Single breath-hold 3D cardiac T1 mapping using through-time spiral GRAPPA.

The quantification of cardiac T1 relaxation time holds great potential for the detection of various cardiac diseases. However, as a result of both cardiac and respiratory motion, only one two-dimensional T1 map can be acquired in one breath-hold with most current techniques, which limits its application for whole heart evaluation in routine clinical practice. In this study, an electrocardiogram (ECG)-triggered three-dimensional Look-Locker method was developed for cardiac T1 measurement. Fast three-dimensional data acquisition was achieved with a spoiled gradient-echo sequence in combination with a stack-of-spirals trajectory and through-time non-Cartesian generalized autocalibrating partially parallel acquisition (GRAPPA) acceleration. The effects of different magnetic resonance parameters on T1 quantification with the proposed technique were first examined by simulating data acquisition and T1 map reconstruction using Bloch equation simulations. Accuracy was evaluated in studies with both phantoms and healthy subjects. These results showed that there was close agreement between the proposed technique and the reference method for a large range of T1 values in phantom experiments. In vivo studies further demonstrated that rapid cardiac T1 mapping for 12 three-dimensional partitions (spatial resolution, 2 × 2 × 8 mm3 ) could be achieved in a single breath-hold of ~12 s. The mean T1 values of myocardial tissue and blood obtained from normal volunteers at 3 T were 1311 ± 66 and 1890 ± 159 ms, respectively. In conclusion, a three-dimensional T1 mapping technique was developed using a non-Cartesian parallel imaging method, which enables fast and accurate T1 mapping of cardiac tissues in a single short breath-hold.

Motion-Corrected Real-Time Cine Magnetic Resonance Imaging of the Heart: Initial Clinical Experience.

OBJECTIVES: Free-breathing real-time (RT) imaging can be used in patients with difficulty in breath-holding; however, RT cine imaging typically experiences poor image quality compared with segmented cine imaging because of low resolution. Here, we validate a novel unsupervised motion-corrected (MOCO) reconstruction technique for free-breathing RT cardiac images, called MOCO-RT. Motion-corrected RT uses elastic image registration to generate a single heartbeat of high-quality data from a free-breathing RT acquisition. MATERIALS AND METHODS: Segmented balanced steady-state free precession (bSSFP) cine images and free-breathing RT images (Cartesian, TGRAPPA factor 4) were acquired with the same spatial/temporal resolution in 40 patients using clinical 1.5 T magnetic resonance scanners. The respiratory cycle was estimated using the reconstructed RT images, and nonrigid unsupervised motion correction was applied to eliminate breathing motion. Conventional segmented RT and MOCO-RT single-heartbeat cine images were analyzed to evaluate left ventricular (LV) function and volume measurements. Two radiologists scored images for overall image quality, artifact, noise, and wall motion abnormalities. Intraclass correlation coefficient was used to assess the reliability of MOCO-RT measurement. RESULTS: Intraclass correlation coefficient showed excellent reliability (intraclass correlation coefficient ≥ 0.95) of MOCO-RT with segmented cine in measuring LV function, mass, and volume. Comparison of the qualitative ratings indicated comparable image quality for MOCO-RT (4.80 ± 0.35) with segmented cine (4.45 ± 0.88, P = 0.215) and significantly higher than conventional RT techniques (3.51 ± 0.41, P < 0.001). Artifact and noise ratings for MOCO-RT (1.11 ± 0.26 and 1.08 ± 0.19) and segmented cine (1.51 ± 0.90, P = 0.088 and 1.23 ± 0.45, P = 0.182) were not different. Wall motion abnormality ratings were comparable among different techniques (P = 0.96). CONCLUSIONS: The MOCO-RT technique can be used to process conventional free-breathing RT cine images and provides comparable quantitative assessment of LV function and volume measurements to conventional segmented cine imaging while providing improved image quality and less artifact and noise. The free-breathing MOCO-RT reconstruction method may have considerable clinical utility in cardiac magnetic resonance imaging for patients with difficulty breath-holding.

