Harnessing the frontal aslant tract's structure to assess its involvement in cognitive functions: new insights from 7-T diffusion imaging

The first therapeutical goal followed by neurooncological surgeons dealing with prefrontal gliomas is attempting supramarginal tumor resection preserving relevant neurological function. Therefore, advanced knowledge of the frontal aslant tract (FAT) functional neuroanatomy in high-order cognitive domains beyond language and speech processing would help refine neurosurgeries, predicting possible relevant cognitive adverse events and maximizing the surgical efficacy. To this aim we performed the recently developed correlational tractography analyses to evaluate the possible relationship between FAT's microstructural properties and cognitive functions in 27 healthy subjects having ultra-high-field (7-Tesla) diffusion MRI. We independently assessed FAT segments innervating the dorsolateral prefrontal cortices (dlPFC-FAT) and the supplementary motor area (SMA-FAT). FAT microstructural robustness, measured by the tract's quantitative anisotropy (QA), was associated with a better performance in episodic memory, visuospatial orientation, cognitive processing speed and fluid intelligence but not sustained selective attention tests. Overall, the percentual tract volume showing an association between QA-index and improved cognitive scores (pQACV) was higher in the SMA-FAT compared to the dlPFC-FAT segment. This effect was right-lateralized for verbal episodic memory and fluid intelligence and bilateralized for visuospatial orientation and cognitive processing speed. Our results provide novel evidence for a functional specialization of the FAT beyond the known in language and speech processing, particularly its involvement in several higher-order cognitive domains. In light of these findings, further research should be encouraged to focus on neurocognitive deficits and their impact on patient outcomes after FAT damage, especially in the context of glioma surgery.

The effect of elimination of gibbs ringing, noise and systematic errors on the DTI metrics and tractography in a rat brain

Diffusion tensor imaging (DTI) metrics and tractography can be biased due to low signal-to-noise ratio (SNR) and systematic errors resulting from image artifacts and imperfections in magnetic field gradients. The imperfections include non-uniformity and nonlinearity, effects caused by eddy currents, and the influence of background and imaging gradients. We investigated the impact of systematic errors on DTI metrics of an isotropic phantom and DTI metrics and tractography of a rat brain measured at high resolution. We tested denoising and Gibbs ringing removal methods combined with the B matrix spatial distribution (BSD) method for magnetic field gradient calibration. The results showed that the performance of the BSD method depends on whether Gibbs ringing is removed and the effectiveness of stochastic error removal. Region of interest (ROI)-based analysis of the DTI metrics showed that, depending on the size of the ROI and its location in space, correction methods can remove systematic bias to varying degrees. The preprocessing pipeline proposed and dedicated to this type of data together with the BSD method resulted in an even - 90% decrease in fractional anisotropy (FA) (globally and locally) in the isotropic phantom and - 45% in the rat brain. The largest global changes in the rat brain tractogram compared to the standard method without preprocessing (sDTI) were noticed after denoising. The direction of the first eigenvector obtained from DTI after denoising, Gibbs ringing removal and BSD differed by an average of 56 and 10 degrees in the ROI from sDTI and from sDTI after denoising and Gibbs ringing removal, respectively. The latter can be identified with the amount of improvement in tractography due to the elimination of systematic errors related to imperfect magnetic field gradients. Based on the results, the systematic bias for high resolution data mainly depended on SNR, but the influence of non-uniform gradients could also be seen. After denoising, the BSD method was able to further correct both the metrics and tractography of the diffusion tensor in the rat brain by taking into account the actual distribution of magnetic field gradients independent of the examined object and uniquely dependent on the scanner and sequence. This means that in vivo studies are also subject to this type of errors, which should be taken into account when processing such data.

Reduced cross-scanner variability using vendor-agnostic sequences for single-shell diffusion MRI

PURPOSE

To reduce the inter-scanner variability of diffusion MRI (dMRI) measures between scanners from different vendors by developing a vendor-neutral dMRI pulse sequence using the open-source vendor-agnostic Pulseq platform.

METHODS

We implemented a standard EPI based dMRI sequence in Pulseq. We tested it on two clinical scanners from different vendors (Siemens Prisma and GE Premier), systematically evaluating and comparing the within- and inter-scanner variability across the vendors, using both the vendor-provided and Pulseq dMRI sequences. Assessments covered both a diffusion phantom and three human subjects, using standard error (SE) and Lin's concordance correlation to measure the repeatability and reproducibility of standard DTI metrics including fractional anisotropy (FA) and mean diffusivity (MD).

RESULTS

Identical dMRI sequences were executed on both scanners using Pulseq. On the phantom, the Pulseq sequence showed more than a 2.5× reduction in SE (variability) across Siemens and GE scanners. Furthermore, Pulseq sequences exhibited markedly reduced SE in-vivo, maintaining scan-rescan repeatability while delivering lower variability in FA and MD (more than 50% reduction in cortical/subcortical regions) compared to vendor-provided sequences.

CONCLUSION

The Pulseq diffusion sequence reduces the cross-scanner variability for both phantom and in-vivo data, which will benefit multi-center neuroimaging studies and improve the reproducibility of neuroimaging studies.

Structural-functional brain network coupling predicts human cognitive ability

Individual differences in general cognitive ability (GCA) have a biological basis within the structure and function of the human brain. Network neuroscience investigations revealed neural correlates of GCA in structural as well as in functional brain networks. However, whether the relationship between structural and functional networks, the structural-functional brain network coupling (SC-FC coupling), is related to individual differences in GCA remains an open question. We used data from 1030 adults of the Human Connectome Project, derived structural connectivity from diffusion weighted imaging, functional connectivity from resting-state fMRI, and assessed GCA as a latent g-factor from 12 cognitive tasks. Two similarity measures and six communication measures were used to model possible functional interactions arising from structural brain networks. SC-FC coupling was estimated as the degree to which these measures align with the actual functional connectivity, providing insights into different neural communication strategies. At the whole-brain level, higher GCA was associated with higher SC-FC coupling, but only when considering path transitivity as neural communication strategy. Taking region-specific variations in the SC-FC coupling strategy into account and differentiating between positive and negative associations with GCA, allows for prediction of individual cognitive ability scores in a cross-validated prediction framework (correlation between predicted and observed scores: r = 0.25, p < .001). The same model also predicts GCA scores in a completely independent sample (N = 567, r = 0.19, p < .001). Our results propose structural-functional brain network coupling as a neurobiological correlate of GCA and suggest brain region-specific coupling strategies as neural basis of efficient information processing predictive of cognitive ability.

Predictive Value of the Diffusion Magnetic Resonance Imaging Technique for the Postoperative Outcome of Cervical Spondylotic Myelopathy

BACKGROUND

Diffusion magnetic resonsance imaging (dMRI) can potentially predict the postoperative outcome of cervical spondylotic myelopathy (CSM).

PURPOSE

To explore preoperative dMRI parameters to predict the postoperative outcome of CSM through multifactor correlation analysis.

STUDY TYPE

Prospective.

POPULATION

Post-surgery CSM patients; 102 total, 73 male (52.42 ± 10.60 years old) and 29 female (52.0 ± 11.45 years old).

FIELD STRENGTH/SEQUENCE

3.0 T/Turbo spin echo T1/T2-weighted, T2*-weighted multiecho gradient echo and dMRI.

ASSESSMENT

Spinal cord function was evaluated using modified Japanese Orthopedic Association (mJOA) scoring at different time points: preoperative and 3, 6, and 12 months postoperative. Single-factor correlation and t test analyses were conducted based on fractional anisotropy (FA), mean diffusivity, intracellular volume fraction, isotropic volume fraction, orientation division index, increased signal intensity, compression ratio, age, sex, symptom duration and operation method, and multicollinearity was calculated. The linear quantile mixed model (LQMM) and the linear mixed-effects regression model (LMER) were used for multifactor correlation analysis using the combinations of the above variables.

STATISTICAL TESTS

Distance correlation, Pearson's correlation, multiscale graph correlation and t tests were used for the single-factor correlation analyses. The variance inflation factor (VIF) was used to calculate multicollinearity. LQMM and LMER were used for multifactor correlation analyses. P < 0.05 was considered statistically significant.

RESULTS

The single-factor correlation between all variables and the postoperative mJOA score was weak (all r < 0.3). The linear relationship was stronger than the nonlinear relationship, and there was no significant multicollinearity (VIF = 1.10-1.94). FA values in the LQMM and LMER models had a significant positive correlation with the mJOA score (r = 5.27-6.04), which was stronger than the other variables.

DATA CONCLUSION

The FA value based on dMRI significantly positively correlated with CSM patient postoperative outcomes, helping to predict the surgical outcome and formulate a treatment plan before surgery.

EVIDENCE LEVEL

1 TECHNICAL EFFICACY: Stage 2.

Fast and robust quantification of uncertainty in non-linear diffusion MRI models

Diffusion MRI (dMRI) allows for non-invasive investigation of brain tissue microstructure. By fitting a model to the dMRI signal, various quantitative measures can be derived from the data, such as fractional anisotropy, neurite density and axonal radii maps. We investigate the Fisher Information Matrix (FIM) and uncertainty propagation as a generally applicable method for quantifying the parameter uncertainties in linear and non-linear diffusion MRI models. In direct comparison with Markov Chain Monte Carlo (MCMC) sampling, the FIM produces similar uncertainty estimates at much lower computational cost. Using acquired and simulated data, we then list several characteristics that influence the parameter variances, including data complexity and signal-to-noise ratio. For practical purposes we investigate a possible use of uncertainty estimates in decreasing intra-group variance in group statistics by uncertainty-weighted group estimates. This has potential use cases for detection and suppression of imaging artifacts.

Current perspectives on tractography-guided deep brain stimulation for the treatment of mood disorders

INTRODUCTION

Deep brain stimulation (DBS) is an emerging therapy for mood disorders, particularly treatment-resistant depression (TRD). Different brain areas implicated in depression-related brain networks have been investigated as DBS targets and variable clinical outcomes highlight the importance of target identification. Tractography has provided insight into how DBS modulates disorder-related brain networks and is being increasingly used to guide DBS for psychiatric disorders.

