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Magnetic Resonance Spectroscopy

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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.

A deep learning approach for magnetic resonance fingerprinting: Scaling capabilities and good training practices investigated by simulations.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
MR fingerprinting (MRF) is an innovative approach to quantitative MRI. A typical disadvantage of dictionary-based MRF is the explosive growth of the dictionary as a function of the number of reconstructed parameters, an instance of the curse of dimen...

Applications of deep learning techniques for automated multiple sclerosis detection using magnetic resonance imaging: A review.

Computers in biology and medicine
Multiple Sclerosis (MS) is a type of brain disease which causes visual, sensory, and motor problems for people with a detrimental effect on the functioning of the nervous system. In order to diagnose MS, multiple screening methods have been proposed ...

Versatile Convolutional Networks Applied to Computed Tomography and Magnetic Resonance Image Segmentation.

Journal of medical systems
Medical image segmentation has seen positive developments in recent years but remains challenging with many practical obstacles to overcome. The applications of this task are wide-ranging in many fields of medicine, and used in several imaging modali...

k-Space-based coil combination via geometric deep learning for reconstruction of non-Cartesian MRSI data.

Magnetic resonance in medicine
PURPOSE: State-of-the-art whole-brain MRSI with spatial-spectral encoding and multichannel acquisition generates huge amounts of data, which must be efficiently processed to stay within reasonable reconstruction times. Although coil combination signi...

Artificial Intelligence in magnetic Resonance guided Radiotherapy: Medical and physical considerations on state of art and future perspectives.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Over the last years, technological innovation in Radiotherapy (RT) led to the introduction of Magnetic Resonance-guided RT (MRgRT) systems. Due to the higher soft tissue contrast compared to on-board CT-based systems, MRgRT is expected to significant...

Artificial intelligence development in pediatric body magnetic resonance imaging: best ideas to adapt from adults.

Pediatric radiology
Emerging manifestations of artificial intelligence (AI) have featured prominently in virtually all industries and facets of our lives. Within the radiology literature, AI has shown great promise in improving and augmenting radiologist workflow. In pe...