MRSCloud: A cloud-based MRS tool for basis set simulation.

PURPOSE: The purpose of this study is to present a cloud-based spectral simulation tool "MRSCloud," which allows MRS users to simulate a vendor-specific and sequence-specific basis set online in a convenient and time-efficient manner. This tool can simulate basis sets for GE, Philips, and Siemens MR scanners, including conventional acquisitions and spectral editing schemes with PRESS and semi-LASER localization at 3 T. METHODS: The MRSCloud tool was built on the spectral simulation functionality in the FID-A software package. We added three extensions to accelerate computation (ie, one-dimensional projection method, coherence pathways filters, and precalculation of propagators). The RF waveforms were generated based on vendors' generic pulse shapes and timings. Simulations were compared within MRSCloud using different numbers of spatial resolution (21 × 21, 41 × 41, and 101 × 101). Simulated metabolite basis functions from MRSCloud were compared with those generated by the generic FID-A and MARSS, and a phantom-acquired basis set from LCModel. Intraclass correlation coefficients were calculated to measure the agreement between individual metabolite basis functions. Statistical analysis was performed using R in RStudio. RESULTS: Simulation time for a full PRESS basis set is approximately 11 min on the server. The interclass correlation coefficients ICCs were at least 0.98 between MRSCloud and FID-A and were at least 0.96 between MRSCloud and MARSS. The interclass correlation coefficients between simulated MRSCloud basis spectra and acquired LCModel basis spectra were lowest for glutamine at 0.68 and highest for N-acetylaspartate at 0.96. CONCLUSIONS: Substantial reductions in runtime have been achieved. High ICC values indicated that the accelerating features are running correctly and produce comparable and accurate basis sets.

Whole-Brain Functional and Diffusion Tensor MRI in Human Participants with Metallic Orthodontic Braces.

Background MRI performed with echo-planar imaging (EPI) sequences is sensitive to susceptibility artifacts in the presence of metallic objects, which presents a substantial barrier for performing functional MRI and diffusion tensor imaging (DTI) in patients with metallic orthodontic material and other head implants. Purpose To evaluate the ability to reduce susceptibility artifacts in healthy human participants wearing metallic orthodontic braces for two alternative approaches: T2-prepared functional MRI and diffusion-prepared DTI with three-dimensional fast gradient-echo readout. Materials and Methods In this prospective study conducted from February to September 2018, T2-prepared functional MRI and diffusion-prepared DTI were performed in healthy human participants. Removable dental braces with bonding trays were used so that MRI could be performed with braces and without braces in the same participants. Results were evaluated in regions with strong (EPI dropout regions for functional MRI and the inferior fronto-occipital fasciculus for DTI) and minimal (motor cortex for functional MRI and the posterior limb of internal capsule for DTI) susceptibility artifacts. Signal-to-noise ratio (SNR), contrast-to-noise ratio for functional MRI, apparent diffusion coefficient and fractional anisotropy for DTI, and degree of distortion (quantified with the Jaccard index, which measures the similarity of geometric shapes) were compared in regions with strong or minimal susceptibility effects between the current standard EPI sequences and the proposed alternatives by using paired t test. Results Six participants were evaluated (mean age ± standard deviation, 40 years ± 6; three women). In brain regions with strong susceptibility effects from the metallic braces, T2-prepared functional MRI showed significantly higher SNR (37.8 ± 2.4 vs 15.5 ± 5.3; P < .001) and contrast-to-noise ratio (0.83 ± 0.16 vs 0.29 ± 0.10; P < .001), whereas diffusion-prepared DTI showed higher SNR (5.8 ± 1.5 vs 3.8 ± 0.7; P = .03) than did conventional EPI methods. Apparent diffusion coefficient and fractional anisotropy were consistent with the literature. Geometric distortion was substantially reduced throughout the brain with the proposed methods (significantly higher Jaccard index, 0.95 ± 0.12 vs 0.81 ± 0.61; P < .001). Conclusion T2-prepared functional MRI and diffusion-prepared diffusion tensor imaging can acquire functional and diffusion MRI, respectively, in healthy human participants wearing metallic dental braces with less susceptibility artifacts and geometric distortion than with conventional echo-planar imaging. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Dietrich in this issue.

