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Recent advances in achieving ultrahigh spatial resolution (e.g. sub-millimeter) diffusion MRI (dMRI) data have proven highly beneficial in characterizing tissue microstructures in organs such as the brain. However, the routine acquisition of in-vivo dMRI data at such high spatial resolutions has been largely prohibited by factors that include prolonged acquisition times, motion induced artifacts, and low SNR. To overcome these limitations, we present here a framework for acquiring and reconstructing 3D multi-slab, multi-band and interleaved multi-shot EPI data, termed 3D-MB-MUSE. Through multi-band excitations, the simultaneous acquisition of multiple 3D slabs enables whole brain dMRI volumes to be acquired in-vivo on a 3 T clinical MRI scanner at high spatial resolution within a reasonably short amount of time. Representing a true 3D model, 3D-MB-MUSE reconstructs an entire 3D multi-band, multi-shot dMRI slab at once while simultaneously accounting for coil sensitivity variations across the slab as well as motion induced artifacts commonly associated with both 3D and multi-shot diffusion imaging. Such a reconstruction fully preserves the SNR advantages of both 3D and multi-shot acquisitions in high resolution dMRI images by removing both motion and aliasing artifacts across multiple dimensions. By enabling ultrahigh resolution dMRI for routine use, the 3D-MB-MUSE framework presented here may prove highly valuable in both clinical and research applications., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Background: Both HIV infection and chronic cocaine use alter the neural circuitry of decision making, but the interactive effects of these commonly comorbid conditions have not been adequately examined. This study tested how cocaine moderates HIV-related neural activation during an intertemporal decision-making task., Methods: The sample included 73 participants who differed on cocaine and HIV status (18 COC+/HIV+, 19 COC+/HIV-, 19 COC-/HIV+, 17 COC-/HIV-). Participants made choices between smaller, sooner and larger, delayed rewards while undergoing functional MRI. Choices varied in difficulty based on subjective value: hard (equivalently valued), easy (disparately valued), and control choices. A mixed-effects model controlling for education and smoking identified main and interactive effects of HIV and COC during hard relative to easy choices (difficulty contrast)., Results: COC+ status was associated with lower activation in bilateral frontal gyri and right insular and posterior parietal cortices. HIV+ status was associated with higher activation in the visual cortex, but lower activation in bilateral prefrontal cortices and cerebellum and left posterior parietal cortex. COC moderated the effects of HIV in several clusters centered in the bilateral prefrontal cortices and cerebellum. In post-hoc analyses, there were significant effects of HIV status on activation for COC+, but not COC-, participants; interaction effects remained after controlling for polysubstance use., Conclusion: Cocaine use may diminish the compensatory neural activation often seen among HIV+ samples during decision making. Our results highlight the importance of examining the neuropsychiatric effects of comorbid medical conditions to identify potential neural targets for cognitive remediation interventions., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Age-related decline in fluid cognition can be characterized as a disconnection among specific brain structures, leading to a decline in functional efficiency. The potential sources of disconnection, however, are unclear. We investigated imaging measures of cerebral white-matter integrity, resting-state functional connectivity, and white-matter hyperintensity volume as mediators of the relation between age and fluid cognition, in 145 healthy, community-dwelling adults 19-79 years of age. At a general level of analysis, with a single composite measure of fluid cognition and single measures of each of the 3 imaging modalities, age exhibited an independent influence on the cognitive and imaging measures, and the imaging variables did not mediate the age-cognition relation. At a more specific level of analysis, resting-state functional connectivity of sensorimotor networks was a significant mediator of the age-related decline in executive function. These findings suggest that different levels of analysis lead to different models of neurocognitive disconnection, and that resting-state functional connectivity, in particular, may contribute to age-related decline in executive function., (Copyright © 2017 Elsevier Inc. All rights reserved.)

