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Non-water-suppressed H FID-MRSI at 3T and 9.4T.

Magnetic resonance in medicine
PURPOSE: This study investigates metabolite concentrations using metabolite-cycled H free induction decay (FID) magnetic resonance spectroscopic imaging (MRSI) at ultra-high fields.

Machine learning methods for the classification of gliomas: Initial results using features extracted from MR spectroscopy.

The neuroradiology journal
CONTEXT: With the advent of new imaging modalities, radiologists are faced with handling increasing volumes of data for diagnosis and treatment planning. The use of automated and intelligent systems is becoming essential in such a scenario. Machine l...

Applying machine learning to the flagellar motor for biosensing.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Escherichia coli detects and follows chemical gradients in its environment in a process known as chemotaxis. The performance of chemotaxis approaches fundamental biosensor speed and sensitivity limits, but there have been relatively few attempts to i...

MultiNet PyGRAPPA: Multiple neural networks for reconstructing variable density GRAPPA (a H FID MRSI study).

NeuroImage
Magnetic resonance spectroscopic imaging (MRSI) is a powerful tool for mapping metabolite levels across the brain, however, it generally suffers from long scan times. This severely hinders its application in clinical settings. Additionally, the prese...

Incorporation of a spectral model in a convolutional neural network for accelerated spectral fitting.

Magnetic resonance in medicine
PURPOSE: MRSI has shown great promise in the detection and monitoring of neurologic pathologies such as tumor. A necessary component of data processing includes the quantitation of each metabolite, typically done through fitting a model of the spectr...

Comparison of convolutional-neural-networks-based method and LCModel on the quantification of in vivo magnetic resonance spectroscopy.

Magma (New York, N.Y.)
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...