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Existing parallel MRI methods are limited by a fundamental trade-off in that suppressing noise introduces aliasing artifacts. Bayesian methods with an appropriately chosen image prior offer a promising alternative; however, previous methods with spatial priors assume that intensities vary smoothly over the entire image, resulting in blurred edges. Here we introduce an edge-preserving prior (EPP) that instead assumes that intensities are piecewise smooth, and propose a new approach to efficiently compute its Bayesian estimate. The estimation task is formulated as an optimization problem that requires a non-convex objective function to be minimized in a space with thousands of dimensions. As a result, traditional continuous minimization methods cannot be applied. This optimization task is closely related to some problems in the field of computer vision for which discrete optimization methods have been developed in the last few years. We adapt these algorithms, which are based on graph cuts, to address our optimization problem. The results of several parallel imaging experiments on brain and torso regions performed under challenging conditions with high acceleration factors are shown and compared with the results of conventional sensitivity encoding (SENSE) methods. An empirical analysis indicates that the proposed method visually improves overall quality compared to conventional methods.

Short echo time proton MR Spectroscopic Imaging (MRSI) suffers from low signal-to-noise ratio (SNR), limiting accuracy to estimate metabolite intensities. A method to coherently sum spectra in a region of interest of the human brain by appropriate peak alignment was developed to yield a mean spectrum with increased SNR. Furthermore, principal component (PC) spectra were calculated to estimate the variance of the mean spectrum. The mean or alternatively the first PC (PC1) spectrum from the same region can be used for quantitation of peak areas of metabolites in the human brain at increased SNR. Monte Carlo simulations showed that both mean and PC1 spectra were more accurate in estimating regional metabolite concentrations than solutions that regress individual spectra against the tissue compositions of MRSI voxels. Back-to-back MRSI studies on 10 healthy volunteers showed that mean spectra markedly improved reliability of brain metabolite measurements, most notably for myo-inositol, as compared to regression methods. Magn Reson Med, 2006. Published 2006 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

Purpose: Establishing a framework to evaluate performances of prospective motion correction (PMC) MRI considering motion variability between MRI scans., Methods: A framework was developed to obtain quantitative comparisons between different motion correction setups, considering that varying intrinsic motion patterns between acquisitions can induce bias. Intrinsic motion was considered by replaying in a phantom experiment the recorded motion trajectories from subjects. T1-weighted MRI on five volunteers and two different marker fixations (mouth guard and nose bridge fixations) were used to test the framework. Two metrics were investigated to quantify the improvement of the image quality with PMC., Results: Motion patterns vary between subjects as well as between repeated scans within a subject. This variability can be approximated by replaying the motion in a distinct phantom experiment and used as a covariate in models comparing motion corrections. We show that considering the intrinsic motion alters the statistical significance in comparing marker fixations. As an example, two marker fixations, a mouth guard and a nose bridge, were evaluated in terms of their effectiveness for PMC. A mouth guard achieved better PMC performance., Conclusion: Intrinsic motion patterns can bias comparisons between PMC configurations and must be considered for robust evaluations. A framework for evaluating intrinsic motion patterns in PMC is presented., (© 2015 Wiley Periodicals, Inc.)

Purpose: To accelerate denoising of magnitude diffusion-weighted images subject to joint rank and edge constraints., Methods: We extend a previously proposed majorize-minimize method for statistical estimation that involves noncentral χ distributions to incorporate joint rank and edge constraints. A new algorithm is derived which decomposes the constrained noncentral χ denoising problem into a series of constrained Gaussian denoising problems each of which is then solved using an efficient alternating minimization scheme., Results: The performance of the proposed algorithm has been evaluated using both simulated and experimental data. Results from simulations based on ex vivo data show that the new algorithm achieves about a factor of 10 speed up over the original Quasi-Newton-based algorithm. This improvement in computational efficiency enabled denoising of large datasets containing many diffusion-encoding directions. The denoising performance of the new efficient algorithm is found to be comparable to or even better than that of the original slow algorithm. For an in vivo high-resolution Q-ball acquisition, comparison of fiber tracking results around hippocampus region before and after denoising will also be shown to demonstrate the denoising effects of the new algorithm., Conclusion: The optimization problem associated with denoising noncentral χ distributed diffusion-weighted images subject to joint rank and edge constraints can be solved efficiently using a majorize-minimize-based algorithm., (© 2015 Wiley Periodicals, Inc.)

Purpose: MRI is used to obtain quantitative oxygenation and blood volume information from the susceptibility-related MR signal dephasing induced by blood vessels. However, analytical models that fit the MR signal are usually not accurate over the range of small blood vessels. Moreover, recent studies have demonstrated limitations in the simultaneous assessment of oxygenation and blood volume. In this study, a multiparametric MRI framework that aims to measure vessel radii in addition to magnetic susceptibility and volume fraction was introduced., Methods: The protocol consisted of gradient-echo sampling of the spin-echo, diffusion, T2, and B0 acquisitions. After correction steps, the data were postprocessed with a versatile numerical model of the MR signal. An important analytical model was implemented for comparison. The approach was validated in phantoms with coiling strings as proxy for blood vessels., Results: The feasibility of the vessel radius measurement is demonstrated. The numerical model shows an improved accuracy compared with the analytical approach. However, both methods overestimate the radius. The simultaneous measurement of the magnetic susceptibility and the volume fraction remains challenging., Conclusion: The results suggest that this approach could be interesting in vivo to better characterize the microvasculature without contrast agent., (Copyright © 2013 Wiley Periodicals, Inc.)

Purpose: To improve signal-to-noise ratio for diffusion-weighted magnetic resonance images., Methods: A new method is proposed for denoising diffusion-weighted magnitude images. The proposed method formulates the denoising problem as an maximum a posteriori} estimation problem based on Rician/noncentral χ likelihood models, incorporating an edge prior and a low-rank model. The resulting optimization problem is solved efficiently using a half-quadratic method with an alternating minimization scheme., Results: The performance of the proposed method has been validated using simulated and experimental data. Diffusion-weighted images and noisy data were simulated based on the diffusion tensor imaging model and Rician/noncentral χ distributions. The simulation study (with known gold standard) shows substantial improvements in single-to-noise ratio and diffusion tensor estimation after denoising. In vivo diffusion imaging data at different b-values were acquired. Based on the experimental data, qualitative improvement in image quality and quantitative improvement in diffusion tensor estimation were demonstrated. Additionally, the proposed method is shown to outperform one of the state-of-the-art nonlocal means-based denoising algorithms, both qualitatively and quantitatively., Conclusion: The single-to-noise ratio of diffusion-weighted images can be effectively improved with rank and edge constraints, resulting in an improvement in diffusion parameter estimation accuracy., (Copyright © 2013 Wiley Periodicals, Inc.)

Quantitative diffusion imaging is a powerful technique for the characterization of complex tissue microarchitecture. However, long acquisition times and limited signal-to-noise ratio represent significant hurdles for many in vivo applications. This article presents a new approach to reduce noise while largely maintaining resolution in diffusion weighted images, using a statistical reconstruction method that takes advantage of the high level of structural correlation observed in typical datasets. Compared to existing denoising methods, the proposed method performs reconstruction directly from the measured complex k-space data, allowing for gaussian noise modeling and theoretical characterizations of the resolution and signal-to-noise ratio of the reconstructed images. In addition, the proposed method is compatible with many different models of the diffusion signal (e.g., diffusion tensor modeling and q-space modeling). The joint reconstruction method can provide significant improvements in signal-to-noise ratio relative to conventional reconstruction techniques, with a relatively minor corresponding loss in image resolution. Results are shown in the context of diffusion spectrum imaging tractography and diffusion tensor imaging, illustrating the potential of this signal-to-noise ratio-enhancing joint reconstruction approach for a range of different diffusion imaging experiments., (Copyright © 2011 Wiley Periodicals, Inc.)

Normal aging is associated with diminished brain perfusion measured as cerebral blood flow (CBF), but previously it is difficult to accurately measure various aspects of perfusion hemodynamics including: bolus arrival times and delays through small arterioles, expressed as arterial-arteriole transit time. To study hemodynamics in greater detail, volumetric arterial spin labeling MRI with variable postlabeling delays was used together with a distributed, dual-compartment tracer model. The main goal was to determine how CBF and other perfusion hemodynamics vary with aging. Twenty cognitive normal female and 15 male subjects (age: 23-84 years old) were studied at 4 T. Arterial spin labeling measurements were performed in the posterior cingulate cortex, precuneus, and whole brain gray matter. CBF declined with advancing age (P < 0.001). Separately from variations in bolus arrival times, arterial-arteriole transit time increased with advancing age (P < 0.01). Finally, women had overall higher CBF values (P < 0.01) and shorter arterial-arteriole transit time (P < 0.01) than men, regardless of age. The findings imply that CBF and blood transit times are compromised in aging, and these changes together with differences between genders should be taken into account when studying brain perfusion., (Copyright © 2011 Wiley Periodicals, Inc.)

For magnetic resonance spectroscopic imaging studies of the brain, it is important to measure the distribution of metabolites in a regionally unbiased way; that is, without restrictions to a priori defined regions of interest. Since magnetic resonance spectroscopic imaging provides measures of multiple metabolites simultaneously at each voxel, there is furthermore great interest in utilizing the multidimensional nature of magnetic resonance spectroscopic imaging for gains in statistical power. Voxelwise multivariate statistical mapping is expected to address both of these issues, but it has not been previously employed for spectroscopic imaging (SI) studies of brain. The aims of this study were to (1) develop and validate multivariate voxel-based statistical mapping for magnetic resonance spectroscopic imaging and (2) demonstrate that multivariate tests can be more powerful than univariate tests in identifying patterns of altered brain metabolism. Specifically, we compared multivariate to univariate tests in identifying known regional patterns in simulated data and regional patterns of metabolite alterations due to amyotrophic lateral sclerosis, a devastating brain disease of the motor neurons., (Copyright (c) 2009 Wiley-Liss, Inc.)

Whole-brain echo-planar spectroscopic imaging (EPSI) often substantially lengthens MRI/MRSI (magnetic resonance spectroscopic imaging) protocols. To halve acquisition time, application of a blipped phase-encoding (PE) gradient during the EPSI readout (RO) was previously suggested by PE of the even RO echoes in k-space at an interstitial location along k(PE), separated from the odd RO echoes, effectively reducing the number of PEs by a factor of 2. However, the approach is very susceptible to phase inconsistencies between even and odd RO echoes in the presence of B(0) inhomogeneities and gradient imbalance, leading to ghosting in the PE direction. In this work, the blipped PE gradient is placed in between pairs of even/odd RO gradient lobes to avoid these problems. This approach is demonstrated in a phantom and in normal human brain in vivo at 4T. While the proposed method allows substantial reduction in metabolite ghosting, it may be limited by the presence of a relatively large spurious signal at the Nyquist frequency., (Copyright 2007 Wiley-Liss, Inc.)

MR spectroscopic (MRS) images from a large volume of brain can be obtained using a 3D echo-planar spectroscopic imaging (3D-EPSI) sequence. However, routine applications of 3D-EPSI are still limited by a long scan time. In this communication, a new approach termed "spectral phase-corrected generalized autocalibrating partially parallel acquisitions" (SPC-GRAPPA) is introduced for the reconstruction of 3D-EPSI data to accelerate data acquisition while preserving the accuracy of quantitation of brain metabolites. In SPC-GRAPPA, voxel-by-voxel spectral phase alignment between metabolite 3D-EPSI from individual coil elements is performed in the frequency domain, utilizing the whole spectrum from interleaved water reference 3D-EPSI for robust estimation of the zero-order phase correction. The performance of SPC-GRAPPA was compared with that of fully encoded 3D-EPSI and conventional GRAPPA. Analysis of whole-brain 3D-EPSI data reconstructed by SPC-GRAPPA demonstrates that SPC-GRAPPA with an acceleration factor of 1.5 yields results very similar to those obtained by fully encoded 3D-EPSI, and is more accurate than conventional GRAPPA., ((c) 2007 Wiley-Liss, Inc.)

Complete coverage of the in vivo proton metabolite spectrum, including downfield resonances, requires a spectral bandwidth of approximately 9 ppm. Spectral bandwidth of in vivo echo-planar spectroscopic imaging (EPSI) is primarily limited by gradient strength of the oscillating readout gradient, gradient slew rate, and limits on peripheral nerve stimulation for human subjects. Furthermore, conventional EPSI reconstruction, which utilizes even and odd readout echoes separately, makes use of only half the spectral bandwidth. In order to regain full spectral bandwidth in EPSI, it has previously been suggested to apply an interlaced Fourier transform (iFT), which uses even and odd echoes simultaneously. However, this method has not been thoroughly analyzed regarding its usefulness for in vivo 3D EPSI. In this Note, limitations of the iFT method are discussed and an alternative, cyclic spectral unwrapping, is proposed, which is based on prior knowledge of typical in vivo spectral patterns., (Copyright (c) 2005 Wiley-Liss, Inc.)

An extended model for extracting measures of brain perfusion from pulsed arterial spin labeling (ASL) data while considering transit effects and restricted permeability of capillaries to blood water is proposed. We divided the time course of the signal difference between control and labeled images into four phases with respect to the arrival time of labeled blood water at the voxel of interest (t(A)), transit time through the arteries in the voxel (t(ex)), and duration of the bolus of labeled spins (tau). Dividing the labeled slab of blood water into many discrete segments, and adapting numerical integration methods allowed us to conveniently model restricted capillary-tissue exchange based on a modified distributed parameter model. We compared this four-phase single-capillary stepwise (FPSCS) model with models that treat water as a freely diffusible tracer, using both simulations and experimental ASL brain imaging data at 1.5T from eight healthy subjects (24-80 years old). The FPSCS model yielded less errors in the least-squares sense in fitting brain ASL data in comparison with freely diffusible tracer models of water (P = 0.055). These results imply that restricted permeability of capillaries to water should be considered when brain ASL data are analyzed., ((c) 2005 Wiley-Liss, Inc.)

A new method, based on a deformable shape-intensity model (DSM), was developed to improve the signal-to-noise ratio (SNR) of multidimensional magnetic resonance spectroscopic imaging (MRSI) data sets without affecting spectral lineshapes and linewidths. Improvements with DSM, compared to digital filters using conventional signal apodization, were demonstrated on both simulated and experimental in vivo (1)H MRS images from 22 cognitively normal (CN) elderly subjects and 25 patients with Alzheimer's disease (AD). Simulated MRSI data showed that DSM achieved superior noise suppression compared to a matched apodization filter. Experimental MRSI data showed that SNR could be increased 2.1-fold with DSM without distorting spectral resolution, thus maintaining all spectral features of the raw, unfiltered data. In conclusion, DSM should be used to achieve high SNR in reconstructing MRSI data., (Copyright 2003 Wiley-Liss, Inc.)

A novel pulsed arterial spin labeling (PASL) technique for multislice perfusion-weighted imaging is proposed that compensates for magnetization transfer (MT) effects without sacrificing tag efficiency, and balances transient magnetic field effects (eddy currents) induced by pulsed field gradients. Improved compensation for MT is demonstrated using a phantom. Improvement in perfusion measurement was compared to other PASL techniques by acquiring perfusion images from 13 healthy volunteers (nine women and four men; age range 29-64 years; mean age 45 +/- 14 years) and second-order image texture analysis. The main improvements with the new method were significantly higher image contrast, higher mean signal intensity, and better signal uniformity across slices. In conclusion, this new PASL method should provide improved accuracy in measuring brain perfusion., (Copyright 2003 Wiley-Liss, Inc.)

Quantitative measurements of regional and tissue specific concentrations of brain metabolites were measured in elderly subjects using multislice proton magnetic resonance spectroscopic imaging ((1)H MRSI). Selective k-space extrapolation and an inversion-recovery sequence were used to minimize lipid contamination and linear regression was used to account for partial volume problems. The technique was applied to measure the concentrations of N-acetyl aspartate (NAA), and creatine (Cr)- and choline (Cho)-containing compounds in cortical gray and white matter, and white matter lesions of the frontal and the parietal lobe in 40 normal elderly subjects (22 females and 18 males, 56-89 years old, mean age 74 +/- 8). NAA was about 15% lower in cortical gray matter and 23% lower in white matter lesions when compared to normal white matter. Cr was 11% higher in cortical gray matter than in white matter, and also about 15% higher in the parietal cortex than in the frontal cortex. Cho was 28% lower in cortical gray matter than in white matter. Furthermore, NAA and Cr changes correlated with age. In conclusion, regional and tissue differences of brain metabolites must be considered in addition to age-related changes when interpreting (1)H MRSI data.

The in vivo neuronal contribution to human cerebral metabolic rate of glucose (CMRglc), measured by 18FDG-PET, is unknown. Examining the effect of 1H MRSI-derived N-acetyl aspartate (NAA) concentration on positron emission tomography (PET) measures of metabolic activity might indicate the relationship of CMRglc to neuron density. In a population of 19 demented, cognitively impaired, and control subjects, the Miller-Gartner algorithm was applied to whole-brain PET data to isolate the PET signal originating in cortical gray matter alone (GMPET). An analogous procedure applied to multislice proton MRSI data yielded the N-acetyl aspartate concentration in cortical gray matter (GMNAA). In 18 of 19 subjects, a significant linear regression (P < 0.05) resulted when GMPET was plotted against GMNAA, whereby GMPET was higher for higher GMNAA. This suggests that CMRglc rises linearly with increasing neuron density in gray matter. This method may be used to investigate the relationship of CMRglc to neurons in various conditions.

Proton MR spectroscopic imaging (MRSI) of human cerebral cortex is complicated by the presence of an intense signal from subcutaneous lipids, which, if not suppressed before Fourier reconstruction, causes ringing and signal contamination throughout the metabolite images as a result of limited k-space sampling. In this article, an improved reconstruction of the lipid region is obtained using the Papoulis-Gerchberg algorithm. This procedure makes use of the narrow-band-limited nature of the subcutaneous lipid signal to extrapolate to higher k-space values without alteration of the metabolite signal region. Using computer simulations and in vivo experimental studies, the implementation and performance of this algorithm were examined. This method was found to permit MRSI brain spectra to be obtained without applying any lipid suppression during data acquisition, at echo times of 50 ms and longer. When applied together with optimized acquisition methods, this provides an effective procedure for imaging metabolite distributions in cerebral cortical surface regions.