AIMC Topic: Magnetic Resonance Spectroscopy

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Correspondence between three-dimensional ear depth information derived from two-dimensional images and magnetic resonance imaging: Use of a neural-network model.

JASA express letters
There is much interest in anthropometric-derived head-related transfer functions (HRTFs) for simulating audio for virtual-reality systems. Three-dimensional (3D) anthropometric measures can be measured directly from individuals, or indirectly simulat...

Deep Learning-Based Superresolution Reconstruction for Upper Abdominal Magnetic Resonance Imaging: An Analysis of Image Quality, Diagnostic Confidence, and Lesion Conspicuity.

Investigative radiology
OBJECTIVES: The aim of this study was to investigate the impact of a deep learning-based superresolution reconstruction technique for T1-weighted volume-interpolated breath-hold examination (VIBESR) on image quality in comparison with standard VIBE i...

A Deep Learning System for Synthetic Knee Magnetic Resonance Imaging: Is Artificial Intelligence-Based Fat-Suppressed Imaging Feasible?

Investigative radiology
MATERIALS AND METHODS: This single-center study was approved by the institutional review board. Artificial intelligence-based FS MRI scans were created from non-FS images using a deep learning system with a modified convolutional neural network-based...

Potential Role of Artificial Intelligence in Cardiac Magnetic Resonance Imaging: Can It Help Clinicians in Making a Diagnosis?

Journal of thoracic imaging
In the era of modern medicine, artificial intelligence (AI) is a growing field of interest which is experiencing a steady development. Several applications of AI have been applied to various aspects of cardiac magnetic resonance to assist clinicians ...

Fourier and Laplace-like low-field NMR spectroscopy: The perspectives of multivariate and artificial neural networks analyses.

Journal of magnetic resonance (San Diego, Calif. : 1997)
Low field Nuclear Magnetic Resonance (LF-NMR) is a rich source of information for a wide range of samples types. These can be hard or soft solids, such as plastics or elastomers; bulk liquids or liquids absorbed in porous materials, and can come from...

A Scalable Natural Language Processing for Inferring BT-RADS Categorization from Unstructured Brain Magnetic Resonance Reports.

Journal of digital imaging
The aim of this study is to develop an automated classification method for Brain Tumor Reporting and Data System (BT-RADS) categories from unstructured and structured brain magnetic resonance imaging (MR) reports. This retrospective study included 14...

Training Deep Neural Networks for Small and Highly Heterogeneous MRI Datasets for Cancer Grading.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Using medical images recorded in clinical practice has the potential to be a game-changer in the application of machine learning for medical decision support. Thousands of medical images are produced in daily clinical activity. The diagnosis of medic...

Cardiac Chamber Segmentation Using Deep Learning on Magnetic Resonance Images from Patients Before and After Atrial Septal Occlusion Surgery.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
We propose a robust technique for segmenting magnetic resonance images of post-atrial septal occlusion intervention in the cardiac chamber. The technique can be used to determine the surgical outcomes of atrial septal defects before and after implant...

Recent advances and prospects of computational methods for metabolite identification: a review with emphasis on machine learning approaches.

Briefings in bioinformatics
Metabolomics involves studies of a great number of metabolites, which are small molecules present in biological systems. They play a lot of important functions such as energy transport, signaling, building block of cells and inhibition/catalysis. Und...