ASL-MRICloud: An online tool for the processing of ASL MRI data.

Arterial spin labeling (ASL) MRI is increasingly used in research and clinical settings. The purpose of this work is to develop a cloud-based tool for ASL data processing, referred to as ASL-MRICloud, which may be useful to the MRI community. In contrast to existing ASL toolboxes, which are based on software installation on the user's local computer, ASL-MRICloud uses a web browser for data upload and results download, and the computation is performed on the remote server. As such, this tool is independent of the user's operating system, software version, and CPU speed. The ASL-MRICloud tool was implemented to be compatible with data acquired by scanners from all major MRI manufacturers, is capable of processing several common forms of ASL, including pseudo-continuous ASL and pulsed ASL, and can process single-delay and multi-delay ASL data. The outputs of ASL-MRICloud include absolute and relative values of cerebral blood flow, arterial transit time, voxel-wise masks indicating regions with potential hyper-perfusion and hypo-perfusion, and an image quality index. The ASL tool is also integrated with a T1 -based brain segmentation and normalization tool in MRICloud to allow generation of parametric maps in standard brain space as well as region-of-interest values. The tool was tested on a large data set containing 309 ASL scans as well as on publicly available ASL data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study.

Anatomical characterization of athetotic and spastic cerebral palsy using an atlas-based analysis.

PURPOSE: To analyze diffusion tensor imaging (DTI) in two types of cerebral palsy (CP): the athetotic-type and the spastic-type, using an atlas-based anatomical analysis of the entire brain, and to investigate whether these images have unique anatomical characteristics that can support functional diagnoses. MATERIALS AND METHODS: We retrospectively analyzed the DTI of seven children with athetotic-type, 11 children with spastic-type, and 20 healthy control children, all age-matched. The severity of motor dysfunction was evaluated with the Gross Motor Function Classification System (GMFCS). The images were normalized using a linear transformation, followed by large deformation diffeomorphic metric mapping. For 205 parcellated brain areas, the volume, fractional anisotropy, and mean diffusivity were measured. Principal component analysis (PCA) was performed for the Z-scores of these parameters. RESULTS: The GMFCS scores in athetotic-type were significantly higher than those in spastic-type (P < 0.001). PCA extracted anatomical components that comprised the two types of CP, as well as the severity of motor dysfunction. In the athetotic group, the abnormalities were more severe than in the spastic group. In the spastic group, significant changes were concentrated in the lateral ventricle and periventricular structures. CONCLUSION: Our results quantitatively delineated anatomical characteristics that reflected the functional findings in two types of CP.

Quantitative analysis of brain pathology based on MRI and brain atlases--applications for cerebral palsy.

We have developed a new method to provide a comprehensive quantitative analysis of brain anatomy in cerebral palsy patients, which makes use of two techniques: diffusion tensor imaging and automated 3D whole brain segmentation based on our brain atlas and a nonlinear normalization technique (large-deformation diffeomorphic metric mapping). This method was applied to 13 patients and normal controls. The reliability of the automated segmentation revealed close agreement with the manual segmentation. We illustrate some potential applications for individual characterization and group comparison. This technique also provides a framework for determining the impact of various neuroanatomic features on brain functions.

An MRI-based atlas and database of the developing mouse brain.

The advent of mammalian gene engineering and genetically modified mouse models has led to renewed interest in developing resources for referencing and quantitative analysis of mouse brain anatomy. In this study, we used diffusion tensor imaging (DTI) for quantitative characterization of anatomical phenotypes in the developing mouse brain. As an anatomical reference for neuroscience research using mouse models, this paper presents DTI based atlases of ex vivo C57BL/6 mouse brains at several developmental stages. The atlas complements existing histology and MRI-based atlases by providing users access to three-dimensional, high-resolution images of the developing mouse brain, with distinct tissue contrasts and segmentations of major gray matter and white matter structures. The usefulness of the atlas and database was demonstrated by quantitative measurements of the development of major gray matter and white matter structures. Population average images of the mouse brain at several postnatal stages were created using large deformation diffeomorphic metric mapping and their anatomical variations were quantitatively characterized. The atlas and database enhance our ability to examine the neuroanatomy in normal or genetically engineered mouse strains and mouse models of neurological diseases.

