Dynamic shimming in the cervical spinal cord for multi-echo gradient-echo imaging at 3 T.

Obtaining high quality images of the spinal cord with MRI is difficult, partly due to the fact that the spinal cord is surrounded by a number of structures that have differing magnetic susceptibility. This causes inhomogeneities in the magnetic field, which in turn lead to image artifacts. In order to address this issue, linear compensation gradients can be employed. The latter can be generated using an MRI scanner's first order gradient coils and adjusted on a per-slice basis, in order to correct for through-plane ("z") magnetic field gradients. This approach is referred to as z-shimming. The aim of this study is two-fold. The first aim was to replicate aspects of a previous study wherein z-shimming was found to improve image quality in T2*-weighted echo-planar imaging. Our second aim was to improve upon the z-shimming approach by including in-plane compensation gradients and adjusting the compensation gradients during the image acquisition process so that they take into account respiration-induced magnetic field variations. We refer to this novel approach as realtime dynamic shimming. Measurements performed in a group of 12 healthy volunteers at 3 T show improved signal homogeneity along the spinal cord when using z-shimming. Signal homogeneity may be further improved by including realtime compensation for respiration-induced field gradients and by also doing this for gradients along the in-plane axes.

Atlas-Based Quantification of DTI Measures in a Typically Developing Pediatric Spinal Cord.

BACKGROUND AND PURPOSE: Multi-parametric MRI, provides a variety of biomarkers sensitive to white matter integrity, However, spinal cord MRI data in pediatrics is rare compared to adults. The purpose of this work was 3-fold: 1) to develop a processing pipeline for atlas-based generation of the typically developing pediatric spinal cord WM tracts, 2) to derive atlas-based normative values of the DTI indices for various WM pathways, and 3) to investigate age-related changes in the obtained normative DTI indices along the extracted tracts. MATERIALS AND METHODS: DTI scans of 30 typically developing subjects (age range, 6-16 years) were acquired on a 3T MR imaging scanner. The data were registered to the PAM50 template in the Spinal Cord Toolbox. Next, the DTI indices for various WM regions were extracted at a single section centered at the C3 vertebral body in all the 30 subjects. Finally, an ANOVA test was performed to examine the effects of the following: 1) laterality, 2) functionality, and 3) age, with DTI-derived indices in 34 extracted WM regions. RESULTS: A postprocessing pipeline was developed and validated to delineate pediatric spinal cord WM tracts. The results of ANOVA on fractional anisotropy values showed no effect for laterality (P = .72) but an effect for functionality (P < .001) when comparing the 30 primary WM labels. There was a significant (P < .05) effect of age and maturity of the left spinothalamic tract on mean diffusivity, radial diffusivity, and axial diffusivity values. CONCLUSIONS: The proposed automated pipeline in this study incorporates unique postprocessing steps followed by template registration and quantification of DTI metrics using atlas-based regions. This method eliminates the need for manual ROI analysis of WM tracts and, therefore, increases the accuracy and speed of the measurements.

Convolutional Neural Network-Based Automated Segmentation of the Spinal Cord and Contusion Injury: Deep Learning Biomarker Correlates of Motor Impairment in Acute Spinal Cord Injury.

BACKGROUND AND PURPOSE: Our aim was to use 2D convolutional neural networks for automatic segmentation of the spinal cord and traumatic contusion injury from axial T2-weighted MR imaging in a cohort of patients with acute spinal cord injury. MATERIALS AND METHODS: Forty-seven patients who underwent 3T MR imaging within 24 hours of spinal cord injury were included. We developed an image-analysis pipeline integrating 2D convolutional neural networks for whole spinal cord and intramedullary spinal cord lesion segmentation. Linear mixed modeling was used to compare test segmentation results between our spinal cord injury convolutional neural network (Brain and Spinal Cord Injury Center segmentation) and current state-of-the-art methods. Volumes of segmented lesions were then used in a linear regression analysis to determine associations with motor scores. RESULTS: Compared with manual labeling, the average test set Dice coefficient for the Brain and Spinal Cord Injury Center segmentation model was 0.93 for spinal cord segmentation versus 0.80 for PropSeg and 0.90 for DeepSeg (both components of the Spinal Cord Toolbox). Linear mixed modeling showed a significant difference between Brain and Spinal Cord Injury Center segmentation compared with PropSeg (P < .001) and DeepSeg (P < .05). Brain and Spinal Cord Injury Center segmentation showed significantly better adaptability to damaged areas compared with PropSeg (P < .001) and DeepSeg (P < .02). The contusion injury volumes based on automated segmentation were significantly associated with motor scores at admission (P = .002) and discharge (P = .009). CONCLUSIONS: Brain and Spinal Cord Injury Center segmentation of the spinal cord compares favorably with available segmentation tools in a population with acute spinal cord injury. Volumes of injury derived from automated lesion segmentation with Brain and Spinal Cord Injury Center segmentation correlate with measures of motor impairment in the acute phase. Targeted convolutional neural network training in acute spinal cord injury enhances algorithm performance for this patient population and provides clinically relevant metrics of cord injury.