This study investigated the association of HIV infection and cocaine dependence with cerebral white matter integrity using diffusion tensor imaging (DTI). One hundred thirty-five participants stratified by HIV and cocaine status (26 HIV+/COC+, 37 HIV+/COC-, 37 HIV-/COC+, and 35 HIV-/COC-) completed a comprehensive substance abuse assessment, neuropsychological testing, and MRI with DTI. Among HIV+ participants, all were receiving HIV care and 46% had an AIDS diagnosis. All COC+ participants were current users and met criteria for cocaine use disorder. We used tract-based spatial statistics (TBSS) to assess the relation of HIV and cocaine to fractional anisotropy (FA) and mean diffusivity (MD). In whole-brain analyses, HIV+ participants had significantly reduced FA and increased MD compared to HIV- participants. The relation of HIV and FA was widespread throughout the brain, whereas the HIV-related MD effects were restricted to the corpus callosum and thalamus. There were no significant cocaine or HIV-by-cocaine effects. These DTI metrics correlated significantly with duration of HIV disease, nadir CD4+ cell count, and AIDS diagnosis, as well as some measures of neuropsychological functioning. These results suggest that HIV is related to white matter integrity throughout the brain, and that HIV-related effects are more pronounced with increasing duration of infection and greater immune compromise. We found no evidence for independent effects of cocaine dependence on white matter integrity, and cocaine dependence did not appear to exacerbate the effects of HIV.

Diffusion-weighted imaging (DWI) obtained with interleaved echo-planar imaging (EPI) pulse sequence has great potential of characterizing brain tissue properties at high spatial-resolution. However, interleaved EPI based DWI data may be corrupted by various types of aliasing artifacts. First, inconsistencies in k-space data obtained with opposite readout gradient polarities result in Nyquist artifact, which is usually reduced with 1D phase correction in post-processing. When there exist eddy current cross terms (e.g., in oblique-plane EPI), 2D phase correction is needed to effectively reduce Nyquist artifact. Second, minuscule motion induced phase inconsistencies in interleaved DWI scans result in image-domain aliasing artifact, which can be removed with reconstruction procedures that take shot-to-shot phase variations into consideration. In existing interleaved DWI reconstruction procedures, Nyquist artifact and minuscule motion-induced aliasing artifact are typically removed subsequently in two stages. Although the two-stage phase correction generally performs well for non-oblique plane EPI data obtained from well-calibrated system, the residual artifacts may still be pronounced in oblique-plane EPI data or when there exist eddy current cross terms. To address this challenge, here we report a new composite 2D phase correction procedure, which effective removes Nyquist artifact and minuscule motion induced aliasing artifact jointly in a single step. Our experimental results demonstrate that the new 2D phase correction method can much more effectively reduce artifacts in interleaved EPI based DWI data as compared with the existing two-stage artifact correction procedures. The new method robustly enables high-resolution DWI, and should prove highly valuable for clinical uses and research studies of DWI., (Copyright © 2016 Elsevier Inc. All rights reserved.)

HIV infection can cause direct and indirect damage to the brain and is consistently associated with neurocognitive disorders, including impairments in decision-making capacities. The tendency to devalue rewards that are delayed (temporal discounting) is relevant to a range of health risk behaviors. Making choices about delayed rewards engages the executive control network of the brain, which has been found to be affected by HIV. In this case-control study of 18 HIV-positive and 17 HIV-negative adults, we examined the effects of HIV on brain activation during a temporal discounting task. Functional MRI (fMRI) data were collected while participants made choices between smaller, sooner rewards and larger, delayed rewards. Choices were individualized based on participants' unique discount functions, so each participant experienced hard (similarly valued), easy (disparately valued), and control choices. fMRI data were analyzed using a mixed-effects model to identify group-related differences associated with choice difficulty. While there was no difference between groups in behavioral performance, the HIV-positive group demonstrated significantly larger increases in activation within left parietal regions and bilateral prefrontal regions during easy trials and within the right prefrontal cortex and anterior cingulate during hard trials. Increasing activation within the prefrontal regions was associated with lower nadir CD4 cell count and risk-taking propensity. These results support the hypothesis that HIV infection can alter brain functioning in regions that support decision making, providing further evidence for HIV-associated compensatory activation within fronto-parietal cortices. A history of immunosuppression may contribute to these brain changes. Hum Brain Mapp 37:2455-2467, 2016. © 2016 Wiley Periodicals, Inc., (© 2016 Wiley Periodicals, Inc.)

Purpose: To develop new techniques for reducing the effects of microscopic and macroscopic patient motion in diffusion imaging acquired with high-resolution multishot echo-planar imaging., Theory: The previously reported multiplexed sensitivity encoding (MUSE) algorithm is extended to account for macroscopic pixel misregistrations, as well as motion-induced phase errors in a technique called augmented MUSE (AMUSE). Furthermore, to obtain more accurate quantitative diffusion-tensor imaging measures in the presence of subject motion, we also account for the altered diffusion encoding among shots arising from macroscopic motion., Methods: MUSE and AMUSE were evaluated on simulated and in vivo motion-corrupted multishot diffusion data. Evaluations were made both on the resulting imaging quality and estimated diffusion tensor metrics., Results: AMUSE was found to reduce image blurring resulting from macroscopic subject motion compared to MUSE but yielded inaccurate tensor estimations when neglecting the altered diffusion encoding. Including the altered diffusion encoding in AMUSE produced better estimations of diffusion tensors., Conclusion: The use of AMUSE allows for improved image quality and diffusion tensor accuracy in the presence of macroscopic subject motion during multishot diffusion imaging. These techniques should facilitate future high-resolution diffusion imaging., (© 2015 Wiley Periodicals, Inc.)

Advances in molecular and structural and functional neuroimaging are rapidly expanding the complexity of neurobiological understanding of Parkinson's disease (PD). This review article begins with an introduction to PD neurobiology as a foundation for interpreting neuroimaging findings that may further lead to more integrated and comprehensive understanding of PD. Diverse areas of PD neuroimaging are then reviewed and summarized, including positron emission tomography, single photon emission computed tomography, magnetic resonance spectroscopy and imaging, transcranial sonography, magnetoencephalography, and multimodal imaging, with focus on human studies published over the last five years. These included studies on differential diagnosis, co-morbidity, genetic and prodromal PD, and treatments from L-DOPA to brain stimulation approaches, transplantation and gene therapies. Overall, neuroimaging has shown that PD is a neurodegenerative disorder involving many neurotransmitters, brain regions, structural and functional connections, and neurocognitive systems. A broad neurobiological understanding of PD will be essential for translational efforts to develop better treatments and preventive strategies. Many questions remain and we conclude with some suggestions for future directions of neuroimaging of PD., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

Purpose: A projection onto convex sets reconstruction of multiplexed sensitivity encoded MRI (POCSMUSE) is developed to reduce motion-related artifacts, including respiration artifacts in abdominal imaging and aliasing artifacts in interleaved diffusion-weighted imaging., Theory: Images with reduced artifacts are reconstructed with an iterative projection onto convex sets (POCS) procedure that uses the coil sensitivity profile as a constraint. This method can be applied to data obtained with different pulse sequences and k-space trajectories. In addition, various constraints can be incorporated to stabilize the reconstruction of ill-conditioned matrices., Methods: The POCSMUSE technique was applied to abdominal fast spin-echo imaging data, and its effectiveness in respiratory-triggered scans was evaluated. The POCSMUSE method was also applied to reduce aliasing artifacts due to shot-to-shot phase variations in interleaved diffusion-weighted imaging data corresponding to different k-space trajectories and matrix condition numbers., Results: Experimental results show that the POCSMUSE technique can effectively reduce motion-related artifacts in data obtained with different pulse sequences, k-space trajectories and contrasts., Conclusion: POCSMUSE is a general post-processing algorithm for reduction of motion-related artifacts. It is compatible with different pulse sequences, and can also be used to further reduce residual artifacts in data produced by existing motion artifact reduction methods., (© 2014 Wiley Periodicals, Inc.)

The advantages of high-resolution diffusion tensor imaging (DTI) have been demonstrated in a recent post-mortem human brain study (Miller et al., NeuroImage 2011;57(1):167-181), showing that white matter fiber tracts can be much more accurately detected in data at a submillimeter isotropic resolution. To our knowledge, in vivo human brain DTI at a submillimeter isotropic resolution has not been routinely achieved yet because of the difficulty in simultaneously achieving high resolution and high signal-to-noise ratio (SNR) in DTI scans. Here we report a 3D multi-slab interleaved EPI acquisition integrated with multiplexed sensitivity encoded (MUSE) reconstruction, to achieve high-quality, high-SNR and submillimeter isotropic resolution (0.85×0.85×0.85mm(3)) in vivo human brain DTI on a 3Tesla clinical MRI scanner. In agreement with the previously reported post-mortem human brain DTI study, our in vivo data show that the structural connectivity networks of human brains can be mapped more accurately and completely with high-resolution DTI as compared with conventional DTI (e.g., 2×2×2mm(3))., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Purpose: We report a series of techniques to reliably eliminate artifacts in interleaved echo-planar imaging (EPI) based diffusion-weighted imaging (DWI)., Methods: First, we integrate the previously reported multiplexed sensitivity encoding (MUSE) algorithm with a new adaptive Homodyne partial-Fourier reconstruction algorithm, so that images reconstructed from interleaved partial-Fourier DWI data are free from artifacts even in the presence of either (a) motion-induced k-space energy peak displacement, or (b) susceptibility field gradient induced fast phase changes. Second, we generalize the previously reported single-band MUSE framework to multiband MUSE, so that both through-plane and in-plane aliasing artifacts in multiband multishot interleaved DWI data can be effectively eliminated., Results: The new adaptive Homodyne-MUSE reconstruction algorithm reliably produces high-quality and high-resolution DWI, eliminating residual artifacts in images reconstructed with previously reported methods. Furthermore, the generalized MUSE algorithm is compatible with multiband and high-throughput DWI., Conclusion: The integration of the multiband and adaptive Homodyne-MUSE algorithms significantly improves the spatial-resolution, image quality, and scan throughput of interleaved DWI. We expect that the reported reconstruction framework will play an important role in enabling high-resolution DWI for both neuroscience research and clinical uses., (© 2014 Wiley Periodicals, Inc.)

Importance: Repetitive transcranial magnetic stimulation (rTMS) is a noninvasive neuromodulation technique that has been closely examined as a possible treatment for Parkinson disease (PD). However, results evaluating the effectiveness of rTMS in PD are mixed, mostly owing to low statistical power or variety in individual rTMS protocols., Objectives: To determine the rTMS effects on motor dysfunction in patients with PD and to examine potential factors that modulate the rTMS effects., Data Sources: Databases searched included PubMed, EMBASE, Web of Knowledge, Scopus, and the Cochrane Library from inception to June 30, 2014., Study Selection: Eligible studies included sham-controlled, randomized clinical trials of rTMS intervention for motor dysfunction in patients with PD., Data Extraction and Synthesis: Relevant measures were extracted independently by 2 investigators. Standardized mean differences (SMDs) were calculated with random-effects models., Main Outcomes and Measures: Motor examination of the Unified Parkinson's Disease Rating Scale., Results: Twenty studies with a total of 470 patients were included. Random-effects analysis revealed a pooled SMD of 0.46 (95% CI, 0.29-0.64), indicating an overall medium effect size favoring active rTMS over sham rTMS in the reduction of motor symptoms (P<.001). Subgroup analysis showed that the effect sizes estimated from high-frequency rTMS targeting the primary motor cortex (SMD, 0.77; 95% CI, 0.46-1.08; P<.001) and low-frequency rTMS applied over other frontal regions (SMD, 0.50; 95% CI, 0.13-0.87; P=.008) were significant. The effect sizes obtained from the other 2 combinations of rTMS frequency and rTMS site (ie, high-frequency rTMS at other frontal regions: SMD, 0.23; 95% CI, -0.02 to 0.48, and low primary motor cortex: SMD, 0.28; 95% CI, -0.23 to 0.78) were not significant. Meta-regression revealed that a greater number of pulses per session or across sessions is associated with larger rTMS effects. Using the Grading of Recommendations, Assessment, Development, and Evaluation criteria, we characterized the quality of evidence presented in this meta-analysis as moderate quality., Conclusions and Relevance: The pooled evidence suggests that rTMS improves motor symptoms for patients with PD. Combinations of rTMS site and frequency as well as the number of rTMS pulses are key modulators of rTMS effects. The findings of our meta-analysis may guide treatment decisions and inform future research.

Activation of frontal and parietal brain regions is associated with attentional control during visual search. We used fMRI to characterize age-related differences in frontoparietal activation in a highly efficient feature search task, detection of a shape singleton. On half of the trials, a salient distractor (a color singleton) was present in the display. The hypothesis was that frontoparietal activation mediated the relation between age and attentional capture by the salient distractor. Participants were healthy, community-dwelling individuals, 21 younger adults (19-29 years of age) and 21 older adults (60-87 years of age). Top-down attention, in the form of target predictability, was associated with an improvement in search performance that was comparable for younger and older adults. The increase in search reaction time (RT) associated with the salient distractor (attentional capture), standardized to correct for generalized age-related slowing, was greater for older adults than for younger adults. On trials with a color singleton distractor, search RT increased as a function of increasing activation in frontal regions, for both age groups combined, suggesting increased task difficulty. Mediational analyses disconfirmed the hypothesized model, in which frontal activation mediated the age-related increase in attentional capture, but supported an alternative model in which age was a mediator of the relation between frontal activation and capture., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Resting-state functional magnetic resonance imaging (fMRI) aims to measure baseline neuronal connectivity independent of specific functional tasks and to capture changes in the connectivity due to neurological diseases. Most existing network detection methods rely on a fixed threshold to identify functionally connected voxels under the resting state. Due to fMRI non-stationarity, the threshold cannot adapt to variation of data characteristics across sessions and subjects, and generates unreliable mapping results. In this study, a new method is presented for resting-state fMRI data analysis. Specifically, the resting-state network mapping is formulated as an outlier detection process that is implemented using one-class support vector machine (SVM). The results are refined by using a spatial-feature domain prototype selection method and two-class SVM reclassification. The final decision on each voxel is made by comparing its probabilities of functionally connected and unconnected instead of a threshold. Multiple features for resting-state analysis were extracted and examined using an SVM-based feature selection method, and the most representative features were identified. The proposed method was evaluated using synthetic and experimental fMRI data. A comparison study was also performed with independent component analysis (ICA) and correlation analysis. The experimental results show that the proposed method can provide comparable or better network detection performance than ICA and correlation analysis. The method is potentially applicable to various resting-state quantitative fMRI studies., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Functional magnetic resonance imaging (fMRI) technique with blood oxygenation level dependent (BOLD) contrast is a powerful tool for noninvasive mapping of brain function under task and resting states. The removal of cardiac- and respiration-induced physiological noise in fMRI data has been a significant challenge as fMRI studies seek to achieve higher spatial resolutions and characterize more subtle neuronal changes. The low temporal sampling rate of most multi-slice fMRI experiments often causes aliasing of physiological noise into the frequency range of BOLD activation signal. In addition, changes of heartbeat and respiration patterns also generate physiological fluctuations that have similar frequencies with BOLD activation. Most existing physiological noise-removal methods either place restrictive limitations on image acquisition or utilize filtering or regression based post-processing algorithms, which cannot distinguish the frequency-overlapping BOLD activation and the physiological noise. In this work, we address the challenge of physiological noise removal via the kernel machine technique, where a nonlinear kernel machine technique, kernel principal component analysis, is used with a specifically identified kernel function to differentiate BOLD signal from the physiological noise of the frequency. The proposed method was evaluated in human fMRI data acquired from multiple task-related and resting state fMRI experiments. A comparison study was also performed with an existing adaptive filtering method. The results indicate that the proposed method can effectively identify and reduce the physiological noise in fMRI data. The comparison study shows that the proposed method can provide comparable or better noise removal performance than the adaptive filtering approach., (© 2013.)