Multi-contrast human neonatal brain atlas: application to normal neonate development analysis.

MRI is a sensitive method for detecting subtle anatomic abnormalities in the neonatal brain. To optimize the usefulness for neonatal and pediatric care, systematic research, based on quantitative image analysis and functional correlation, is required. Normalization-based image analysis is one of the most effective methods for image quantification and statistical comparison. However, the application of this methodology to neonatal brain MRI scans is rare. Some of the difficulties are the rapid changes in T1 and T2 contrasts and the lack of contrast between brain structures, which prohibits accurate cross-subject image registration. Diffusion tensor imaging (DTI), which provides rich and quantitative anatomical contrast in neonate brains, is an ideal technology for normalization-based neonatal brain analysis. In this paper, we report the development of neonatal brain atlases with detailed anatomic information derived from DTI and co-registered anatomical MRI. Combined with a diffeomorphic transformation, we were able to normalize neonatal brain images to the atlas space and three-dimensionally parcellate images into 122 regions. The accuracy of the normalization was comparable to the reliability of human raters. This method was then applied to babies of 37-53 post-conceptional weeks to characterize developmental changes of the white matter, which indicated a posterior-to-anterior and a central-to-peripheral direction of maturation. We expect that future applications of this atlas will include investigations of the effect of prenatal events and the effects of preterm birth or low birth weights, as well as clinical applications, such as determining imaging biomarkers for various neurological disorders.

Atlas-based analysis of neurodevelopment from infancy to adulthood using diffusion tensor imaging and applications for automated abnormality detection.

Quantification of normal brain maturation is a crucial step in understanding developmental abnormalities in brain anatomy and function. The aim of this study was to develop atlas-based tools for time-dependent quantitative image analysis, and to characterize the anatomical changes that occur from 2years of age to adulthood. We used large deformation diffeomorphic metric mapping to register diffusion tensor images of normal participants into the common coordinates and used a pre-segmented atlas to segment the entire brain into 176 structures. Both voxel- and atlas-based analyses reported a structure that showed distinctive changes in terms of its volume and diffusivity measures. In the white matter, fractional anisotropy (FA) linearly increased with age in logarithmic scale, while diffusivity indices, such as apparent diffusion coefficient (ADC), and axial and radial diffusivity, decreased at a different rate in several regions. The average, variability, and the time course of each measured parameter are incorporated into the atlas, which can be used for automated detection of developmental abnormalities. As a demonstration of future application studies, the brainstem anatomy of cerebral palsy patients was evaluated and the altered anatomy was delineated.

Atlas-guided tract reconstruction for automated and comprehensive examination of the white matter anatomy.

Tractography based on diffusion tensor imaging (DTI) is widely used to quantitatively analyze the status of the white matter anatomy in a tract-specific manner in many types of diseases. This approach, however, involves subjective judgment in the tract-editing process to extract only the tracts of interest. This process, usually performed by manual delineation of regions of interest, is also time-consuming, and certain tracts, especially the short cortico-cortical association fibers, are difficult to reconstruct. In this paper, we propose an automated approach for reconstruction of a large number of white matter tracts. In this approach, existing anatomical knowledge about tract trajectories (called the Template ROI Set or TRS) were stored in our DTI-based brain atlas with 130 three-dimensional anatomical segmentations, which were warped non-linearly to individual DTI data. We examined the degree of matching with manual results for selected fibers. We established 30 TRSs to reconstruct 30 prominent and previously well-described fibers. In addition, TRSs were developed to delineate 29 short association fibers that were found in all normal subjects examined in this paper (N=20). Probabilistic maps of the 59 tract trajectories were created from the normal subjects and were incorporated into our image analysis tool for automated tract-specific quantification.

