AI Medical Compendium Journal:
NMR in biomedicine

Showing 41 to 50 of 76 articles

Deep learning methods for automatic segmentation of lower leg muscles and bones from MRI scans of children with and without cerebral palsy.

NMR in biomedicine
Cerebral palsy is a neurological condition that is known to affect muscle growth. Detailed investigations of muscle growth require segmentation of muscles from MRI scans, which is typically done manually. In this study, we evaluated the performance o...

Automated segmentation of biventricular contours in tissue phase mapping using deep learning.

NMR in biomedicine
Tissue phase mapping (TPM) is an MRI technique for quantification of regional biventricular myocardial velocities. Despite its potential, clinical use is limited due to the requisite labor-intensive manual segmentation of cardiac contours for all tim...

Fast and accurate modeling of transient-state, gradient-spoiled sequences by recurrent neural networks.

NMR in biomedicine
Fast and accurate modeling of MR signal responses are typically required for various quantitative MRI applications, such as MR fingerprinting. This work uses a new extended phase graph (EPG)-Bloch model for accurate simulation of transient-state, gra...

Na MRI in ischemic stroke: Acquisition time reduction using postprocessing with convolutional neural networks.

NMR in biomedicine
Quantitative Na magnetic resonance imaging (MRI) provides tissue sodium concentration (TSC), which is connected to cell viability and vitality. Long acquisition times are one of the most challenging aspects for its clinical establishment. K-space un...

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...

xQSM: quantitative susceptibility mapping with octave convolutional and noise-regularized neural networks.

NMR in biomedicine
Quantitative susceptibility mapping (QSM) provides a valuable MRI contrast mechanism that has demonstrated broad clinical applications. However, the image reconstruction of QSM is challenging due to its ill-posed dipole inversion process. In this stu...

Retrospective respiratory motion correction in cardiac cine MRI reconstruction using adversarial autoencoder and unsupervised learning.

NMR in biomedicine
The aim of this study was to develop a deep neural network for respiratory motion compensation in free-breathing cine MRI and evaluate its performance. An adversarial autoencoder network was trained using unpaired training data from healthy volunteer...

Multi-task convolutional neural network-based design of radio frequency pulse and the accompanying gradients for magnetic resonance imaging.

NMR in biomedicine
Modern MRI systems usually load the predesigned RFs and the accompanying gradients during clinical scans, with minimal adaption to the specific requirements of each scan. Here, we describe a neural network-based method for real-time design of excitat...

Rapid MR relaxometry using deep learning: An overview of current techniques and emerging trends.

NMR in biomedicine
Quantitative mapping of MR tissue parameters such as the spin-lattice relaxation time (T ), the spin-spin relaxation time (T ), and the spin-lattice relaxation in the rotating frame (T ), referred to as MR relaxometry in general, has demonstrated imp...

Classification of parotid gland tumors by using multimodal MRI and deep learning.

NMR in biomedicine
Various MRI sequences have shown their potential to discriminate parotid gland tumors, including but not limited to T -weighted, postcontrast T -weighted, and diffusion-weighted images. In this study, we present a fully automatic system for the diagn...