Journal of magnetic resonance (San Diego, Calif. : 1997)
May 15, 2025
Undersampling accelerates signal acquisition at the expense of introducing artifacts. Removing these artifacts is a fundamental problem in signal processing and this task is also called signal reconstruction. Through modeling signals as the superimpo...
Journal of magnetic resonance (San Diego, Calif. : 1997)
Nov 29, 2024
In this study, we introduce a denoising method aimed at improving the contrast ratio in low-field MRI (LFMRI) using an advanced 3D deep convolutional residual network model. Our approach employs synthetic brain imaging datasets that closely mimic the...
Journal of magnetic resonance (San Diego, Calif. : 1997)
Sep 12, 2024
In the case of limited sampling windows or truncation of free induction decays (FIDs) for artifact removal in proton magnetic resonance spectroscopy (H-MRS) and spectroscopic imaging (H-MRSI), metabolite quantification needs to be performed on incomp...
Journal of magnetic resonance (San Diego, Calif. : 1997)
Nov 29, 2023
Magnetic resonance spectroscopy (MRS) is an important clinical imaging method for diagnosis of diseases. MRS spectrum is used to observe the signal intensity of metabolites or further infer their concentrations. Although the magnetic resonance vendor...
Journal of magnetic resonance (San Diego, Calif. : 1997)
Dec 8, 2022
Multi-contrast magnetic resonance imaging (MRI) can provide richer diagnosis information. The data acquisition time, however, is increased than single-contrast imaging. To reduce this time, k-space undersampling is an effective way but a smart recons...
Journal of magnetic resonance (San Diego, Calif. : 1997)
Nov 24, 2022
A new deep neural network based on the WaveNet architecture (WNN) is presented, which is designed to grasp specific patterns in the NMR spectra. When trained at a fixed non-uniform sampling (NUS) schedule, the WNN benefits from pattern recognition of...
Journal of magnetic resonance (San Diego, Calif. : 1997)
Sep 14, 2022
Bias field is one of the main artifacts that degrade the quality of magnetic resonance images. It introduces intensity inhomogeneity and affects image analysis such as segmentation. In this work, we proposed a deep learning approach to jointly estima...
Journal of magnetic resonance (San Diego, Calif. : 1997)
Mar 8, 2022
This is a methodological guide to the use of deep neural networks in the processing of pulsed dipolar spectroscopy (PDS) data encountered in structural biology, organic photovoltaics, photosynthesis research, and other domains featuring long-lived ra...
Journal of magnetic resonance (San Diego, Calif. : 1997)
Feb 13, 2021
The applicability of generative adversarial networks (GANs) capable of unsupervised anomaly detection (AnoGAN) was investigated in the management of quality of H-MRS human brain spectra at 3.0 T. The AnoGAN was trained in an unsupervised manner solel...
Journal of magnetic resonance (San Diego, Calif. : 1997)
Jul 16, 2019
Machine learning has been used in NMR in for decades, but recent developments signal explosive growth is on the horizon. An obstacle to the application of machine learning in NMR is the relative paucity of available training data, despite the existen...