Real-time free-breathing cardiac imaging with self-calibrated through-time radial GRAPPA.

PURPOSE: Real-time free-breathing cardiac imaging with highly undersampled radial trajectories has previously been successfully demonstrated using calibrated radial generalized autocalibrating partially parallel acquisition (rGRAPPA). A self-calibrated approach for rGRAPPA is proposed that removes the need for the calibration prescan. METHODS: To investigate the effect of various parameters on image quality, a comprehensive imaging study on one normal swine was performed. Root mean squared errors (RMSEs) were computed with respect to gold standard acquisitions, and several acquisition/reconstruction strategies were compared. Additionally, the method was tested on 13 human subjects, and RMSEs relative to standard through-time radial GRAPPA were computed. RESULTS: Real-time images with high spatiotemporal resolution were obtained. Image quality was comparable to calibrated through-time rGRAPPA with endocardial and epicardial borders clearly delineated. In the swine, the average RMSE between self-calibrated and gold-standard calibrated images was 5.18 ± 0.84%. In normal human subjects, the average RMSE between self-calibrated and calibrated through-time rGRAPPA was 3.79 ± 0.64%. For lower accelerations rates (R = 6-8) image quality was similar to comparable calibrated scans though RMSE increased for higher degrees of undersampling (R = 12-16). CONCLUSION: Highly accelerated real-time imaging with undersampled radial trajectories without additional calibration data is feasible. Image quality was acceptable for real-time cardiac MRI applications demanding high speed. Magn Reson Med 77:250-264, 2017. © 2016 Wiley Periodicals, Inc.

SVD compression for magnetic resonance fingerprinting in the time domain.

Magnetic resonance (MR) fingerprinting is a technique for acquiring and processing MR data that simultaneously provides quantitative maps of different tissue parameters through a pattern recognition algorithm. A predefined dictionary models the possible signal evolutions simulated using the Bloch equations with different combinations of various MR parameters and pattern recognition is completed by computing the inner product between the observed signal and each of the predicted signals within the dictionary. Though this matching algorithm has been shown to accurately predict the MR parameters of interest, one desires a more efficient method to obtain the quantitative images. We propose to compress the dictionary using the singular value decomposition, which will provide a low-rank approximation. By compressing the size of the dictionary in the time domain, we are able to speed up the pattern recognition algorithm, by a factor of between 3.4-4.8, without sacrificing the high signal-to-noise ratio of the original scheme presented previously.

Quantitative high-resolution renal perfusion imaging using 3-dimensional through-time radial generalized autocalibrating partially parallel acquisition.

OBJECTIVES: Dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) examinations of the kidneys provide quantitative information on renal perfusion and filtration. However, these examinations are often difficult to implement because of respiratory motion and their need for a high spatiotemporal resolution and 3-dimensional coverage. Here, we present a free-breathing quantitative renal DCE-MRI examination acquired with a highly accelerated stack-of-stars trajectory and reconstructed with 3-dimensional (3D) through-time radial generalized autocalibrating partially parallel acquisition (GRAPPA), using half and quarter doses of gadolinium contrast. MATERIALS AND METHODS: Data were acquired in 10 asymptomatic volunteers using a stack-of-stars trajectory that was undersampled in-plane by a factor of 12.6 with respect to Nyquist sampling criterion and using partial Fourier of 6/8 in the partition direction. Data had a high temporal (2.1-2.9 seconds per frame) and spatial (approximately 2.2 mm) resolution with full 3D coverage of both kidneys (350-370 mm × 79-92 mm). Images were successfully reconstructed with 3D through-time radial GRAPPA, and interframe respiratory motion was compensated by using an algorithm developed to automatically use images from multiple points of enhancement as references for registration. Quantitative pharmacokinetic analysis was performed using a separable dual-compartment model. RESULTS: Region-of-interest (ROI) pharmacokinetic analysis provided estimates (mean (SD)) of quantitative renal parameters after a half dose: 218.1 (57.1) mL/min per 100 mL; plasma mean transit time, 4.8 (2.2) seconds; renal filtration, 28.7 (10.0) mL/min per 100 mL; and tubular mean transit time, 131.1 (60.2) seconds in 10 kidneys. The ROI pharmacokinetic analysis provided estimates (mean (SD)) of quantitative renal parameters after a quarter dose: 218.1 (57.1) mL/min per 100 mL; plasma mean transit time, 4.8 (2.2) seconds; renal filtration, 28.7 (10.0) mL/min per 100 mL; and tubular mean transit time, 131.1 (60.2) seconds in the 10 kidneys. Three-dimensional pixelwise parameter maps were also evaluated. CONCLUSIONS: Highly undersampled data were successfully reconstructed with 3D through-time radial GRAPPA to achieve a high-resolution 3-dimensional renal DCE-MRI examination. The acquisition was completely free breathing, and the images were registered to compensate for respiratory motion. This allowed for an accurate high-resolution 3D quantitative renal functional mapping of perfusion and filtration parameters.

