no code implementations • 4 Apr 2024 • Hossein Askari, Fred Roosta, Hongfu Sun
A central challenge in this approach, however, is how to guide an unconditional prediction to conform to the measurement information.
no code implementations • 21 Mar 2024 • Zhuang Xiong, Wei Jiang, Yang Gao, Feng Liu, Hongfu Sun
In this work, we developed a 3D image patch-based diffusion model, namely QSMDiff, for robust QSM reconstruction across different scan parameters, alongside simultaneous super-resolution and image-denoising tasks.
1 code implementation • 20 Nov 2023 • Wei Jiang, Zhuang Xiong, Feng Liu, Nan Ye, Hongfu Sun
Supervised deep learning methods have shown promise in undersampled Magnetic Resonance Imaging (MRI) reconstruction, but their requirement for paired data limits their generalizability to the diverse MRI acquisition parameters.
1 code implementation • 14 Nov 2023 • Yang Gao, Zhuang Xiong, Shanshan Shan, Yin Liu, Pengfei Rong, Min Li, Alan H Wilman, G. Bruce Pike, Feng Liu, Hongfu Sun
The proposed OA-LFE-empowered iQSM, which we refer to as iQSM+, is trained in a self-supervised manner on a specially-designed simulation brain dataset.
no code implementations • 7 Oct 2023 • Wendi Ma, Marlon Bran Lorenzana, Wei Dai, Hongfu Sun, Shekhar S. Chandra
As aliasing artefacts are highly structural and non-local, many MRI reconstruction networks use pooling to enlarge filter coverage and incorporate global context.
no code implementations • 18 Aug 2023 • Zhuang Xiong, Yang Gao, Yin Liu, Amir Fazlollahi, Peter Nestor, Feng Liu, Hongfu Sun
The data-driven approach of supervised learning methods has limited applicability in solving dipole inversion in Quantitative Susceptibility Mapping (QSM) with varying scan parameters across different objects.
no code implementations • 22 Feb 2023 • Yu Ren, Guoli Wang, PingPing Wang, Kunmeng Liu, Quanjin Liu, Hongfu Sun, Xiang Li, Benzheng Wei
Conclusions: The experimental result demonstrates the effectiveness of the proposed MM-SFENet on the localization and classification of bladder cancer.
no code implementations • 25 Nov 2022 • Zhuang Xiong, Yang Gao, Feng Liu, Hongfu Sun
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.
1 code implementation • 6 Apr 2022 • Xuanyu Zhu, Yang Gao, Feng Liu, Stuart Crozier, Hongfu Sun
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.
2 code implementations • 15 Nov 2021 • Yang Gao, Zhuang Xiong, Amir Fazlollahi, Peter J Nestor, Viktor Vegh, Fatima Nasrallah, Craig Winter, G. Bruce Pike, