Distinguishing type II focal cortical dysplasias from normal cortex: A novel normative modeling approach.

OBJECTIVE: Focal cortical dysplasias (FCDs) are a common cause of apparently non-lesional drug-resistant focal epilepsy. Visual detection of subtle FCDs on MRI is clinically important and often challenging. In this study, we implement a set of 3D local image filters adapted from computer vision applications to characterize the appearance of normal cortex surrounding the gray-white junction. We create a normative model to serve as the basis for a novel multivariate constrained outlier approach to automated FCD detection. METHODS: Standardized MPRAGE, T2 and FLAIR MR images were obtained in 15 patients with radiologically or histologically diagnosed FCDs and 30 healthy volunteers. Multiscale 3D local image filters were computed for each MR contrast then sampled onto the gray-white junction surface. Using an iterative Gaussianization procedure, we created a normative model of cortical variability in healthy volunteers, allowing for identification of outlier regions and estimates of similarity in normal cortex and FCD lesions. We used a constrained outlier approach following local normalization to automatically detect FCD lesions based on projection onto the mean FCD feature vector. RESULTS: FCDs as well as some normal cortical regions such as primary sensorimotor and paralimbic regions appear as outliers. Regions such as the paralimbic regions and the anterior insula have similar features to FCDs. Our constrained outlier approach allows for automated FCD detection with 80% sensitivity and 70% specificity. SIGNIFICANCE: A normative model using multiscale local image filters can be used to describe the normal cortical variability. Although FCDs appear similar to some cortical regions such as the anterior insula and paralimbic cortices, they can be identified using a constrained outlier detection approach. Our method for detecting outliers and estimating similarity is generic and could be extended to identification of other types of lesions or atypical cortical areas.

Robust, atlas-free, automatic segmentation of brain MRI in health and disease.

BACKGROUND: Brain- and lesion-volumes derived from magnetic resonance images (MRI) serve as important imaging markers of disease progression in neurodegenerative diseases and aging. While manual segmentation of these volumes is both tedious and impractical in large cohorts of subjects, automated segmentation methods often fail in accurate segmentation of brains with severe atrophy or high lesion loads. The purpose of this study was to develop an atlas-free brain Classification using DErivative-based Features (C-DEF), which utilizes all scans that may be acquired during the course of a routine MRI study at any center. METHODS: Proton-density, T2-weighted, T1-weighted, brain-free water, 3D FLAIR, 3D T2-weighted, and 3D T2*-weighted images, collected routinely on patients with neuroinflammatory diseases at the NIH, were used to optimize the C-DEF algorithm on healthy volunteers and HIV + subjects (cohort 1). First, manually marked lesions and eroded FreeSurfer brain segmentation masks (compiled into gray and white matter, globus pallidus, CSF labels) were used in training. Next, the optimized C-DEF was applied on a separate cohort of HIV + subjects (cohort two), and the results were compared with that of FreeSurfer and Lesion-TOADS. Finally, C-DEF segmentation was evaluated on subjects clinically diagnosed with various other neurological diseases (cohort three). RESULTS: C-DEF algorithm was optimized using leave-one-out cross validation on five healthy subjects (age 36 ± 11 years), and five subjects infected with HIV (age 57 ± 2.6 years) in cohort one. The optimized C-DEF algorithm outperformed FreeSurfer and Lesion-TOADS segmentation in 49 other subjects infected with HIV (cohort two, age 54 ± 6 years) in qualitative and quantitative comparisons. Although trained only on HIV brains, sensitivity to detect lesions using C-DEF increased by 45% in HTLV-I-associated myelopathy/tropical spastic paraparesis (n = 5; age 58 ± 7 years), 33% in multiple sclerosis (n = 5; 42 ± 9 years old), and 4% in subjects with polymorphism of the cytotoxic T-lymphocyte-associated protein 4 gene (n = 5; age 24 ± 12 years) compared to Lesion-TOADS. CONCLUSION: C-DEF outperformed other segmentation algorithms in the various neurological diseases explored herein, especially in lesion segmentation. While the results reported are from routine images acquired at the NIH, the algorithm can be easily trained and optimized for any set of contrasts and protocols for wider application. We are currently exploring various technical aspects of optimal implementation of CDEF in a clinical setting and evaluating a larger cohort of patients with other neurological diseases. Improving the accuracy of brain segmentation methodology will help better understand the relationship of imaging abnormalities to clinical and neuropsychological markers in disease.

ISMRM Raw data format: A proposed standard for MRI raw datasets.

PURPOSE: This work proposes the ISMRM Raw Data format as a common MR raw data format, which promotes algorithm and data sharing. METHODS: A file format consisting of a flexible header and tagged frames of k-space data was designed. Application Programming Interfaces were implemented in C/C++, MATLAB, and Python. Converters for Bruker, General Electric, Philips, and Siemens proprietary file formats were implemented in C++. Raw data were collected using magnetic resonance imaging scanners from four vendors, converted to ISMRM Raw Data format, and reconstructed using software implemented in three programming languages (C++, MATLAB, Python). RESULTS: Images were obtained by reconstructing the raw data from all vendors. The source code, raw data, and images comprising this work are shared online, serving as an example of an image reconstruction project following a paradigm of reproducible research. CONCLUSION: The proposed raw data format solves a practical problem for the magnetic resonance imaging community. It may serve as a foundation for reproducible research and collaborations. The ISMRM Raw Data format is a completely open and community-driven format, and the scientific community is invited (including commercial vendors) to participate either as users or developers. Magn Reson Med 77:411-421, 2017. © 2016 Wiley Periodicals, Inc.

Improved cardiac magnetic resonance thermometry and dosimetry for monitoring lesion formation during catheter ablation.

PURPOSE: A new real-time MR-thermometry pipeline was developed to measure multiple temperature images per heartbeat with 1.6×1.6×3 mm3 spatial resolution. The method was evaluated on 10 healthy volunteers and during radiofrequency ablation (RFA) in sheep. METHODS: Multislice, electrocardiogram-triggered, echo-planar imaging was combined with parallel imaging, under free breathing conditions. In-plane respiratory motion was corrected on magnitude images by an optical flow algorithm. Motion-related susceptibility artifacts were compensated on phase images by an algorithm based on Principal Component Analysis. Correction of phase drift and temporal filter were included in the pipeline implemented in the Gadgetron framework. Contact electrograms were recorded simultaneously with MR thermometry by an MR-compatible ablation catheter. RESULTS: The temporal standard deviation of temperature in the left ventricle remained below 2 °C on each volunteer. In sheep, focal heated regions near the catheter tip were observed on temperature images (maximal temperature increase of 38 °C) during RFA, with contact electrograms of acceptable quality. Thermal lesion dimensions at gross pathology were in agreement with those observed on thermal dose images. CONCLUSION: This fully automated MR thermometry pipeline (five images/heartbeat) provides direct assessment of lesion formation in the heart during catheter-based RFA, which may improve treatment of cardiac arrhythmia by ablation. Magn Reson Med 77:673-683, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Evaluation of multi-echo ICA denoising for task based fMRI studies: Block designs, rapid event-related designs, and cardiac-gated fMRI.

Multi-echo fMRI, particularly the multi-echo independent component analysis (ME-ICA) algorithm, has previously proven useful for increasing the sensitivity and reducing false positives for functional MRI (fMRI) based resting state connectivity studies. Less is known about its efficacy for task-based fMRI, especially at the single subject level. This work, which focuses exclusively on individual subject results, compares ME-ICA to single-echo fMRI and a voxel-wise T2(⁎) weighted combination of multi-echo data for task-based fMRI under the following scenarios: cardiac-gated block designs, constant repetition time (TR) block designs, and constant TR rapid event-related designs. Performance is evaluated primarily in terms of sensitivity (i.e., activation extent, activation magnitude, percent detected trials and effect size estimates) using five different tasks expected to evoke neuronal activity in a distributed set of regions. The ME-ICA algorithm significantly outperformed all other evaluated processing alternatives in all scenarios. Largest improvements were observed for the cardiac-gated dataset, where ME-ICA was able to reliably detect and remove non-neural T1 signal fluctuations caused by non-constant repetition times. Although ME-ICA also outperformed the other options in terms of percent detection of individual trials for rapid event-related experiments, only 46% of all events were detected after ME-ICA; suggesting additional improvements in sensitivity are required to reliably detect individual short event occurrences. We conclude the manuscript with a detailed evaluation of ME-ICA outcomes and a discussion of how the ME-ICA algorithm could be further improved. Overall, our results suggest that ME-ICA constitutes a versatile, powerful approach for advanced denoising of task-based fMRI, not just resting-state data.

