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BackgroundCardiovascular magnetic resonance (CMR) stress perfusion imaging provides important diagnostic and prognostic information in coronary artery disease (CAD). Current clinical sequences have limited temporal and/or spatial resolution, and incomplete heart coverage. Techniques such as k-t principal component analysis (PCA) or k-t sparcity and low rank structure (SLR), which rely on the high degree of spatiotemporal correlation in first-pass perfusion data, can significantly accelerate image acquisition mitigating these problems. However, in the presence of respiratory motion, these techniques can suffer from significant degradation of image quality. A number of techniques based on non-rigid registration have been developed. However, to first approximation, breathing motion predominantly results in rigid motion of the heart. To this end, a simple robust motion correction strategy is proposed for k-t accelerated and compressed sensing (CS) perfusion imaging.MethodsA simple respiratory motion compensation (MC) strategy for k-t accelerated and compressed-sensing CMR perfusion imaging to selectively correct respiratory motion of the heart was implemented based on linear k-space phase shifts derived from rigid motion registration of a region-of-interest (ROI) encompassing the heart. A variable density Poisson disk acquisition strategy was used to minimize coherent aliasing in the presence of respiratory motion, and images were reconstructed using k-t PCA and k-t SLR with or without motion correction. The strategy was evaluated in a CMR-extended cardiac torso digital (XCAT) phantom and in prospectively acquired first-pass perfusion studies in 12 subjects undergoing clinically ordered CMR studies. Phantom studies were assessed using the Structural Similarity Index (SSIM) and Root Mean Square Error (RMSE). In patient studies, image quality was scored in a blinded fashion by two experienced cardiologists.ResultsIn the phantom experiments, images reconstructed with the MC strategy had higher SSIM (p < 0.01) and lower RMSE (p < 0.01) in the presence of respiratory motion. For patient studies, the MC strategy improved k-t PCA and k-t SLR reconstruction image quality (p < 0.01). The performance of k-t SLR without motion correction demonstrated improved image quality as compared to k-t PCA in the setting of respiratory motion (p < 0.01), while with motion correction there is a trend of better performance in k-t SLR as compared with motion corrected k-t PCA.ConclusionsOur simple and robust rigid motion compensation strategy greatly reduces motion artifacts and improves image quality for standard k-t PCA and k-t SLR techniques in setting of respiratory motion due to imperfect breath-holding.

Purpose To develop a continuous‐acquisition cardiac self‐gated spiral pulse sequence and a respiratory motion‐compensated reconstruction strategy for free‐breathing cine imaging. Methods Cine data were acquired continuously on a 3T scanner for 8 seconds per slice without ECG gating or breath‐holding, using a golden‐angle gradient echo spiral pulse sequence. Cardiac motion information was extracted by applying principal component analysis on the gridded 8 × 8 k‐space center data. Respiratory motion was corrected by rigid registration on each heartbeat. Images were reconstructed using a low‐rank and sparse (L+S) technique. This strategy was evaluated in 37 healthy subjects and 8 subjects undergoing clinical cardiac MR studies. Image quality was scored (1–5 scale) in a blinded fashion by 2 experienced cardiologists. In 13 subjects with whole‐heart coverage, left ventricular ejection fraction (LVEF) from SPiral Acquisition with Respiratory correction and Cardiac Self‐gating (SPARCS) was compared to that from a standard ECG‐gated breath‐hold balanced steady‐state free precession (bSSFP) cine sequence. Results The self‐gated signal was successfully extracted in all cases and demonstrated close agreement with the acquired ECG signal (mean bias, –0.22 ms). The mean image score across all subjects was 4.0 for reconstruction using the L+S model. There was good agreement between the LVEF derived from SPARCS and the gold‐standard bSSFP technique. Conclusion SPARCS successfully images cardiac function without the need for ECG gating or breath‐holding. With an 8‐second data acquisition per slice, whole‐heart cine images with clinically acceptable spatial and temporal resolution and image quality can be acquired in <90 seconds of free‐breathing acquisition.