Traumatic brain injuries (TBI) are induced by sudden acceleration-deceleration and/or rotational forces acting on the brain. Diffuse axonal injury (DAI) has been identified as one of the chief underlying causes of morbidity and mortality in head trauma incidents. DAIs refer to microscopic white matter (WM) injuries as a result of shearing forces that induce pathological and anatomical changes within the brain, which potentially contribute to significant impairments later in life. These microscopic injuries are often unidentifiable by the conventional computed tomography (CT) and magnetic resonance (MR) scans employed by emergency departments to initially assess head trauma patients and, as a result, TBIs are incredibly difficult to diagnose. The impairments associated with TBI may be caused by secondary mechanisms that are initiated at the moment of injury, but often have delayed clinical presentations that are difficult to assess due to the initial misdiagnosis. As a result, the true consequences of these head injuries may go unnoticed at the time of injury and for many years thereafter. The purpose of this review is to investigate these consequences of TBI and their potential link to neurodegenerative disease (ND). This review will summarize the current epidemiological findings, the pathological similarities, and new neuroimaging techniques that may help delineate the relationship between TBI and ND. Lastly, this review will discuss future directions and propose new methods to overcome the limitations that are currently impeding research progress. It is imperative that improved techniques are developed to adequately and retrospectively assess TBI history in patients that may have been previously undiagnosed in order to increase the validity and reliability across future epidemiological studies. The authors introduce a new surveillance tool (Retrospective Screening of Traumatic Brain Injury Questionnaire, RESTBI) to address this concern.

Functional magnetic resonance imaging (fMRI) aims to localize task-related brain activation or resting-state functional connectivity. Most existing fMRI data analysis techniques rely on fixed thresholds to identify active voxels under a task condition or functionally connected voxels in the resting state. Due to fMRI non-stationarity, a fixed threshold cannot adapt to intra- and inter-subject variation and provide a reliable mapping of brain function. In this work, a machine learning method is proposed for a unified analysis of both task-related and resting state fMRI data. Specifically, the mapping of brain function in a task condition or resting state is formulated as an outlier detection process. Support vector machines are used to provide an initial mapping and refine mapping results. The method does not require a fixed threshold for the final decision, and can adapt to fMRI non-stationarity. The proposed method was evaluated using experimental data acquired from multiple human subjects. The results indicate that the proposed method can provide reliable mapping of brain function, and is applicable to various quantitative fMRI studies.