AREAS COVERED

In this perspective, an overview of the current state of DBS for TRD and the principles of tractography is provided. Next, a comprehensive review of DBS targets is presented with a focus on tractography. Finally, the challenges and future directions of tractography-guided DBS are discussed.

EXPERT OPINION

Tractography-guided DBS is a promising tool for improving DBS outcomes for mood disorders. Tractography is particularly useful for targeting patient-specific white matter tracts that are not visible using conventional structural MRI. Developments in tractography methods will help refine DBS targeting for TRD and may facilitate symptom-specific precision neuromodulation. Ultimately, the standardization of tractography methods will be essential to transforming DBS into an established therapy for mood disorders.

Traveling Subject-Informed Harmonization Increases Reliability of Brain Diffusion Tensor and Neurite Mapping

Diffusion-weighted magnetic resonance imaging (dMRI) of brain has helped elucidate the microstructural changes of psychiatric and neurodegenerative disorders. Inconsistency between MRI models has hampered clinical application of dMRI-based metrics. Using harmonized dMRI data of 300 scans from 69 traveling subjects (TS) scanning the same individuals at multiple conditions with 13 MRI models and 2 protocols, the widely-used metrics such as diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) were evaluated before and after harmonization with a combined association test (ComBat) or TS-based general linear model (TS-GLM). Results showed that both ComBat and TS-GLM significantly reduced the effects of the MRI site, model, and protocol for diffusion metrics while maintaining the intersubject biological effects. The harmonization power of TS-GLM based on TS data model is more powerful than that of ComBat. In conclusion, our research demonstrated that although ComBat and TS-GLM harmonization approaches were effective at reducing the scanner effects of the site, model, and protocol for DTI and NODDI metrics in WM, they exhibited high retainability of biological effects. Therefore, we suggest that, after harmonizing DTI and NODDI metrics, a multisite study with large cohorts can accurately detect small pathological changes by retaining pathological effects.

Anesthetic modulation of water diffusion: Insights from a diffusion tensor imaging study

Diffusion tensor imaging (DTI) in animal models are essential for translational neuroscience studies. A critical step in animal studies is the use of anesthetics. Understanding the influence of specific anesthesia regimes on DTI-derived parameters, such as fractional anisotropy (FA) and mean diffusivity (MD), is imperative when comparing results between animal studies using different anesthetics. Here, the quantification of FA and MD under different anesthetic regimes, alpha-chloralose and isoflurane, is discussed. We also used a range of b-values to determine whether the anesthetic effect was b-value dependent. The first group of rats (n = 6) was anesthetized with alpha-chloralose (80 mg/kg), whereas the second group of rats (n = 7) was anesthetized with isoflurane (1.5%). DTI was performed with b-values of 500, 1500, and 1500s/mm2, and the MD and FA were assessed individually. Anesthesia-specific differences in MD were apparent, as manifested by the higher estimated MD under isoflurane anesthesia than that under alpha-chloralose anesthesia (P < 0.001). MD values increased with decreasing b-value in all regions studied, and the degree of increase when rats were anesthetized with isoflurane was more pronounced than that associated with alpha-chloralose (P < 0.05). FA quantitation was also influenced by anesthesia regimens to varying extents, depending on the brain regions and b-values. In conclusion, both scanning parameters and the anesthesia regimens significantly impacted the quantification of DTI indices.

Influence of adaptive denoising on Diffusion Kurtosis Imaging at 3T and 7T

BACKGROUND AND OBJECTIVE

Choosing the most appropriate denoising method to improve the quality of diagnostic images maximally is key in pre-processing of diffusion MRI images. Recent advancements in acquisition and reconstruction techniques have questioned traditional noise estimation methods favoring adaptive denoising frameworks, circumventing the need to know a priori information that is hardly available in a clinical setting. In this observational study, we compared two innovative adaptive techniques sharing some features, Patch2Self and Nlsam, through application on reference adult data at 3T and 7T. The primary aim was identifying the most effective method in case of Diffusion Kurtosis Imaging (DKI) data - particularly susceptible to noise and signal fluctuations - at 3T and 7T fields. A side goal consisted of investigating the dependence of kurtosis metrics' variability with respect to the magnetic field on the adopted denoising methodology.

METHODS

For comparison purposes, we focused on qualitative and quantitative analysis of DKI data and related microstructural maps before and after applying the two denoising approaches. Specifically, we assessed computational efficiency, preservation of anatomical details via perceptual metrics, consistency of microstructure model fitting, alleviation of degeneracies in model estimation, and joint variability with varying field strength and denoising method.

RESULTS

Accounting for all these factors, Patch2Self framework has turned out to be specifically suitable for DKI data, with improving performance at 7T. Nlsam method is more robust in alleviating degenerate black voxels while introducing some blurring, which in turn is reflected in an overall loss of image sharpness. Regarding the impact of denoising on field-dependent variability, both methods have been shown to make variations from standard to Ultra-High Field more concordant with theoretical evidence, claiming that kurtosis metrics are sensitive to susceptibility-induced background gradients, directly proportional to the magnetic field strength and sensitive to the microscopic distribution of iron and myelin.

CONCLUSIONS

This study serves as a proof-of-concept stressing the need for an accurate choice of a denoising methodology, specifically tailored for the data under analysis and allowing higher spatial resolution acquisition within clinically compatible timings, with all the potential benefits that improving suboptimal quality of diagnostic images entails.

High-resolution diffusion-weighted imaging at 7 Tesla: single-shot readout trajectories and their impact on signal-to-noise ratio, spatial resolution and accuracy

Diffusion MRI (dMRI) is a valuable imaging technique to study the connectivity and microstructure of the brain in vivo. However, the resolution of dMRI is limited by the low signal-to-noise ratio (SNR) of this technique. Various multi-shot acquisition strategies have been developed to achieve sub-millimeter resolution, but they require long scan times which can be restricting for patient scans. Alternatively, the SNR of single-shot acquisitions can be increased by using a spiral readout trajectory to minimize the sequence echo time. Imaging at ultra-high fields (UHF) could further increase the SNR of single-shot dMRI; however, the shorter T2* of brain tissue and the greater field non-uniformities at UHFs will degrade image quality, causing image blurring, distortions, and signal loss. In this study, we investigated the trade-off between the SNR and resolution of different k-space trajectories, including echo planar imaging (EPI), partial Fourier EPI, and spiral trajectories, over a range of dMRI resolutions at 7T. The effective resolution, spatial specificity and sharpening effect were measured from the point spread function (PSF) of the simulated diffusion sequences for a nominal resolution range of 0.6-1.8 mm. In-vivo partial brain scans at a nominal resolution of 1.5 mm isotropic were acquired using the three readout trajectories to validate the simulation results. Field probes were used to measure dynamic magnetic fields offline up to the 3^rd order of spherical harmonics. Image reconstruction was performed using static ΔB₀ field maps and the measured trajectories to correct image distortions and artifacts, leaving T2* effects as the primary source of blurring. The effective resolution was examined in fractional anisotropy (FA) maps calculated from a multi-shell dataset with b-values of 300, 1000, and 2000 s/mm² in 5, 16, and 48 directions, respectively. In-vivo scans at nominal resolutions of 1, 1.2, and 1.5 mm were acquired and the SNR of the different trajectories calculated using the multiple replica method to investigate the SNR. Finally, in-vivo whole brain scans with an effective resolution of 1.5 mm isotropic were acquired to explore the SNR and efficiency of different trajectories at a matching effective resolution. FA and intra-cellular volume fraction (ICVF) maps calculated using neurite orientation dispersion and density imaging (NODDI) were used for the comparison. The simulations and in vivo imaging results showed that for matching nominal resolutions, EPI trajectories had the highest specificity and effective resolution with maximum image sharpening effect. However, spirals have a significantly higher SNR, in particular at higher resolutions and even when the effective image resolutions are matched. Overall, this work shows that the higher SNR of single-shot spiral trajectories at 7T allows us to achieve higher effective resolutions compared to EPI and PF-EPI to map the microstructure and connectivity of small brain structures.

Inter-vendor and inter-observer reliability of diffusion tensor imaging in the musculoskeletal system: a multiscanner MR study

BACKGROUND

To evaluate the inter-observer and inter-vendor reliability of diffusion tensor imaging parameters in the musculoskeletal system.

METHODS

This prospective study included six healthy volunteers three men (mean age: 42; range: 31-52 years) and three women (mean age: 36; range: 30-44 years). Each subject was scanned using different 3 Tesla magnetic resonance scanners from three different vendors at three different sites bilaterally. First, the intra-class correlation coefficient was used to determine between-observers agreement for overall measurements and clinical sites. Next, between-group comparisons were made through the nonparametric Friedman's test. Finally, the Bland-Altman method was used to determine agreement among the three scanner measurements, comparing them two by two.

RESULTS

A total of 792 measurement were calculated. ICC reported high levels of agreement between the two observers. ICC related to MD, FA, and RD measurements ranged from 0.88 (95% CI 0.85-0.90) to 0.95 (95% CI 0.94-0.96), from 0.85 (95% CI 0.81-0.88) to 0.95 (95% CI 0.93-0.96), and from 0.89 (0.85-0.90) to 0.92 (0.90-0.94). No statistically significant inter-vendor differences were observed. The Bland-Altmann method confirmed a high correlation between parameter values.

CONCLUSION

An excellent inter-observer and inter-vendor reliability was found in our study.

7 T Musculoskeletal MRI: Fundamentals and Clinical Implementation

ABSTRACT

This review summarizes the current state-of-the-art of musculoskeletal 7 T magnetic resonance imaging (MRI), the associated technological challenges, and gives an overview of current and future clinical applications of 1 H-based 7 T MRI. The higher signal-to-noise ratio at 7 T is predominantly used for increased spatial resolution and thus the visualization of anatomical details or subtle lesions rather than to accelerate the sequences. For musculoskeletal MRI, turbo spin echo pulse sequences are particularly useful, but with altered relaxation times, B1 inhomogeneity, and increased artifacts at 7 T; specific absorption rate limitation issues quickly arise for turbo spin echo pulse sequences. The development of dedicated pulse sequence techniques in the last 2 decades and the increasing availability of specialized coils now facilitate several clinical musculoskeletal applications. 7 T MRI is performed in vivo in a wide range of applications for the knee joint and other anatomical areas, such as ultra-high-resolution nerve imaging or bone trabecular microarchitecture imaging. So far, however, it has not been shown systematically whether the higher field strength compared with the established 3 T MRI systems translates into clinical advantages, such as an early-stage identification of tissue damage allowing for preventive therapy or an influence on treatment decisions and patient outcome. At the moment, results tend to suggest that 7 T MRI will be reserved for answering specific, targeted musculoskeletal questions rather than for a broad application, as is the case for 3 T MRI. Future data regarding the implementation of clinical use cases are expected to clarify if 7 T musculoskeletal MRI applications with higher diagnostic accuracy result in patient benefits compared with MRI at lower field strengths.

