AIMC Topic: Magnetic Resonance Imaging

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Application of MR morphologic, diffusion tensor, and perfusion imaging in the classification of brain tumors using machine learning scheme.

Neuroradiology
PURPOSE: While MRI is the modality of choice for the assessment of patients with brain tumors, differentiation between various tumors based on their imaging characteristics might be challenging due to overlapping imaging features. The purpose of this...

Predicting anxiety from wholebrain activity patterns to emotional faces in young adults: a machine learning approach.

NeuroImage. Clinical
BACKGROUND: It is becoming increasingly clear that pathophysiological processes underlying psychiatric disorders categories are heterogeneous on many levels, including symptoms, disease course, comorbidity and biological underpinnings. This heterogen...

Using natural language processing to extract structured epilepsy data from unstructured clinic letters: development and validation of the ExECT (extraction of epilepsy clinical text) system.

BMJ open
OBJECTIVE: Routinely collected healthcare data are a powerful research resource but often lack detailed disease-specific information that is collected in clinical free text, for example, clinic letters. We aim to use natural language processing techn...

Adolescent binge drinking disrupts normal trajectories of brain functional organization and personality maturation.

NeuroImage. Clinical
Adolescent binge drinking has been associated with higher risks for the development of many health problems throughout the lifespan. Adolescents undergo multiple changes that involve the co-development processes of brain, personality and behavior; th...

Ultrafast (milliseconds), multidimensional RF pulse design with deep learning.

Magnetic resonance in medicine
PURPOSE: Some advanced RF pulses, like multidimensional RF pulses, are often long and require substantial computation time because of a number of constraints and requirements, sometimes hampering clinical use. However, the pulses offer opportunities ...

DeepQSM - using deep learning to solve the dipole inversion for quantitative susceptibility mapping.

NeuroImage
Quantitative susceptibility mapping (QSM) is based on magnetic resonance imaging (MRI) phase measurements and has gained broad interest because it yields relevant information on biological tissue properties, predominantly myelin, iron and calcium in ...

Artificial intelligence to diagnose meniscus tears on MRI.

Diagnostic and interventional imaging
PURPOSE: The purpose of this study was to build and evaluate a high-performance algorithm to detect and characterize the presence of a meniscus tear on magnetic resonance imaging examination (MRI) of the knee.

Convolutional Neural Network-Based Automated Segmentation of the Spinal Cord and Contusion Injury: Deep Learning Biomarker Correlates of Motor Impairment in Acute Spinal Cord Injury.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Our aim was to use 2D convolutional neural networks for automatic segmentation of the spinal cord and traumatic contusion injury from axial T2-weighted MR imaging in a cohort of patients with acute spinal cord injury.

An Adolescent with Progressive Enlargement of Digits: Case report and proposed diagnostic criteria for macrodystrophia lipomatosa.

Sultan Qaboos University medical journal
Macrodystrophia lipomatosa (ML) is a rare congenital non-hereditary condition caused by an increase in all mesenchymal elements. We report a 14-year-old girl who presented to the Medical Outpatient Department, Kunhitharuvai Memorial Charitable Trust ...