Reducing sensitivity losses due to respiration and motion in accelerated echo planar imaging by reordering the autocalibration data acquisition.

PURPOSE: To reduce the sensitivity of echo-planar imaging (EPI) auto-calibration signal (ACS) data to patient respiration and motion to improve the image quality and temporal signal-to-noise ratio (tSNR) of accelerated EPI time-series data. METHODS: ACS data for accelerated EPI are generally acquired using segmented, multishot EPI to distortion-match the ACS and time-series data. The ACS data are, therefore, typically collected over multiple TR periods, leading to increased vulnerability to motion and dynamic B0 changes. The fast low-angle excitation echo-planar technique (FLEET) is adopted to reorder the ACS segments so that segments within any given slice are acquired consecutively in time, thereby acquiring ACS data for each slice as rapidly as possible. RESULTS: Subject breathhold and motion phantom experiments demonstrate that artifacts in the ACS data reduce tSNR and produce tSNR discontinuities across slices in the accelerated EPI time-series data. Accelerated EPI data reconstructed using FLEET-ACS exhibit improved tSNR and increased tSNR continuity across slices. Additionally, image quality is improved dramatically when bulk motion occurs during the ACS acquisition. CONCLUSION: FLEET-ACS provides reduced respiration and motion sensitivity in accelerated EPI, which yields higher tSNR and image quality. Benefits are demonstrated in both conventional-resolution 3T and high-resolution 7T EPI time-series data.

Improvement of temporal signal-to-noise ratio of GRAPPA accelerated echo planar imaging using a FLASH based calibration scan.

PURPOSE: To demonstrate that the temporal signal-to-noise ratio (SNR) of generalized autocalibrating partially parallel acquisitions (GRAPPA) accelerated echo planar imaging (EPI) can be enhanced and made more spatially uniform by using a fast low angle shot (FLASH) based calibration scan. METHODS: EPI of a phantom and human brains were acquired at 3 Tesla without and with GRAPPA acceleration factor of 2. The GRAPPA accelerated data were reconstructed using calibration scans acquired with EPI and FLASH acquisition schemes. The increase in temporal signal fluctuation due to GRAPPA reconstruction was quantified and compared. Simulated g-factor maps were also created for different calibration scans. RESULTS: GRAPPA accelerated phantom data exhibited areas with high g values when using the EPI based calibration for reconstruction. The g-factor maps were uniform when using the FLASH calibration scan. g was greater than 1.1 in 74% of pixels in 64 × 64 data reconstructed with the EPI calibration compared with only 15% when using the FLASH calibration scan. Human data also showed abnormally high g regions when using the EPI calibration but not when using the FLASH calibration scan. Use of the FLASH calibration scan increased the whole brain temporal SNR by ∼12% without affecting the image quality. Experimental observations were confirmed by simulations. CONCLUSION: A calibration scan based on a FLASH acquisition scheme can be used to improve the temporal SNR of GRAPPA accelerated EPI time series. Magn Reson Med 75:2362-2371, 2016. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.

Task Dependence, Tissue Specificity, and Spatial Distribution of Widespread Activations in Large Single-Subject Functional MRI Datasets at 7T.

It was recently shown that when large amounts of task-based blood oxygen level-dependent (BOLD) data are combined to increase contrast- and temporal signal-to-noise ratios, the majority of the brain shows significant hemodynamic responses time-locked with the experimental paradigm. Here, we investigate the biological significance of such widespread activations. First, the relationship between activation extent and task demands was investigated by varying cognitive load across participants. Second, the tissue specificity of responses was probed using the better BOLD signal localization capabilities of a 7T scanner. Finally, the spatial distribution of 3 primary response types--namely positively sustained (pSUS), negatively sustained (nSUS), and transient--was evaluated using a newly defined voxel-wise waveshape index that permits separation of responses based on their temporal signature. About 86% of gray matter (GM) became significantly active when all data entered the analysis for the most complex task. Activation extent scaled with task load and largely followed the GM contour. The most common response type was nSUS BOLD, irrespective of the task. Our results suggest that widespread activations associated with extremely large single-subject functional magnetic resonance imaging datasets can provide valuable information about the functional organization of the brain that goes undetected in smaller sample sizes.

