AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Magnetic Resonance Spectroscopy

Showing 121 to 130 of 200 articles

Clear Filters

Machine learning in Magnetic Resonance Imaging: Image reconstruction.

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)
Magnetic Resonance Imaging (MRI) plays a vital role in diagnosis, management and monitoring of many diseases. However, it is an inherently slow imaging technique. Over the last 20 years, parallel imaging, temporal encoding and compressed sensing have...

An evaluation of MR based deep learning auto-contouring for planning head and neck radiotherapy.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
INTRODUCTION: Auto contouring models help consistently define volumes and reduce clinical workload. This study aimed to evaluate the cross acquisition of a Magnetic Resonance (MR) deep learning auto contouring model for organ at risk (OAR) delineatio...

Accelerated white matter lesion analysis based on simultaneous and quantification using magnetic resonance fingerprinting and deep learning.

Magnetic resonance in medicine
PURPOSE: To develop an accelerated postprocessing pipeline for reproducible and efficient assessment of white matter lesions using quantitative magnetic resonance fingerprinting (MRF) and deep learning.

DeepMC: a deep learning method for efficient Monte Carlo beamlet dose calculation by predictive denoising in magnetic resonance-guided radiotherapy.

Physics in medicine and biology
Emerging magnetic resonance (MR) guided radiotherapy affords significantly improved anatomy visualization and, subsequently, more effective personalized treatment. The new therapy paradigm imposes significant demands on radiation dose calculation qua...

Application of deep learning for automatic segmentation of brain tumors on magnetic resonance imaging: a heuristic approach in the clinical scenario.

Neuroradiology
PURPOSE: Accurate brain tumor segmentation on magnetic resonance imaging (MRI) has wide-ranging applications such as radiosurgery planning. Advances in artificial intelligence, especially deep learning (DL), allow development of automatic segmentatio...

Magnetic resonance parameter mapping using model-guided self-supervised deep learning.

Magnetic resonance in medicine
PURPOSE: To develop a model-guided self-supervised deep learning MRI reconstruction framework called reference-free latent map extraction (RELAX) for rapid quantitative MR parameter mapping.

Deep learning magnetic resonance spectroscopy fingerprints of brain tumours using quantum mechanically synthesised data.

NMR in biomedicine
Metabolic fingerprints are valuable biomarkers for diseases that are associated with metabolic disorders. 1H magnetic resonance spectroscopy (MRS) is a unique noninvasive diagnostic tool that can depict the metabolic fingerprint based solely on the p...

Frequency and phase correction of J-difference edited MR spectra using deep learning.

Magnetic resonance in medicine
PURPOSE: To investigate whether a deep learning-based (DL) approach can be used for frequency-and-phase correction (FPC) of MEGA-edited MRS data.

Machine learning assisted intraoperative assessment of brain tumor margins using HRMAS NMR spectroscopy.

PLoS computational biology
Complete resection of the tumor is important for survival in glioma patients. Even if the gross total resection was achieved, left-over micro-scale tissue in the excision cavity risks recurrence. High Resolution Magic Angle Spinning Nuclear Magnetic ...

Predictive diagnosis of chronic obstructive pulmonary disease using serum metabolic biomarkers and least-squares support vector machine.

Journal of clinical laboratory analysis
OBJECTIVE: Development of biofluid-based biomarkers is attractive for the diagnosis of chronic obstructive pulmonary disease (COPD) but still lacking. Thus, here we aimed to identify serum metabolic biomarkers for the diagnosis of COPD.