Microstructural imaging in the spinal cord and validation strategies.

In vivo histology using magnetic resonance imaging (MRI) is a newly emerging research field that aims to non-invasively characterize tissue microstructure. The implications of in vivo histology are many, from discovering novel biomarkers to studying human development, to providing tools for disease diagnosis and monitoring the effects of novel treatments on tissue. This review focuses on quantitative MRI (qMRI) techniques that are used to map spinal cord microstructure. Opening with a rationale for non-invasive imaging of the spinal cord, this article continues with a brief overview of the existing MRI techniques for axon and myelin imaging, followed by the specific challenges and potential solutions for acquiring and processing such data. The final part of this review focuses on histological validation, with suggested tissue preparation, acquisition and processing protocols for large-scale microscopy.

Spinal Cord Gray Matter Atrophy in Amyotrophic Lateral Sclerosis.

BACKGROUND AND PURPOSE: There is an emerging need for biomarkers to better categorize clinical phenotypes and predict progression in amyotrophic lateral sclerosis. This study aimed to quantify cervical spinal gray matter atrophy in amyotrophic lateral sclerosis and investigate its association with clinical disability at baseline and after 1 year. MATERIALS AND METHODS: Twenty-nine patients with amyotrophic lateral sclerosis and 22 healthy controls were scanned with 3T MR imaging. Standard functional scale was recorded at the time of MR imaging and after 1 year. MR imaging data were processed automatically to measure the spinal cord, gray matter, and white matter cross-sectional areas. A statistical analysis assessed the difference in cross-sectional areas between patients with amyotrophic lateral sclerosis and controls, correlations between spinal cord and gray matter atrophy to clinical disability at baseline and at 1 year, and prediction of clinical disability at 1 year. RESULTS: Gray matter atrophy was more sensitive to discriminate patients with amyotrophic lateral sclerosis from controls (P = .004) compared with spinal cord atrophy (P = .02). Gray matter and spinal cord cross-sectional areas showed good correlations with clinical scores at baseline (R = 0.56 for gray matter and R = 0.55 for spinal cord; P < .01). Prediction at 1 year with clinical scores (R2 = 0.54) was improved when including a combination of gray matter and white matter cross-sectional areas (R2 = 0.74). CONCLUSIONS: Although improvements over spinal cord cross-sectional areas were modest, this study suggests the potential use of gray matter cross-sectional areas as an MR imaging structural biomarker to monitor the evolution of amyotrophic lateral sclerosis.

A Novel MRI Biomarker of Spinal Cord White Matter Injury: T2*-Weighted White Matter to Gray Matter Signal Intensity Ratio.