Distributed MRI reconstruction using Gadgetron-based cloud computing.

PURPOSE: To expand the open source Gadgetron reconstruction framework to support distributed computing and to demonstrate that a multinode version of the Gadgetron can be used to provide nonlinear reconstruction with clinically acceptable latency. METHODS: The Gadgetron framework was extended with new software components that enable an arbitrary number of Gadgetron instances to collaborate on a reconstruction task. This cloud-enabled version of the Gadgetron was deployed on three different distributed computing platforms ranging from a heterogeneous collection of commodity computers to the commercial Amazon Elastic Compute Cloud. The Gadgetron cloud was used to provide nonlinear, compressed sensing reconstruction on a clinical scanner with low reconstruction latency (eg, cardiac and neuroimaging applications). RESULTS: The proposed setup was able to handle acquisition and 11 -SPIRiT reconstruction of nine high temporal resolution real-time, cardiac short axis cine acquisitions, covering the ventricles for functional evaluation, in under 1 min. A three-dimensional high-resolution brain acquisition with 1 mm(3) isotropic pixel size was acquired and reconstructed with nonlinear reconstruction in less than 5 min. CONCLUSION: A distributed computing enabled Gadgetron provides a scalable way to improve reconstruction performance using commodity cluster computing. Nonlinear, compressed sensing reconstruction can be deployed clinically with low image reconstruction latency.

Noise propagation in region of interest measurements.

PURPOSE: The purpose of this work was to develop and validate a technique for predicting the standard deviation (SD) associated with thermal noise propagation in region of interest measurements. THEORY AND METHODS: Standard methods for error propagation estimation were used to derive equations for the SDs of linear combinations of complex, magnitude, or phase pixel values. The equations were applied to common imaging scenarios in which the image pixels were correlated due to anisotropic pixel resolutions and parallel imaging. All SD estimates were evaluated efficiently using only vector-vector multiplications and Fourier transforms. The estimated SDs were compared to those obtained using repeated experiments and pseudo replica reconstructions. RESULTS: The proposed method was able to predict region of interest SDs in all the tested analysis scenarios. Positive and negative noise correlations caused by different parallel-imaging aliasing point spread functions were accurately predicted, and the method predicted the confidence intervals (CI) of time-intensity curves for in vivo cardiac perfusion measurements. CONCLUSION: An intuitive technique for region of interest CIs was developed and validated using phantom experiments and in vivo data.

Method for calculating confidence intervals for phase contrast flow measurements.

BACKGROUND: Phase contrast (PC) measurements play an important role in several cardiovascular magnetic resonance (CMR) protocols but considerable variation is observed in such measurements. Part of this variation stems from the propagation of thermal noise from the measurement data through the image reconstruction to the region of interest analysis used in flow measurement, which limits the precision. The purpose of this study was to develop a method for direct estimation of the variation caused by thermal noise and to validate this method in phantom and in vivo data. METHODS: The estimation of confidence intervals in flow measurements is complicated by noise correlation among the image pixels and cardiac phases. This correlation is caused by sequence and reconstruction parameters. A method for the calculation of the standard deviation of region of interest measurements was adapted and expanded to accommodate typical clinical PC measurements and the region-of-interest analysis used for such measurements. This included the dependency between cardiac phases that arises due to retrospective cardiac gating used in such studies. The proposed method enables calculation of standard deviations of flow measurements without the need for repeated experiments or repeated reconstructions. The method was compared to repeated trials in phantom measurements and pseudo replica reconstructions of in vivo data. Three different flow protocols (free breathing and breath hold with various accelerations) were compared in terms of the confidence interval ranges caused by thermal noise in the measurement data. RESULTS: Using the proposed method it was possible to accurately predict confidence intervals for flow measurements. The method was in good agreement with repeated measurements in phantom experiments and there was also good agreement with confidence intervals predicted by pseudo replica reconstructions in both phantom and in vivo data. The proposed method was used to demonstrate that the variation in cardiac output caused by thermal noise is on the order of 1% in clinically used free breathing protocols, and on the order of 3-5% in breath-hold protocols with higher parallel imaging factors. CONCLUSIONS: It is possible to calculate confidence intervals for Cartesian PC contrast flow measurements directly without the need for time-consuming pseudo replica reconstructions.

