Automated landmark-based symmetric and standard alignment of skull base structures on CT.

Introduction: Symmetry and standard alignment are crucial in both clinical interpretation and research on head CT studies. Registration to a standard template is the traditional method for alignment, yet registration does not guarantee precise alignment of any given structure. This study introduces a method for aligning skull base structures while still achieving a standard anterior commissure-posterior commissure (AC-PC)-like orientation on head CT studies using landmarks, specifically the cochleas and nasal bridge.

Deep learning to predict risk of lateral skull base cerebrospinal fluid leak or encephalocele.

Purpose: Skull base features, including increased foramen ovale (FO) cross-sectional area, are associated with lateral skull base spontaneous cerebrospinal fluid (sCSF) leak and encephalocele. Manual measurement requires skill in interpreting imaging studies and is time consuming. The goal of this study was to develop a fully automated deep learning method for FO segmentation and to determine the predictive value in identifying patients with sCSF leak or encephalocele.

Multimodal deep learning for Alzheimer's disease dementia assessment.

Worldwide, there are nearly 10 million new cases of dementia annually, of which Alzheimer's disease (AD) is the most common. New measures are needed to improve the diagnosis of individuals with cognitive impairment due to various etiologies. Here, we report a deep learning framework that accomplishes multiple diagnostic steps in successive fashion to identify persons with normal cognition (NC), mild cognitive impairment (MCI), AD, and non-AD dementias (nADD).

Fully automated intracranial ventricle segmentation on CT with 2D regional convolutional neural network to estimate ventricular volume.

Purpose: Hydrocephalus is a clinically significant condition which can have devastating consequences if left untreated. Currently available methods for quantifying this condition using CT imaging are unreliable and prone to error. The purpose of this study is to investigate the clinical utility of using convolutional neural networks to calculate ventricular volume and explore limitations.

Reliability and reproducibility of individual differences in functional connectivity acquired during task and resting state.

Objectives: Application of fMRI connectivity metrics as diagnostic biomarkers at the individual level will require reliability, sensitivity and specificity to longitudinal changes in development, aging, neurocognitive, and behavioral performance and pathologies. Such metrics have not been well characterized for recent advances in BOLD acquisition.

Experimental Design: Analysis of multiband BOLD data from the HCP 500 Subjects Release was performed with FIX ICA and with WM, CSF and motion parameter regression.