AIMC Topic:
Magnetic Resonance Imaging
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Voxel-based morphometry in single subjects without a scanner-specific normal database using a convolutional neural network.
European radiology
Nov 9, 2023
OBJECTIVES: Reliable detection of disease-specific atrophy in individual T1w-MRI by voxel-based morphometry (VBM) requires scanner-specific normal databases (NDB), which often are not available. The aim of this retrospective study was to design, trai...
Classification of brain tumours from MRI images using deep learning-enabled hybrid optimization algorithm.
Network (Bristol, England)
Nov 9, 2023
Brain tumours are produced by the uncontrolled, and unusual tissue growth of brain. Because of the wide range of brain tumour locations, potential shapes, and image intensities, segmentation of the brain tumour by magnetic resonance imaging (MRI) is ...
A Machine Learning-Based Unenhanced Radiomics Approach to Distinguishing Between Benign and Malignant Breast Lesions Using T2-Weighted and Diffusion-Weighted MRI.
Journal of magnetic resonance imaging : JMRI
Nov 7, 2023
BACKGROUND: Breast MRI has been recommended as supplemental screening tool to mammography and breast ultrasound of breast cancer by international guidelines, but its long examination time and use of contrast material remains concerning.
Female
Image Interpretation, Computer-Assisted
Adult
Reproducibility of Results
Prospective Studies
Middle Aged
Breast Neoplasms
Humans
Aged
Diffusion Magnetic Resonance Imaging
Radiomics
Machine Learning
Diagnosis, Differential
Contrast Media
Retrospective Studies
Magnetic Resonance Imaging
Area Under Curve
Breast
Automatic Detection and Classification of Modic Changes in MRI Images Using Deep Learning: Intelligent Assisted Diagnosis System.
Orthopaedic surgery
Nov 7, 2023
OBJECTIVE: Modic changes (MCs) are the most prevalent classification system for describing intravertebral MRI signal intensity changes. However, interpreting these intricate MRI images is a complex and time-consuming process. This study investigates ...
Individualized Assessment of Brain Aβ Deposition With fMRI Using Deep Learning.
IEEE journal of biomedical and health informatics
Nov 7, 2023
PET-based Alzheimer's disease (AD) assessment has many limitations in large-scale screening. Non-invasive techniques such as resting-state functional magnetic resonance imaging (rs-fMRI) have been proven valuable in early AD diagnosis. This study inv...
Deep learning-assisted preclinical MR fingerprinting for sub-millimeter T and T mapping of entire macaque brain.
Magnetic resonance in medicine
Nov 6, 2023
PURPOSE: Preclinical MR fingerprinting (MRF) suffers from long acquisition time for organ-level coverage due to demanding image resolution and limited undersampling capacity. This study aims to develop a deep learning-assisted fast MRF framework for ...
High-resolution spiral real-time cardiac cine imaging with deep learning-based rapid image reconstruction and quantification.
NMR in biomedicine
Nov 5, 2023
The objective of the current study was to develop and evaluate a DEep learning-based rapid Spiral Image REconstruction (DESIRE) and deep learning (DL)-based segmentation approach to quantify the left ventricular ejection fraction (LVEF) for high-reso...
Quantitative image signature and machine learning-based prediction of outcomes in cerebral cavernous malformations.
Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
Nov 4, 2023
PURPOSE: There is increasing interest in novel prognostic tools and predictive biomarkers to help identify, with more certainty, cerebral cavernous malformations (CCM) susceptible of bleeding if left untreated. We developed explainable quantitative-b...
A deep-learning approach for segmentation of liver tumors in magnetic resonance imaging using UNet+.
BMC cancer
Nov 3, 2023
OBJECTIVE: Radiomic and deep learning studies based on magnetic resonance imaging (MRI) of liver tumor are gradually increasing. Manual segmentation of normal hepatic tissue and tumor exhibits limitations.
Deep learning system for automated detection of posterior ligamentous complex injury in patients with thoracolumbar fracture on MRI.
Scientific reports
Nov 3, 2023
This study aimed to develop a deep learning (DL) algorithm for automated detection and localization of posterior ligamentous complex (PLC) injury in patients with acute thoracolumbar (TL) fracture on magnetic resonance imaging (MRI) and evaluate its ...
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