Integrated strategy for improving functional connectivity mapping using multiecho fMRI.

Functional connectivity analysis of resting state blood oxygen level-dependent (BOLD) functional MRI is widely used for noninvasively studying brain functional networks. Recent findings have indicated, however, that even small (≤1 mm) amounts of head movement during scanning can disproportionately bias connectivity estimates, despite various preprocessing efforts. Further complications for interregional connectivity estimation from time domain signals include the unaccounted reduction in BOLD degrees of freedom related to sensitivity losses from high subject motion. To address these issues, we describe an integrated strategy for data acquisition, denoising, and connectivity estimation. This strategy builds on our previously published technique combining data acquisition with multiecho (ME) echo planar imaging and analysis with spatial independent component analysis (ICA), called ME-ICA, which distinguishes BOLD (neuronal) and non-BOLD (artifactual) components based on linear echo-time dependence of signals-a characteristic property of BOLD T*2 signal changes. Here we show for 32 control subjects that this method provides a physically principled and nearly operator-independent way of removing complex artifacts such as motion from resting state data. We then describe a robust estimator of functional connectivity based on interregional correlation of BOLD-independent component coefficients. This estimator, called independent components regression, considerably simplifies statistical inference for functional connectivity because degrees of freedom equals the number of independent coefficients. Compared with traditional connectivity estimation methods, the proposed strategy results in fourfold improvements in signal-to-noise ratio, functional connectivity analysis with improved specificity, and valid statistical inference with nominal control of type 1 error in contrasts of connectivity between groups with different levels of subject motion.

Initial investigation of the blood-brain barrier in MS lesions at 7 tesla.

BACKGROUND: We previously described two dynamics of contrast enhancement in scans of active multiple sclerosis lesions: Medium-sized, early lesions enhance centrifugally, whereas larger, slightly older lesions enhance centripetally. Due to technical limitations, our previous study did not characterize lesions < 5 mm in diameter, cortical enhancement, and anatomical structures within lesions. OBJECTIVE: The objective of this paper is to obtain initial observations of these important aspects of lesion development on a 7 tesla scanner at high spatial resolution. METHODS: We scanned eight patients, acquiring precontrast T2*-weighted scans, T1-weighted scans before and after contrast, and high-resolution dynamic contrast-enhanced scans during and up to 30 min after contrast. RESULTS: We detected 15 enhancing lesions, obtaining dynamic data in 10: Five lesions < 4 mm enhanced centrifugally (initial central enhancement expanded outward), and five lesions > 4 mm enhanced centripetally (initial peripheral enhancement gradually filled the lesion). A leukocortical lesion initially showed enhancement in its white matter portion, which gradually spread into the cortex. Seventy-three percent of lesions were clearly perivenular. CONCLUSION: Most active lesions are perivenular, and the smallest lesions enhance centrifugally. This supports the idea that lesions grow outward from a central vein.

Whole-brain, time-locked activation with simple tasks revealed using massive averaging and model-free analysis.

The brain is the body's largest energy consumer, even in the absence of demanding tasks. Electrophysiologists report on-going neuronal firing during stimulation or task in regions beyond those of primary relationship to the perturbation. Although the biological origin of consciousness remains elusive, it is argued that it emerges from complex, continuous whole-brain neuronal collaboration. Despite converging evidence suggesting the whole brain is continuously working and adapting to anticipate and actuate in response to the environment, over the last 20 y, task-based functional MRI (fMRI) have emphasized a localizationist view of brain function, with fMRI showing only a handful of activated regions in response to task/stimulation. Here, we challenge that view with evidence that under optimal noise conditions, fMRI activations extend well beyond areas of primary relationship to the task; and blood-oxygen level-dependent signal changes correlated with task-timing appear in over 95% of the brain for a simple visual stimulation plus attention control task. Moreover, we show that response shape varies substantially across regions, and that whole-brain parcellations based on those differences produce distributed clusters that are anatomically and functionally meaningful, symmetrical across hemispheres, and reproducible across subjects. These findings highlight the exquisite detail lying in fMRI signals beyond what is normally examined, and emphasize both the pervasiveness of false negatives, and how the sparseness of fMRI maps is not a result of localized brain function, but a consequence of high noise and overly strict predictive response models.

