Synthesis of High-Resolution Research-Quality MRI Data from Clinical MRI Data in Patients with COVID-19

Pathophysiological mechanisms of neurological disorders in patients with coronavirus disease 2019 (COVID-19) are poorly understood, partly because of a lack of high-resolution neuroimaging data. We applied SynthSR, a convolutional neural network that synthesizes high-resolution isotropic research-quality data from thick-slice clinical MRI data, to a cohort of 11 patients with severe COVID-19. SynthSR successfully synthesized T1-weighted MPRAGE data at 1 mm spatial resolution for all 11 patients, each of whom had at least one brain lesion. Correlations between volumetric measures derived from synthesized and acquired MPRAGE data were strong for the cortical grey matter, subcortical grey matter, brainstem, hippocampus, and hemispheric white matter (r=0.84 to 0.96, p≤0.001), but absent for the cerebellar white matter and corpus callosum (r=0.04 to 0.17, p>0.61). SynthSR creates an opportunity to quantitatively study clinical MRI scans and elucidate the pathophysiology of neurological disorders in patients with COVID-19, including those with focal lesions.

Classification and Morphometric Features of Pterion in Thai Population with Potential Sex Prediction

Background and Objectives: The landmark for neurosurgical approaches to access brain lesion is the pterion. The aim of the present study is to classify and examine the prevalence of all types of pterion variations and perform morphometric measurements from previously defined anthropological landmarks. Materials and methods: One-hundred and twenty-four Thai dried skulls were investigated. Classification and morphometric measurement of the pterion was performed. Machine learning models were also used to interpret the morphometric findings with respect to sex and age estimation. Results: Spheno-parietal type was the most common type (62.1%), followed by epipteric (11.7%), fronto-temporal (5.2%) and stellate (1.2%). Complete synostosis of the pterion suture was present in 18.5% and was only present in males. While most morphometric measurements were similar between males and females, the distances from the pterion center to the mastoid process and to the external occipital protuberance were longer in males. Random forest algorithm could predict sex with 80.7% accuracy (root mean square error = 0.38) when the pterion morphometric data were provided. Correlational analysis indicated that the distances from the pterion center to the anterior aspect of the frontozygomatic suture and to the zygomatic angle were positively correlated with age, which may serve as basis for age estimation in the future. Conclusions: Further studies are needed to explore the use of machine learning in anatomical studies and morphometry-based sex and age estimation. Thorough understanding of the anatomy of the pterion is clinically useful when planning pterional craniotomy, particularly when the position of the pterion may change with age.

Design and Validation of an Expanded Disability Status Scale Model in Multiple Sclerosis

<b><i>Introduction:</i></b> We aimed to develop and validate an Expanded Disability Status Scale (EDSS) model through clinical, optical coherence tomography (OCT), and magnetic resonance imaging (MRI) measures. <b><i>Methods:</i></b> Sixty-four multiple sclerosis (MS) patients underwent peripapillary retinal nerve fiber layer and segmented macular layers evaluation through OCT (Spectralis, Heidelberg Engineering). Brain parenchymal fraction was quantified through Freesurfer, while cervical spinal cord (SC) volume was assessed manually guided by Spinal Cord Toolbox software analysis. EDSS, neuroradiological, and OCT assessment were carried out within 3 months. OCT parameters were calculated as the average of both nonoptic neuritis (ON) eyes, and in case the patient had previous ON, the value of the fellow non-ON eye was taken. Brain lesion volume, sex, age, disease duration, and history of disease-modifying treatment (1st or 2nd line disease-modifying treatments) were tested as covariables of the EDSS score. <b><i>Results:</i></b> EDSS values correlated with patient’s age (<i>r</i> = 0.543, <i>p</i> = 0.001), SC volume (<i>r</i> = −0.301, <i>p</i> = 0.034), and ganglion cell layer (GCL, <i>r</i> = −0.354, <i>p</i> = 0.012). Using these correlations, an ordinal regression model to express probability of diverse EDSS scores were designed, the highest of which was the most probable (Nagelkerke <i>R</i>² = 43.3%). Using EDSS cutoff point of 4.0 in a dichotomous model, compared to a cutoff of 2.0, permits the inclusion of GCL as a disability predictor, in addition to age and SC. <b><i>Conclusions:</i></b> MS disability measured through EDSS is an age-dependent magnitude that is partly conditioned by SC and GCL. Further studies assessing paraclinical disability predictors are needed.