Mapping of functional areas in the human cortex based on connectivity through association fibers.

In the human brain, different regions of the cortex communicate via white matter tracts. Investigation of this connectivity is essential for understanding brain function. It has been shown that trajectories of white matter fiber bundles can be estimated based on orientational information that is obtained from diffusion tensor imaging (DTI). By extrapolating this information, cortical regions associated with a specific white matter tract can be estimated. In this study, we created population-averaged cortical maps of brain connectivity for 4 major association fiber tracts, the corticospinal tract (CST), and commissural fibers. It is shown that these 4 association fibers interconnect all 4 lobes of the hemispheres. Cortical regions that were assigned based on association with the CST and the superior longitudinal fasciculus (SLF) agreed with locations of their known (CST: motor) or putative (SLF: language) functions. The proposed approach can potentially be used for quantitative assessment of the effect of white matter abnormalities on associated cortical regions.

Atlas-based whole brain white matter analysis using large deformation diffeomorphic metric mapping: application to normal elderly and Alzheimer's disease participants.

The purpose of this paper is to establish single-participant white matter atlases based on diffusion tensor imaging. As one of the applications of the atlas, automated brain segmentation was performed and the accuracy was measured using Large Deformation Diffeomorphic Metric Mapping (LDDMM). High-quality diffusion tensor imaging (DTI) data from a single-participant were B0-distortion-corrected and transformed to the ICBM-152 atlas or to Talairach coordinates. The deep white matter structures, which have been previously well documented and clearly identified by DTI, were manually segmented. The superficial white matter areas beneath the cortex were defined, based on a population-averaged white matter probability map. The white matter was parcellated into 176 regions based on the anatomical labeling in the ICBM-DTI-81 atlas. The automated parcellation was achieved by warping this parcellation map to normal controls and to Alzheimer's disease patients with severe anatomical atrophy. The parcellation accuracy was measured by a kappa analysis between the automated and manual parcellation at 11 anatomical regions. The kappa values were 0.70 for both normal controls and patients while the inter-rater reproducibility was 0.81 (controls) and 0.82 (patients), suggesting "almost perfect" agreement. A power analysis suggested that the proposed method is suitable for detecting FA and size abnormalities of the white matter in clinical studies.

Human brain white matter atlas: identification and assignment of common anatomical structures in superficial white matter.

Structural delineation and assignment are the fundamental steps in understanding the anatomy of the human brain. The white matter has been structurally defined in the past only at its core regions (deep white matter). However, the most peripheral white matter areas, which are interleaved between the cortex and the deep white matter, have lacked clear anatomical definitions and parcellations. We used axonal fiber alignment information from diffusion tensor imaging (DTI) to delineate the peripheral white matter, and investigated its relationship with the cortex and the deep white matter. Using DTI data from 81 healthy subjects, we identified nine common, blade-like anatomical regions, which were further parcellated into 21 subregions based on the cortical anatomy. Four short association fiber tracts connecting adjacent gyri (U-fibers) were also identified reproducibly among the healthy population. We anticipate that this atlas will be useful resource for atlas-based white matter anatomical studies.

DtiStudio: resource program for diffusion tensor computation and fiber bundle tracking.

A versatile resource program was developed for diffusion tensor image (DTI) computation and fiber tracking. The software can read data formats from a variety of MR scanners. Tensor calculation is performed by solving an over-determined linear equation system using least square fitting. Various types of map data, such as tensor elements, eigenvalues, eigenvectors, diffusion anisotropy, diffusion constants, and color-coded orientations can be calculated. The results are visualized interactively in orthogonal views and in three-dimensional mode. Three-dimensional tract reconstruction is based on the Fiber Assignment by Continuous Tracking (FACT) algorithm and a brute-force reconstruction approach. To improve the time and memory efficiency, a rapid algorithm to perform the FACT is adopted. An index matrix for the fiber data is introduced to facilitate various types of fiber bundles selection based on approaches employing multiple regions of interest (ROIs). The program is developed using C++ and OpenGL on a Windows platform.