BACKGROUND AND PURPOSE: T2*-weighted imaging provides sharp contrast between spinal cord GM and WM, allowing their segmentation and cross-sectional area measurement. Injured WM demonstrates T2*WI hyperintensity but requires normalization for quantitative use. We introduce T2*WI WM/GM signal-intensity ratio and compare it against cross-sectional area, the DTI metric fractional anisotropy, and magnetization transfer ratio in degenerative cervical myelopathy. MATERIALS AND METHODS: Fifty-eight patients with degenerative cervical myelopathy and 40 healthy subjects underwent 3T MR imaging, covering C1-C7. Metrics were automatically extracted at maximally compressed and uncompressed rostral/caudal levels. Normalized metrics were compared with t tests, area under the curve, and logistic regression. Relationships with clinical measures were analyzed by using Pearson correlation and multiple linear regression. RESULTS: The maximally compressed level cross-sectional area demonstrated superior differences (P = 1 × 10-13), diagnostic accuracy (area under the curve = 0.890), and univariate correlation with the modified Japanese Orthopedic Association score (0.66). T2*WI WM/GM showed strong differences (rostral: P = 8 × 10-7; maximally compressed level: P = 1 × 10-11; caudal: P = 1 × 10-4), correlations (modified Japanese Orthopedic Association score; rostral: -0.52; maximally compressed level: -0.59; caudal: -0.36), and diagnostic accuracy (rostral: 0.775; maximally compressed level: 0.860; caudal: 0.721), outperforming fractional anisotropy and magnetization transfer ratio in most comparisons and cross-sectional area at rostral/caudal levels. Rostral T2*WI WM/GM showed the strongest correlations with focal motor (-0.45) and sensory (-0.49) deficits and was the strongest independent predictor of the modified Japanese Orthopedic Association score (P = .01) and diagnosis (P = .02) in multivariate models (R2 = 0.59, P = 8 × 10-13; area under the curve = 0.954, respectively). CONCLUSIONS: T2*WI WM/GM shows promise as a novel biomarker of WM injury. It detects damage in compressed and uncompressed regions and contributes substantially to multivariate models for diagnosis and correlation with impairment. Our multiparametric approach overcomes limitations of individual measures, having the potential to improve diagnostics, monitor progression, and predict outcomes.

Clinically Feasible Microstructural MRI to Quantify Cervical Spinal Cord Tissue Injury Using DTI, MT, and T2*-Weighted Imaging: Assessment of Normative Data and Reliability.

BACKGROUND AND PURPOSE: DTI, magnetization transfer, T2*-weighted imaging, and cross-sectional area can quantify aspects of spinal cord microstructure. However, clinical adoption remains elusive due to complex acquisitions, cumbersome analysis, limited reliability, and wide ranges of normal values. We propose a simple multiparametric protocol with automated analysis and report normative data, analysis of confounding variables, and reliability. MATERIALS AND METHODS: Forty healthy subjects underwent T2WI, DTI, magnetization transfer, and T2*WI at 3T in <35 minutes using standard hardware and pulse sequences. Cross-sectional area, fractional anisotropy, magnetization transfer ratio, and T2*WI WM/GM signal intensity ratio were calculated. Relationships between MR imaging metrics and age, sex, height, weight, cervical cord length, and rostrocaudal level were analyzed. Test-retest coefficient of variation measured reliability in 24 DTI, 17 magnetization transfer, and 16 T2*WI datasets. DTI with and without cardiac triggering was compared in 10 subjects. RESULTS: T2*WI WM/GM showed lower intersubject coefficient of variation (3.5%) compared with magnetization transfer ratio (5.8%), fractional anisotropy (6.0%), and cross-sectional area (12.2%). Linear correction of cross-sectional area with cervical cord length, fractional anisotropy with age, and magnetization transfer ratio with age and height led to decreased coefficients of variation (4.8%, 5.4%, and 10.2%, respectively). Acceptable reliability was achieved for all metrics/levels (test-retest coefficient of variation < 5%), with T2*WI WM/GM comparing favorably with fractional anisotropy and magnetization transfer ratio. DTI with and without cardiac triggering showed no significant differences for fractional anisotropy and test-retest coefficient of variation. CONCLUSIONS: Reliable multiparametric assessment of spinal cord microstructure is possible by using clinically suitable methods. These results establish normalization procedures and pave the way for clinical studies, with the potential for improving diagnostics, objectively monitoring disease progression, and predicting outcomes in spinal pathologies.

g-Ratio weighted imaging of the human spinal cord in vivo.

The fiber g-ratio is defined as the ratio of the inner to the outer diameter of the myelin sheath. This ratio provides a measure of the myelin thickness that complements axon morphology (diameter and density) for assessment of demyelination in diseases such as multiple sclerosis. Previous work has shown that an aggregate g-ratio map can be computed using a formula that combines axon and myelin density measured with quantitative MRI. In this work, we computed g-ratio weighted maps in the cervical spinal cord of nine healthy subjects. We utilized the 300mT/m gradients from the CONNECTOM scanner to estimate the fraction of restricted water (fr) with high accuracy, using the CHARMED model. Myelin density was estimated using the lipid and macromolecular tissue volume (MTV) method, derived from normalized proton density (PD) mapping. The variability across spinal level, laterality and subject were assessed using a three-way ANOVA. The average g-ratio value obtained in the white matter was 0.76+/-0.03, consistent with previous histology work. Coefficients of variation of fr and MTV were respectively 4.3% and 13.7%. fr and myelin density were significantly different across spinal tracts (p=3×10-7 and 0.004 respectively) and were positively correlated in the white matter (r=0.42), suggesting shared microstructural information. The aggregate g-ratio did not show significant differences across tracts (p=0.6). This study suggests that fr and myelin density can be measured in vivo with high precision and that they can be combined to produce a g-ratio-weighted map robust to free water pool contamination from cerebrospinal fluid or veins. Potential applications include the study of early demyelination in multiple sclerosis, and the quantitative assessment of remyelination drugs.

