Recent work in k-t BLAST and undersampled projection angiography has emphasized the value of using training data sets obtained during the acquisition of a series of images. These techniques have used iterative algorithms guided by the training set information to reconstruct time frames sampled at well below the Nyquist limit. We present here a simple non-iterative unfiltered backprojection algorithm that incorporates the idea of a composite image consisting of portions or all of the acquired data to constrain the backprojection process. This significantly reduces streak artifacts and increases the overall SNR, permitting decreased numbers of projections to be used when acquiring each image in the image time series. For undersampled 2D projection imaging applications, such as cine phase contrast (PC) angiography, our results suggest that the angular undersampling factor, relative to Nyquist requirements, can be increased from the present factor of 4 to about 100 while increasing SNR per individual time frame. Results are presented for a contrast-enhanced PR HYPR TRICKS acquisition in a volunteer using an angular undersampling factor of 75 and a TRICKS temporal undersampling factor of 3 for an overall undersampling factor of 225. There are many applications for which it is desirable to have high spatial and high temporal resolution. K-space sampling that obeys the Nyquist theorem usually precludes simultaneous achievement of these aims in MR imaging. Among other approaches, radial acquisitions have been proposed for accelerated sampling schemes. Peters (1) and Vigen (2) reported on the use of 3D MR angiography acquisitions in which 2 dimensions were encoded using undersampled projection reconstruction and the third was encoded using phase encoding. In these applications, the projections are rotated around a single axis and, even if the planes containing the projections are completely sampled in the Fourier encoded direction, the undersampling factor, relative to that required by the Nyquist theorem, is limited to about 6 due to the streaks in the axial reformatted images.When radial sampling is extended by distributing the projections in all directions in 3D as in VIPR (3), significantly higher acceleration factors relative to fully sampled acquisition can be achieved. We recently reported on a relatively artifact free PC VIPR (phase contrast Vastly undersampled Isotropic PRojection imaging) acquisition in which an acceleration factor of 61 relative to conventional Cartesian 3D PC was achieved (4). This acceleration factor was defined as the ratio of an imaging speed index for PC VIPR and Cartesian 3D PC acquisitions. This index was determined as the volume covered divided by the product of scan duration times voxel size.Despite such large increases in acquisition speed, some applications would benefit from further accelerations. For example, in recent cine PC VIPR measurements with 3D flow encoding for pressure mapping in 1-2 mm thick vessels using an acquisition matrix of 256 ϫ 256 ϫ 256 voxels and 10 cardiac ph...

MR parameter mapping requires sampling along additional (parametric) dimension, which often limits its clinical appeal due to a several-fold increase in scan times compared to conventional anatomic imaging. Data undersampling combined with parallel imaging is an attractive way to reduce scan time in such applications. However, inherent SNR penalties of parallel MRI due to noise amplification often limit its utility even at moderate acceleration factors, requiring regularization by prior knowledge. In this work, we propose a novel regularization strategy, which utilizes smoothness of signal evolution in the parametric dimension within compressed sensing framework (p-CS) to provide accurate and precise estimation of parametric maps from undersampled data. The performance of the method was demonstrated with variable flip angle T1 mapping and compared favorably to two representative reconstruction approaches, image space-based total variation regularization and an analytical model-based reconstruction. The proposed p-CS regularization was found to provide efficient suppression of noise amplification and preservation of parameter mapping accuracy without explicit utilization of analytical signal models. The developed method may facilitate acceleration of quantitative MRI techniques that are not suitable to model-based reconstruction because of complex signal models or when signal deviations from the expected analytical model exist.

We present MRiLab, a new comprehensive simulator for large-scale realistic MRI simulations on a regular PC equipped with a modern graphical processing unit (GPU). MRiLab combines realistic tissue modeling with numerical virtualization of an MRI system and scanning experiment to enable assessment of a broad range of MRI approaches including advanced quantitative MRI methods inferring microstructure on a sub-voxel level. A flexibl representation of tissue microstructure is achieved in MRiLab by employing the generalized tissue model with multiple exchanging water and macromolecular proton pools rather than a system of independent proton isochromats typically used in previous simulators. The computational power needed for simulation of the biologically relevant tissue models in large 3D objects is gained using parallelized execution on GPU. Three simulated and one actual MRI experiments were performed to demonstrate the ability of the new simulator to accommodate a wide variety of voxel composition scenarios and demonstrate detrimental effects of simplifie treatment of tissue micro-organization adapted in previous simulators. GPU execution allowed ∼200× improvement in computational speed over standard CPU. As a cross-platform, open-source, extensible environment for customizing virtual MRI experiments, MRiLab streamlines the development of new MRI methods, especially those aiming to infer quantitatively tissue composition and microstructure.