Magic angle effect on diffusion tensor imaging in ligament and brain

PURPOSE

To evaluate the magic angle effect on diffusion tensor imaging (DTI) measurements in rat ligaments and mouse brains.

METHODS

Three rat knee joints and three mouse brains were scanned at 9.4 T using a modified 3D diffusion-weighted spin echo pulse sequence with the isotropic spatial resolution of 45 μm. The b value was 1000 s/mm2 for rat knee and 4000 s/mm2 for mouse brain. DTI model was used to investigate the quantitative metrics at different orientations with respect to the main magnetic field. The collagen fiber structure of the ligament was validated with polarized light microscopy (PLM) imaging.

RESULTS

The signal intensity, signal-to-noise ratio (SNR), and DTI metrics in the ligament were strongly dependent on the collagen fiber orientation with respect to the main magnetic field from both simulation and actual MRI scans. The variation of fractional anisotropy (FA) was about ~32%, and the variation of mean diffusivity (MD) was ~11%. These findings were further validated with the numerical simulation at different SNRs (~10.0 to 86.0). Compared to the ligament, the DTI metrics showed little orientation dependence in mouse brains.

CONCLUSION

Magic angle effect plays an important role in DTI measurements in the highly ordered collagen-rich tissues, while MD showed less orientation dependence than FA.

Diffusion tensor imaging pipeline measures of cerebral white matter integrity: An overview of recent advances and prospects

Cerebral small vessel disease (CSVD) is a leading cause of age-related microvascular cognitive decline, resulting in significant morbidity and decreased quality of life. Despite a progress on its key pathophysiological bases and general acceptance of key terms from neuroimaging findings as observed on the magnetic resonance imaging (MRI), key questions on CSVD remain elusive. Enhanced relationships and reliable lesion studies, such as white matter tractography using diffusion-based MRI (dMRI) are necessary in order to improve the assessment of white matter architecture and connectivity in CSVD. Diffusion tensor imaging (DTI) and tractography is an application of dMRI that provides data that can be used to non-invasively appraise the brain white matter connections via fiber tracking and enable visualization of individual patient-specific white matter fiber tracts to reflect the extent of CSVD-associated white matter damage. However, due to a lack of standardization on various sets of software or image pipeline processing utilized in this technique that driven mostly from research setting, interpreting the findings remain contentious, especially to inform an improved diagnosis and/or prognosis of CSVD for routine clinical use. In this minireview, we highlight the advances in DTI pipeline processing and the prospect of this DTI metrics as potential imaging biomarker for CSVD, even for subclinical CSVD in at-risk individuals.

Diffusion tensor imaging analysis of rheumatoid arthritis patients with neuropsychiatric features to determine the alteration of white matter integrity due to vascular events

INTRODUCTION

The pathophysiology of neuropsychiatric manifestations in rheumatoid arthritis is not well known. The magnetic resonance imaging of the brain in rheumatoid arthritis demonstrates non-specific findings, and in the majority of cases, magnetic resonance imaging fails to detect an abnormality, even in rheumatoid arthritis patients with neuropsychiatric manifestations. Therefore, we aimed to assess microstructural integrity changes of white matter in patients with rheumatoid arthritis by using different diffusion tensor imaging parameters.

METHODS

Eighteen rheumatoid arthritis patients (10 with neuropsychiatric symptoms and 8 without any neuropsychiatric symptoms) and 14 controls were included. The volume of the T2 hyperintense lesions was assessed. Different diffusion tensor imaging parameters such as fractional anisotropy, apparent diffusion coefficient, trace, axial diffusivity, and radial diffusivity were obtained from six different regions of white matter. Inter group significant difference was determined by one-way analysis of variance followed by Tukey's post hoc test. The accuracy of diffusion tensor imaging matrices was evaluated from the receiver operating characteristic curve.

RESULTS

No significant difference in lesions' volume was detected between rheumatoid arthritis patients with or without neuropsychiatric symptoms. There was an increased apparent diffusion coefficient and radial diffusivity (p < 0.05) as well as decreased fractional anisotropy and axial diffusivity (p < 0.5) in rheumatoid arthritis patients with neuropsychiatric symptoms compared to controls. Moreover, the apparent diffusion coefficient (p < .05) was increased in both positive and negative MRI of patients with neuropsychiatric features compared to the control group. The sensitivity and specificity of the apparent diffusion coefficient parameters was 73% and 72%, respectively.

CONCLUSIONS

The various anisotropic metrics were altered in rheumatoid arthritis patients with neuropsychiatric symptoms by using diffusion tensor imaging analysis, representing that central nervous system vasculitis leads to tissue hypoxia resulting in vasogenic edema. This may lead to axonal and myelin degeneration of white matter fibers and neuronal cell disruption. Key Points • Our study confirms that neurovascular events are not uncommon in RA patients with NP features. Diffusion tensor imaging (DTI) is superior to conventional MRI scan for RA patients with NP features because it distinguishes between gray and white matter structures. • RA patients with NP features are more likely to have microstructural changes detected by DTI than by DWI, and it can provide comprehensive anatomical layouts describing regional disparities in neurodegeneration. • DTI's quantitative association of NP symptoms in a large patient cohort is an important study scope.

Neuroimaging at 7 Tesla: a pictorial narrative review

Neuroimaging using the 7-Tesla (7T) human magnetic resonance (MR) system is rapidly gaining popularity after being approved for clinical use in the European Union and the USA. This trend is the same for functional MR imaging (MRI). The primary advantages of 7T over lower magnetic fields are its higher signal-to-noise and contrast-to-noise ratios, which provide high-resolution acquisitions and better contrast, making it easier to detect lesions and structural changes in brain disorders. Another advantage is the capability to measure a greater number of neurochemicals by virtue of the increased spectral resolution. Many structural and functional studies using 7T have been conducted to visualize details in the white matter and layers of the cortex and hippocampus, the subnucleus or regions of the putamen, the globus pallidus, thalamus and substantia nigra, and in small structures, such as the subthalamic nucleus, habenula, perforating arteries, and the perivascular space, that are difficult to observe at lower magnetic field strengths. The target disorders for 7T neuroimaging range from tumoral diseases to vascular, neurodegenerative, and psychiatric disorders, including Alzheimer's disease, Parkinson's disease, multiple sclerosis, epilepsy, major depressive disorder, and schizophrenia. MR spectroscopy has also been used for research because of its increased chemical shift that separates overlapping peaks and resolves neurochemicals more effectively at 7T than a lower magnetic field. This paper presents a narrative review of these topics and an illustrative presentation of images obtained at 7T. We expect 7T neuroimaging to provide a new imaging biomarker of various brain disorders.

Sensitivity of Diffusion MRI to White Matter Pathology: Influence of Diffusion Protocol, Magnetic Field Strength, and Processing Pipeline in Systemic Lupus Erythematosus

There are many ways to acquire and process diffusion MRI (dMRI) data for group studies, but it is unknown which maximizes the sensitivity to white matter (WM) pathology. Inspired by this question, we analyzed data acquired for diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) at 3T (3T-DTI and 3T-DKI) and DTI at 7T in patients with systemic lupus erythematosus (SLE) and healthy controls (HC). Parameter estimates in 72 WM tracts were obtained using TractSeg. The impact on the sensitivity to WM pathology was evaluated for the diffusion protocol, the magnetic field strength, and the processing pipeline. Sensitivity was quantified in terms of Cohen's d for group comparison. Results showed that the choice of diffusion protocol had the largest impact on the effect size. The effect size in fractional anisotropy (FA) across all WM tracts was 0.26 higher when derived by DTI than by DKI and 0.20 higher in 3T compared with 7T. The difference due to the diffusion protocol was larger than the difference due to magnetic field strength for the majority of diffusion parameters. In contrast, the difference between including or excluding different processing steps was near negligible, except for the correction of distortions from eddy currents and motion which had a clearly positive impact. For example, effect sizes increased on average by 0.07 by including motion and eddy correction for FA derived from 3T-DTI. Effect sizes were slightly reduced by the incorporation of denoising and Gibbs-ringing removal (on average by 0.011 and 0.005, respectively). Smoothing prior to diffusion model fitting generally reduced effect sizes. In summary, 3T-DTI in combination with eddy current and motion correction yielded the highest sensitivity to WM pathology in patients with SLE. However, our results also indicated that the 3T-DKI and 7T-DTI protocols used here may be adjusted to increase effect sizes.

Post-Concussive Vestibular Dysfunction Is Related to Injury to the Inferior Vestibular Nerve

Symptoms of vestibular dysfunction such as dizziness and vertigo are common after sports-related concussions (SRC) and associated with a worse outcome and a prolonged recovery. Vestibular dysfunction after SRC can be because of an impairment of the peripheral or central neural parts of the vestibular system. The aim of the present study was to establish the cause of vestibular impairment in athletes with SRC who have persisting post-concussive symptoms (PPCS). We recruited 42 participants-21 athletes with previous SRCs and PPCS ≥6 months and 21 healthy athletic age- and sex-matched controls-who underwent symptom rating, a detailed test battery of vestibular function and 7T magnetic resonance imaging with diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) of cerebellar white matter tracts, and T1-weighted imaging for cerebellar volumetrics. Vestibular dysfunction was observed in 13 SRC athletes and three controls (p = 0.001). Athletes with vestibular dysfunction reported more pronounced symptoms on the Dizziness Handicap Inventory (DHI; p < 0.001) and the Hospital Anxiety and Depression Scale (HADS; p < 0.001). No significant differences in DTI metrics were found, while in DKI two metrics were observed in the superior and/or inferior cerebellar tracts. Cerebellar gray and white matter volumes were similar in athletes with SRC and controls. Compared with controls, pathological video head impulse test results (vHIT; p < 0.001) and cervical vestibular evoked myogenic potentials (cVEMP; p = 0.002) were observed in athletes with SRC, indicating peripheral vestibular dysfunction and specifically suggesting injury to the inferior vestibular nerve. In athletes with persisting symptoms after SRC, vestibular dysfunction is associated with injury to the inferior vestibular nerve.

