AIMC Topic: Magnetic Resonance Imaging

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A fully convolutional neural network for new T2-w lesion detection in multiple sclerosis.

NeuroImage. Clinical
INTRODUCTION: Longitudinal magnetic resonance imaging (MRI) has an important role in multiple sclerosis (MS) diagnosis and follow-up. Specifically, the presence of new T2-w lesions on brain MR scans is considered a predictive biomarker for the diseas...

Dual-functional neural network for bilateral hippocampi segmentation and diagnosis of Alzheimer's disease.

International journal of computer assisted radiology and surgery
PURPOSE: Knowing the course of Alzheimer's disease is very important to prevent the deterioration of the disease, and accurate segmentation of sensitive lesions can provide a visual basis for the diagnosis results. This study proposes an improved end...

Analyzing brain structural differences associated with categories of blood pressure in adults using empirical kernel mapping-based kernel ELM.

Biomedical engineering online
BACKGROUND: Hypertension increases the risk of angiocardiopathy and cognitive disorder. Blood pressure has four categories: normal, elevated, hypertension stage 1 and hypertension stage 2. The quantitative analysis of hypertension helps determine dis...

DMCNN: A Deep Multiscale Convolutional Neural Network Model for Medical Image Segmentation.

Journal of healthcare engineering
Medical image segmentation is one of the hot issues in the related area of image processing. Precise segmentation for medical images is a vital guarantee for follow-up treatment. At present, however, low gray contrast and blurred tissue boundaries ar...

Assessment of Radiomics and Deep Learning for the Segmentation of Fetal and Maternal Anatomy in Magnetic Resonance Imaging and Ultrasound.

Academic radiology
Recent advances in fetal imaging open the door to enhanced detection of fetal disorders and computer-assisted surgical planning. However, precise segmentation of womb's tissues is challenging due to motion artifacts caused by fetal movements and mate...

Multi-planar 3D breast segmentation in MRI via deep convolutional neural networks.

Artificial intelligence in medicine
Nowadays, Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) has demonstrated to be a valid complementary diagnostic tool for early detection and diagnosis of breast cancer. However, without a CAD (Computer Aided Detection) system, manual...

Arterial spin labeling MR image denoising and reconstruction using unsupervised deep learning.

NMR in biomedicine
Arterial spin labeling (ASL) imaging is a powerful magnetic resonance imaging technique that allows to quantitatively measure blood perfusion non-invasively, which has great potential for assessing tissue viability in various clinical settings. Howev...

Submillimeter MR fingerprinting using deep learning-based tissue quantification.

Magnetic resonance in medicine
PURPOSE: To develop a rapid 2D MR fingerprinting technique with a submillimeter in-plane resolution using a deep learning-based tissue quantification approach.

Deep Transfer Learning and Radiomics Feature Prediction of Survival of Patients with High-Grade Gliomas.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Patient survival in high-grade glioma remains poor, despite the recent developments in cancer treatment. As new chemo-, targeted molecular, and immune therapies emerge and show promising results in clinical trials, image-based...