AIMC Topic: Magnetic Resonance Spectroscopy
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Artificial intelligence-powered four-fold upscaling of human brain synthetic metabolite maps.
The Journal of international medical research
Apr 21, 2025
ObjectiveCompared with anatomical magnetic resonance imaging modalities, metabolite images from magnetic resonance spectroscopic imaging often suffer from low quality and detail due to their larger voxel sizes. Conventional interpolation techniques a...
A machine learning-based nuclear magnetic resonance profiling model to authenticate 'Jerez-Xérès-Sherry' wines.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Mar 23, 2025
Traditionally, wine quality and certification have been assessed through sensory analysis by trained tasters. However, this method has the limitation of relying on highly specialized individuals who are typically trained to evaluate only specific typ...
Rapid and accurate identification and quantification of Lycium barbarum L. components: Integrating deep learning and NMR for nutritional assessment.
Food research international (Ottawa, Ont.)
Mar 15, 2025
Lycium barbarum L. (L. barbarum), revered for its nutritional and commercial value, exhibits variable nutritional contents depending on the consumption method. This study introduces an innovative approach, the Identification and Quantification of L.b...
Rapid discrimination of different primary processing Arabica coffee beans using FT-IR and machine learning.
Food research international (Ottawa, Ont.)
Feb 10, 2025
In this study, fourier transform infrared spectroscopy (FT-IR) analysis was combined with machine learning, while various analytical techniques such as colorimetry, low-field nuclear magnetic resonance spectroscopy, scanning electron microscope, two-...
Deep-ER: Deep Learning ECCENTRIC Reconstruction for fast high-resolution neurometabolic imaging.
NeuroImage
Feb 1, 2025
INTRODUCTION: Altered neurometabolism is an important pathological mechanism in many neurological diseases and brain cancer, which can be mapped non-invasively by Magnetic Resonance Spectroscopic Imaging (MRSI). Advanced MRSI using non-cartesian comp...
Feasibility of using Gramian angular field for preprocessing MR spectroscopy data in AI classification tasks: Differentiating glioblastoma from lymphoma.
European journal of radiology
Jan 29, 2025
OBJECTIVES: To convert 1D spectra into 2D images using the Gramian angular field, to be used as input for convolutional neural network for classification tasks such as glioblastoma versus lymphoma.
WALINET: A water and lipid identification convolutional neural network for nuisance signal removal in MR spectroscopic imaging.
Magnetic resonance in medicine
Dec 31, 2024
PURPOSE: Proton magnetic resonance spectroscopic imaging ( -MRSI) provides noninvasive spectral-spatial mapping of metabolism. However, long-standing problems in whole-brain -MRSI are spectral overlap of metabolite peaks with large lipid signal fro...
Comparative performance of artificial neural networks and support vector Machines in detecting adulteration of apple juice concentrate using spectroscopy and time domain NMR.
Food research international (Ottawa, Ont.)
Dec 30, 2024
The detection of adulteration in apple juice concentrate is critical for ensuring product authenticity and consumer safety. This study evaluates the effectiveness of artificial neural networks (ANN) and support vector machines (SVM) in analyzing spec...
Interpretable machine learning model for predicting clinically significant prostate cancer: integrating intratumoral and peritumoral radiomics with clinical and metabolic features.
BMC medical imaging
Dec 30, 2024
BACKGROUND: To develop and validate an interpretable machine learning model based on intratumoral and peritumoral radiomics combined with clinicoradiological features and metabolic information from magnetic resonance spectroscopy (MRS), to predict cl...
A combined NMR and deep neural network approach for enhancing the spectral resolution of aromatic side chains in proteins.
Science advances
Dec 20, 2024
Nuclear magnetic resonance (NMR) spectroscopy is an important technique for deriving the dynamics and interactions of macromolecules; however, characterizations of aromatic residues in proteins still pose a challenge. Here, we present a deep neural n...
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