Magnetic resonance 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 (SVD), 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.
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 reconstructions times are reported.
Objectives Dynamic contrast enhanced (DCE) MRI exams of the kidneys provide quantitative information on renal perfusion and filtration. However, these exams are often difficult to implement because of respiratory motion and their need for a high spatiotemporal resolution and 3D coverage. Here, we present a free-breathing quantitative renal DCE MRI exam acquired with a highly accelerated stack-of-stars trajectory and reconstructed with 3D through-time radial GRAPPA, utilizing half and quarter doses of gadolinium contrast. Materials and Methods Data were acquired in ten asymptomatic volunteers using a stack-of-stars trajectory that was under sampled 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 s/frame) and spatial (approximately 2.2 mm3) resolution with full 3D coverage of both kidneys (350-370 mm2 × 79-92 mm). Images were successfully reconstructed with 3D through-time radial GRAPPA, and inter-frame respiratory motion was compensated by using an algorithm developed to automatically utilize images from multiple points of enhancement as references for registration. Quantitative pharmacokinetic analysis was performed using a separable dual compartment model. Results ROI pharmacokinetic analysis provided estimates (mean±std.dev.) of renal perfusion after half-dose: 218.1ml/min/100ml±57.1, plasma mean transit time: 4.8s±2.2, renal filtration: 28.7ml/min/100ml±10.0, and tubular mean transit time: 131.1s±60.2in 10 kidneys. ROI pharmacokinetic analysis provided estimates (mean±std.dev.) of renal perfusion after quarter-dose: 218.1ml/min/100ml±57.1, plasma mean transit time: 4.8s±2.2, renal filtration: 28.7ml/min/100ml±10.0, and tubular mean transit time: 131.1s±60.2 in 10 kidneys. 3D pixel wise parameter maps were also evaluated. Conclusion Highly under sampled data were successfully reconstructed with 3D through-time radial GRAPPA to achieve a high resolution 3D renal DCE MRI exam. The acquisition was completely free-breathing, and the images were registered to compensate for respiratory motion. This allowed for accurate high resolution 3D quantitative renal functional mapping of perfusion and filtration parameters.
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