New imaging techniques in the diagnosis of multiple sclerosis

Emerging deep learning techniques using magnetic resonance imaging data applied in multiple sclerosis and clinical isolated syndrome patients (Review)

Computer-aided diagnosis systems aim to assist clinicians in the early identification of abnormal signs in order to optimize the interpretation of medical images and increase diagnostic precision. Multiple sclerosis (MS) and clinically isolated syndrome (CIS) are chronic inflammatory, demyelinating diseases affecting the central nervous system. Recent advances in deep learning (DL) techniques have led to novel computational paradigms in MS and CIS imaging designed for automatic segmentation and detection of areas of interest and automatic classification of anatomic structures, as well as optimization of neuroimaging protocols. To this end, there are several publications presenting artificial intelligence-based predictive models aiming to increase diagnostic accuracy and to facilitate optimal clinical management in patients diagnosed with MS and/or CIS. The current study presents a thorough review covering DL techniques that have been applied in MS and CIS during recent years, shedding light on their current advances and limitations.

Longitudinal Structural MRI in Neurologically Healthy Adults

BACKGROUND

Structural brain MRI measures are frequently examined in both healthy and clinical groups, so an understanding of how these measures vary over time is desirable.

PURPOSE

To test the stability of structural brain MRI measures over time.

POPULATION

In all, 112 healthy volunteers across four sites.

STUDY TYPE

Retrospective analysis of prospectively acquired data.

FIELD STRENGTH/SEQUENCE

3 T, magnetization prepared - rapid gradient echo, and single-shell diffusion sequence.

ASSESSMENT

Diffusion, cortical thickness, and volume data from the sensorimotor network were assessed for stability over time across 3 years. Two sites used a Siemens MRI scanner, two sites a Philips scanner.

STATISTICAL TESTS

The stability of structural measures across timepoints was assessed using intraclass correlation coefficients (ICC) for absolute agreement, cutoff ≥0.80, indicating high reliability. Mixed-factorial analysis of variance (ANOVA) was used to examine between-site and between-scanner type differences in individuals over time.

RESULTS

All cortical thickness and gray matter volume measures in the sensorimotor network, plus all diffusivity measures (fractional anisotropy plus mean, axial and radial diffusivities) for primary and premotor cortices, primary somatosensory thalamic connections, and the cortico-spinal tract met ICC. The majority of measures differed significantly between scanners, with a trend for sites using Siemens scanners to produce larger values for connectivity, cortical thickness, and volume measures than sites using Philips scanners.

DATA CONCLUSION

Levels of reliability over time for all tested structural MRI measures were generally high, indicating that any differences between measurements over time likely reflect underlying biological differences rather than inherent methodological variability.

LEVEL OF EVIDENCE

4.

TECHNICAL EFFICACY STAGE

1.

Comparison of diffusion-weighted imaging and enhanced T1-weighted sequencing in patients with multiple sclerosis

Introduction The purpose of this study was to assess whether demographic, brain anatomical regions and contrast enhancement show differences in multiple sclerosis (MS) patients with increased diffusion lesions (ID group) compared with diffusion restriction (DR group). Method MRI protocol comprised T1- and T2-weighted sequences with and without gadolinium (Gd), and sagittal three-dimensional FLAIR sequence, DWI and ADC maps were prospectively performed in 126 MS patients from January to December 2015. The investigation was conducted to evaluate differences in demographic, cord and brain regional, technical, and positive or negative Gd contrast imaging parameters in two groups of ID and DR. Statistical analysis was performed by using SPSS. Results A total of 9.6% of patients showed DR. In the DR group, 66.6% of the patients showed contrast enhancement of plaques, whereas 29.2% of the IR group showed enhancement of plaques. The most prevalent group was non-enhanced plaques in the ID group, followed by Gd-enhanced plaques in the ID group. Patients in the ID group (90.4%) were significantly more than in the DR group (9.6%). Out of the 40 patients with Gd-enhanced plaques, 80.5% was from the ID group and 19.5% from the DR group. Conclusion MRI of the brain, unlike of the cord, with Gd demonstrates significant difference in enhancement between the two groups ( p < 0.05). No significant difference was seen in demographic, cord and brain regional, and technical parameters, EDSS, disease duration, and attack rate as well as demographic and regional parameters between the ID and decrease diffusion groups ( p > 0.05).

AxonSeg: Open Source Software for Axon and Myelin Segmentation and Morphometric Analysis

Segmenting axon and myelin from microscopic images is relevant for studying the peripheral and central nervous system and for validating new MRI techniques that aim at quantifying tissue microstructure. While several software packages have been proposed, their interface is sometimes limited and/or they are designed to work with a specific modality (e.g., scanning electron microscopy (SEM) only). Here we introduce AxonSeg, which allows to perform automatic axon and myelin segmentation on histology images, and to extract relevant morphometric information, such as axon diameter distribution, axon density and the myelin g-ratio. AxonSeg includes a simple and intuitive MATLAB-based graphical user interface (GUI) and can easily be adapted to a variety of imaging modalities. The main steps of AxonSeg consist of: (i) image pre-processing; (ii) pre-segmentation of axons over a cropped image and discriminant analysis (DA) to select the best parameters based on axon shape and intensity information; (iii) automatic axon and myelin segmentation over the full image; and (iv) atlas-based statistics to extract morphometric information. Segmentation results from standard optical microscopy (OM), SEM and coherent anti-Stokes Raman scattering (CARS) microscopy are presented, along with validation against manual segmentations. Being fully-automatic after a quick manual intervention on a cropped image, we believe AxonSeg will be useful to researchers interested in large throughput histology. AxonSeg is open source and freely available at: https://github.com/neuropoly/axonseg.

Diffusion Tensor Imaging Evaluation of Corticospinal Tract Hyperintensity in Upper Motor Neuron-Predominant ALS Patients

Amyotrophic lateral sclerosis (ALS) patients with predominant upper motor neuron (UMN) signs occasionally have hyperintensity of corticospinal tract (CST) on T2- and proton-density-(PD-) weighted brain images. Diffusion tensor imaging (DTI) was used to assess whether diffusion parameters along intracranial CST differ in presence or absence of hyperintensity and correspond to UMN dysfunction. DTI brain scans were acquired in 47 UMN-predominant ALS patients with (n = 21) or without (n = 26) CST hyperintensity and in 10 control subjects. Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were measured in four regions of interests (ROIs) along CST. Abnormalities (P < 0.05) were observed in FA, AD, or RD in CST primarily at internal capsule (IC) level in ALS patients, especially those with CST hyperintensity. Clinical measures corresponded well with DTI changes at IC level. The IC abnormalities suggest a prominent axonopathy in UMN-predominant ALS and that tissue changes underlying CST hyperintensity have specific DTI changes, suggestive of unique axonal pathology.

Multimodal Nonlinear Optical Microscopy

Because each nonlinear optical (NLO) imaging modality is sensitive to specific molecules or structures, multimodal NLO imaging capitalizes the potential of NLO microscopy for studies of complex biological tissues. The coupling of multiphoton fluorescence, second harmonic generation, and coherent anti-Stokes Raman scattering (CARS) has allowed investigation of a broad range of biological questions concerning lipid metabolism, cancer development, cardiovascular disease, and skin biology. Moreover, recent research shows the great potential of using CARS microscope as a platform to develop more advanced NLO modalities such as electronic-resonance-enhanced four-wave mixing, stimulated Raman scattering, and pump-probe microscopy. This article reviews the various approaches developed for realization of multimodal NLO imaging as well as developments of new NLO modalities on a CARS microscope. Applications to various aspects of biological and biomedical research are discussed.