Niloufar Zarinabad

Perfusion phantom : an efficient and reproducible method to simulate myocardial first-pass perfusion measurements with cardiovascular magnetic resonance

The aim of this article is to describe a novel hardware perfusion phantom that simulates myocardial first‐pass perfusion allowing comparisons between different MR techniques and validation of the results against a true gold standard. MR perfusion images were acquired at different myocardial perfusion rates and variable doses of gadolinium and cardiac output. The system proved to be sensitive to controlled variations of myocardial perfusion rate, contrast agent dose, and cardiac output. It produced distinct signal intensity curves for perfusion rates ranging from 1 to 10 mL/mL/min. Quantification of myocardial blood flow by signal deconvolution techniques provided accurate measurements of perfusion. The phantom also proved to be very reproducible between different sessions and different operators. This novel hardware perfusion phantom system allows reliable, reproducible, and efficient simulation of myocardial first‐pass MR perfusion. Direct comparison between the results of image‐based quantification and reference values of flow and myocardial perfusion will allow development and validation of accurate quantification methods. Magn Reson Med, 2013.

Voxel‐wise quantification of myocardial perfusion by cardiac magnetic resonance. Feasibility and methods comparison

The purpose of this study is to enable high spatial resolution voxel‐wise quantitative analysis of myocardial perfusion in dynamic contrast‐enhanced cardiovascular MR, in particular by finding the most favorable quantification algorithm in this context. Four deconvolution algorithms—Fermi function modeling, deconvolution using B‐spline basis, deconvolution using exponential basis, and autoregressive moving average modeling —were tested to calculate voxel‐wise perfusion estimates. The algorithms were developed on synthetic data and validated against a true gold‐standard using a hardware perfusion phantom. The accuracy of each method was assessed for different levels of spatial averaging and perfusion rate. Finally, voxel‐wise analysis was used to generate high resolution perfusion maps on real data acquired from five patients with suspected coronary artery disease and two healthy volunteers. On both synthetic and perfusion phantom data, the B‐spline method had the highest error in estimation of myocardial blood flow. The autoregressive moving average modeling and exponential methods gave accurate estimates of myocardial blood flow. The Fermi model was the most robust method to noise. Both simulations and maps in the patients and hardware phantom showed that voxel‐wise quantification of myocardium perfusion is feasible and can be used to detect abnormal regions. Magn Reson Med, 2012.

Myocardial Blood Flow Quantification From MRI by Deconvolution Using an Exponential Approximation Basis

We have evaluated the use of deconvolution using an exponential approximation basis for the quantification of myocardial blood flow from perfusion cardiovascular magnetic resonance. Our experiments, based on simulated signal intensity curves, phantom acquisitions, and clinical image data, indicate that exponential deconvolution allows for accurate quantification of myocardial blood flow. Together with automated respiratory motion correction myocardial contour delineation, the exponential deconvolution enables efficient and reproducible quantification of myocardial blood flow in clinical routine.

Quantitative assessment of magnetic resonance derived myocardial perfusion measurements using advanced techniques: microsphere validation in an explanted pig heart system

BackgroundCardiovascular Magnetic Resonance (CMR) myocardial perfusion imaging has the potential to evolve into a method allowing full quantification of myocardial blood flow (MBF) in clinical routine. Multiple quantification pathways have been proposed. However at present it remains unclear which algorithm is the most accurate. An isolated perfused, magnetic resonance (MR) compatible pig heart model allows very accurate titration of MBF and in combination with high-resolution assessment of fluorescently-labeled microspheres represents a near optimal platform for validation. We sought to investigate which algorithm is most suited to quantify myocardial perfusion by CMR at 1.5 and 3 Tesla using state of the art CMR perfusion techniques and quantification algorithms.MethodsFirst-pass perfusion CMR was performed in an MR compatible blood perfused pig heart model. We acquired perfusion images at physiological flow (“rest”), reduced flow (“ischaemia”) and during adenosine-induced hyperaemia (“hyperaemia”) as well as during coronary occlusion. Perfusion CMR was performed at 1.5 Tesla (n = 4 animals) and at 3 Tesla (n = 4 animals). Fluorescently-labeled microspheres and externally controlled coronary blood flow served as reference standards for comparison of different quantification strategies, namely Fermi function deconvolution (Fermi), autoregressive moving average modelling (ARMA), exponential basis deconvolution (Exponential) and B-spline basis deconvolution (B-spline).ResultsAll CMR derived MBF estimates significantly correlated with microsphere results. The best correlation was achieved with Fermi function deconvolution both at 1.5 Tesla (r = 0.93, p < 0.001) and at 3 Tesla (r = 0.9, p < 0.001). Fermi correlated significantly better with the microspheres than all other methods at 3 Tesla (p < 0.002). B-spline performed worse than Fermi and Exponential at 1.5 Tesla and showed the weakest correlation to microspheres (r = 0.74, p < 0.001). All other comparisons were not significant. At 3 Tesla exponential deconvolution performed worst (r = 0.49, p < 0.001).ConclusionsCMR derived quantitative blood flow estimates correlate with true myocardial blood flow in a controlled animal model. Amongst the different techniques, Fermi function deconvolution was the most accurate technique at both field strengths. Perfusion CMR based on Fermi function deconvolution may therefore emerge as a useful clinical tool providing accurate quantitative blood flow assessment.