Differentiating BOLD and non-BOLD signals in fMRI time series using multi-echo EPI.

A central challenge in the fMRI based study of functional connectivity is distinguishing neuronally related signal fluctuations from the effects of motion, physiology, and other nuisance sources. Conventional techniques for removing nuisance effects include modeling of noise time courses based on external measurements followed by temporal filtering. These techniques have limited effectiveness. Previous studies have shown using multi-echo fMRI that neuronally related fluctuations are Blood Oxygen Level Dependent (BOLD) signals that can be characterized in terms of changes in R(2)* and initial signal intensity (S(0)) based on the analysis of echo-time (TE) dependence. We hypothesized that if TE-dependence could be used to differentiate BOLD and non-BOLD signals, non-BOLD signal could be removed to denoise data without conventional noise modeling. To test this hypothesis, whole brain multi-echo data were acquired at 3 TEs and decomposed with Independent Components Analysis (ICA) after spatially concatenating data across space and TE. Components were analyzed for the degree to which their signal changes fit models for R(2)* and S(0) change, and summary scores were developed to characterize each component as BOLD-like or not BOLD-like. These scores clearly differentiated BOLD-like "functional network" components from non BOLD-like components related to motion, pulsatility, and other nuisance effects. Using non BOLD-like component time courses as noise regressors dramatically improved seed-based correlation mapping by reducing the effects of high and low frequency non-BOLD fluctuations. A comparison with seed-based correlation mapping using conventional noise regressors demonstrated the superiority of the proposed technique for both individual and group level seed-based connectivity analysis, especially in mapping subcortical-cortical connectivity. The differentiation of BOLD and non-BOLD components based on TE-dependence was highly robust, which allowed for the identification of BOLD-like components and the removal of non BOLD-like components to be implemented as a fully automated procedure.

Evidence for area CA1 as a match/mismatch detector: a high-resolution fMRI study of the human hippocampus.

The hippocampus is proposed to switch between memory encoding and retrieval by continually computing the overlap between what is expected and what is encountered. Central to this hypothesis is that area CA1 performs this calculation. However, empirical evidence for this is lacking. To test the theoretical role of area CA1 in match/mismatch detection, we had subjects study complex stimuli and then, during high-resolution fMRI scanning, make memory judgments about probes that either matched or mismatched expectations. More than any other hippocampal subfield, area CA1 displayed responses consistent with a match/mismatch detector. Specifically, the responses in area CA1 tracked the total number of changes present in the probe. Additionally, area CA1 was sensitive to both behaviorally relevant and irrelevant changes, a key feature of an automatic comparator. These results are consistent with, and provide the first evidence in humans for, the theoretically important role of area CA1 as a match/mismatch detector.

Sodium inversion recovery MRI of the knee joint in vivo at 7T.

The loss of proteoglycans (PG) in the articular cartilage is an early signature of osteoarthritis (OA). The ensuing changes in the fixed charge density in the cartilage can be directly linked to sodium concentration via charge balance. Sodium ions in the knee joint appear in two pools: in the synovial fluids or joint effusion where the ions are in free motion and bound within the cartilage tissue where the Na(+) ions have a restricted motion. The ions in these two compartments have therefore different T1 and T2 relaxation times. The purpose of this study is to demonstrate the feasibility of a fluid-suppressed 3D ultrashort TE radial sodium sequence by implementing an inversion recovery (IR) preparation of the magnetization at 7T. This method could allow a more accurate and more sensitive quantification of loss of PG in patients with OA. It is shown that adiabatic pulses offer significantly improved performance in terms of robustness to B1 and B0 inhomogeneities when compared to the hard pulse sequence. Power deposition considerations further pose a limit to the RF inversion power, and we demonstrate in simulations and experiments how a practical compromise can be struck between clean suppression of fluid signals and power deposition levels. Two IR sequences with different types of inversion pulses (a rectangular pulse and an adiabatic pulse) were tested on a liquid phantom, ex vivo on a human knee cadaver and then in vivo on five healthy volunteers, with a (Nyquist) resolution of ∼3.6 mm and a signal-to-noise ratio of ∼30 in cartilage without IR and ∼20 with IR. Due to specific absorption rate limitations, the total acquisition time was ∼17 min for the 3D radial sequence without inversion or with the rectangular IR, and 24:30 min for the adiabatic IR sequence. It is shown that the adiabatic IR sequence generates a more uniform fluid suppression over the whole sample than the rectangular IR sequence.

