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Found 6 papers, 4 papers with code
Affine Transformation Edited and Refined Deep Neural Network for Quantitative Susceptibility Mapping
We propose an end-to-end AFfine Transformation Edited and Refined (AFTER) deep neural network for QSM, which is robust against arbitrary acquisition orientation and spatial resolution up to 0. 6 mm isotropic at the finest.
BFRnet: A deep learning-based MR background field removal method for QSM of the brain containing significant pathological susceptibility sources
The BFRnet method is compared with three conventional BFR methods and one previous deep learning method using simulated and in vivo brains from 4 healthy and 2 hemorrhagic subjects.
Instant tissue field and magnetic susceptibility mapping from MR raw phase using Laplacian enabled deep neural networks
In addition, experiments on patients with intracranial hemorrhage and multiple sclerosis were also performed to test the generalization of the novel neural networks.
Deep grey matter quantitative susceptibility mapping from small spatial coverages using deep learning
Method: A recently proposed deep learning-based QSM method, namely xQSM, is investigated to assess the accuracy of dipole inversion on reduced brain coverages.
Accelerating Quantitative Susceptibility Mapping using Compressed Sensing and Deep Neural Network
In this study, a learning-based Deep Complex Residual Network (DCRNet) is proposed to recover both the magnitude and phase images from incoherently undersampled data, enabling high acceleration of QSM acquisition.
xQSM-Quantitative Susceptibility Mapping with Octave Convolutional Neural Networks
Quantitative susceptibility mapping (QSM) is a valuable magnetic resonance imaging (MRI) contrast mechanism that has demonstrated broad clinical applications.
Image and Video Processing