Previous neuroimaging research has documented that patterns of intrinsic (resting state) functional connectivity (FC) among brain regions covary with individual measures of cognitive performance. Here, we examined the relation between intrinsic FC and a reaction time (RT) measure of performance, as a function of age group and task demands. We obtained filtered, event-related functional magnetic resonance imaging data, and RT measures of visual search performance, from 21 younger adults (19-29 years old) and 21 healthy, older adults (60-87 years old). Age-related decline occurred in the connectivity strength in multiple brain regions, consistent with previous findings. Among 8 pairs of regions, across somatomotor, orbitofrontal, and subcortical networks, increasing FC was associated with faster responding (lower RT). Relative to younger adults, older adults exhibited a lower strength of this RT-connectivity relation and greater disruption of this relation by a salient but irrelevant display item (color singleton distractor). Age-related differences in the covariation of intrinsic FC and cognitive performance vary as a function of task demands., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Diffusion weighted magnetic resonance imaging (DWI) data have been mostly acquired with single-shot echo-planar imaging (EPI) to minimize motion induced artifacts. The spatial resolution, however, is inherently limited in single-shot EPI, even when the parallel imaging (usually at an acceleration factor of 2) is incorporated. Multi-shot acquisition strategies could potentially achieve higher spatial resolution and fidelity, but they are generally susceptible to motion-induced phase errors among excitations that are exacerbated by diffusion sensitizing gradients, rendering the reconstructed images unusable. It has been shown that shot-to-shot phase variations may be corrected using navigator echoes, but at the cost of imaging throughput. To address these challenges, a novel and robust multi-shot DWI technique, termed multiplexed sensitivity-encoding (MUSE), is developed here to reliably and inherently correct nonlinear shot-to-shot phase variations without the use of navigator echoes. The performance of the MUSE technique is confirmed experimentally in healthy adult volunteers on 3Tesla MRI systems. This newly developed technique should prove highly valuable for mapping brain structures and connectivities at high spatial resolution for neuroscience studies., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Background and Purpose: The combination of phase demodulation and field mapping is a practical method to correct echo planar imaging (EPI) geometric distortion. However, since phase dispersion accumulates in each phase-encoding step, the calculation complexity of phase modulation is Ny-fold higher than conventional image reconstructions. Thus, correcting EPI images via phase demodulation is generally a time-consuming task., Methods: Parallel computing by employing general-purpose calculations on graphics processing units (GPU) can accelerate scientific computing if the algorithm is parallelized. This study proposes a method that incorporates the GPU-based technique into phase demodulation calculations to reduce computation time. The proposed parallel algorithm was applied to a PROPELLER-EPI diffusion tensor data set., Results: The GPU-based phase demodulation method reduced the EPI distortion correctly, and accelerated the computation. The total reconstruction time of the 16-slice PROPELLER-EPI diffusion tensor images with matrix size of 128 × 128 was reduced from 1,754 seconds to 101 seconds by utilizing the parallelized 4-GPU program., Conclusions: GPU computing is a promising method to accelerate EPI geometric correction. The resulting reduction in computation time of phase demodulation should accelerate postprocessing for studies performed with EPI, and should effectuate the PROPELLER-EPI technique for clinical practice., (Copyright © 2011 by the American Society of Neuroimaging.)

The purpose of this work was to evaluate a previously proposed approach that aims to improve the point spread function (PSF) of MR spectroscopic imaging (MRSI) to avoid corruption by lipid signal arising from neighboring voxels. Retrospective spatial filtering can be used to alter the PSF; however, this either reduces spatial resolution or requires extending the acquisition in k-space at the cost of increased imaging time. Alternatively, the method evaluated here, PSF-choice, can modify the PSF localization to reduce the contamination from adjacent lipids by conforming the signal response more closely to the desired MRSI voxel grid. This is done without increasing scan time or degrading SNR of important metabolites. PSF-choice achieves improvements in spatial localization through modifications to the radiofrequency excitation pulses. An implementation of this method is reported for MRSI of the prostate, where it is demonstrated that, in 13 of 16 pilot prostate MRSI scans, intravoxel spectral contamination from lipid was significantly reduced when using PSF-choice. Phantom studies were also performed that demonstrate, compared with MRSI with standard Fourier phase encoding, out-of-voxel signal contamination of spectra was significantly reduced in MRSI with PSF-choice.