Comparison of Olfactory Tract Diffusion Measures Between Early Stage Parkinson's Disease Patients and Healthy Controls Using Ultra-High Field MRI

BACKGROUND

MRI is a valuable method to assist in the diagnostic work-up of Parkinson's disease (PD). The olfactory tract (OT) has been proposed as a potential MRI biomarker for distinguishing PD patients from healthy controls.

OBJECTIVE

This study aims to further investigate whether diffusion measures of the OT differ between early stage PD patients and healthy controls.

METHODS

Twenty hyposmic/anosmic PD patients, 65 normosmic PD patients, and 36 normosmic healthy controls were evaluated and a 7T diffusion weighted image scan was acquired. Manual seed regions of interest were drawn in the OT region. Tractography of the OT was performed using a deterministic streamlines algorithm. Diffusion measures (fractional anisotropy and mean- radial- and axial diffusivity) of the generated streamlines were compared between groups.

RESULTS

Diffusion measures did not differ between PD patients compared to healthy controls and between hyposmic/anosmic PD patients, normosmic PD patients, and normosmic healthy controls. A positive correlation was found between age and mean- and axial diffusivity within the hyposmic/anosmic PD subgroup, but not in the normosmic groups. A positive correlation was found between MDS-UPDRSIII scores and fractional anisotropy.

CONCLUSION

This study showed that fiber tracking of the OT was feasible in both early stage PD and healthy controls using 7T diffusion weighted imaging data. However, 7T MRI diffusion measures of the OT are not useful as an early clinical biomarker for PD. Future work is needed to clarify the role of other OT measurements as a biomarker for PD and its different subgroups.

Widespread effects of dMRI data quality on diffusion measures in children

Diffusion magnetic resonance imaging (dMRI) datasets are susceptible to several confounding factors related to data quality, which is especially true in studies involving young children. With the recent trend of large‐scale multicenter studies, it is more critical to be aware of the varied impacts of data quality on measures of interest. Here, we investigated data quality and its effect on different diffusion measures using a multicenter dataset. dMRI data were obtained from 691 participants (5–17 years of age) from six different centers. Six data quality metrics—contrast to noise ratio, outlier slices, and motion (absolute, relative, translation, and rotational)—and four diffusion measures—fractional anisotropy, mean diffusivity, tract density, and length—were computed for each of 36 major fiber tracts for all participants. The results indicated that four out of six data quality metrics (all except absolute and translation motion) differed significantly between centers. Associations between these data quality metrics and the diffusion measures differed significantly across the tracts and centers. Moreover, these effects remained significant after applying recently proposed harmonization algorithms that purport to remove unwanted between‐site variation in diffusion data. These results demonstrate the widespread impact of dMRI data quality on diffusion measures. These tracts and measures have been routinely associated with individual differences as well as group‐wide differences between neurotypical populations and individuals with neurological or developmental disorders. Accordingly, for analyses of individual differences or group effects (particularly in multisite dataset), we encourage the inclusion of data quality metrics in dMRI analysis.

Reliability of the freehand region-of-interest method in quantitative cerebral diffusion tensor imaging

Background

Diffusion tensor imaging (DTI) is a magnetic resonance imaging (MRI) technique used for evaluating changes in the white matter in brain parenchyma. The reliability of quantitative DTI analysis is influenced by several factors, such as the imaging protocol, pre-processing and post-processing methods, and selected diffusion parameters. The region-of-interest (ROI) method is most widely used of the post-processing methods because it is found in commercial software. The focus of our research was to study the reliability of the freehand ROI method using various intra- and inter-observer analyses.

Methods

This study included 40 neurologically healthy participants who underwent diffusion MRI of the brain with a 3 T scanner. The measurements were performed at nine different anatomical locations using a freehand ROI method. The data extracted from the ROIs included the regional mean values, intra- and inter-observer variability and reliability. The used DTI parameters were fractional anisotropy (FA), the apparent diffusion coefficient (ADC), and axial (AD) and radial (RD) diffusivity.

Results

The average intra-class correlation coefficient (ICC) of the intra-observer was found to be 0.9 (excellent). The single ICC results were excellent (> 0.8) or adequate (> 0.69) in eight out of the nine regions in terms of FA and ADC. The most reliable results were found in the frontobasal regions. Significant differences between age groups were also found in the frontobasal regions. Specifically, the FA and AD values were significantly higher and the RD values lower in the youngest age group (18–30 years) compared to the other age groups.

Conclusions

The quantitative freehand ROI method can be considered highly reliable for the average ICC and mostly adequate for the single ICC. The freehand method is suitable for research work with a well-experienced observer. Measurements should be performed at least twice in the same region to ensure that the results are sufficiently reliable. In our study, reliability was slightly undermined by artifacts in some regions such as the cerebral peduncle and centrum semiovale. From a clinical point of view, the results are most reliable in adults under the age of 30, when age-related changes in brain white matter have not yet occurred.

Brain diffusion tensor imaging in dogs with degenerative myelopathy

BACKGROUND

Degenerative myelopathy (DM) in dogs shares similarities with superoxide dismutase 1-associated human amyotrophic lateral sclerosis (ALS). Brain microstructural lesions are quantified using diffusion tensor imaging (DTI) in ALS patients.

OBJECTIVE

Characterize brain neurodegenerative changes in DM-affected dogs using DTI.

ANIMALS

Sixteen DM-affected and 8 control dogs.

METHODS

Prospective observational study. Brain DTI was performed at baseline and every 3 months on DM-affected dogs and compared to controls. Fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity were calculated on specified regions of interest. Gait scores (0, normal to 14, tetraplegia) were assigned at each scan. Diffusion tensor imaging values in DM-affected dogs were compared to controls, gait scores, and evaluated over time.

RESULTS

Mean age was 5.7 years (SD 3.2) in controls and 9.7 years (SD 1.4) in DM-affected dogs. In DM-affected dogs, mean baseline gait score was 4 (SD 1), and mean score change from baseline to last scan was 4.82 (SD 2.67). Nine dogs had ≤3 scans; 7 had >3 scans. Accounting for age, no differences in DTI indices were identified for any brain or proximal spinal cord regions between DM-affected dogs and controls (P > .05). Diffusion tensor imaging values poorly correlated with gait scores (R2  < .2). No significant changes were identified in diffusion indices over time (P > .05).

CONCLUSIONS AND CLINICAL IMPORTANCE

Diffusion tensor imaging indices did not differentiate DM-affected from control dogs, detect longitudinal changes, or differentiate disease severity. Findings do not yet support brain DTI as an imaging biomarker.

A Structural Connectivity Disruption One Decade before the Typical Age for Dementia: A Study in Healthy Subjects with Family History of Alzheimer's Disease

The concept of the brain has shifted to a complex system where different subnetworks support the human cognitive functions. Neurodegenerative diseases would affect the interactions among these subnetworks and, the evolution of impairment and the subnetworks involved would be unique for each neurodegenerative disease. In this study, we seek for structural connectivity traits associated with the family history of Alzheimer's disease, that is, early signs of subnetworks impairment due to Alzheimer's disease. The sample in this study consisted of 123 first-degree Alzheimer's disease relatives and 61 nonrelatives. For each subject, structural connectomes were obtained using classical diffusion tensor imaging measures and different resolutions of cortical parcellation. For the whole sample, independent structural-connectome-traits were obtained under the framework of connICA. Finally, we tested the association of the structural-connectome-traits with different factors of relevance for Alzheimer's disease by means of a multiple linear regression. The analysis revealed a structural-connectome-trait obtained from fractional anisotropy associated with the family history of Alzheimer's disease. The structural-connectome-trait presents a reduced fractional anisotropy pattern in first-degree relatives in the tracts connecting posterior areas and temporal areas. The family history of Alzheimer's disease structural-connectome-trait presents a posterior-posterior and posterior-temporal pattern, supplying new evidences to the cascading network failure model.

Ultra-high field imaging in Major Depressive Disorder: a review of structural and functional studies

BACKGROUND

Major depressive disorder (MDD) is a severe and pervasive psychiatric condition with a lifetime prevalence of 15-25%. Numerous Magnetic Resonance Imaging (MRI) studies employing scans at field strengths of 1.5T or 3T have been carried out in the last decades, providing an unprecedented insight into the neural correlates of MDD. However, in recent years, MRI technology has largely progressed and the use of scans at ultra-high field (≥ 7T) has improved the sensitivity and the resolution of MR images. In this context, with this review we aim to summarize evidence of structural and functional brain mechanisms underlying MDD obtained with ultra-high field MRI.

METHODS

We conducted a search on PubMed, Scopus and Web of Science of neuroimaging studies on MDD patients, which employed ultra-high field MRI. We detected six structural MRI studies, two Diffusion Tensor Imaging (DTI) studies and five functional MRI (fMRI) studies.

RESULTS

Overall, the MRI and DTI studies showed volumetric and structural connectivity alterations in the hippocampus and, to a lesser extent, in the amygdala. In contrast, more heterogeneous results were reported by fMRI studies, which, though, described functional abnormalities in the cingulate cortex, thalamus and several other brain areas.

LIMITATIONS

The small sample size and the heterogeneity in patients' samples, processing and study design limit the conclusion of the present review.

CONCLUSIONS

Studies employing scans at ultra-high magnetic field may provide a useful contribution to the mixed body of literature on MDD. This preliminary but promising evidence confirms the importance of performing ultra-high field MRI investigations in order to detect and better characterize subtle brain abnormalities in MDD.

Multiband diffusion tensor imaging for presurgical mapping of motor and language pathways in patients with brain tumors

BACKGROUND AND PURPOSE

Assessment of the essential white matter fibers of arcuate fasciculus and corticospinal tract (CST), required for preoperative planning in brain tumor patients, relies on the reliability of diffusion tensor imaging (DTI). The recent development of multiband DTI (mb-DTI) based on simultaneous multislice excitation could maintain the overall quality of tractography while not exceeding standard clinical care time. To address this potential, we performed quantitative analyses to evaluate tractography results of arcuate fasciculus and CST acquired by mb-DTI in brain tumor patients.

