AIMC Topic: Magnetic Resonance Imaging

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Machine learning in neuroimaging and computational pathophysiology of Parkinson's disease: A comprehensive review and meta-analysis.

Asian journal of psychiatry
In recent years, machine learning and deep learning have shown potential for improving Parkinson's disease (PD) diagnosis, one of the most common neurodegenerative diseases. This comprehensive analysis examines machine learning and deep learning-base...

Brain Fractal Dimension and Machine Learning can predict first-episode psychosis and risk for transition to psychosis.

Computers in biology and medicine
Although there are notable structural abnormalities in the brain associated with psychotic diseases, it is still unclear how these abnormalities relate to clinical presentation. However, the fractal dimension (FD), which offers details on the complex...

Relational Bi-level aggregation graph convolutional network with dynamic graph learning and puzzle optimization for Alzheimer's classification.

Computers in biology and medicine
Alzheimer's disease (AD) is a neurodegenerative disorder characterized by a progressive cognitive decline, necessitating early diagnosis for effective treatment. This study presents the Relational Bi-level Aggregation Graph Convolutional Network with...

Improve robustness to mismatched sampling rate: An alternating deep low-rank approach for exponential function reconstruction and its biomedical magnetic resonance applications.

Journal of magnetic resonance (San Diego, Calif. : 1997)
Undersampling accelerates signal acquisition at the expense of introducing artifacts. Removing these artifacts is a fundamental problem in signal processing and this task is also called signal reconstruction. Through modeling signals as the superimpo...

Accelerating CEST MRI With Deep Learning-Based Frequency Selection and Parameter Estimation.

NMR in biomedicine
Chemical exchange saturation transfer (CEST) MRI is a powerful molecular imaging technique for detecting metabolites through proton exchange. While CEST MRI provides high sensitivity, its clinical application is hindered by prolonged scan time due to...

A Minimal Annotation Pipeline for Deep Learning Segmentation of Skeletal Muscles.

NMR in biomedicine
Translating quantitative skeletal muscle MRI biomarkers into clinics requires efficient automatic segmentation methods. The purpose of this work is to investigate a simple yet effective iterative methodology for building a high-quality automatic segm...

The implementation of artificial intelligence in serial monitoring of post gamma knife vestibular schwannomas: A pilot study.

Clinical imaging
BACKGROUND: Vestibular schwannomas (VS) are benign tumors that can lead to hearing loss, balance issues, and tinnitus. Gamma Knife Radiosurgery (GKS) is a common treatment for VS, aimed at halting tumor growth and preserving neurological function. Ac...

Computational modeling of breast tissue mechanics and machine learning in cancer diagnostics: enhancing precision in risk prediction and therapeutic strategies.

Expert review of anticancer therapy
INTRODUCTION: Breast cancer remains a significant global health issue. Despite advances in detection and treatment, its complexity is driven by genetic, environmental, and structural factors. Computational methods like Finite Element Modeling (FEM) h...

Global, mesoscale and local investigation into resting state fMRI brain network attributes of sleep quality.

International journal of psychophysiology : official journal of the International Organization of Psychophysiology
Poor sleep quality has been found to be associated with functional abnormalities in a few regions of the human brain. However, the brain is a dynamic network cooperation system, and it is necessary to study the relationship between sleep quality and ...

Fast cortical thickness estimation using deep learning-based anatomy segmentation and diffeomorphic registration.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Accurately and efficiently estimating the cortical thickness from magnetic resonance images (MRIs) is crucial for neuroscientific studies and clinical applications with various large-scale datasets. Diffeomorphic registration-based cortical thickness...