Alexey A. Samsonov

Characterization of Cerebral White Matter Properties Using Quantitative Magnetic Resonance Imaging Stains

The image contrast in magnetic resonance imaging (MRI) is highly sensitive to several mechanisms that are modulated by the properties of the tissue environment. The degree and type of contrast weighting may be viewed as image filters that accentuate specific tissue properties. Maps of quantitative measures of these mechanisms, akin to microstructural/environmental-specific tissue stains, may be generated to characterize the MRI and physiological properties of biological tissues. In this article, three quantitative MRI (qMRI) methods for characterizing white matter (WM) microstructural properties are reviewed. All of these measures measure complementary aspects of how water interacts with the tissue environment. Diffusion MRI, including diffusion tensor imaging, characterizes the diffusion of water in the tissues and is sensitive to the microstructural density, spacing, and orientational organization of tissue membranes, including myelin. Magnetization transfer imaging characterizes the amount and degree of magnetization exchange between free water and macromolecules like proteins found in the myelin bilayers. Relaxometry measures the MRI relaxation constants T1 and T2, which in WM have a component associated with the water trapped in the myelin bilayers. The conduction of signals between distant brain regions occurs primarily through myelinated WM tracts; thus, these methods are potential indicators of pathology and structural connectivity in the brain. This article provides an overview of the qMRI stain mechanisms, acquisition and analysis strategies, and applications for these qMRI stains.

Independent estimation of T*2 for water and fat for improved accuracy of fat quantification

Noninvasive biomarkers of intracellular accumulation of fat within the liver (hepatic steatosis) are urgently needed for detection and quantitative grading of nonalcoholic fatty liver disease, the most common cause of chronic liver disease in the United States. Accurate quantification of fat with MRI is challenging due the presence of several confounding factors, including T*2 decay. The specific purpose of this work is to quantify the impact of T*2 decay and develop a multiexponential T*2 correction method for improved accuracy of fat quantification, relaxing assumptions made by previous T*2 correction methods. A modified Gauss‐Newton algorithm is used to estimate the T*2 for water and fat independently. Improved quantification of fat is demonstrated, with independent estimation of T*2 for water and fat using phantom experiments. The tradeoffs in algorithm stability and accuracy between multiexponential and single exponential techniques are discussed. Magn Reson Med 63:849–857, 2010.

Accelerating MR parameter mapping using sparsity-promoting regularization in parametric dimension

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 uses 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. Magn Reson Med 70:1263–1273, 2013.

PC HYPR flow: A technique for rapid imaging of contrast dynamics

To improve spatial and temporal resolution and signal‐to‐noise ratio (SNR) in three‐dimensional (3D) radial contrast‐enhanced (CE) time‐resolved MR angiography by means of a novel hybrid phase contrast (PC) and CE MRA acquisition and HYPR reconstruction (PC HYPR Flow).

Advances in locally constrained k-space-based parallel MRI

In this article, several theoretical and methodological developments regarding k‐space‐based, locally constrained parallel MRI (pMRI) reconstruction are presented. A connection between Parallel MRI with Adaptive Radius in k‐Space (PARS) and GRAPPA methods is demonstrated. The analysis provides a basis for unified treatment of both methods. Additionally, a weighted PARS reconstruction is proposed, which may absorb different weighting strategies for improved image reconstruction. Next, a fast and efficient method for pMRI reconstruction of data sampled on non‐Cartesian trajectories is described. In the new technique, the computational burden associated with the numerous matrix inversions in the original PARS method is drastically reduced by limiting direct calculation of reconstruction coefficients to only a few reference points. The rest of the coefficients are found by interpolating between the reference sets, which is possible due to the similar configuration of points participating in reconstruction for highly symmetric trajectories, such as radial and spirals. As a result, the time requirements are drastically reduced, which makes it practical to use pMRI with non‐Cartesian trajectories in many applications. The new technique was demonstrated with simulated and actual data sampled on radial trajectories. Magn Reson Med, 2006.

Characterizing and correcting gradient errors in non‐cartesian imaging: Are gradient errors linear time‐invariant (LTI)?

Non‐Cartesian and rapid imaging sequences are more sensitive to scanner imperfections such as gradient delays and eddy currents. These imperfections vary between scanners and over time and can be a significant impediment to successful implementation and eventual adoption of non‐Cartesian techniques by scanner manufacturers. Differences between the k‐space trajectory desired and the trajectory actually acquired lead to misregistration and reduction in image quality. While early calibration methods required considerable scan time, more recent methods can work more quickly by making certain approximations. We examine a rapid gradient calibration procedure applied to multiecho three‐dimensional projection reconstruction (3DPR) acquisitions in which the calibration runs as part of every scan. After measuring the trajectories traversed for excitations on each of the orthogonal gradient axes, trajectories for the oblique projections actually acquired during the scan are synthesized as linear combinations of these measurements. The ability to do rapid calibration depends on the assumption that gradient errors are linear and time‐invariant (LTI). This work examines the validity of these assumptions and shows that the assumption of linearity is reasonable, but that gradient errors can vary over short time periods (due to changes in gradient coil temperature) and thus it is important to use calibration data matched to the scan data. Magn Reson Med, 2009.