High-resolution diffusion tensor imaging of the brain stem at 3 T.

Diffusion tensor imaging with 1.8-mm isotropic resolution was performed to delineate structures of the brain stem. High-resolution single-shot imaging was achieved by the combination of a high-field magnet (3T) and the SENSitivity Encoding (or SENSE) parallel imaging technique. Various structures in the brain stem, such as the inferior olivary nuclei, deep cerebellar nuclei, some cranial nerves, and white matter tracts were identified, which have been difficult to appreciate by conventional MR techniques.

Techniques to improve the accuracy and to reduce the variance in noise power spectrum measurement.

Several techniques to increase the accuracy and to reduce the variance of the noise power spectrum (NPS) measurement for digital X-ray imaging systems are investigated. These techniques include: 1) averaging the outputs from subblocks of the entire image; 2) averaging the two-dimensional NPS data along a circular route centered on the origin of spectral domain; and 3) masking a window function on each subblock before Fourier transforms. Techniques 1) and 2) are used mainly to reduce the variance of the NPS measurement. Technique 3) serves to improve the accuracy of the final result. Experiments with two different charge-coupled device-based X-ray imaging systems demonstrated that the precision and accuracy of the NPS measurement could be significantly improved using these techniques. The impact of the image partition for averaging is discussed and the corresponding NPS estimations are presented for the number of subblocks ranging from 4 to 64. The effect of masking on the NPS is also studied using four different window functions.

AtlasGuide: software for stereotaxic guidance using 3D CT/MRI hybrid atlases of developing mouse brains.

Stereotaxic operations of the mouse brain are critically important for various types of neuroscience research studies, which include electrical recording of neural activities or site-targeted injection of stem cells, chemical tracers, and vectors, to name a few. To guide such operations, two-dimensional histology-based mouse brain atlases, such as the Paxinos and Franklin atlas, are widely used. Recently, computed tomography (CT) and magnetic resonance imaging (MRI) based hybrid three-dimensional (3D) atlases of developing mouse brains have been introduced. In this study, a new stereotaxic guidance software, called AtlasGuide, is introduced, which was developed to fully utilize the benefits of the 3D atlases for high-precision stereotaxic targeting. The AtlasGuide software provides functions to visualize oblique needle paths in 2D and 3D views, which allow investigators to simultaneously examine brain structures that could be damaged by the needle path and optimize the injection angles for high-precision trajectory selection through critical neural tissue. It allows reorientation and scaling of the atlases dynamically to match the orientation of the animal brain prepared for surgery, thereby eliminating the need to manually align the subject to the atlas, a procedure which is essential while using conventional 2D atlases. In addition, the software enables loading user-defined atlases when researchers need image-based guidance for different age groups, strains, or species. The software with integrated 3D stereotaxic mouse atlases is available for download at the http://lbam.med.jhmi.edu website.

Image corruption detection in diffusion tensor imaging for post-processing and real-time monitoring.

Due to the high sensitivity of diffusion tensor imaging (DTI) to physiological motion, clinical DTI scans often suffer a significant amount of artifacts. Tensor-fitting-based, post-processing outlier rejection is often used to reduce the influence of motion artifacts. Although it is an effective approach, when there are multiple corrupted data, this method may no longer correctly identify and reject the corrupted data. In this paper, we introduce a new criterion called "corrected Inter-Slice Intensity Discontinuity" (cISID) to detect motion-induced artifacts. We compared the performance of algorithms using cISID and other existing methods with regard to artifact detection. The experimental results show that the integration of cISID into fitting-based methods significantly improves the retrospective detection performance at post-processing analysis. The performance of the cISID criterion, if used alone, was inferior to the fitting-based methods, but cISID could effectively identify severely corrupted images with a rapid calculation time. In the second part of this paper, an outlier rejection scheme was implemented on a scanner for real-time monitoring of image quality and reacquisition of the corrupted data. The real-time monitoring, based on cISID and followed by post-processing, fitting-based outlier rejection, could provide a robust environment for routine DTI studies.