METHODS

We retrospectively analyzed 44 patients with brain lesions who underwent presurgical single-shot DTI (s-DTI) and mb-DTI. We measured DTI parameters: fractional anisotropy (FA) and mean diffusivity (MD [mm² s^-1 ]) in whole brain and tumor regions; and the tractography parameters: fiber FA, MD (mm² s^-1 ), volume (mm³ ), and length (mm) in the whole brain, arcuate fasciculus, and CST. Additionally, three neuroradiologists performed a blinded visual assessment comparing s-DTI with mb-DTI.

RESULTS

The mb-DTI showed higher mean FA and lower MD (r > .95, p < .002) in whole brain and tumor regions of interest; slightly higher fiber FA, volume, and length; and slightly lower fiber MD in whole brain, arcuate fasciculus, and CST than in s-DTI. These differences were significant for fiber FA in all tracts; length (mm) in arcuate fasciculus; and fiber MD (mm² s^-1 ) and volume (mm³ ) in all patients with tumor involved in the arcuate fasciculus, CST, and whole brain tracts (p = .001). Visual assessment demonstrated that both techniques produced visually similar tracts.

CONCLUSIONS

This study demonstrated the clinical potential and significant advantages of preoperative mb-DTI in brain tumor patients.

Diffusion Tensor Imaging-Based Analysis of Baseline Neurocognitive Function and Posttreatment White Matter Changes in Pediatric Patients With Craniopharyngioma Treated With Surgery and Proton Therapy

PURPOSE

To determine the preirradiation baseline association of white matter integrity with neurocognitive function and to assess posttreatment changes in pediatric patients with craniopharyngioma treated with proton therapy.

METHODS AND MATERIALS

Ninety children and adolescents (2-20 years old) with craniopharyngioma were treated with proton therapy (54 Gy[RBE]) in a prospective therapeutic trial. Neurocognitive performance at the postoperative baseline before proton therapy and diffusion tensor imaging (DTI) data acquired at baseline and at annual follow-up were analyzed. Tract-based spatial statistics and structural connectomics were used to derive global and local white matter features from DTI. Baseline DTI features were compared for patients with average and below-average neurocognitive performance. Longitudinal DTI data were analyzed to determine the proton dose effect on white matter structures in relation to the irradiated brain volume and baseline age.

RESULTS

Before proton therapy, patients with below-average working memory, processing speed, verbal fluency, verbal learning, or fine motor dexterity exhibited more globally degraded white matter structures compared with their counterparts with average performance, as indicated by lower mean fractional anisotropy, decreased global efficiency, or higher modularity. Surgery, obstructive hydrocephalus, and preoperative hypothalamic involvement appeared to be related to this degradation. In local analyses, tract-based spatial statistics revealed left-lateralized associations with verbal and motor functions, which supported surgical approaches to midline tumors via the right hemisphere. The mean fractional anisotropy of the brain and the global efficiency derived from DTI increased over the 5 years after proton therapy. The rate of increase was lower with larger irradiated brain volumes and in older children.

CONCLUSIONS

Below-average baseline neurocognitive performance in patients with craniopharyngioma before proton therapy appeared to be related to structural degradation of white matter tracts. Posttherapy longitudinal DTI showed improving trends in global integrity and efficiency measures, particularly in children in whom a smaller brain volume was irradiated.

Non-local means based Rician noise filtering for diffusion tensor and kurtosis imaging in human brain and spinal cord

BACKGROUND

To investigate the effect of using a Rician nonlocal means (NLM) filter on quantification of diffusion tensor (DT)- and diffusion kurtosis (DK)-derived metrics in various anatomical regions of the human brain and the spinal cord, when combined with a constrained linear least squares (CLLS) approach.

METHODS

Prospective brain data from 9 healthy subjects and retrospective spinal cord data from 5 healthy subjects from a 3 T MRI scanner were included in the study. Prior to tensor estimation, registered diffusion weighted images were denoised by an optimized blockwise NLM filter with CLLS. Mean kurtosis (MK), radial kurtosis (RK), axial kurtosis (AK), mean diffusivity (MD), radial diffusivity (RD), axial diffusivity (AD) and fractional anisotropy (FA), were determined in anatomical structures of the brain and the spinal cord. DTI and DKI metrics, signal-to-noise ratio (SNR) and Chi-square values were quantified in distinct anatomical regions for all subjects, with and without Rician denoising.

RESULTS

The averaged SNR significantly increased with Rician denoising by a factor of 2 while the averaged Chi-square values significantly decreased up to 61% in the brain and up to 43% in the spinal cord after Rician NLM filtering. In the brain, the mean MK varied from 0.70 (putamen) to 1.27 (internal capsule) while AK and RK varied from 0.58 (corpus callosum) to 0.92 (cingulum) and from 0.70 (putamen) to 1.98 (corpus callosum), respectively. In the spinal cord, FA varied from 0.78 in lateral column to 0.81 in dorsal column while MD varied from 0.91 × 10^-3 mm²/s (lateral) to 0.93 × 10^-3 mm²/s (dorsal). RD varied from 0.34 × 10^-3 mm²/s (dorsal) to 0.38 × 10^-3 mm²/s (lateral) and AD varied from 1.96 × 10^-3 mm²/s (lateral) to 2.11 × 10^-3 mm²/s (dorsal).

CONCLUSIONS

Our results show a Rician denoising NLM filter incorporated with CLLS significantly increases SNR and reduces estimation errors of DT- and KT-derived metrics, providing the reliable metrics estimation with adequate SNR levels.

Longitudinal Reproducibility of Neurite Orientation Dispersion and Density Imaging (NODDI) Derived Metrics in the White Matter

Diffusion-weighted magnetic resonance imaging (DWI) is undergoing constant evolution with the ambitious goal of developing in-vivo histology of the brain. A recent methodological advancement is Neurite Orientation Dispersion and Density Imaging (NODDI), a histologically validated multi-compartment model to yield microstructural features of brain tissue such as geometric complexity and neurite packing density, which are especially useful in imaging the white matter. Since NODDI is increasingly popular in clinical research and fields such as developmental neuroscience and neuroplasticity, it is of vast importance to characterize its reproducibility (or reliability). We acquired multi-shell DWI data in 29 healthy young subjects twice over a rescan interval of 4 weeks to assess the within-subject coefficient of variation (CV_WS), between-subject coefficient of variation (CV_BS) and the intraclass correlation coefficient (ICC), respectively. Using these metrics, we compared regional and voxel-by-voxel reproducibility of the most common image analysis approaches (tract-based spatial statistics [TBSS], voxel-based analysis with different extents of smoothing ["VBM-style"], ROI-based analysis). We observed high test-retest reproducibility for the orientation dispersion index (ODI) and slightly worse results for the neurite density index (NDI). Our findings also suggest that the choice of analysis approach might have significant consequences for the results of a study. Collectively, the voxel-based approach with Gaussian smoothing kernels of ≥4 mm FWHM and ROI-averaging yielded the highest reproducibility across NDI and ODI maps (CV_WS mostly ≤3%, ICC mostly ≥0.8), respectively, whilst smaller kernels and TBSS performed consistently worse. Furthermore, we demonstrate that image quality (signal-to-noise ratio [SNR]) is an important determinant of NODDI metric reproducibility. We discuss the implications of these results for longitudinal and cross-sectional research designs commonly employed in the neuroimaging field.

Diffusion tensor imaging analysis in scrub typhus meningoencephalitis to determine the alteration of microstructural subcortical white-matter integrity

BACKGROUND AND PURPOSE

Magnetic resonance imaging (MRI) of the brain in scrub typhus meningoencephalitis is non-specific, and in the majority of the cases, conventional MRI fails to detect any abnormality. However, autopsy reports depict central nervous system involvement in almost all patients. There is therefore a need for research on the quantitative assessment of brain parenchyma that can detect microstructural abnormalities. The study aimed to assess the microstructural integrity changes of scrub typhus meningoencephalitis by using different diffusion tensor imaging (DTI) parameters.

METHODS

This was a retrospective analysis of scrub typhus meningoencephalitis. Seven patients and seven age- and sex-matched healthy controls were included. Different DTI parameters such as apparent diffusion coefficient (ADC), fractional anisotropy (FA), relative anisotropy (RA), trace, volume ratio (VR) and geodesic anisotropy (GA) were obtained from six different regions of subcortical white matter at the level of the centrum semiovale. Intergroup significant difference was determined by one-way analysis of variance followed by Tukey's post hoc test. Receiver operating characteristic curves were constructed to determine the accuracy of the DTI matrices.

RESULTS

There was a significant decrease in FA, RA and GA as well as an increase in ADC and VR in the subcortical white matter in patients with scrub typhus meningoencephalitis compared to controls (p < 0.001). The maximum sensitivity of the DTI parameters was 85.7%, and the maximum specificity was 81%.

CONCLUSION

There was an alteration of subcortical white-matter integrity in scrub typhus meningoencephalitis that represents the axonal degeneration, myelin breakdown and neuronal degeneration. DTI may be a useful tool to detect white-matter abnormalities in scrub typhus meningoencephalitis in clinical practice, particularly in patients with negative conventional MRI.

On the signal-to-noise ratio benefit of spiral acquisition in diffusion MRI

PURPOSE

Spiral readouts combine several favorable properties that promise superior net sensitivity for diffusion imaging. The purpose of this study is to verify the signal-to-noise ratio (SNR) benefit of spiral acquisition in comparison with current echo-planar imaging (EPI) schemes.

METHODS

Diffusion-weighted in vivo brain data from three subjects were acquired with a single-shot spiral sequence and several variants of single-shot EPI, including full-Fourier and partial-Fourier readouts as well as different diffusion-encoding schemes. Image reconstruction was based on an expanded signal model including field dynamics obtained by concurrent field monitoring. The effective resolution of each sequence was matched to that of full-Fourier EPI with 1 mm nominal resolution. SNR maps were generated by determining the noise statistics of the raw data and analyzing the propagation of equivalent synthetic noise through image reconstruction. Using the same approach, maps of noise amplification due to parallel imaging (g-factor) were calculated for different acceleration factors.