MRI Atlas-Based Measurement of Spinal Cord Injury Predicts Outcome in Acute Flaccid Myelitis.

BACKGROUND AND PURPOSE: Recent advances in spinal cord imaging analysis have led to the development of a robust anatomic template and atlas incorporated into an open-source platform referred to as the Spinal Cord Toolbox. Using the Spinal Cord Toolbox, we sought to correlate measures of GM, WM, and cross-sectional area pathology on T2 MR imaging with motor disability in patients with acute flaccid myelitis. MATERIALS AND METHODS: Spinal cord imaging for 9 patients with acute flaccid myelitis was analyzed by using the Spinal Cord Toolbox. A semiautomated pipeline using the Spinal Cord Toolbox measured lesion involvement in GM, WM, and total spinal cord cross-sectional area. Proportions of GM, WM, and cross-sectional area affected by T2 hyperintensity were calculated across 3 ROIs: 1) center axial section of lesion; 2) full lesion segment; and 3) full cord atlas volume. Spearman rank order correlation was calculated to compare MR metrics with clinical measures of disability. RESULTS: Proportion of GM metrics at the center axial section significantly correlated with measures of motor impairment upon admission (r [9] = -0.78; P = .014) and at 3-month follow-up (r [9] = -0.66; P = .05). Further, proportion of GM extracted across the full lesion segment significantly correlated with initial motor impairment (r [9] = -0.74, P = .024). No significant correlation was found for proportion of WM or proportion of cross-sectional area with clinical disability. CONCLUSIONS: Atlas-based measures of proportion of GM T2 signal abnormality measured on a single axial MR imaging section and across the full lesion segment correlate with motor impairment and outcome in patients with acute flaccid myelitis. This is the first atlas-based study to correlate clinical outcomes with segmented measures of T2 signal abnormality in the spinal cord.

Modeling white matter microstructure.

Quantitative magnetic resonance imaging can be combined with advanced biophysical models to measure microstructural features of white matter. Non-invasive microstructural imaging has the potential to revolutionize neuroscience, and acquiring these measures in clinically feasible times would greatly improve patient monitoring and clinical studies of drug efficacy. However, a good understanding of microstructural imaging techniques is essential to set realistic expectations and to prevent over-interpretation of results. This review explains the methodology behind microstructural modeling and imaging, and gives an overview of the breakthroughs and challenges associated with it.

White matter atlas of the human spinal cord with estimation of partial volume effect.

Template-based analysis has proven to be an efficient, objective and reproducible way of extracting relevant information from multi-parametric MRI data. Using common atlases, it is possible to quantify MRI metrics within specific regions without the need for manual segmentation. This method is therefore free from user-bias and amenable to group studies. While template-based analysis is common procedure for the brain, there is currently no atlas of the white matter (WM) spinal pathways. The goals of this study were: (i) to create an atlas of the white matter tracts compatible with the MNI-Poly-AMU template and (ii) to propose methods to quantify metrics within the atlas that account for partial volume effect. The WM atlas was generated by: (i) digitalizing an existing WM atlas from a well-known source (Gray's Anatomy), (ii) registering this atlas to the MNI-Poly-AMU template at the corresponding slice (C4 vertebral level), (iii) propagating the atlas throughout all slices of the template (C1 to T6) using regularized diffeomorphic transformations and (iv) computing partial volume values for each voxel and each tract. Several approaches were implemented and validated to quantify metrics within the atlas, including weighted-average and Gaussian mixture models. Proof-of-concept application was done in five subjects for quantifying magnetization transfer ratio (MTR) in each tract of the atlas. The resulting WM atlas showed consistent topological organization and smooth transitions along the rostro-caudal axis. The median MTR across tracts was 26.2. Significant differences were detected across tracts, vertebral levels and subjects, but not across laterality (right-left). Among the different tested approaches to extract metrics, the maximum a posteriori showed highest performance with respect to noise, inter-tract variability, tract size and partial volume effect. This new WM atlas of the human spinal cord overcomes the biases associated with manual delineation and partial volume effect. Combined with multi-parametric data, the atlas can be applied to study demyelination and degeneration in diseases such as multiple sclerosis and will facilitate the conduction of longitudinal and multi-center studies.