On optimality of parallel MRI reconstruction in k‐space

Parallel MRI reconstruction in k‐space has several advantages, such as tolerance to calibration data errors and efficient non‐Cartesian data processing. These benefits largely accrue from the approximation that a given unsampled k‐space datum can be synthesized from only a few local samples. In this study, several aspects of parallel MRI reconstruction in k‐space are studied: the design of optimized reconstruction kernels, the effect of regularization on image error, and the accuracy of different k‐space–based parallel MRI methods. Reconstruction of parallel MRI data in k‐space is posed as the problem of approximating the pseudoinverse with a sparse matrix. The error of the approximation is used as an optimization criterion to find reconstruction kernels optimized for the given coil setup. An efficient algorithm for automatic selection of reconstruction kernels is described. Additionally, a total error metric is introduced for validation of the reconstruction kernel and choice of regularization parameters. The new methods yield reduced reconstruction and noise errors in both simulated and real data studies when compared with existing methods. The new methods may be useful for reduction of image errors, faster data processing, and validation of parallel MRI reconstruction design for a given coil system and k‐space trajectory. Magn Reson Med, 2007.

High b-value and diffusion tensor imaging in a canine model of dysmyelination and brain maturation

Recent studies in rodents have demonstrated that diffusion imaging is highly sensitive to differences in myelination. These studies suggest that demyelination/dysmyelination cause increases in the radial diffusivity from diffusion tensor imaging (DTI) measurements and decreases in the restricted diffusion component from high b-value diffusion-weighted imaging experiments. In this study, the shaking pup (sh pup), a canine model of dysmyelination, was studied on a clinical MRI scanner using a combination of conventional diffusion tensor imaging and high b-value diffusion-weighted imaging methods. Diffusion measurements were compared between control dogs and sh pups in the age range 3 months to 16 months, which is similar to the period of early childhood through adolescence in humans. The study revealed significant group differences in nearly all diffusion measures with the largest differences in the zero-displacement probability (Po) from high b-value DWI and the radial diffusivity from DTI, which are consistent with the observations from the published rodent studies. Age-related changes in Po, FA, mean diffusivity, radial diffusivity and axial diffusivity were observed in whole brain white matter for the control dogs, but not the sh pups. Regionally, age-related changes in the sh pup white matter were observed for Po, mean diffusivity and radial diffusivity in the internal capsule, which may be indicative of mild myelination. These studies demonstrate that DWI may be used to study myelin abnormalities and brain development in large animal models on clinical MRI scanners, which are more amenable to translation to human studies.

Self-calibrated GRAPPA method for 2D and 3D radial data

A fast parallel MRI (pMRI) reconstruction method is presented for 2D and 3D radial trajectories. A limitation of the radial generalized autocalibrating partially parallel acquisitions (GRAPPA) method is the need to acquire training data prior to the actual scan. This can be eliminated by the use of self‐calibration when synthesizing the missing data for each coil reconstruction. The training data for each coil are estimated by multiplying the conventionally reconstructed composite image, which contains the aliasing artifacts from undersampling, with each coils spatial sensitivity profile. An estimate of the individual receiver spatial sensitivity profiles is obtained from the k‐space data that fulfill the Nyquist sampling criterion. The frequency domain representation of the training data is then calculated at the acquired k‐space sample points and at the unacquired locations at which we desire to synthesize k‐space data. Fitting the acquired k‐space samples to the unacquired points creates reconstruction weights that are used to synthesize unacquired radial lines. The in vivo feasibility of the method for 2D radial trajectories is illustrated with an example of 2D abdominal imaging. Preliminary results obtained after applying the method on a 3D radial steady‐state free precession (SSFP) data set are also demonstrated. Magn Reson Med 57:931–938, 2007.

Improved least squares MR image reconstruction using estimates of k-Space data consistency

This study describes a new approach to reconstruct data that has been corrupted by unfavorable magnetization evolution. In this new framework, images are reconstructed in a weighted least squares fashion using all available data and a measure of consistency determined from the data itself. The reconstruction scheme optimally balances uncertainties from noise error with those from data inconsistency, is compatible with methods that model signal corruption, and may be advantageous for more accurate and precise reconstruction with many least squares‐based image estimation techniques including parallel imaging and constrained reconstruction/compressed sensing applications. Performance of the several variants of the algorithm tailored for fast spin echo and self‐gated respiratory gating applications was evaluated in simulations, phantom experiments, and in vivo scans. The data consistency weighting technique substantially improved image quality and reduced noise as compared to traditional reconstruction approaches. Magn Reson Med, 2011.