RESULTS

Relative to full-Fourier EPI at b = 0 s/mm² , spiral acquisition yielded SNR gains of 42-88% and 40-89% in white and gray matter, respectively, depending on the diffusion-encoding scheme. Relative to partial-Fourier EPI, the gains were 36-44% and 34-42%. Spiral g-factor maps exhibited less spatial variation and lower maxima than their EPI counterparts.

CONCLUSION

Spiral readouts achieve significant SNR gains in the order of 40-80% over EPI in diffusion imaging at 3T. Combining systematic effects of shorter echo time, readout efficiency, and favorable g-factor behavior, similar benefits are expected across clinical and neurosciences uses of diffusion imaging.

Evaluation of Urinary Sphincter Function by Rapid Magnetic Resonance Diffusion Tensor Imaging

PURPOSE

This study aimed to assess the feasibility of a rapid diffusion tensor imaging (DTI) for evaluation of the female urinary sphincter function based on differentiation between rest and muscle contraction.

METHODS

Magnetic resonance imaging (MRI) of the lower pelvis was performed at 3 Tesla in 10 healthy female volunteers (21-36 years; body mass index, 20.8±3.6 kg/m2) between June and July 2019. High-resolution T1- and T2-weighted images were acquired for anatomical reference, and following DTI performed in 4 experiment phases: twice during rest (denoted rest-1, rest-2) and contraction (contraction-1, contraction-2). Manual segmentation of the urinary sphincter and the levator ani muscles were performed by 2 independent readers. Mean diffusivity (MD) and fractional anisotropy (FA) values derived from DTI volumes were compared in search for significant differences between the experiment phases. Interreader agreement was assessed by intraclass correlation coefficient (ICC).

RESULTS

Kruskal-Wallis test showed significant differences between MD values among all the experiment phases, by both independent readers (1st: X2 [3,76]=17.16, P<0.001 and 2nd: X2 [3,76]=15.88, P<0.01). Post hoc analysis revealed differences in MD values by both readers between: rest-1 vs. contraction-1 (least P<0.05), rest-1 vs. contraction-2 (P<0.01), rest-2 vs. contraction-1 (P<0.03), rest-2 vs. contraction-2 (P=0.02) with overall mean 'rest' to 'contraction' ΔMD=20.6%. No MD or FA differences were found between rest-1 vs. rest-2 and contraction-1 vs. contraction-2 among all the experiment phases, and interreader agreement was ICC=0.85 (MD) and ICC=0.79 (FA).

CONCLUSION

Rapid DTI might prospectively act as a supporting tool for the evaluation of female pelvic floor muscle function, and incontinence assessment.

Mesoscale diffusion magnetic resonance imaging of the ex vivo human hippocampus

Mesoscale diffusion magnetic resonance imaging (MRI) endeavors to bridge the gap between macroscopic white matter tractography and microscopic studies investigating the cytoarchitecture of human brain tissue. To ensure a robust measurement of diffusion at the mesoscale, acquisition parameters were arrayed to investigate their effects on scalar indices (mean, radial, axial diffusivity, and fractional anisotropy) and streamlines (i.e., graphical representation of axonal tracts) in hippocampal layers. A mesoscale resolution afforded segementation of the pyramidal cell layer (CA1-4), the dentate gyrus, as well as stratum moleculare, radiatum, and oriens. Using ex vivo samples, surgically excised from patients with intractable epilepsy (n = 3), we found that shorter diffusion times (23.7 ms) with a b-value of 4,000 s/mm2 were advantageous at the mesoscale, providing a compromise between mean diffusivity and fractional anisotropy measurements. Spatial resolution and sample orientation exerted a major effect on tractography, whereas the number of diffusion gradient encoding directions minimally affected scalar indices and streamline density. A sample temperature of 15°C provided a compromise between increasing signal-to-noise ratio and increasing the diffusion properties of the tissue. Optimization of the acquisition afforded a system's view of intra- and extra-hippocampal connections. Tractography reflected histological boundaries of hippocampal layers. Individual layer connectivity was visualized, as well as streamlines emanating from individual sub-fields. The perforant path, subiculum and angular bundle demonstrated extra-hippocampal connections. Histology of the samples confirmed individual cell layers corresponding to ROIs defined on MR images. We anticipate that this ex vivo mesoscale imaging will yield novel insights into human hippocampal connectivity.

White Matter Microstructure of the Cerebellar Peduncles Is Associated with Balance Performance during Sensory Re-Weighting in People with Multiple Sclerosis

People with multiple sclerosis (PwMS) exhibit impaired balance during different sensory environments and poor cerebellar peduncle microstructure. We aimed to examine associations between microstructures of the superior, middle and inferior cerebellar peduncles (CP) with visual, vestibular, and proprioceptive-based balance in PwMS. Twenty-seven PwMS and twenty-nine healthy controls (HC) underwent MRI and balance assessments. We assessed CP microstructure with radial diffusivity (RD) and fractional anisotropy (FA) and balance with center of pressure-derived measures of path length and root mean square of sway during proprioceptive (C2), visual (C3), and vestibular (C4) balance conditions of the modified clinical test of sensory integration on balance (mCTSIB). PwMS exhibited significantly lower FA (p < 0.001) and greater RD (p < 0.001) across all CP and greater path length (p < 0.05) in the mCTSIB compared with HC. In PwMS, significant associations were detected between inferior CP white matter microstructure and proprioceptive-based balance control (rho = -0.43, p < 0.05) and middle CP white matter microstructure and visual-based balance control (rho = 0.39, p < 0.05). PwMS may rely more on cerebellar-regulated proprioceptive- and visual-based balance control than HC.

Pre-operative MRI measurements versus anthropometric data: Which is more accurate in predicting 4-stranded hamstring graft size in anterior cruciate ligament reconstruction?

Background/objective

Graft diameter has been shown to play an important role in anterior cruciate ligament (ACL) autograft failure rates. The roles of pre-operative MRI measurement of graft size and anthropometric data have been studied in the prediction of hamstring graft size. Pre-operative knowledge of hamstring graft diameter allows surgeons to perform better surgical planning and provides an opportunity to discuss with patients on alternative graft options such as allografts should the need arises. The purpose of this study was to compare the accuracy of pre-operative anthropometric data and MRI measurements in the prediction of 4-stranded hamstring autograft size in anterior cruciate ligament reconstruction.

Method

This was a cohort study involving 141 subjects (115 males and 26 females) who underwent a single bundle ACL reconstruction utilising a 4-stranded hamstring graft by a single surgeon from 2008 to 2012. Pre-operatively, the height, weight, body mass index (BMI), age, gender and smoking status was recorded. The MRI scans used for diagnosis were utilized to measure the gracilis (GT) and semi-tendinosus (ST) cross sectional area (CSA).

Result

We found the strongest correlation between Combined (ST + GT) CSA and intra-operative graft size (r = 0.596, p < 0.001). This was followed by ST CSA (r = 0.570,p < 0.001), Body surface area (r = 0.507,p < 0.001), and GT CSA (r = 0.460,p < 0.001). No significant correlation was found between 2 anthropometric data (Age and BMI). There was also no significant difference between different strengths of MRIs (1.5T vs 3.0T) in determining the intra-operative graft size (p = 0.438).

Conclusion

We conclude that pre-operative MRI is superior to anthropometric variables in predicting the size of 4-stranded hamstring autografts used in ACL reconstruction.

Denoising and Multiple Tissue Compartment Visualization of Multi-b-Valued Breast Diffusion MRI

BACKGROUND

Multi-b-valued/multi-shell diffusion provides potentially valuable metrics in breast MRI but suffers from low signal-to-noise ratio and has potentially long scan times.

PURPOSE

To investigate the effects of model-based denoising with no loss of spatial resolution on multi-shell breast diffusion MRI; to determine the effects of downsampling on multi-shell diffusion; and to quantify these effects in multi-b-valued (three directions per b-value) acquisitions.

STUDY TYPE

Prospective ("fully-sampled" multi-shell) and retrospective longitudinal (multi-b).

SUBJECTS

One normal subject (multi-shell) and 10 breast cancer subjects imaging at four timepoints (multi-b).

FIELD STRENGTH/SEQUENCE

3T multi-shell acquisition and 1.5T multi-b acquisition.

ASSESSMENT

The "fully-sampled" multi-shell acquisition was retrospectively downsampled to determine the bias and error from downsampling. Mean, axial/parallel, radial diffusivity, and fractional anisotropy (FA) were analyzed. Denoising was applied retrospectively to the multi-b-valued breast cancer subject dataset and assessed subjectively for image noise level and tumor conspicuity.

STATISTICAL TESTS

Parametric paired t-test (P < 0.05 considered statistically significant) on mean and coefficient of variation of each metric-the apparent diffusion coefficient (ADC) from all b-values, fast ADC, slow ADC, and perfusion fraction. Paired and two-sample t-tests for each metric comparing normal and tumor tissue.

RESULTS

In the multi-shell data, denoising effectively suppressed FA (-45% to -78%), with small biases in mean diffusivity (-5% in normal, +23% in tumor, and -4% in vascular compartments). In the multi-b data, denoising resulted in small biases to the ADC metrics in tumor and normal contralateral tissue (by -3% to +11%), but greatly reduced the coefficient of variation for every metric (by -1% to -24%). Denoising improved differentiation of tumor and normal tissue regions in most metrics and timepoints; subjectively, image noise level and tumor conspicuity were improved in the fast ADC maps.

DATA CONCLUSION

Model-based denoising effectively suppressed erroneously high FA and improved the accuracy of diffusivity metrics.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY STAGE: 1.

Sensorimotor white matter projections and disease severity in primary Restless Legs Syndrome/Willis-Ekbom disease: a multimodal DTI analysis

BACKGROUND

Restless Legs Syndrome, a potentially disabling sleep disorder, also known as Willis-Ekbom disease (RLS/WED), may be caused by loss of inhibitory modulation of descending central motor pathways, structural changes in the somatosensory cortex, abnormal connectivity between motor and sensory areas, as well as by subtle abnormalities in white matter micro-organization.

OBJECTIVE

To compare diffusion-tensor imaging (DTI) metrics in areas associated with sensory or motor function, as well as sensorimotor integration, between subjects with primary mild-to-severe RLS/WED and controls.

