AIMC Topic: Magnetic Resonance Imaging

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Deep Learning Models Unveiled Functional Difference Between Cortical Gyri and Sulci.

IEEE transactions on bio-medical engineering
It is largely unknown whether there is functional role difference between cortical gyral and sulcal regions. Recent advancements in neuroimaging studies demonstrate clear difference of structural connection profiles in gyral and sulcal areas, suggest...

The application of artificial intelligence to understand the pathophysiological basis of psychogenic nonepileptic seizures.

Epilepsy & behavior : E&B
Psychogenic nonepileptic seizures (PNES) are episodes of paroxysmal impairment associated with a range of motor, sensory, and mental manifestations, which perfectly mimic epileptic seizures. Several patterns of neural abnormalities have been describe...

Resting state dynamics meets anatomical structure: Temporal multiple kernel learning (tMKL) model.

NeuroImage
Over the last decade there has been growing interest in understanding the brain activity, in the absence of any task or stimulus, captured by the resting-state functional magnetic resonance imaging (rsfMRI). The resting state patterns have been obser...

Adversarial Similarity Network for Evaluating Image Alignment in Deep Learning based Registration.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
This paper introduces an unsupervised adversarial similarity network for image registration. Unlike existing deep learning registration frameworks, our approach does not require ground-truth deformations and specific similarity metrics. We connect a ...

Preoperative prediction of cavernous sinus invasion by pituitary adenomas using a radiomics method based on magnetic resonance images.

European radiology
OBJECTIVES: To predict cavernous sinus (CS) invasion by pituitary adenomas (PAs) pre-operatively using a radiomics method based on contrast-enhanced T1 (CE-T1) and T2-weighted magnetic resonance (MR) imaging.

Automatic Age Estimation and Majority Age Classification From Multi-Factorial MRI Data.

IEEE journal of biomedical and health informatics
Age estimation from radiologic data is an important topic both in clinical medicine as well as in forensic applications, where it is used to assess unknown chronological age or to discriminate minors from adults. In this paper, we propose an automati...

Identifying a neuroanatomical signature of schizophrenia, reproducible across sites and stages, using machine learning with structured sparsity.

Acta psychiatrica Scandinavica
OBJECTIVE: Structural MRI (sMRI) increasingly offers insight into abnormalities inherent to schizophrenia. Previous machine learning applications suggest that individual classification is feasible and reliable and, however, is focused on the predicti...

Instance-Based Representation Using Multiple Kernel Learning for Predicting Conversion to Alzheimer Disease.

International journal of neural systems
The early detection of Alzheimer's disease and quantification of its progression poses multiple difficulties for machine learning algorithms. Two of the most relevant issues are related to missing data and results interpretability. To deal with both ...

Deep learning-based quantification of abdominal fat on magnetic resonance images.

PloS one
Obesity is increasingly prevalent and associated with increased risk of developing type 2 diabetes, cardiovascular diseases, and cancer. Magnetic resonance imaging (MRI) is an accurate method for determination of body fat volume and distribution. How...