AIMC Topic: Magnetic Resonance Imaging

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Inexpensive, non-invasive biomarkers predict Alzheimer transition using machine learning analysis of the Alzheimer's Disease Neuroimaging (ADNI) database.

PloS one
The Alzheimer's Disease Neuroimaging (ADNI) database is an expansive undertaking by government, academia, and industry to pool resources and data on subjects at various stage of symptomatic severity due to Alzheimer's disease. As expected, magnetic r...

Deep Learning Approach for Anterior Cruciate Ligament Lesion Detection: Evaluation of Diagnostic Performance Using Arthroscopy as the Reference Standard.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: MRI is the most commonly used imaging method for diagnosing anterior cruciate ligament (ACL) injuries. However, the interpretation of knee MRI is time-intensive and depends on the clinical experience of the reader. An automated detection ...

Three-Dimensional Neural Network to Automatically Assess Liver Tumor Burden Change on Consecutive Liver MRIs.

Journal of the American College of Radiology : JACR
BACKGROUND: Tumor response to therapy is often assessed by measuring change in liver lesion size between consecutive MRIs. However, these evaluations are both tedious and time-consuming for clinical radiologists.

Restored pelvic anatomy is preserved after laparoscopic and robot-assisted ventral rectopexy: MRI-based 5-year follow-up of a randomized controlled trial.

Colorectal disease : the official journal of the Association of Coloproctology of Great Britain and Ireland
AIM: Our aim was to compare the long-term anatomical outcomes between robot-assisted ventral mesh rectopexy (RVMR) and laparoscopic ventral mesh rectopexy (LVMR) for external or internal rectal prolapse.

Prediction of pituitary adenoma surgical consistency: radiomic data mining and machine learning on T2-weighted MRI.

Neuroradiology
PURPOSE: Pituitary macroadenoma consistency can influence the ease of lesion removal during surgery, especially when using a transsphenoidal approach. Unfortunately, it is not assessable on standard qualitative MRI. Radiomic texture analysis could he...

The effect of deep convolutional neural networks on radiologists' performance in the detection of hip fractures on digital pelvic radiographs.

European journal of radiology
PURPOSE: The purpose of our study is to develop deep convolutional neural network (DCNN) for detecting hip fractures using CT and MRI as a gold standard, and to evaluate the diagnostic performance of 7 readers with and without DCNN.

Alzheimer's Disease stage identification using deep learning models.

Journal of biomedical informatics
OBJECTIVE: The aim of this research is to identify the stage of Alzheimer's Disease (AD) patients through the use of mobility data and deep learning models. This process facilitates the monitoring of the disease and allows actions to be taken in orde...

Accelerating cardiac cine MRI using a deep learning-based ESPIRiT reconstruction.

Magnetic resonance in medicine
PURPOSE: To propose a novel combined parallel imaging and deep learning-based reconstruction framework for robust reconstruction of highly accelerated 2D cardiac cine MRI data.

Prediction and classification of sleep quality based on phase synchronization related whole-brain dynamic connectivity using resting state fMRI.

NeuroImage
Recently, functional network connectivity (FNC) has been extended from static to dynamic analysis to explore the time-varying functional organization of brain networks. Nowadays, a majority of dynamic FNC (dFNC) analysis frameworks identified recurri...