Multivariate combination of magnetization transfer, T2* and B0 orientation to study the myelo-architecture of the in vivo human cortex.

Recently, T2* imaging at 7Tesla (T) MRI was shown to reveal microstructural features of the cortical myeloarchitecture thanks to an increase in contrast-to-noise ratio. However, several confounds hamper the specificity of T2* measures (iron content, blood vessels, tissues orientation). Another metric, magnetization transfer ratio (MTR), is known to also be sensitive to myelin content and thus would be an excellent complementary measure because its underlying contrast mechanisms are different than that from T2*. The goal of this study was thus to combine MTR and T2* using multivariate statistics in order to gain insights into cortical myelin content. Seven healthy subjects were scanned at 7T and 3T to obtain T2* and MTR data, respectively. A multivariate myelin estimation model (MMEM) was developed, and consists in (i) normalizing T2* and MTR values and (ii) extracting their shared information using independent component analysis (ICA). B0 orientation dependence and cortical thickness were also computed and included in the model. Results showed high correlation between MTR and T2* in the whole cortex (r=0.76, p<10(-16)), suggesting that both metrics are partly driven by a common source of contrast, here assumed to be the myelin. Average MTR and T2* were respectively 31.0+/-0.3% and 32.1+/-1.4 ms. Results of the MMEM spatial distribution showed similar trends to that from histological work stained for myelin (r=0.77, p<0.01). Significant right-left differences were detected in the primary motor cortex (p<0.05), the posterior cingulate cortex (p<0.05) and the visual cortex (p<0.05). This study demonstrates that MTR and T2* are highly correlated in the cortex. The combination of MTR, T2*, CT and B0 orientation may be a useful means to study cortical myeloarchitecture with more specificity than using any of the individual methods. The MMEM framework is extendable to other contrasts such as T1 and diffusion MRI.

Characterizing the location of spinal and vertebral levels in the human cervical spinal cord.

BACKGROUND AND PURPOSE: Advanced MR imaging techniques are critical to understanding the pathophysiology of conditions involving the spinal cord. We provide a novel, quantitative solution to map vertebral and spinal cord levels accounting for anatomic variability within the human spinal cord. For the first time, we report a population distribution of the segmental anatomy of the cervical spinal cord that has direct implications for the interpretation of advanced imaging studies most often conducted across groups of subjects. MATERIALS AND METHODS: Twenty healthy volunteers underwent a T2-weighted, 3T MRI of the cervical spinal cord. Two experts marked the C3-C8 cervical nerve rootlets, C3-C7 vertebral bodies, and pontomedullary junction. A semiautomated algorithm was used to locate the centerline of the spinal cord and measure rostral-caudal distances from a fixed point in the brain stem, the pontomedullary junction, to each of the spinal rootlets and vertebral bodies. Distances to each location were compared across subjects. Six volunteers had 2 additional scans in neck flexion and extension to measure the effects of patient positioning in the scanner. RESULTS: We demonstrated that substantial variation exists in the rostral-caudal position of spinal cord segments among individuals and that prior methods of predicting spinal segments are imprecise. We also show that neck flexion or extension has little effect on the relative location of vertebral-versus-spinal levels. CONCLUSIONS: Accounting for spinal level variation is lacking in existing imaging studies. Future studies should account for this variation for accurate interpretation of the neuroanatomic origin of acquired MR signals.

Framework for integrated MRI average of the spinal cord white and gray matter: the MNI-Poly-AMU template.

