Nervous System Commons^™

Magnetization Transfer Ratio In The Typically Developing Pediatric Spinal Cord: Normative Data And Age Correlation, Sara Naghizadeh Kashani, Iswarya Vel, Zahra Sadeghi Adl, Shiva Shahrampour, Devon Middleton, Mahdi Alizadeh, Laura Krisa, Scott Faro, Slimane Tounekti, Julien Cohen-Adad, Feroze Mohamed

Department of Radiology Faculty Papers

BACKGROUND AND PURPOSE: This study presents automated atlas-based magnetization transfer (MT) measurements of the typically developing pediatric cervical spinal cord (SC). We report normative MT ratio (MTR) values from the whole cervical cord white matter (WM) and WM tracts, examining variations with age, sex, height, and weight.

METHODS: MT scans of 33 healthy females (mean age = 12.8) and 22 males (mean age = 13.09) were acquired from the cervical SC (C2-C7) using a 3.0 T MRI. Data were processed using the SC Toolbox, segmented, and registered to the PAM50 template. Affine and non-rigid transformations co-registered the PAM50 WM atlas …

Multi-Modal Mri Based Segmentation Of Brain Metastases Using Adaptive Self-Attention, Evan Savaria

Computer Science Faculty Publications

Brain metastases (BMs) are the most common adult central nervous system malignancy, affecting 20–40% of cancer patients. Accurate segmentation of metastatic lesions in multi-modal MRI is essential for treatment planning and prognosis however, manual delineation is time consuming and prone to variability. Traditional deep learning models such as U-Net, have improved segmentation accuracy but capture limited long-range dependencies and struggle with variations in metastasis size, shape, and distribution. This study introduces the Adaptive Integrated Multi-modal Segmentation (AIMS) model, an adaptive self-attention framework within a hybrid U-Net and Transformer architecture to enhance BM segmentation by leveraging multi-modal MRI integration. The proposed …

Automated Segmentation Of The Ulnar Nerve In Mri Using Deep Learning Techniques, Akhil Nagulapalli

Theses

Cubital Tunnel Syndrome (CuTS), a condition caused by compression of the ulnar nerve, results in numbness, tingling, pain, and even muscle atrophy, affecting fine motor skills and diminishing patient quality of life. Accurate diagnosis of CuTS is challenging, as current diagnostic methods—including clinical exams, nerve conduction studies, and unaided MRI—often lack the precision to reliably identify the nerve and detect compression in its early stages. Deep learning-based segmentation offers a promising solution, enabling precise and automated identification of nerve structures in MRI images, which could significantly improve diagnostic accuracy and support timely intervention.

A novel deep learning model for segmenting …

Atlas-Based Shared-Boundary Deformable Multi-Surface Models Through Multi-Material And Two-Manifold Dual Contouring, Tanweer Rashid, Sharmin Sultana, Mallar Chakravarty, Michel Albert Audette

Electrical & Computer Engineering Faculty Publications

This paper presents a multi-material dual “contouring” method used to convert a digital 3D voxel-based atlas of basal ganglia to a deformable discrete multi-surface model that supports surgical navigation for an intraoperative MRI-compatible surgical robot, featuring fast intraoperative deformation computation. It is vital that the final surface model maintain shared boundaries where appropriate so that even as the deep-brain model deforms to reflect intraoperative changes encoded in ioMRI, the subthalamic nucleus stays in contact with the substantia nigra, for example, while still providing a significantly sparser representation than the original volumetric atlas consisting of hundreds of millions of voxels. The …

Extended Functional Connectivity Of Convergent Structural Alterations Among Individuals With Ptsd: A Neuroimaging Meta-Analysis, Brianna S. Pankey, Michael C. Riedel, Isis Cowan, Jessica E. Bartley, Rosario Pintos Lobo, Lauren D. Hill-Bowen, Taylor Sato, Erica D. Musser, Matthew T. Sutherland, Angela R. Laird

Psychology Faculty Publications

Background: Post-traumatic stress disorder (PTSD) is a debilitating disorder defined by the onset of intrusive, avoidant, negative cognitive or affective, and/or hyperarousal symptoms after witnessing or experiencing a traumatic event. Previous voxel-based morphometry studies have provided insight into structural brain alterations associated with PTSD with notable heterogeneity across these studies. Furthermore, how structural alterations may be associated with brain function, as measured by task-free and task-based functional connectivity, remains to be elucidated.

Methods: Using emergent meta-analytic techniques, we sought to first identify a consensus of structural alterations in PTSD using the anatomical likelihood estimation (ALE) approach. Next, we generated functional …

A Descriptive Study To Find Possible Correlation Between Mri Findings Of Pituitary Gland And Serum Prolactin Level, Muhammad Azeemuddin, Rohana Naqi, Mohammad Wasay

Department of Medicine

Objective: To explore equation, if any, between findings of magnetic resonance imaging of pituitary gland and serum prolactin level.

Methods: The retrospective, descriptive study was conducted at the Department of Radiology, Aga Khan University Hospital, Karachi, and related to patients\' records from April 19, 2006 to April 23, 2009. Seventy patients underwent magnetic resonance imaging of brain for pituitary gland. Inclusion criteria were all patients referred with relevant clinical symptoms or deranged serum prolactin level. Patients who were claustrophobic or had a pacemaker, aneurysm clip, metallic foreign body in the orbit or with no laboratory investigation were excluded from the …