Cortico-cortical, cortico-striatal, and cortico-thalamic white matter fiber tracts generated in the macaque brain via dynamic programming.

Probabilistic methods have the potential to generate multiple and complex white matter fiber tracts in diffusion tensor imaging (DTI). Here, a method based on dynamic programming (DP) is introduced to reconstruct fibers pathways whose complex anatomical structures cannot be resolved beyond the resolution of standard DTI data. DP is based on optimizing a sequentially additive cost function derived from a Gaussian diffusion model whose covariance is defined by the diffusion tensor. DP is used to determine the optimal path between initial and terminal nodes by efficiently searching over all paths, connecting the nodes, and choosing the path in which the total probability is maximized. An ex vivo high-resolution scan of a macaque hemi-brain is used to demonstrate the advantages and limitations of DP. DP can generate fiber bundles between distant cortical areas (superior longitudinal fasciculi, arcuate fasciculus, uncinate fasciculus, and fronto-occipital fasciculus), neighboring cortical areas (dorsal and ventral banks of the principal sulcus), as well as cortical projections to the hippocampal formation (cingulum bundle), neostriatum (motor cortical projections to the putamen), thalamus (subcortical bundle), and hippocampal formation projections to the mammillary bodies via the fornix. Validation is established either by comparison with in vivo intracellular transport of horseradish peroxidase in another macaque monkey or by comparison with atlases. DP is able to generate known pathways, including crossing and kissing tracts. Thus, DP has the potential to enhance neuroimaging studies of cortical connectivity.

Quantitative analysis of gray and white matter in Williams syndrome.

Williams syndrome is a developmental disorder with a genetic basis, which results in an uneven cognitive profile with relatively strong language skills and severely impaired visuospatial abilities. To better understand the brain structure underlying this profile, we compared individuals with Williams syndrome with controls using multimodal neuroimaging data and new analytic methods (diffeomorphic mapping and atlas-based analysis). People with Williams syndrome had basal ganglia atrophy, while the fusiform, the medium temporal gyri, and the cerebellar cortex were relatively preserved. The right superior longitudinal fasciculus, the left frontooccipital fasciculus, the caudate, and the cingulum demonstrated increased fractional anisotropy, whereas the corticospinal tract revealed decreased values. These findings may be linked to the uneven cognitive profile evident in Williams syndrome.

Multi-modal MRI analysis with disease-specific spatial filtering: initial testing to predict mild cognitive impairment patients who convert to Alzheimer's disease.

BACKGROUND: Alterations of the gray and white matter have been identified in Alzheimer's disease (AD) by structural magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI). However, whether the combination of these modalities could increase the diagnostic performance is unknown. METHODS: Participants included 19 AD patients, 22 amnestic mild cognitive impairment (aMCI) patients, and 22 cognitively normal elderly (NC). The aMCI group was further divided into an "aMCI-converter" group (converted to AD dementia within 3 years), and an "aMCI-stable" group who did not convert in this time period. A T(1)-weighted image, a T(2) map, and a DTI of each participant were normalized, and voxel-based comparisons between AD and NC groups were performed. Regions-of-interest, which defined the areas with significant differences between AD and NC, were created for each modality and named "disease-specific spatial filters" (DSF). Linear discriminant analysis was used to optimize the combination of multiple MRI measurements extracted by DSF to effectively differentiate AD from NC. The resultant DSF and the discriminant function were applied to the aMCI group to investigate the power to differentiate the aMCI-converters from the aMCI-stable patients. RESULTS: The multi-modal approach with AD-specific filters led to a predictive model with an area under the receiver operating characteristic curve (AUC) of 0.93, in differentiating aMCI-converters from aMCI-stable patients. This AUC was better than that of a single-contrast-based approach, such as T(1)-based morphometry or diffusion anisotropy analysis. CONCLUSION: The multi-modal approach has the potential to increase the value of MRI in predicting conversion from aMCI to AD.