The field of spinal cord MRI is lacking a common template, as existing for the brain, which would allow extraction of multi-parametric data (diffusion-weighted, magnetization transfer, etc.) without user bias, thereby facilitating group analysis and multi-center studies. This paper describes a framework to produce an unbiased average anatomical template of the human spinal cord. The template was created by co-registering T2-weighted images (N = 16 healthy volunteers) using a series of pre-processing steps followed by non-linear registration. A white and gray matter probabilistic template was then merged to the average anatomical template, yielding the MNI-Poly-AMU template, which currently covers vertebral levels C1 to T6. New subjects can be registered to the template using a dedicated image processing pipeline. Validation was conducted on 16 additional subjects by comparing an automatic template-based segmentation and manual segmentation, yielding a median Dice coefficient of 0.89. The registration pipeline is rapid (~15 min), automatic after one C2/C3 landmark manual identification, and robust, thereby reducing subjective variability and bias associated with manual segmentation. The template can notably be used for measurements of spinal cord cross-sectional area, voxel-based morphometry, identification of anatomical features (e.g., vertebral levels, white and gray matter location) and unbiased extraction of multi-parametric data.

What can we learn from T2* maps of the cortex?

Studies have shown that T2* contrast can reveal features of cortical anatomy. However, understanding the relationship between T2* contrast and the underlying cyto- and myelo-architecture is not an easy task, given the number of confounds, such as myelin, iron, blood vessels and structure orientation. Moreover, it is difficult to obtain reliable T2* measurements in the cortex due to its thin and folded geometry and the presence of artifacts. This review addresses issues associated with T2* mapping in the human cortex. After describing the theory behind T2* relaxation, a list of practical steps is proposed to reliably acquire and process T2* data and then map these values within the cortex using surface-based analysis. The last section addresses the question: "What can we gain from T2* cortical mapping?", with particular emphasis on Brodmann mapping.

The current state-of-the-art of spinal cord imaging: applications.

A first-ever spinal cord imaging meeting was sponsored by the International Spinal Research Trust and the Wings for Life Foundation with the aim of identifying the current state-of-the-art of spinal cord imaging, the current greatest challenges, and greatest needs for future development. This meeting was attended by a small group of invited experts spanning all aspects of spinal cord imaging from basic research to clinical practice. The greatest current challenges for spinal cord imaging were identified as arising from the imaging environment itself; difficult imaging environment created by the bone surrounding the spinal canal, physiological motion of the cord and adjacent tissues, and small crosssectional dimensions of the spinal cord, exacerbated by metallic implants often present in injured patients. Challenges were also identified as a result of a lack of "critical mass" of researchers taking on the development of spinal cord imaging, affecting both the rate of progress in the field, and the demand for equipment and software to manufacturers to produce the necessary tools. Here we define the current state-of-the-art of spinal cord imaging, discuss the underlying theory and challenges, and present the evidence for the current and potential power of these methods. In two review papers (part I and part II), we propose that the challenges can be overcome with advances in methods, improving availability and effectiveness of methods, and linking existing researchers to create the necessary scientific and clinical network to advance the rate of progress and impact of the research.

The current state-of-the-art of spinal cord imaging: methods.

A first-ever spinal cord imaging meeting was sponsored by the International Spinal Research Trust and the Wings for Life Foundation with the aim of identifying the current state-of-the-art of spinal cord imaging, the current greatest challenges, and greatest needs for future development. This meeting was attended by a small group of invited experts spanning all aspects of spinal cord imaging from basic research to clinical practice. The greatest current challenges for spinal cord imaging were identified as arising from the imaging environment itself; difficult imaging environment created by the bone surrounding the spinal canal, physiological motion of the cord and adjacent tissues, and small cross-sectional dimensions of the spinal cord, exacerbated by metallic implants often present in injured patients. Challenges were also identified as a result of a lack of "critical mass" of researchers taking on the development of spinal cord imaging, affecting both the rate of progress in the field, and the demand for equipment and software to manufacturers to produce the necessary tools. Here we define the current state-of-the-art of spinal cord imaging, discuss the underlying theory and challenges, and present the evidence for the current and potential power of these methods. In two review papers (part I and part II), we propose that the challenges can be overcome with advances in methods, improving availability and effectiveness of methods, and linking existing researchers to create the necessary scientific and clinical network to advance the rate of progress and impact of the research.

Pushing the limits of in vivo diffusion MRI for the Human Connectome Project.