Vehicular networks are being developed for efficient broadcast of safety alerts, real-time traffic congestion probing and for distribution of on-road multimedia content. In order to investigate vehicular networking protocols and evaluate the effects of incremental deployment it is essential to have a topology-aware simulation and test-bed infrastructure. While several traffic simulators have been developed under the Intelligent Transport System initiative, their primary motivation has been to model and forecast vehicle traffic flow and congestion from a queuing perspective. GrooveNet is a hybrid simulator which enables communication between simulated vehicles, real vehicles and between real and simulated vehicles. By modeling inter-vehicular communication within a real street map-based topography it facilitates protocol design and also in-vehicle deployment. GrooveNet's modular architecture incorporates mobility, trip and message broadcast models over a variety of link and physical layer communication models. It is easy to run simulations of thousands of vehicles in any US city and to add new models for networking, security, applications and vehicle interaction. GrooveNet supports multiple network interfaces, GPS and events triggered from the vehicle's on-board computer. Through simulation, we are able to study the message latency, and coverage under various traffic conditions. On-road tests over 400 miles lend insight to required market penetration. ©2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Abstract-Vehicular networks are being developed for efficient broadcast of safety alerts, real-time traffic congestion probing and for distribution of on-road multimedia content. In order to investigate vehicular networking protocols and evaluate the effects of incremental deployment it is essential to have a topology-aware simulation and test-bed infrastructure. While several traffic simulators have been developed under the Intelligent Transport System initiative, their primary motivation has been to model and forecast vehicle traffic flow and congestion from a queuing perspective. GrooveNet is a hybrid simulator which enables communication between simulated vehicles, real vehicles and between real and simulated vehicles. By modeling inter-vehicular communication within a real street map-based topography it facilitates protocol design and also in-vehicle deployment. GrooveNet's modular architecture incorporates mobility, trip and message broadcast models over a variety of link and physical layer communication models. It is easy to run simulations of thousands of vehicles in any US city and to add new models for networking, security, applications and vehicle interaction. GrooveNet supports multiple network interfaces, GPS and ev...

AbstractWe consider the following problem: Given a proof of the Skolemization of a formulaF, what is the length of the shortest proof ofF? For the restriction of this question to cut-free proofs we prove corresponding exponential upper and lower bounds.

To develop a free-breathing cardiac self-gated technique that provides cine images and B + 1 slice profile-corrected T 1 maps from a single acquisition. Methods: Without breath-holding or electrocardiogram gating, data were acquired continuously on a 3T scanner using a golden-angle gradient-echo spiral pulse sequence, with an inversion RF pulse applied every 4 seconds. Flip angles of 3° and 15° were used for readouts after the first four and second four inversions. Self-gating cardiac triggers were extracted from heart image navigators, and respiratory motion was corrected by rigid registration on each heartbeat. Cine images were reconstructed from the steady-state portion of 15° readouts using a low-rank plus sparse reconstruction. The T 1 maps were fit using a projection onto convex sets approach from images reconstructed using slice profile-corrected dictionary learning. This strategy was evaluated in a phantom and 14 human subjects. Results: The self-gated signal demonstrated close agreement with the acquired electrocardiogram signal. The image quality for the proposed cine images and standard clinical balanced SSFP images were 4.31 (±0.50) and 4.65 (±0.30), respectively. The slice profile-corrected T 1 values were similar to those of the inversion-recovery spinecho technique in phantom, and had a higher global T 1 value than that of MOLLI in human subjects. Conclusions: Cine and T 1 mapping using spiral acquisition with respiratory and cardiac self-gating successfully acquired T 1 maps and cine images in a single acquisition without the need for electrocardiogram gating or breath-holding. This dual-excitation flip-angle approach provides a novel approach for measuring T 1 while accounting for B + 1 and slice profile effect on the apparent T * 1. K E Y W O R D S cardiac MRI, cine, dictionary learning, self-gating, spiral trajectory, T 1 mapping | 83 ZHOU et al.

SPIRiT (iterative self-consistent parallel imaging reconstruction), and its sparsity-regularized variant L1-SPIRiT, are compatible with both Cartesian and non-Cartesian MRI sampling trajectories. However, the non-Cartesian framework is more expensive computationally, involving a nonuniform Fourier transform with a nontrivial Gram matrix. We propose a novel implementation of the regularized reconstruction problem using variable splitting, alternating minimization of the augmented La-grangian, and careful preconditioning. Our new method based on the alternating direction method of multipliers converges much faster than existing methods because of the preconditioners' heightened effectiveness. We demonstrate such rapid convergence substantially improves image quality for a fixed computation time. Our framework is a step forward towards rapid non-Cartesian L1-SPIRiT reconstructions.

Purpose Regularizing parallel MRI reconstruction significantly improves image quality but requires tuning parameter selection. We propose a Monte Carlo method for automatic parameter selection based on Stein’s unbiased risk estimate (SURE) that minimizes the multi-channel k-space mean squared error (MSE). We automatically tune parameters for image reconstruction methods that preserve the undersampled acquired data, which cannot be accomplished using existing techniques. Theory We derive a weighted MSE criterion appropriate for data-preserving regularized parallel imaging reconstruction and the corresponding weighted SURE. We describe a Monte Carlo approximation of the weighted SURE that uses two evaluations of the reconstruction method per candidate parameter value. Methods We reconstruct images using the sparsity-promoting methods DESIGN and L1-SPIRiT. We validate Monte Carlo SURE against the weighted MSE. We select the regularization parameter using these methods for various noise levels and undersampling factors and compare the results to those using MSE-optimal parameters. Results Our method selects nearly MSE-optimal regularization parameters for both DESIGN and L1-SPIRiT over a range of noise levels and undersampling factors. Conclusion The proposed method automatically provides nearly MSE-optimal choices of regularization parameters for data-preserving nonlinear parallel MRI reconstruction methods.