Nathan A. Pack

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 (∼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. Magn Reson Med 64:125–137, 2010.

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

BackgroundModel-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.ResultsAn 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.ConclusionThis 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.

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

BackgroundQuantitative 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).MethodsPerfusion 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 flowsResultsThe 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).ConclusionsThe 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.

Quantification of myocardial perfusion MRI using radial data acquisition: comparison of Ktrans from dual-bolus and T1 estimation methods

Introduction Myocardial perfusion MRI is a useful modality to detect myocardial ischemia. Quantitative perfusion estimates require an accurate arterial input function (AIF). Recently, a method for estimating T1 and thus gadolinium concentration from a radial k-space perfusion sequence was proposed [1]. The method created four sub-images with differing effective saturation recovery times (eSRTs) from 96 ray acquisitions to estimate T1. No measures of truth were used to evaluate the method in vivo. In this work, we employ a similar technique for obtaining T1 estimates and compare to perfusion estimates from a dual-bolus method, a current standard for quantifying myocardial perfusion [2].

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 the systole using cine-gated nontagged MRI with strains measured from tagged MRI. Tagged and non-tagged cine images were obtained on a 1.5 T Siemens Avanto clinical scanner with a TrueFISP imaging sequence. The Hyperelastic Warping solution was evolved using a series of non-tagged images in 10 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 the FE model, and then Warping penalty forces were utilized to generate the final registration. Warping results for circumferential strain were correlated (R2 =0.59) with results obtain from tagged MR images analyzed with a HARP algorithm. Results for fiber stretch, LV twist, and transmural strain distribution were similar to values in the literature. Hyperelastic Warping represents a novel approach for quantifying 3-D regional strains within the myocardium with a high resolution.

A direct comparison of adenosine and regadenoson myocardial perfusion reserves measured by MRI

Methods 8 subjects (5 female, 3 male) without ischemia were imaged on a 3 T Siemens Trio system. Imaging was done first at rest, and then during adenosine infusion (140 ug/ kg/min) and 34 ± 4 minutes later with regadenoson injection (0.4 mg/5 ml). A 5 cc/sec injection of Gd-BOPTA (MultihanceTM) was used, with doses of 0.02, 0.03 and 0.03 mmol/kg, respectively. The contrast was injected ~3 minutes after the start of the adenosine infusion, and ~90 seconds after the regadenoson injection. A saturation recovery radial turboFLASH sequence was used with 72 rays acquired after each saturation pulse. Scan parameters were TR = 2.6 msec, TE = 1.14 msec, flip = 14, slice thickness = 8 mm. Reconstruction was performed by iteratively minimizing a cost function as in [1] with total variation constraints in both space and time dimensions. Processing was performed in a manner similar to [2] to convert the arterial input functions into gadolinium concentration to remove the effects of saturation. Images were registered and segmented to give time curves from 6 tissue regions per slice. The curves were fit to a two compartment model and Ktrans used as an index of perfusion.