Perhaps more than any other "-omics" endeavor, the accuracy and level of detail obtained from mapping the major connection pathways in the living human brain with diffusion MRI depend on the capabilities of the imaging technology used. The current tools are remarkable; allowing the formation of an "image" of the water diffusion probability distribution in regions of complex crossing fibers at each of half a million voxels in the brain. Nonetheless our ability to map the connection pathways is limited by the image sensitivity and resolution, and also the contrast and resolution in encoding of the diffusion probability distribution. The goal of our Human Connectome Project (HCP) is to address these limiting factors by re-engineering the scanner from the ground up to optimize the high b-value, high angular resolution diffusion imaging needed for sensitive and accurate mapping of the brain's structural connections. Our efforts were directed based on the relative contributions of each scanner component. The gradient subsection was a major focus since gradient amplitude is central to determining the diffusion contrast, the amount of T2 signal loss, and the blurring of the water PDF over the course of the diffusion time. By implementing a novel 4-port drive geometry and optimizing size and linearity for the brain, we demonstrate a whole-body sized scanner with G(max) = 300 mT/m on each axis capable of the sustained duty cycle needed for diffusion imaging. The system is capable of slewing the gradient at a rate of 200 T/m/s as needed for the EPI image encoding. In order to enhance the efficiency of the diffusion sequence we implemented a FOV shifting approach to Simultaneous MultiSlice (SMS) EPI capable of unaliasing 3 slices excited simultaneously with a modest g-factor penalty allowing us to diffusion encode whole brain volumes with low TR and TE. Finally we combine the multi-slice approach with a compressive sampling reconstruction to sufficiently undersample q-space to achieve a DSI scan in less than 5 min. To augment this accelerated imaging approach we developed a 64-channel, tight-fitting brain array coil and show its performance benefit compared to a commercial 32-channel coil at all locations in the brain for these accelerated acquisitions. The technical challenges of developing the over-all system are discussed as well as results from SNR comparisons, ODF metrics and fiber tracking comparisons. The ultra-high gradients yielded substantial and immediate gains in the sensitivity through reduction of TE and improved signal detection and increased efficiency of the DSI or HARDI acquisition, accuracy and resolution of diffusion tractography, as defined by identification of known structure and fiber crossing.

Reduction of physiological noise with independent component analysis improves the detection of nociceptive responses with fMRI of the human spinal cord.

The evaluation of spinal cord neuronal activity in humans with functional magnetic resonance imaging (fMRI) is technically challenging. Major difficulties arise from cardiac and respiratory movement artifacts that constitute significant sources of noise. In this paper we assessed the Correction of Structured noise using spatial Independent Component Analysis (CORSICA). FMRI data of the cervical spinal cord were acquired in 14 healthy subjects using gradient-echo EPI. Nociceptive electrical stimuli were applied to the thumb. Additional data with short TR (250 ms, to prevent aliasing) were acquired to generate a spatial map of physiological noise derived from Independent Component Analysis (ICA). Physiological noise was subsequently removed from the long-TR data after selecting independent components based on the generated noise map. Stimulus-evoked responses were analyzed using the general linear model, with and without CORSICA and with a regressor generated from the cerebrospinal fluid region. Results showed higher sensitivity to detect stimulus-related activation in the targeted dorsal segment of the cord after CORSICA. Furthermore, fewer voxels showed stimulus-related signal changes in the CSF and outside the spinal region, suggesting an increase in specificity. ICA can be used to effectively reduce physiological noise in spinal cord fMRI time series.

Improving diffusion MRI using simultaneous multi-slice echo planar imaging.

In diffusion MRI, simultaneous multi-slice single-shot EPI acquisitions have the potential to increase the number of diffusion directions obtained per unit time, allowing more diffusion encoding in high angular resolution diffusion imaging (HARDI) acquisitions. Nonetheless, unaliasing simultaneously acquired, closely spaced slices with parallel imaging methods can be difficult, leading to high g-factor penalties (i.e., lower SNR). The CAIPIRINHA technique was developed to reduce the g-factor in simultaneous multi-slice acquisitions by introducing inter-slice image shifts and thus increase the distance between aliased voxels. Because the CAIPIRINHA technique achieved this by controlling the phase of the RF excitations for each line of k-space, it is not directly applicable to single-shot EPI employed in conventional diffusion imaging. We adopt a recent gradient encoding method, which we termed "blipped-CAIPI", to create the image shifts needed to apply CAIPIRINHA to EPI. Here, we use pseudo-multiple replica SNR and bootstrapping metrics to assess the performance of the blipped-CAIPI method in 3× simultaneous multi-slice diffusion studies. Further, we introduce a novel image reconstruction method to reduce detrimental ghosting artifacts in these acquisitions. We show that data acquisition times for Q-ball and diffusion spectrum imaging (DSI) can be reduced 3-fold with a minor loss in SNR and with similar diffusion results compared to conventional acquisitions.