficiently reproducible to detect treatment response.

Using dynamic contrast-enhanced magnetic resonance imaging data to constrain a positron emission tomography kinetic model: theory and simulations.

We show how dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data can constrain a compartmental model for analyzing dynamic positron emission tomography (PET) data. We first develop the theory that enables the use of DCE-MRI data to separate whole tissue time activity curves (TACs) available from dynamic PET data into individual TACs associated with the blood space, the extravascular-extracellular space (EES), and the extravascular-intracellular space (EIS). Then we simulate whole tissue TACs over a range of physiologically relevant kinetic parameter values and show that using appropriate DCE-MRI data can separate the PET TAC into the three components with accuracy that is noise dependent. The simulations show that accurate blood, EES, and EIS TACs can be obtained as evidenced by concordance correlation coefficients >0.9 between the true and estimated TACs. Additionally, provided that the estimated DCE-MRI parameters are within 10% of their true values, the errors in the PET kinetic parameters are within approximately 20% of their true values. The parameters returned by this approach may provide new information on the transport of a tracer in a variety of dynamic PET studies.

The effect of temporal sampling on quantitative pharmacokinetic and three-time-point analysis of breast DCE-MRI.

The effects of temporal sampling on the previously published three-time-point (3TP) method are compared with those of a Tofts-Kety model using an arterial input function from the alternating minimization with model (AMM) method. Computer simulations are done to estimate the expected error in both the 3TP and Tofts-Kety models as a function of the temporal sampling rate of the data. The error in the 3TP model parameters remained essentially constant with respect to temporal sampling. The Tofts-Kety model showed a linear increase in parameter error with respect to temporal sampling. Both analysis methods were also applied to 87 clinically acquired breast scans. These scans were downsampled in time by a factor of 2 and 4, and the methods were reapplied. The spatial resolution was held constant throughout this study. At temporal resolutions less than 19.4 s, the Tofts-Kety model outperformed the 3TP model using receiver operating characteristic curve analysis (area under the ROC curve [AUC] of 0.94 compared to 0.91). As the temporal sampling rate decreased, the 3TP model outperformed the Tofts-Kety model (AUC of 0.89 versus 0.85). When the temporal sampling rate of the data was less than 20 s, the Tofts-Kety model with the AMM method had lower parameter error than the 3TP method.

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.

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.