Quantitative 3D myocardial perfusion with an efficient arterial input function.

PURPOSE: The purpose of this study was to further develop and combine several innovative sequence designs to achieve quantitative 3D myocardial perfusion. These developments include an optimized 3D stack-of-stars readout (150 ms per beat), efficient acquisition of a 2D arterial input function, tailored saturation pulse design, and potential whole heart coverage during quantitative stress perfusion. THEORY AND METHODS: All studies were performed free-breathing on a Prisma 3T MRI scanner. Phantom validation was used to verify sequence accuracy. A total of 21 subjects (3 patients with known disease) were scanned, 12 with a rest only protocol and 9 with both stress (regadenoson) and rest protocols. First pass quantitative perfusion was performed with gadoteridol (0.075 mmol/kg). RESULTS: Implementation and quantitative perfusion results are shown for healthy subjects and subjects with known coronary disease. Average rest perfusion for the 15 included healthy subjects was 0.79 ± 0.19 mL/g/min, the average stress perfusion for 6 healthy subject studies was 2.44 ± 0.61 mL/g/min, and the average global myocardial perfusion reserve ratio for 6 healthy subjects was 3.10 ± 0.24. Perfusion deficits for 3 patients with ischemia are shown. Average resting heart rate was 59 ± 7 bpm and the average stress heart rate was 81 ± 10 bpm. CONCLUSION: This work demonstrates that a quantitative 3D myocardial perfusion sequence with the acquisition of a 2D arterial input function is feasible at high stress heart rates such as during stress. T1 values and gadolinium concentrations of the sequence match the reference standard well in a phantom, and myocardial rest and stress perfusion and myocardial perfusion reserve values are consistent with those published in literature.

Interstudy repeatability of self-gated quantitative myocardial perfusion MRI.

PURPOSE: To evaluate the interstudy repeatability of multislice quantitative cardiovascular magnetic resonance myocardial blood flow (MBF), myocardial perfusion reserve (MPR), and extracellular volume (ECV). A unique saturation recovery self-gated acquisition was used for the perfusion scans. MATERIALS AND METHODS: An ungated golden angle radial turboFLASH pulse sequence was used to scan 10 subjects on two separate days on a 3T scanner. A single saturation pulse was followed by a set of four slices. Rest and hyperemia scans were acquired during free breathing. The images were reconstructed using an iterative algorithm with spatiotemporal constraints. The ungated images were retrospectively binned (self-gated) into near-systole and near-diastole. Deformable registration was performed to adjust for respiratory and residual cardiac motion, and the data were fit with a Fermi model to estimate the interstudy repeatability of quantitative self-gated MBF and MPR. RESULTS: The coefficient of variation (CoV) of the territorial MPR using the self-gated near-systole data was 18.6%. The self-gated near-diastole data gave less good CoV of MPR, equal to 46.2%. For MBFs, and using smaller (segmental) regions, the CoVs were 20.1% and 22.7% for the estimation of myocardial blood flow at stress and rest, respectively, using the self-gated near-systole data. The self-gated near-diastole data gave CoV = 48.6% and 44.9% for stress and rest. CONCLUSION: The self-gated free-breathing technique for quantification of myocardial blood flow showed good repeatability for near-systole, with results comparable to published studies on interstudy repeatability of quantitative myocardial perfusion MRI using ECG-gating and breath-holds. Self-gated near-diastole data results were less repeatable. J. Magn. Reson. Imaging 2016;43:1369-1378.

Quantification of myocardial perfusion with self-gated cardiovascular magnetic resonance.