Object: To develop an ultrafast MRI-based temperature monitoring method for application during rapid ultrasound exposures in moving organs., Materials and Methods: A slice selective 90° - 180° pair of RF pulses was used to solicit an echo from a column, which was then sampled with a train of gradient echoes. In a gel phantom, phase changes of each echo were compared to standard gradient-echo thermometry, and temperature monitoring was tested during focused ultrasound sonications. Signal-to-noise ratio (SNR) performance was evaluated in vivo in a rabbit brain, and feasibility was tested in a human heart., Results: The correlation between each echo in the acquisition and MRI-based temperature measurements was good (R = 0.98 ± 0.03). A temperature sampling rate of 19 Hz was achieved at 3T in the gel phantom. It was possible to acquire the water frequency in the beating heart muscle with 5-Hz sampling rate during a breath hold., Conclusion: Ultrafast thermometry via phase or frequency monitoring along single columns was demonstrated. With a temporal resolution around 50 ms, it may be possible to monitor focal heating produced by short ultrasound pulses.

Functional magnetic resonance imaging (fMRI) techniques enable noninvasive studies of brain functional activity under task and resting states. However, the analysis of brain activity could be significantly affected by the cardiac- and respiration-induced physiological noise in fMRI data. In most multi-slice fMRI experiments, the temporal sampling rates are not high enough to critically sample the physiological noise, and the noise is aliased into frequency bands where useful brain functional signal exists, compromising the analysis. Most existing approaches cannot distinguish between the aliased noise and signal if they overlap in the frequency domain. In this work, we further developed a kernel principal component analysis based physiological removal method based on our previous work. Specifically, two kernel functions were evaluated based on a newly proposed criterion that can measure the capability of a kernel to separate the aliased physiological noise from fMRI signal. In addition, a mutual information based criterion was designed to select principal components for noise removal. The method was evaluated by human experimental fMRI studies, and the results demonstrate that the proposed method can effectively identify and attenuate the aliased physiological noise in fMRI data.

Functional connectivity (FC) reflects the coherence of spontaneous, low-frequency fluctuations in functional magnetic resonance imaging (fMRI) data. We report a behavior-based connectivity analysis method, in which whole-brain data are used to identify behaviorally relevant, intrinsic FC networks. Nineteen younger adults (20-28 years) and 19 healthy, older adults (63-78 years) were assessed with fMRI and diffusion tensor imaging (DTI). Results indicated that FC involving a distributed network of brain regions, particularly the inferior frontal gyri, exhibited age-related change in the correlation with perceptual-motor speed (choice reaction time; RT). No relation between FC and RT was evident for younger adults, whereas older adults exhibited a significant age-related slowing of perceptual-motor speed, which was mediated by decreasing FC. Older adults' FC values were in turn associated positively with white matter integrity (from DTI) within the genu of the corpus callosum. The developed FC analysis illustrates the value of identifying connectivity by combining structural, functional, and behavioral data.

Computation of B1 fields is a key issue for determination and correction of intensity nonuniformity in magnetic resonance images. This paper presents a new method for computing transmitted and received B1 fields. Our method combines a modified MRI acquisition protocol and an estimation technique based on the Levenberg-Marquardt algorithm and spatial filtering. It enables accurate estimation of transmitted and received B1 fields for both homogeneous and heterogeneous objects. The method is validated using numerical simulations and experimental data from phantom and human scans. The experimental results are in agreement with theoretical expectations.

Diffusion Weighted Imaging (DWI) and Diffusion Tensor Imaging (DTI) are widely used in the study and diagnosis of neurological diseases involving the White Matter (WM). However, many neurological and neurodegenerative diseases (e.g., Alzheimer's disease and Creutzfeldt-Jakob disease) are generally considered to involve the Grey Matter (GM). Investigation of GM diffusivity of normal aging and pathological brains has both scientific significance and clinical applications. Most of previous research reports on quantification of GM diffusivity were based on the manually labeled Region of Interests (ROI) analysis of specific neuroanatomic regions. The well-known drawbacks of ROI analysis include inter-rater variations, irreproducible results, tediousness, and requirement of a priori definition of interested regions. In this paper, we present a new framework of automated 76-space analysis of GM diffusivity using DWI/DTI. The framework will be evaluated using clinical data, and applied for study of normal brain, Creutzfeldt-Jakob disease and Schizophrenia.