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Magnetic Resonance Spectroscopy

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A deep learning approach to real-time volumetric measurements without image reconstruction for cardiovascular magnetic resonance.

Physiological measurement
Cardiovascular magnetic resonance (CMR) can measure ventricular volumes for the quantitative assessment of cardiac function in clinical cardiology. Conventionally, CMR volumetric measurements require image reconstruction and segmentation. There are l...

Breast PET/MRI Hybrid Imaging and Targeted Tracers.

Journal of magnetic resonance imaging : JMRI
The recent introduction of hybrid positron emission tomography/magnetic resonance imaging (PET/MRI) as a promising imaging modality for breast cancer assessment has prompted fervent research activity on its clinical applications. The current knowledg...

In vivo magnetic resonance P-Spectral Analysis With Neural Networks: 31P-SPAWNN.

Magnetic resonance in medicine
PURPOSE: We have introduced an artificial intelligence framework, 31P-SPAWNN, in order to fully analyze phosphorus-31 ( P) magnetic resonance spectra. The flexibility and speed of the technique rival traditional least-square fitting methods, with th...

Artificial intelligence in multiparametric magnetic resonance imaging: A review.

Medical physics
Multiparametric magnetic resonance imaging (mpMRI) is an indispensable tool in the clinical workflow for the diagnosis and treatment planning of various diseases. Machine learning-based artificial intelligence (AI) methods, especially those adopting ...

Deep Learning Classification of Treatment Response in Diabetic Painful Neuropathy: A Combined Machine Learning and Magnetic Resonance Neuroimaging Methodological Study.

Neuroinformatics
Functional magnetic resonance imaging (fMRI) has been shown successfully to assess and stratify patients with painful diabetic peripheral neuropathy (pDPN). This supports the idea of using neuroimaging as a mechanism-based technique to individualise ...

Double U-Net CycleGAN for 3D MR to CT image synthesis.

International journal of computer assisted radiology and surgery
PURPOSE: CycleGAN and its variants are widely used in medical image synthesis, which can use unpaired data for medical image synthesis. The most commonly used method is to use a Generative Adversarial Network (GAN) model to process 2D slices and ther...

Design and applications of water irradiation devoid RF pulses for ultra-high field biomolecular NMR spectroscopy.

Physical chemistry chemical physics : PCCP
Water suppression is of paramount importance for many biological and analytical NMR spectroscopy applications. Here, we report the design of a new set of binomial-like radio frequency (RF) pulses that elude water irradiation while exciting or refocus...

Prediction of self-diffusion coefficients of chemically diverse pure liquids by all-atom molecular dynamics simulations.

Journal of computational chemistry
Molecular self-diffusion coefficients underlie various kinetic properties of the liquids involved in chemistry, physics, and pharmaceutics. In this study, 547 self-diffusion coefficients are calculated based on all-atom molecular dynamics (MD) simula...

Predicting H NMR acyl chain order parameters with graph neural networks.

Computational biology and chemistry
H NMR order parameters of the acyl chain of phospholipid membranes are an important indicator of the effects of molecules on membrane order, mobility, and permeability. So far, the evaluation procedures are case-by-case studies for every type of smal...

Convolutional Neural Networks in Spinal Magnetic Resonance Imaging: A Systematic Review.

World neurosurgery
OBJECTIVE: Convolutional neural networks (CNNs) are being increasingly used in the medical field, especially for image recognition in high-resolution, large-volume data sets. The study represents the current state of research on the application of CN...