BACKGROUND: Current myocardial perfusion measurements make use of an ECG-gated pulse sequence to track the uptake and washout of a gadolinium-based contrast agent. The use of a gated acquisition is a problem in situations with a poor ECG signal. Recently, an ungated perfusion acquisition was proposed but it is not known how accurately quantitative perfusion estimates can be made from such datasets that are acquired without any triggering signal. METHODS: An undersampled saturation recovery radial turboFLASH pulse sequence was used in 7 subjects to acquire dynamic contrast-enhanced images during free-breathing. A single saturation pulse was followed by acquisition of 4-5 slices after a delay of ~40 msec. This was repeated without pause and without any type of gating. The same pulse sequence, with ECG-gating, was used to acquire gated data as a ground truth. An iterative spatio-temporal constrained reconstruction was used to reconstruct the undersampled images. After reconstruction, the ungated images were retrospectively binned ("self-gated") into two cardiac phases using a region of interest based technique and deformably registered into near-systole and near-diastole. The gated and the self-gated datasets were then quantified with standard methods. RESULTS: Regional myocardial blood flow estimates (MBFs) obtained using self-gated systole (0.64 ± 0.26 ml/min/g), self-gated diastole (0.64 ± 0.26 ml/min/g), and ECG-gated scans (0.65 ± 0.28 ml/min/g) were similar. Based on the criteria for interchangeable methods listed in the statistical analysis section, the MBF values estimated from self-gated and gated methods were not significantly different. CONCLUSION: The self-gated technique for quantification of regional myocardial perfusion matched ECG-gated perfusion measurements well in normal subjects at rest. Self-gated systolic perfusion values matched ECG-gated perfusion values better than did diastolic values.

Estimating extraction fraction and blood flow by combining first-pass myocardial perfusion and T1 mapping results.

BACKGROUND: Quantifying myocardial perfusion is complicated by the complexity of pharmacokinetic model being used and the reliability of perfusion parameter estimates. More complex modeling provides more information about the underlying physiology, but too many parameters in complex models introduce a new problem of reliable estimation. To overcome the problem of multiple parameters, we have developed a technique that combines knowledge from two different cardiac magnetic resonance (MR) imaging techniques: dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and T1 mapping. Using extracellular volume (ECV) estimates from T1 mapping may allow more robust model parameter estimates. METHODS: Simulations and human scans were performed. The myocardial perfusion scans used an ungated saturation recovery prepared TurboFLASH pulse sequence. Four short-axis (SA) slices were acquired after a single saturation pulse with a saturation recovery time of ~25 ms before the first slice. Gadoteridol was injected and ~240 frames were acquired over a minute with shallow breathing and no electrocardiograph (ECG) gating. This was followed 20±5 minutes later by an injection of regadenoson to induce hyperemia. The data were acquired using an under-sampled golden angle radial acquisition. Modified look-locker inversion recovery (MOLLI) T1 mapping was performed in 3 slices pre- and post-contrast. The pre- and post-contrast T1 maps were used for ECV estimation. Quantification of perfusion was done using a 4-parameter model with additional information about ECV supplied during model fitting. Phase contrast scans of the coronary sinus (CS) were acquired at rest and immediately after the stress perfusion acquisition to estimate global flow. RESULTS: Without ECV information, the 5-parameter model fails to converge to a unique solution and often gives incorrect estimates for the perfusion parameters. The myocardial blood flow (MBF) estimates during rest and stress were 0.9±0.1 and 2.3±0.6 mL/min/g, respectively. The extraction fraction estimates were 0.49±0.04 and 0.34±0.05 during rest and stress, respectively. CONCLUSIONS: These results show that it is possible to successfully fit a dynamic perfusion model with an extraction fraction parameter by using information from T1 mapping scans. This hybrid approach is especially important when the 5-parameter model alone fails to converge on a unique solution. This work is a good example of exploiting information overlaps between various cardiac MR imaging techniques.

An Open Benchmark Challenge for Motion Correction of Myocardial Perfusion MRI.