Real-time imaging with radial GRAPPA: Implementation on a heterogeneous architecture for low-latency reconstructions.

Combination of non-Cartesian trajectories with parallel MRI permits to attain unmatched acceleration rates when compared to traditional Cartesian MRI during real-time imaging. However, computationally demanding reconstructions of such imaging techniques, such as k-space domain radial generalized auto-calibrating partially parallel acquisitions (radial GRAPPA) and image domain conjugate gradient sensitivity encoding (CG-SENSE), lead to longer reconstruction times and unacceptable latency for online real-time MRI on conventional computational hardware. Though CG-SENSE has been shown to work with low-latency using a general purpose graphics processing unit (GPU), to the best of our knowledge, no such effort has been made for radial GRAPPA. Radial GRAPPA reconstruction, which is robust even with highly undersampled acquisitions, is not iterative, requiring only significant computation during initial calibration while achieving good image quality for low-latency imaging applications. In this work, we present a very fast, low-latency, reconstruction framework based on a heterogeneous system using multi-core CPUs and GPUs. We demonstrate an implementation of radial GRAPPA that permits reconstruction times on par with or faster than acquisition of highly accelerated datasets in both cardiac and dynamic musculoskeletal imaging scenarios. Acquisition and reconstruction times are reported.

RT-GROG: parallelized self-calibrating GROG for real-time MRI.

A real-time implementation of self-calibrating Generalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) operator gridding for radial acquisitions is presented. Self-calibrating GRAPPA operator gridding is a parallel-imaging-based, parameter-free gridding algorithm, where coil sensitivity profiles are used to calculate gridding weights. Self-calibrating GRAPPA operator gridding's weight-set calculation and image reconstruction steps are decoupled into two distinct processes, implemented in C++ and parallelized. This decoupling allows the weights to be updated adaptively in the background while image reconstruction threads use the most recent gridding weights to grid and reconstruct images. All possible combinations of two-dimensional gridding weights G(x)(m)G(y)(n) are evaluated for m,n = {-0.5, -0.4, ..., 0, 0.1, ..., 0.5} and stored in a look-up table. Consequently, the per-sample two-dimensional weights calculation during gridding is eliminated from the reconstruction process and replaced by a simple look-up table access. In practice, up to 34x faster reconstruction than conventional (parallelized) self-calibrating GRAPPA operator gridding is achieved. On a 32-coil dataset of size 128 x 64, reconstruction performance is 14.5 frames per second (fps), while the data acquisition is 6.6 fps.

Visualization of active devices and automatic slice repositioning ("SnapTo") for MRI-guided interventions.

