AI Medical Compendium Topic:
Magnetic Resonance Imaging

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Machine learning-based estimation of respiratory fluctuations in a healthy adult population using resting state BOLD fMRI and head motion parameters.

Magnetic resonance in medicine
PURPOSE: External physiological monitoring is the primary approach to measure and remove effects of low-frequency respiratory variation from BOLD-fMRI signals. However, the acquisition of clean external respiratory data during fMRI is not always poss...

Effective deep-learning brain MRI super resolution using simulated training data.

Computers in biology and medicine
BACKGROUND: In the field of medical imaging, high-resolution (HR) magnetic resonance imaging (MRI) is essential for accurate disease diagnosis and analysis. However, HR imaging is prone to artifacts and is not universally available. Consequently, low...

Prediction of femoral head collapse in osteonecrosis using deep learning segmentation and radiomics texture analysis of MRI.

BMC medical informatics and decision making
BACKGROUND: Femoral head collapse is a critical pathological change and is regarded as turning point in disease progression in osteonecrosis of the femoral head (ONFH). In this study, we aim to build an automatic femoral head collapse prediction pipe...

Early Detection of Breast Cancer in MRI Using AI.

Academic radiology
RATIONALE AND OBJECTIVES: To develop and evaluate an AI algorithm that detects breast cancer in MRI scans up to one year before radiologists typically identify it, potentially enhancing early detection in high-risk women.

SPINEPS-automatic whole spine segmentation of T2-weighted MR images using a two-phase approach to multi-class semantic and instance segmentation.

European radiology
OBJECTIVES: Introducing SPINEPS, a deep learning method for semantic and instance segmentation of 14 spinal structures (ten vertebra substructures, intervertebral discs, spinal cord, spinal canal, and sacrum) in whole-body sagittal T2-weighted turbo ...

GeSeNet: A General Semantic-Guided Network With Couple Mask Ensemble for Medical Image Fusion.

IEEE transactions on neural networks and learning systems
At present, multimodal medical image fusion technology has become an essential means for researchers and doctors to predict diseases and study pathology. Nevertheless, how to reserve more unique features from different modal source images on the prem...

Hierarchical Graph Convolutional Network Built by Multiscale Atlases for Brain Disorder Diagnosis Using Functional Connectivity.

IEEE transactions on neural networks and learning systems
Functional connectivity network (FCN) data from functional magnetic resonance imaging (fMRI) is increasingly used for the diagnosis of brain disorders. However, state-of-the-art studies used to build the FCN using a single brain parcellation atlas at...

Characterization of Brain Abnormalities in Lactational Neurodevelopmental Poly I:C Rat Model of Schizophrenia and Depression Using Machine-Learning and Quantitative MRI.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: A recent neurodevelopmental rat model, utilizing lactational exposure to polyriboinosinic-polyribocytidilic acid (Poly I:C) leads to mimics of behavioral phenotypes resembling schizophrenia-like symptoms in male offspring and depression-l...

Decision Fusion Model for Predicting Microvascular Invasion in Hepatocellular Carcinoma Based on Multi-MR Habitat Imaging and Machine-Learning Classifiers.

Academic radiology
RATIONALE AND OBJECTIVES: Accurate prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is crucial for guiding treatment. This study evaluates and compares the performance of clinicoradiologic, traditional radiomics, deep-lear...

Subcortical and insula functional connectivity aberrations and clinical implications in first-episode schizophrenia.

Asian journal of psychiatry
INTRODUCTION: Schizophrenia is a complex mental disorder whose pathophysiology remains elusive, particularly in the roles of subcortex. This study aims to explore the role of subcortex and insula and their relationship with symptom changes in first-e...