Modelling parameter role on accuracy of cardiac perfusion quantification

Cardiovascular magnetic resonance (CMR) perfusion data are suitable for quantitative measurement of myocardial blood flow. The goal of perfusion-CMR post- processing is to recover tissue impulse-response from observed signal-intensity curves. While several deconvolution techniques are available for this purpose, all of them use models with varying parameters for the representation of the impulse-response. However this variation influences the accuracy of the deconvolution and introduces possible variations in the results. Using an appropriate order for quantification is essential to allow CMR-perfusion-quantification to develop into a useful clinical tool. The aim of this study was to evaluate the effect of parameter variation in Fermi modelling, autoregressive moving-average model (ARMA), B-spline-basis and exponential-basis deconvolution. Whilst Fermi is the least dependent method on the modelling parameter determination, the B-spline and ARMA were the most sensitive models to this variation. ARMA upon a correct choice of order showed to be the superior to other methods.

Effects of tracer arrival time on the accuracy of high-resolution (voxel-wise) myocardial perfusion maps from contrast-enhanced first-pass perfusion magnetic resonance

First-pass perfusion cardiac magnetic resonance (CMR) allows the quantitative assessment of myocardial blood flow (MBF). However, flow estimates are sensitive to the delay between the arterial and myocardial tissue tracer arrival time (tOnset) and the accurate estimation of MBF relies on the precise identification of tOnset. The aim of this study is to assess the sensitivity of the quantification process to tOnset at voxel level. Perfusion data were obtained from series of simulated data, a hardware perfusion phantom, and patients. Fermi deconvolution has been used for analysis. A novel algorithm, based on sequential deconvolution, which minimizes the error between myocardial curves and fitted curves obtained after deconvolution, has been used to identify the optimal tOnset for each region. Voxel-wise analysis showed to be more sensitive to tOnset compared to segmental analysis. The automated detection of the tOnset allowed a net improvement of the accuracy of MBF quantification and in patients the identification of perfusion abnormalities in territories that were missed when a constant user-selected tOnset was used. Our results indicate that high-resolution MBF quantification should be performed with optimized tOnset values at voxel level.

Myocardial Blood Flow Quantification from MRI - an Image Analysis Perspective

First-pass perfusion imaging allows for a very high spatial resolution, noninvasive and radiation free quantification of myocardial blood flow. True quantification of perfusion images offers a unique capability to localize and measure subendocardial ischemia. Several techniques such as semiquantitative, model independent and model dependent methods are available for calculating MBF from perfusion CMR. However, for accurate perfusion quantification a few requirements need to be addressed beforehand. These include but are not limited to the relationship between the amount of injected contrast agent and the signal intensity of the MR image, used pulse sequence, and the extraction of an arterial input function. Moreover, with the new advances in CMR perfusion imaging, high spatial resolution voxel-wise quantitative analysis of myocardial perfusion is feasible. Voxel-wise quantification has the potential to combine the advantage of visual analysis with the objective and reproducible evaluation made by true quantitative methods.

Effect of tracer arrival time on the estimation of the myocardial perfusion in DCE-CMR

Background An accurate segmental myocardial blood flow (MBF) quantification can be performed by means of signal deconvolution techniques. The accuracy of MBF estimates relies on the precise identification of the tracer arrival time in the myocardium (tOnset). Voxelwise MBF quantification is likel yt o be more subject to such error than segmental MBF analysis due to the lower signal-to-noise ratio of myocardial signal intensity curves. Automated tOnset detection methods would be therefore warranted. The aim of this study was to assess the importance to of tOnset identification on voxelwise MBF quantification and to describe an automated algorithm to detect the optimal tOnset which minimizes the error in MBF estimates. Methods Perfusion data were obtained from an hardware perfusion phantom (validated MBF 5 ml/g/min) and from patients during adenosine-induced hyperaemia (140µg/ kg/min) using 0.075mmol/kg Gadobutrol (Gadovist, Schering, Germany) injected at 4ml/minute followed by a 20 ml saline flush. A pre-bolus technique was used for quantification.

Multiclass imbalance learning: Improving classification of pediatric brain tumors from magnetic resonance spectroscopy: Imbalanced learning for MRS tumor classification

Classification of pediatric brain tumors from 1H‐magnetic resonance spectroscopy (MRS) can aid diagnosis and management of brain tumors. However, varied incidence of the different tumor types leads to imbalanced class sizes and introduces difficulties in classifying rare tumor groups. This study assessed different imbalanced multiclass learning techniques and compared the use of complete spectra and quantified metabolite profiles for classification of three main childhood brain tumor types.

Multiclass imbalance learning: Improving classification of pediatric brain tumors from magnetic resonance spectroscopy.

Classification of pediatric brain tumors from 1H‐magnetic resonance spectroscopy (MRS) can aid diagnosis and management of brain tumors. However, varied incidence of the different tumor types leads to imbalanced class sizes and introduces difficulties in classifying rare tumor groups. This study assessed different imbalanced multiclass learning techniques and compared the use of complete spectra and quantified metabolite profiles for classification of three main childhood brain tumor types.