METHODS

DTI metrics were assessed in 38 subjects with RLS/WED (14 mild to moderate, 24 severe to very severe) and 24 healthy age-matched controls with whole-brain Tract Based Spatial Statistics (TBSS), Region-of-interest (ROI) and probabilistic tractography based analyses. The ROIs corresponded to the corticospinal tract (CST) at the level of the cerebral peduncle; the superior, middle and inferior cerebellar peduncles. Subgroup analyses were made according to the severity of RLS/WED symptoms. The corticospinal tract was evaluated with probabilistic tractography. We also explored associations between significant findings and severity of symptoms with the Spearman's correlation coefficient.

RESULTS

TBSS analysis revealed decreased axial diffusivity (AD) in the left posterior thalamic radiation in RLS/WED. In subjects with severe RLS/WED, AD was reduced in the left posterior corona radiata and this reduction was negatively correlated with severity of symptoms. ROI-based analysis showed that radial diffusivity (RD) was increased in the superior cerebellar peduncles of individuals with severe RLS/WED. Tractography did not show between-group or subgroup differences.

CONCLUSIONS

Our results are consistent with subtle white matter changes, prominently in RLS/WED subjects with more severe symptoms, in areas related to sensory or motor function, as well as to sensorimotor integration, compared to controls. These findings support the hypothesis, raised by prior pathophysiological studies, of defective integration within these networks.

The impact of position-orientation adaptive smoothing in diffusion weighted imaging-From diffusion metrics to fiber tractography

In contrast to commonly used approaches to improve data quality in diffusion weighted imaging, position-orientation adaptive smoothing (POAS) provides an edge-preserving post-processing approach. This study aims to investigate its potential and effects on image quality, diffusion metrics, and fiber tractography of the corticospinal tract in relation to non-post-processed and averaged data. 22 healthy volunteers were included in this study. For each volunteer five clinically applicable diffusion weighted imaging data sets were acquired and post-processed by standard averaging and POAS. POAS post-processing led to significantly higher signal-to-noise-ratios (p < 0.001), lower fractional anisotropy across the whole brain (p < 0.05) and reduced intra-subject variability of diffusion weighted imaging signal intensity and fractional anisotropy (p < 0.001, p = 0.006). Fiber tractography of the corticospinal tract resulted in significantly (p = 0.027, p = 0.014) larger tract volumes while fiber density was the lowest. Similarity across tractography results was highest for POAS post-processed data (p < 0.001). POAS post-processing enhances image quality, decreases the intra-subject variability of signal intensity and fractional anisotropy, increases fiber tract volume of the corticospinal tract, and leads to higher reproducibility of tractography results. Thus, POAS post-processing supports a reliable and more accurate fiber tractography of the corticospinal tract, being mandatory for the clinical use.

Higher sensitivity for traumatic cerebral microbleeds at 7 T ultra-high field MRI: is it clinically significant for the acute state of the patients and later quality of life?

Background

The present study evaluates the possible prognostic benefits of 7 T susceptibility weighted imaging (SWI) of traumatic cerebral microbleeds (TMBs) over 3 T SWI to predict the acute clinical state and subjective impairments, including health-related quality of life (HRQOL), after closed head injury (CHI).

Methods

The study group comprised 10 participants with known TMBs All subjects underwent 3 T magnetic resonance imaging (MRI) and 7 T MRI, respectively. Location and count of TMBs were independently evaluated by two neuroradiologists. The initial Glasgow Coma Scale (GCS), the duration of coma and further clinical data were taken from the patients records. HRQOL was assessed by means of a questionnaire. Memory complaints and neurological symptoms were inquired at the time of the MRI examinations.

Results

SWI revealed a total of 485 TMBs at 3 T, 584 TMBs at 7 T with similar spatial resolution, and 684 TMBs at 7 T with a factor of 10 higher spatial resolution. The TMBs depicted by 7 T high-resolution SWI were correlated with the duration of coma (Spearman's rho of 0.77). The corresponding association with TMBs in 3 T MRI SWI showed a Spearman's rho of 0.71. The initial GCS score and TMBs correlated with a Spearman's rho of -0.35 at 3 T SWI MRI and a rho of -0.33 at 7 T high-resolution SWI, respectively. The physical aspect of HRQOL correlated substantially with the count of TMBs (rho = 0.44 for 3 T SWI and rho = 0.35 for both 7 T SWI sequences, respectively).

Conclusions

The number of TMBs showed a substantial association with indicators of the acute clinical state and chronic neurobehavioral parameters after CHI, but there was no additional advantage of 7 T MRI. These preliminary findings warrant a larger prospective study for the future.

On PTV definition for glioblastoma based on fiber tracking of diffusion tensor imaging data

Radiotherapy (RT) is commonly applied for the treatment of glioblastoma multiforme (GBM). Following the planning target volume (PTV) definition procedure standardized in guidelines, a 20% risk of missing non-local recurrences is present. Purpose of this study was to evaluate whether diffusion tensor imaging (DTI)-based fiber tracking may be beneficial for PTV definition taking into account the prediction of distant recurrences. 56 GBM patients were examined with magnetic resonance imaging (MRI) including DTI performed before RT after resection of the primary tumor. Follow-up MRIs were acquired in three month intervals. For the seven patients with a distant recurrence, fiber tracking was performed with three algorithms and it was evaluated whether connections existed from the primary tumor region to the distant recurrence. It depended strongly on the used tracking algorithm and the used tracking parameters whether a connection was observed. Most of the connections were weak and thus not usable for PTV definition. Only in one of the seven patients with a recurring tumor, a clear connection was present. It seems unlikely that DTI-based fiber tracking can be beneficial for predicting distant recurrences in the planning of PTVs for glioblastoma multiforme.

Feasibility of diffusion and probabilistic white matter analysis in patients implanted with a deep brain stimulator

Deep brain stimulation (DBS) for Parkinson's disease (PD) is an established advanced therapy that produces therapeutic effects through high frequency stimulation. Although this therapeutic option leads to improved clinical outcomes, the mechanisms of the underlying efficacy of this treatment are not well understood. Therefore, investigation of DBS and its postoperative effects on brain architecture is of great interest. Diffusion weighted imaging (DWI) is an advanced imaging technique, which has the ability to estimate the structure of white matter fibers; however, clinical application of DWI after DBS implantation is challenging due to the strong susceptibility artifacts caused by implanted devices. This study aims to evaluate the feasibility of generating meaningful white matter reconstructions after DBS implantation; and to subsequently quantify the degree to which these tracts are affected by post-operative device-related artifacts. DWI was safely performed before and after implanting electrodes for DBS in 9 PD patients. Differences within each subject between pre- and post-implantation FA, MD, and RD values for 123 regions of interest (ROIs) were calculated. While differences were noted globally, they were larger in regions directly affected by the artifact. White matter tracts were generated from each ROI with probabilistic tractography, revealing significant differences in the reconstruction of several white matter structures after DBS. Tracts pertinent to PD, such as regions of the substantia nigra and nigrostriatal tracts, were largely unaffected. The aim of this study was to demonstrate the feasibility and clinical applicability of acquiring and processing DWI post-operatively in PD patients after DBS implantation. The presence of global differences provides an impetus for acquiring DWI shortly after implantation to establish a new baseline against which longitudinal changes in brain connectivity in DBS patients can be compared. Understanding that post-operative fiber tracking in patients is feasible on a clinically-relevant scale has significant implications for increasing our current understanding of the pathophysiology of movement disorders, and may provide insights into better defining the pathophysiology and therapeutic effects of DBS.

One-year changes in brain microstructure differentiate preclinical Huntington's disease stages

OBJECTIVE

To determine whether brain imaging markers of tissue microstructure can detect the effect of disease progression across the preclinical stages of Huntington's disease.

METHODS

Longitudinal microstructural changes in diffusion imaging metrics (mean diffusivity and fractional anisotropy) were investigated in participants with presymptomatic Huntington's disease (N = 35) stratified into three preclinical subgroups according to their estimated time until onset of symptoms, compared with age- and gender-matched healthy controls (N = 19) over a 1y period.

RESULTS

Significant differences were found over the four groups in change of mean diffusivity in the posterior basal ganglia and the splenium of the corpus callosum. This overall effect was driven by significant differences between the group far-from-onset (FAR) of symptoms and the groups midway- (MID) and near-the-onset (NEAR) of symptoms. In particular, an initial decrease of mean diffusivity in the FAR group was followed by a subsequent increase in groups closer to onset of symptoms. The seemingly counter-intuitive decrease of mean diffusivity in the group furthest from onset of symptoms might be an early indicator of neuroinflammatory process preceding the neurodegenerative phase. In contrast, the only clinical measure that was able to capture a difference in 1y changes between the preclinical stages was the UHDRS confidence in motor score.

CONCLUSIONS

With sensitivity to longitudinal changes in brain microstructure within and between preclinical stages, and potential differential response to distinct pathophysiological mechanisms, diffusion imaging is a promising state marker for monitoring treatment response and identifying the optimal therapeutic window of opportunity in preclinical Huntington's disease.

Denoising of diffusion MRI improves peripheral nerve conspicuity and reproducibility

BACKGROUND

Quantitative diffusion MRI is a promising technique for evaluating peripheral nerve integrity but low signal-to-noise ratio (SNR) can impede measurement accuracy.

PURPOSE

To evaluate principal component analysis (PCA) and generalized spherical deconvolution (genSD) denoising techniques to improve within-subject reproducibility and peripheral nerve conspicuity.

STUDY TYPE

Prospective.

SUBJECTS

Seven healthy volunteers and three peripheral neuropathy patients.

FIELD STRENGTH/SEQUENCE

3T/multiband single-shot echo planar diffusion sequence using multishell 55-direction scheme.

ASSESSMENT

Images were processed using four methods: "original" (no denoising), "average" (10 repetitions), "PCA-only," and "PCA + genSD." Tibial and common peroneal nerve segmentations and masks were generated from volunteer diffusion data. Quantitative (SNR and contrast-to-noise ratio [CNR]) values were calculated. Three radiologists qualitatively evaluated nerve conspicuity for each method. The two denoising methods were also performed in three patients with peripheral neuropathies.

