AIMC Topic: Magnetic Resonance Spectroscopy
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Deep learning segmentation of peri-sinus structures from structural magnetic resonance imaging: validation and normative ranges across the adult lifespan.
Fluids and barriers of the CNS
Feb 13, 2024
BACKGROUND: Peri-sinus structures such as arachnoid granulations (AG) and the parasagittal dural (PSD) space have gained much recent attention as sites of cerebral spinal fluid (CSF) egress and neuroimmune surveillance. Neurofluid circulation dysfunc...
Geographical discrimination of Asian red pepper powders using H NMR spectroscopy and deep learning-based convolution neural networks.
Food chemistry
Dec 6, 2023
This study investigated an innovative approach to discriminate the geographical origins of Asian red pepper powders by analyzing one-dimensional H NMR spectra through a deep learning-based convolution neural network (CNN). H NMR spectra were collecte...
Automatic segmentation of the interscapular brown adipose tissue in rats based on deep learning using the dynamic magnetic resonance fat fraction images.
Magma (New York, N.Y.)
Nov 29, 2023
OBJECTIVE: The study aims to propose an accurate labelling method of interscapular BAT (iBAT) in rats using dynamic MR fat fraction (FF) images with noradrenaline (NE) stimulation and then develop an automatic iBAT segmentation method using a U-Net m...
CloudBrain-MRS: An intelligent cloud computing platform for in vivo magnetic resonance spectroscopy preprocessing, quantification, and analysis.
Journal of magnetic resonance (San Diego, Calif. : 1997)
Nov 29, 2023
Magnetic resonance spectroscopy (MRS) is an important clinical imaging method for diagnosis of diseases. MRS spectrum is used to observe the signal intensity of metabolites or further infer their concentrations. Although the magnetic resonance vendor...
Deep Learning-Assisted Segmentation and Classification of Brain Tumor Types on Magnetic Resonance and Surgical Microscope Images.
World neurosurgery
Nov 28, 2023
OBJECTIVE: The primary aim of this research was to harness the capabilities of deep learning to enhance neurosurgical procedures, focusing on accurate tumor boundary delineation and classification. Through advanced diagnostic tools, we aimed to offer...
Time-optimized protein NMR assignment with an integrative deep learning approach using AlphaFold and chemical shift prediction.
Science advances
Nov 22, 2023
Chemical shift assignment is vital for nuclear magnetic resonance (NMR)-based studies of protein structures, dynamics, and interactions, providing crucial atomic-level insight. However, obtaining chemical shift assignments is labor intensive and requ...
Letter to the Editor: "Accurate measurement of magnetic resonance parkinsonism index by a fully automatic and deep learning quantification pipeline".
European radiology
Nov 2, 2023
Deep-Learning-Based Mixture Identification for Nuclear Magnetic Resonance Spectroscopy Applied to Plant Flavors.
Molecules (Basel, Switzerland)
Nov 1, 2023
Nuclear magnetic resonance (NMR) is a crucial technique for analyzing mixtures consisting of small molecules, providing non-destructive, fast, reproducible, and unbiased benefits. However, it is challenging to perform mixture identification because o...
Reply to Letter to the Editor: "Accurate measurement of magnetic resonance parkinsonism index by a fully automatic and deep learning quantification pipeline".
European radiology
Oct 13, 2023
Comparison of convolutional-neural-networks-based method and LCModel on the quantification of in vivo magnetic resonance spectroscopy.
Magma (New York, N.Y.)
Sep 15, 2023
BACKGROUND: Quantification of metabolites concentrations in institutional unit (IU) is important for inter-subject and long-term comparisons in the applications of magnetic resonance spectroscopy (MRS). Recently, deep learning (DL) algorithms have fo...
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