The accurate visualization of interventional devices is crucial for the safety and effectiveness of MRI-guided interventional procedures. In this paper, we introduce an improvement to the visualization of active devices. The key component is a fast, robust method ("CurveFind") that reconstructs the three-dimensional trajectory of the device from projection images in a fraction of a second. CurveFind is an iterative prediction-correction algorithm that acts on a product of orthogonal projection images. By varying step size and search direction, it is robust to signal inhomogeneities. At the touch of a key, the imaged slice is repositioned to contain the relevant section of the device ("SnapTo"), the curve of the device is plotted in a three-dimensional display, and the point on a target slice, which the device will intersect, is displayed. These features have been incorporated into a real-time MRI system. Experiments in vitro and in vivo (in a pig) have produced successful results using a variety of single- and multichannel devices designed to produce both spatially continuous and discrete signals. CurveFind is typically able to reconstruct the device curve, with an average error of approximately 2 mm, even in the case of complex geometries.

Interventional MRI using multiple 3D angiography roadmaps with real-time imaging.

PURPOSE: To enhance real-time magnetic resonance (MR)-guided catheter navigation by overlaying colorized multiphase MR angiography (MRA) and cholangiopancreatography (MRCP) roadmaps in an anatomic context. MATERIALS AND METHODS: Time-resolved MRA and respiratory-gated MRCP were acquired prior to real-time imaging in a pig model. MRA and MRCP data were loaded into a custom real-time MRI reconstruction and visualization workstation where they were displayed as maximum intensity projections (MIPs) in distinct colors. The MIPs were rendered in 3D together with real-time multislice imaging data using alpha blending. Interactive rotation allowed different views of the combined data. RESULTS: Fused display of the previously acquired MIP angiography data with real-time imaging added anatomical context during endovascular interventions in swine. The use of multiple MIPs rendered in different colors facilitated differentiation of vascular structures, improving visual feedback during device navigation. CONCLUSION: Interventional real-time MRI may be enhanced by combining with previously acquired multiphase angiograms. Rendered as 3D MIPs together with 2D slice data, this technique provided useful anatomical context that enhanced MRI-guided interventional applications.

HTGRAPPA: real-time B1-weighted image domain TGRAPPA reconstruction.

The temporal generalized autocalibrating partially parallel acquisitions (TGRAPPA) algorithm for parallel MRI was modified for real-time low latency imaging in interventional procedures using image domain, B(1)-weighted reconstruction. GRAPPA coefficients were calculated in k-space, but applied in the image domain after appropriate transformation. Convolution-like operations in k-space were thus avoided, resulting in improved reconstruction speed. Image domain GRAPPA weights were combined into composite unmixing coefficients using adaptive B(1)-map estimates and optimal noise weighting. Images were reconstructed by pixel-by-pixel multiplication in the image domain, rather than time-consuming convolution operations in k-space. Reconstruction and weight-set calculation computations were parallelized and implemented on a general-purpose multicore architecture. The weight calculation was performed asynchronously to the real-time image reconstruction using a dedicated parallel processing thread. The weight-set coefficients were computed in an adaptive manner with updates linked to changes in the imaging scan plane. In this implementation, reconstruction speed is not dependent on acceleration rate or GRAPPA kernel size.

Parallelized hybrid TGRAPPA reconstruction for real-time interactive MRI.

Real-time parallel MRI reconstruction was demonstrated using a hybrid implementation of the TGRAPPA algorithm. The GRAPPA coefficients were calculated in k-space and applied in the image domain after appropriate transformation, thereby achieving improved speed and excellent image quality. Adaptive B1-weighted combining of the per coil images permitted use of pre-calculated composite image domain weights providing significant decrease in computation. The weight calculation was decoupled from the real-time image reconstruction as a parallel processing thread which was updated in an adaptive manner to speed convergence in the event of interactive change in scan plane. The computation was parallelized and implemented on a general purpose multi-core architecture. Reconstruction speeds of 65-70 frames per second were achieved with a matrix of 192 x 144 with 15 coils.