High efficiency, low distortion 3D diffusion tensor imaging with variable density spiral fast spin echoes (3D DW VDS RARE).

We present an acquisition and reconstruction method designed to acquire high resolution 3D fast spin echo diffusion tensor images while mitigating the major sources of artifacts in DTI-field distortions, eddy currents and motion. The resulting images, being 3D, are of high SNR, and being fast spin echoes, exhibit greatly reduced field distortions. This sequence utilizes variable density spiral acquisition gradients, which allow for the implementation of a self-navigation scheme by which both eddy current and motion artifacts are removed. The result is that high resolution 3D DTI images are produced without the need for eddy current compensating gradients or B(0) field correction. In addition, a novel method for fast and accurate reconstruction of the non-Cartesian data is employed. Results are demonstrated in the brains of normal human volunteers.

Distortion and signal loss in medial temporal lobe.

BACKGROUND: The medial temporal lobe (MTL) contains subregions that are subject to severe distortion and signal loss in functional MRI. Air/tissue and bone/tissue interfaces in the vicinity of the MTL distort the local magnetic field due to differences in magnetic susceptibility. Fast image acquisition and thin slices can reduce the amount of distortion and signal loss, but at the cost of image signal-to-noise ratio (SNR). METHODOLOGY/PRINCIPAL FINDINGS: In this paper, we quantify the severity of distortion and signal loss in MTL subregions for three different echo planar imaging (EPI) acquisitions at 3 Tesla: a conventional moderate-resolution EPI (3x3x3 mm), a conventional high-resolution EPI (1.5x1.5x2 mm), and a zoomed high-resolution EPI. We also demonstrate the advantage of reversing the phase encode direction to control the direction of distortion and to maximize efficacy of distortion compensation during data post-processing. With the high-resolution zoomed acquisition, distortion is not significant and signal loss is present only in the most anterior regions of the parahippocampal gyrus. Furthermore, we find that the severity of signal loss is variable across subjects, with some subjects showing negligible loss and others showing more dramatic loss. CONCLUSIONS/SIGNIFICANCE: Although both distortion and signal loss are minimized in a zoomed field of view acquisition with thin slices, this improvement in accuracy comes at the cost of reduced SNR. We quantify this trade-off between distortion and SNR in order to provide a decision tree for design of high-resolution experiments investigating the function of subregions in MTL.

Neural representations of faces and body parts in macaque and human cortex: a comparative FMRI study.

Single-cell studies in the macaque have reported selective neural responses evoked by visual presentations of faces and bodies. Consistent with these findings, functional magnetic resonance imaging studies in humans and monkeys indicate that regions in temporal cortex respond preferentially to faces and bodies. However, it is not clear how these areas correspond across the two species. Here, we directly compared category-selective areas in macaques and humans using virtually identical techniques. In the macaque, several face- and body part-selective areas were found located along the superior temporal sulcus (STS) and middle temporal gyrus (MTG). In the human, similar to previous studies, face-selective areas were found in ventral occipital and temporal cortex and an additional face-selective area was found in the anterior temporal cortex. Face-selective areas were also found in lateral temporal cortex, including the previously reported posterior STS area. Body part-selective areas were identified in the human fusiform gyrus and lateral occipitotemporal cortex. In a first experiment, both monkey and human subjects were presented with pictures of faces, body parts, foods, scenes, and man-made objects, to examine the response profiles of each category-selective area to the five stimulus types. In a second experiment, face processing was examined by presenting upright and inverted faces. By comparing the responses and spatial relationships of the areas, we propose potential correspondences across species. Adjacent and overlapping areas in the macaque anterior STS/MTG responded strongly to both faces and body parts, similar to areas in the human fusiform gyrus and posterior STS. Furthermore, face-selective areas on the ventral bank of the STS/MTG discriminated both upright and inverted faces from objects, similar to areas in the human ventral temporal cortex. Overall, our findings demonstrate commonalities and differences in the wide-scale brain organization between the two species and provide an initial step toward establishing functionally homologous category-selective areas.