AIMC Topic: Magnetic Resonance Spectroscopy

Clear Filters Showing 121 to 130 of 224 articles

SpinSPJ: a novel NMR scripting system to implement artificial intelligence and advanced applications.

BMC bioinformatics
BACKGROUND: Software for nuclear magnetic resonance (NMR) spectrometers offer general functionality of instrument control and data processing; these applications are often developed with non-scripting languages. NMR users need to flexibly integrate r...

Deep learning-based 3Ddose reconstruction with an electronic portal imaging device for magnetic resonance-linear accelerators: a proof of concept study.

Physics in medicine and biology
To develop a novel deep learning-based 3Ddose reconstruction framework with an electronic portal imaging device (EPID) for magnetic resonance-linear accelerators (MR-LINACs).The proposed method directly back-projected 2D portal dose into 3D patient c...

Quantitative imaging of apoptosis following oncolytic virotherapy by magnetic resonance fingerprinting aided by deep learning.

Nature biomedical engineering
Non-invasive imaging methods for detecting intratumoural viral spread and host responses to oncolytic virotherapy are either slow, lack specificity or require the use of radioactive or metal-based contrast agents. Here we show that in mice with gliob...

Atri-U: assisted image analysis in routine cardiovascular magnetic resonance volumetry of the left atrium.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
BACKGROUND: Artificial intelligence can assist in cardiac image interpretation. Here, we achieved a substantial reduction in time required to read a cardiovascular magnetic resonance (CMR) study to estimate left atrial volume without compromising acc...

Application of Medical Imaging Based on Deep Learning in the Treatment of Lumbar Degenerative Diseases and Osteoporosis with Bone Cement Screws.

Computational and mathematical methods in medicine
OBJECTIVE: To explore the application value of magnetic resonance spectroscopy (MRS) and GSI-energy spectrum electronic computed tomography (CT) medical imaging based on the deep convolutional neural network (CNN) in the treatment of lumbar degenerat...

A pilot study for fragment identification using 2D NMR and deep learning.

Magnetic resonance in chemistry : MRC
This paper presents a proof of concept of a method to identify substructures in 2D NMR spectra of mixtures using a bespoke image-based convolutional neural network application. This is done using HSQC and HMBC spectra separately and in combination. T...

A Fully Automated Analytic System for Measuring Endolymphatic Hydrops Ratios in Patients With Ménière Disease via Magnetic Resonance Imaging: Deep Learning Model Development Study.

Journal of medical Internet research
BACKGROUND: Recently, the analysis of endolymphatic hydropses (EHs) via inner ear magnetic resonance imaging (MRI) for patients with Ménière disease has been attempted in various studies. In addition, artificial intelligence has rapidly been incorpor...

DEEP picker is a deep neural network for accurate deconvolution of complex two-dimensional NMR spectra.

Nature communications
The analysis of nuclear magnetic resonance (NMR) spectra for the comprehensive and unambiguous identification and characterization of peaks is a difficult, but critically important step in all NMR analyses of complex biological molecular systems. Her...

Prediction of N chemical shifts by machine learning.

Magnetic resonance in chemistry : MRC
We demonstrate the potential for machine learning systems to predict three-dimensional (3D)-relevant NMR properties beyond traditional H- and C-based data, with comparable accuracy to density functional theory (DFT) (but orders of magnitude faster)...

Molecular subtyping of diffuse gliomas using magnetic resonance imaging: comparison and correlation between radiomics and deep learning.

European radiology
OBJECTIVES: The molecular subtyping of diffuse gliomas is important. The aim of this study was to establish predictive models based on preoperative multiparametric MRI.