Dynamic MR imaging applications often require compromises in spatial and/or temporal resolution when standard reconstruction schemes are used. Acquisition windows are limited by the passage of contrast agents, as with hyperpolarized nuclei and contrast enhanced angiography, and/or clinical feasibility, as in 3D cine flow imaging. Recently, several alternative sampling and reconstruction methods have been introduced that explore data redundancies in such applications. These methods include model-based reconstructions (1-3) that rely on a priori information and compressed sensing methods (4,5), which aim to reduce the number of k-space points to represent a given object.Recently, HighlY constrained backPRojection (HYPR) (3) reconstruction has been used in conjunction with undersampled radial acquisitions to permit radial undersampling factors of up to 80 in 2D and 1000 in 3D (6 -8) in selected time-resolved applications in which the images are sparse and have a high degree of spatiotemporal correlation. Unlike other acceleration methods, where signalto-noise ratio (SNR) tends to decrease in proportion to the square root of the acceleration factor, HYPR maintains SNR from the composite image used to constrain the unfiltered backprojection process. While originally formulated for angiography, HYPR has been applied to a wide range of imaging methods including hyperpolarized gas imaging, cerebral diffusion, and cine phase contrast, all of which have temporal information that is spatially correlated.In the original HYPR method, a series of radial acquisitions with interleaved k-space projection sets is acquired. Using 1D discrete Fourier transform, we obtain image space profiles P t i , i ϭ 1…N p , where N p is the number of projections acquired at each timeframe. Each of these Radon projections is then normalized by the corresponding Radon projections P c i , i ϭ 1…N p , of the composite image I c that is reconstructed by conventional methods from the projections in several or all of the acquired timeframes. An unfiltered backprojection operator B is applied to each normalized projection. The average of all the backprojected information for each timeframe may be regarded as a weighting image I w . The individual timeframe weighting images provide dynamic information. The final HYPR images I H are obtained by multiplication of the individual timeframe weighting images with the composite image, and can be described as:In the limit of extremely sparse images or images with complete spatiotemporal correlation the HYPR algorithm provides near exact reconstruction. However, as the sparsity and spatiotemporal correlation deteriorate, there can be crosstalk of signals from different portions of the imaging volume. This crosstalk has generally forced the use of narrow sliding window composites to improve waveform fidelity. Since the sliding window composite has fewer projections, it has more artifact than a full-length composite would. A HYPR-based method presented here uses the concept of local reconstruction (HYPR LR) by con...

BACKGROUND AND PURPOSE We have developed PC HYPRFlow, a comprehensive MRA technique that includes a whole-brain CE dynamic series followed by PC velocity-encoding, yielding a time series of high-resolution morphologic angiograms with associated velocity information. In this study, we present velocity data acquired by using the PC component of PC HYPRFlow (PC-VIPR). MATERIALS AND METHODS Ten healthy volunteers (6 women, 4 men) were scanned by using PC HYPRFlow and 2D-PC imaging, immediately followed by velocity measurements by using TCD. Velocity measurements were made in the M1 segments of the MCAs from the PC-VIPR, 2D-PC, and TCD examinations. RESULTS PC-VIPR showed approximately 30% lower mean velocity compared with TCD, consistent with other comparisons of TCD with PC-MRA. The correlation with TCD was r = 0.793, and the correlation of PC-VIPR with 2D-PC was r = 0.723. CONCLUSIONS PC-VIPR is a technique capable of acquiring high-resolution MRA of diagnostic quality with velocity data comparable with TCD and 2D-PC. The combination of velocity information and fast high-resolution whole-brain morphologic angiograms makes PC HYPRFlow an attractive alternative to current MRA methods.