A New Approach for Deep Gray Matter Analysis Using Partial-Volume Estimation

Automatic segmentation of Deep Grey Nuclei using a high-resolution 7T MRI Atlas - quantification of T1 values in healthy volunteers

We present a new consensus atlas of deep grey nuclei obtained by shape-based averaging of manual segmentation of two experienced neuroradiologists and optimized from 7T MP2RAGE images acquired at (0.6mm)³ in 60 healthy subjects. A group-wise normalization method was used to build a high-contrast and high-resolution T₁ -weighted brain template (0.5mm)³ using data from 30 out of the 60 controls. Delineation of 24 deep grey nuclei per hemisphere, including the claustrum and twelve thalamic nuclei, was then performed by two expert neuroradiologists and reviewed by a third neuroradiologist according to tissue contrast and external references based on the Morel atlas. Corresponding deep grey matter structures were also extracted from the Morel and CIT168 atlases. The data-derived, Morel and CIT168 atlases were all applied at the individual level using non-linear registration to fit the subject reference and to extract absolute mean quantitative T₁ values derived from the 3D-MP2RAGE volumes, after correction for residual B₁ ⁺ biases. Three metrics (The Dice and the volumetric similarity coefficients, and a novel Hausdorff distance) were used to estimate the inter-rater agreement of manual MRI segmentation and inter-atlas variability, and these metrics were measured to quantify biases due to image registration and their impact on the measurements of the quantitative T₁ values was highlighted. This represents a fully-automated segmentation process permitting the extraction of unbiased normative T₁ values in a population of young healthy controls as a reference for characterizing subtle structural alterations of deep grey nuclei relevant to a range of neurological diseases.

Ultrahigh field in vivo characterization of microstructural abnormalities in the orbitofrontal cortex and amygdala in autism

There are currently no biomarkers for autism spectrum disorder (ASD). This neurodevelopmental condition has previously been associated with histopathological findings, including increased neuronal packing density in the amygdala, abnormal laminar cytoarchitecture and increased average neuronal density in the prefrontal cortex. The present study examined whether new brain imaging technologies could reveal in vivo, in adults with ASD, the manifestation of previously described histopathological changes. Using quantitative mapping at ultrahigh field (7 Tesla), we show that we can observe microstructural alterations in the right lateral orbitofrontal cortex and the bilateral amygdala in adult individuals with ASD in vivo. These imaging alterations point to an abnormal laminar cytoarchitecture and to an increased neuronal density, similar to what has been previously described in post-mortem data in ASD. Our data demonstrate that it is possible to visualize, in vivo and at the individual level, alterations of cortical and subcortical microstructure in ASD. Future studies will be needed to extend these findings to a larger group of individuals and evaluate their association with symptomatology as well as their specificity among the different neurodevelopmental disorders.

Partial-volume modeling reveals reduced gray matter in specific thalamic nuclei early in the time course of psychosis and chronic schizophrenia

The structural complexity of the thalamus, due to its mixed composition of gray and white matter, make it challenging to disjoint and quantify each tissue contribution to the thalamic anatomy. This work promotes the use of partial‐volume‐based over probabilistic‐based tissue segmentation approaches to better capture thalamic gray matter differences between patients at different stages of psychosis (early and chronic) and healthy controls. The study was performed on a cohort of 23 patients with schizophrenia, 41 with early psychosis and 69 age and sex‐matched healthy subjects. Six tissue segmentation approaches were employed to obtain the gray matter concentration/probability images. The statistical tests were applied at three different anatomical scales: whole thalamus, thalamic subregions and voxel‐wise. The results suggest that the partial volume model estimation of gray matter is more sensitive to detect atrophies within the thalamus of patients with psychosis. However all the methods detected gray matter deficit in the pulvinar, particularly in early stages of psychosis. This study demonstrates also that the gray matter decrease varies nonlinearly with age and between nuclei. While a gray matter loss was found in the pulvinar of patients in both stages of psychosis, reduced gray matter in the mediodorsal was only observed in early psychosis subjects. Finally, our analyses point to alterations in a sub‐region comprising the lateral posterior and ventral posterior nuclei. The obtained results reinforce the hypothesis that thalamic gray matter assessment is more reliable when the tissues segmentation method takes into account the partial volume effect.

Cerebral Gray and White Matter Involvement in Anorexia Nervosa Evaluated by T1, T2, and T2* Mapping

BACKGROUND AND PURPOSE

Changes in the brain composition of anorexics could potentially be expected, opening the door to new imaging approaches where quantitative and qualitative MRI have a role. Our purpose was to investigate anorexia-related brain dehydration and myelin depletion by analyzing T1, T2, and T2* relaxation times of different brain structures in anorexics and controls.

METHODS

Thirty-eight anorexic female patients (mean age, 26.2 years; age range, 16.2-48.7 years; mean BMI, 14.5 kg/m² ; BMI range, 10.0-18.4 kg/m² ) underwent brain MRI between August 2014 and August 2018. Controls were 16 healthy females (mean age, 28.0 years; age range, 22.3-34.7 years; mean BMI, 20.9 kg/m² ; BMI range, 18.4-26.6 kg/m² ). T1, T2, and T2* relaxation times were obtained for different brain structures in anorexics and controls as part of this retrospective case-control study.

RESULTS

The T1 relaxation times of gray and white matter were significantly lower in anorexics (P = .009), whereas the T2 relaxation times of gray matter were higher (P < .001). There were no statistically significant differences in gray matter T2* relaxation times or in white matter T2 and T2* relaxation times between anorexics and controls. Occipital lobe gray matter showed the shortest T1, T2, and T2* relaxation times of all brain regions (P < .05).

CONCLUSIONS

T1 shortening in anorexics suggests both dehydration and myelin loss, whereas T2 prolongation points toward myelin loss (myelin water has lower T2), which seems to be less discernible in white matter. Shorter overall relaxation times in the most posterior regions of the brain suggest higher iron content.

Wavelet Entropy and Directed Acyclic Graph Support Vector Machine for Detection of Patients with Unilateral Hearing Loss in MRI Scanning

Highlights

We develop computer-aided diagnosis system for unilateral hearing loss detection in structural magnetic resonance imaging.

Wavelet entropy is introduced to extract image global features from brain images. Directed acyclic graph is employed to endow support vector machine an ability to handle multi-class problems.

The developed computer-aided diagnosis system achieves an overall accuracy of 95.1% for this three-class problem of differentiating left-sided and right-sided hearing loss from healthy controls.

Aim: Sensorineural hearing loss (SNHL) is correlated to many neurodegenerative disease. Now more and more computer vision based methods are using to detect it in an automatic way.

Materials: We have in total 49 subjects, scanned by 3.0T MRI (Siemens Medical Solutions, Erlangen, Germany). The subjects contain 14 patients with right-sided hearing loss (RHL), 15 patients with left-sided hearing loss (LHL), and 20 healthy controls (HC).

Method: We treat this as a three-class classification problem: RHL, LHL, and HC. Wavelet entropy (WE) was selected from the magnetic resonance images of each subjects, and then submitted to a directed acyclic graph support vector machine (DAG-SVM).

Results: The 10 repetition results of 10-fold cross validation shows 3-level decomposition will yield an overall accuracy of 95.10% for this three-class classification problem, higher than feedforward neural network, decision tree, and naive Bayesian classifier.

Conclusions: This computer-aided diagnosis system is promising. We hope this study can attract more computer vision method for detecting hearing loss.