STATISTICAL TESTS

For healthy volunteers, calculations included SNR and CNR_FA (computed using FA values). Coefficient of variation (CV%) of CNR_FA quantified within-subject reproducibility. Groups were compared with two-sample t-tests (significance P < 0.05; two-tailed, Bonferroni-corrected). Odds ratios (ORs) quantified the relative rates of each of three radiologists confidently identifying a nerve, per slice, for the four methods.

RESULTS

"PCA + genSD" yielded the highest SNR (mean_overall = 14.83 ± 1.99) and tibial and common peroneal nerve CNR_FA (mean_tibial = 3.45, mean_peroneal = 2.34) compared to "original" (P _SNR < 0.001; P _CNR = 0.011) and "PCA-only" (P _SNR < 0.001, P _CNR < 0.001). "PCA + genSD" had higher within-subject reproducibility (low CV%) for tibial (6.04 ± 1.98) and common peroneal nerves (8.27 ± 2.75) compared to "original" and "PCA-only." The mean FA was higher for "original" than "average" (P < 0.001), but did not differ significantly between "average" and "PCA + genSD" (P = 0.14). "PCA + genSD" had higher tibial and common peroneal nerve conspicuity than "PCA-only" (OR_tibial = 2.50, P < 0.001; OR_peroneal = 1.86, P < 0.001) and "original" (OR_tibial = 2.73, P < 0.001; OR_peroneal = 2.43, P < 0.001).

DATA CONCLUSION

PCA + genSD denoising method improved SNR, CNR_FA , and within-subject reproducibility (CV%) without biasing FA and nerve conspicuity. This technique holds promise for facilitating more reliable, unbiased diffusion measurements of peripheral nerves.

LEVEL OF EVIDENCE

2 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2020;51:1128-1137.

Population-based Bayesian regularization for microstructural diffusion MRI with NODDIDA

PURPOSE

Information on the brain microstructure can be probed by Diffusion Magnetic Resonance Imaging (dMRI). Neurite Orientation Dispersion and Density Imaging with Diffusivities Assessment (NODDIDA) is one of the simplest microstructural model proposed. However, the estimation of the NODDIDA parameters from clinically plausible dMRI acquisition is ill-posed, and different parameter sets can describe the same measurements equally well. A few approaches to resolve this problem focused on developing better optimization strategies for this non-convex optimization. However, this fundamentally does not resolve ill-posedness. This article introduces a Bayesian estimation framework, which is regularized through knowledge from an extensive dMRI measurement set on a population of healthy adults (henceforth population-based prior).

METHODS

We reformulate the problem as a Bayesian maximum a posteriori estimation, which includes as a special case previous approach using non-informative uniform priors. A population-based prior is estimated from 35 subjects of the MGH Adult Diffusion data (Human Connectome Project), acquired with an extensive acquisition protocol including high b-values. The accuracy and robustness of different approaches with and without the population-based prior is tested on subsets of the MGH dataset, and an independent dataset from a clinically comparable scanner, with only clinically plausible dMRI measurements.

RESULTS

The population-based prior produced substantially more accurate and robust parameter estimates, compared to the conventional uniform priors, for clinically feasible protocols, without introducing any evident bias.

CONCLUSIONS

The use of the proposed Bayesian population-based prior can lead to clinically feasible and robust estimation of NODDIDA parameters without changing the acquisition protocol.

Association between postoperative DTI metrics and neurological deficits after posterior fossa tumor resection in children

OBJECTIVE

Resection of posterior fossa tumors in children may be associated with persistent neurological deficits. It is unclear if these neurological deficits are associated with persistent structural damage to the cerebellar pathways. The purpose of this research was to define longitudinal changes in diffusion tensor imaging (DTI) metrics in white matter cerebellar tracts and the clinical correlates of these metrics in children undergoing resection of posterior fossa tumors.

METHODS

Longitudinal brain DTI was performed in a cohort of pediatric patients who underwent resection of posterior fossa tumors. Fractional anisotropy (FA) of the superior cerebellar peduncles (SCPs) and middle cerebellar peduncles (MCPs) was measured on preoperative, postoperative, and follow-up DTI. Early postoperative (< 48 hours) and longer-term follow-up neurological deficits (mutism, ataxia, and extraocular movement dysfunction) were documented. Statistical analysis was performed to determine differences in FA values based on presence or absence of neurological deficits. Statistical significance was set at p < 0.05.

RESULTS

Twenty children (mean age 6.1 ± 4.1 years [SD], 12 males and 8 females) were included in this study. Follow-up DTI was performed at a median duration of 14.3 months after surgery, and the median duration of follow-up was 19.7 months. FA of the left SCP was significantly reduced on postoperative DTI in comparison with preoperative DTI (0.44 ± 0.07 vs 0.53 ± 0.1, p = 0.003). Presence of ataxia at follow-up was associated with a persistent reduction in the left SCP FA on follow-up DTI (0.43 ± 0.1 vs 0.55 ± 0.1, p = 0.016). Patients with early postoperative mutism who did not recover at follow-up had significantly decreased FA of the left SCP on early postoperative DTI in comparison with those who recovered (0.38 ± 0.05 vs 0.48 ± 0.06, p = 0.04).

CONCLUSIONS

DTI after resection of posterior fossa tumors in children shows that persistent reduction of SCP FA is associated with ataxia at follow-up.

Acute ex vivo changes in brain white matter diffusion tensor metrics

Purpose

Diffusion magnetic resonance imaging and tractography has an important role in the visualization of brain white matter and assessment of tissue microstructure. There is a lack of correspondence between diffusion metrics of live tissue, ex vivo tissue, and histological findings. The objective of this study is to elucidate this connection by determining the specific diffusion alterations between live and ex vivo brain tissue. This may have an important role in the incorporation of diffusion imaging in ex vivo studies as a complement to histological sectioning as well as investigations of novel neurosurgical techniques.

Methods

This study presents a method of high angular resolution diffusion imaging and tractography of intact and non-fixed ex vivo piglet brains. Most studies involving ex vivo brain specimens have been formalin-fixed or excised from their original biological environment, processes both of which are known to affect diffusion parameters. Thus, non-fixed ex vivo tissue is used. A region-of-interest based analysis of diffusion tensor metrics are compared to in vivo subjects in a selection of major white matter bundles in order to assess the translatability of ex vivo diffusion measurements.

Results

Tractography was successfully achieved in both in vivo and ex vivo groups. No significant differences were found in tract connectivity, average streamline length, or apparent fiber density. Significantly decreased diffusivity (mean, axial, and radial; p<0.0005) in the non-fixed ex vivo group and unaltered fractional anisotropy (p>0.059) between groups were observed.

Conclusion

This study validates the extrapolation of non-fixed fractional anisotropy measurements to live tissue and the potential use of ex vivo tissue for methodological development.

Atrophic Changes and Diffusion Abnormalities of Affected Trigeminal Nerves in Trigeminal Neuralgia Using 7-T MRI

BACKGROUND

Magnetic resonance imaging (MRI) has been widely used for visualizing trigeminal nerves in trigeminal neuralgia.

OBJECTIVE

To assess atrophy and diffusion abnormalities of affected trigeminal nerves in trigeminal neuralgia with 7-T MRI.

METHODS

In this prospective study, 14 patients (mean age 49 years; range 31-64 years) with trigeminal neuralgia underwent 7-T MRI. We measured trigeminal nerve volumes along their course through the pontocerebellar cistern. We also evaluated fractional anisotropy (FA) and quantitative anisotropy (QA) values within cisternal segment and pontine nuclei of the affected-side and unaffected-side trigeminal nerves, using diffusion tensor imaging (DTI). Associations between DTI metrics and Barrow Neurological Institute (BNI) pain scores were examined.

RESULTS

The volumes were significantly smaller for the affected trigeminal nerves (33.83 ± 23.12 mm3) than for the unaffected ones (47.76 ± 32.48 mm3; p = 0.008). Cisternal segment FA and QA values were significantly lower in affected trigeminal nerves than in unaffected ones. However, DTI measurements in the pontine nuclei revealed no significant differences between affected-side and unaffected-side trigeminal nerves. No DTI metrics significantly correlated with BNI pain scores.

CONCLUSION

Our results suggest that 7-T MRI allows identifications of atrophy and diffusion abnormalities of trigeminal nerves in trigeminal neuralgia.

Using the Wild Bootstrap to Quantify Uncertainty in Mean Apparent Propagator MRI

Purpose: Estimation of uncertainty of MAP-MRI metrics is an important topic, for several reasons. Bootstrap derived uncertainty, such as the standard deviation, provides valuable information, and can be incorporated in MAP-MRI studies to provide more extensive insight. Methods: In this paper, the uncertainty of different MAP-MRI metrics was quantified by estimating the empirical distributions using the wild bootstrap. We applied the wild bootstrap to both phantom data and human brain data, and obtain empirical distributions for the MAP-MRI metrics return-to-origin probability (RTOP), non-Gaussianity (NG), and propagator anisotropy (PA). Results: We demonstrated the impact of diffusion acquisition scheme (number of shells and number of measurements per shell) on the uncertainty of MAP-MRI metrics. We demonstrated how the uncertainty of these metrics can be used to improve group analyses, and to compare different preprocessing pipelines. We demonstrated that with uncertainty considered, the results for a group analysis can be different. Conclusion: Bootstrap derived uncertain measures provide additional information to the MAP-MRI derived metrics, and should be incorporated in ongoing and future MAP-MRI studies to provide more extensive insight.

Diffusion Tensor Imaging in Acute Spinal Cord Injury: A Review of Animal and Human Studies

Diffusion tensor imaging (DTI), based on the property of preferential diffusion of water molecules in biological tissue, is seeing increasing clinical application in the pathologies of the central nervous system. Spinal cord injury (SCI) is one such area where the use of DTI allows for the evaluation of changes to microstructure of the spinal cord not detected on routine conventional magnetic resonance imaging. The insights obtained from pre-clinical models of SCI indicate correlation of quantitative DTI indices with histology and function, which points to the potential of DTI as a non-invasive, viable biomarker for integrity of white matter tracts in the spinal cord. In this review, we describe DTI alterations in the acute phase of SCI in both animal models and human subjects and explore the underlying pathophysiology behind these changes.