Cardiac magnetic resonance perfusion examinations enable noninvasive quantification of myocardial blood flow. However, motion between frames due to breathing must be corrected for quantitative analysis. Although several methods have been proposed, there is a lack of widely available benchmarks to compare different algorithms. We sought to compare many algorithms from several groups in an open benchmark challenge. Nine clinical studies from two different centers comprising normal and diseased myocardium at both rest and stress were made available for this study. The primary validation measure was regional myocardial blood flow based on the transfer coefficient (Ktrans), which was computed using a compartment model and the myocardial perfusion reserve (MPR) index. The ground truth was calculated using contours drawn manually on all frames by a single observer, and visually inspected by a second observer. Six groups participated and 19 different motion correction algorithms were compared. Each method used one of three different motion models: rigid, global affine, or local deformation. The similarity metric also varied with methods employing either sum-of-squared differences, mutual information, or cross correlation. There were no significant differences in Ktrans or MPR compared across different motion models or similarity metrics. Compared with the ground truth, only Ktrans for the sum-of-squared differences metric, and for local deformation motion models, had significant bias. In conclusion, the open benchmark enabled evaluation of clinical perfusion indices over a wide range of methods. In particular, there was no benefit of nonrigid registration techniques over the other methods evaluated in this study. The benchmark data and results are available from the Cardiac Atlas Project ( www.cardiacatlas.org).

Technical Note: Evaluation of pre-reconstruction interpolation methods for iterative reconstruction of radial k-space data.

PURPOSE: To evaluate the use of three different pre-reconstruction interpolation methods to convert non-Cartesian k-space data to Cartesian samples such that iterative reconstructions can be performed more simply and more rapidly. METHODS: Phantom as well as cardiac perfusion radial datasets were reconstructed by four different methods. Three of the methods used pre-reconstruction interpolation once followed by a fast Fourier transform (FFT) at each iteration. The methods were: bilinear interpolation of nearest-neighbor points (BINN), 3-point interpolation, and a multi-coil interpolator called GRAPPA Operator Gridding (GROG). The fourth method performed a full non-Uniform FFT (NUFFT) at each iteration. An iterative reconstruction with spatiotemporal total variation constraints was used with each method. Differences in the images were quantified and compared. RESULTS: The GROG multicoil interpolation, the 3-point interpolation, and the NUFFT-at-each-iteration approaches produced high quality images compared to BINN, with the GROG-derived images having the fewest streaks among the three preinterpolation approaches. However, all reconstruction methods produced approximately equal results when applied to perfusion quantitation tasks. Pre-reconstruction interpolation gave approximately an 83% reduction in reconstruction time. CONCLUSION: Image quality suffers little from using a pre-reconstruction interpolation approach compared to the more accurate NUFFT-based approach. GROG-based pre-reconstruction interpolation appears to offer the best compromise by using multicoil information to perform the interpolation to Cartesian sample points prior to image reconstruction. Speed gains depend on the implementation and relatively standard optimizations on a MATLAB platform result in preinterpolation speedups of ~ 6 compared to using NUFFT at every iteration, reducing the reconstruction time from around 42 min to 7 min.

Split Bregman multicoil accelerated reconstruction technique: A new framework for rapid reconstruction of cardiac perfusion MRI.

PURPOSE: Rapid reconstruction of undersampled multicoil MRI data with iterative constrained reconstruction method is a challenge. The authors sought to develop a new substitution based variable splitting algorithm for faster reconstruction of multicoil cardiac perfusion MRI data. METHODS: The new method, split Bregman multicoil accelerated reconstruction technique (SMART), uses a combination of split Bregman based variable splitting and iterative reweighting techniques to achieve fast convergence. Total variation constraints are used along the spatial and temporal dimensions. The method is tested on nine ECG-gated dog perfusion datasets, acquired with a 30-ray golden ratio radial sampling pattern and ten ungated human perfusion datasets, acquired with a 24-ray golden ratio radial sampling pattern. Image quality and reconstruction speed are evaluated and compared to a gradient descent (GD) implementation and to multicoil k-t SLR, a reconstruction technique that uses a combination of sparsity and low rank constraints. RESULTS: Comparisons based on blur metric and visual inspection showed that SMART images had lower blur and better texture as compared to the GD implementation. On average, the GD based images had an ∼18% higher blur metric as compared to SMART images. Reconstruction of dynamic contrast enhanced (DCE) cardiac perfusion images using the SMART method was ∼6 times faster than standard gradient descent methods. k-t SLR and SMART produced images with comparable image quality, though SMART was ∼6.8 times faster than k-t SLR. CONCLUSIONS: The SMART method is a promising approach to reconstruct good quality multicoil images from undersampled DCE cardiac perfusion data rapidly.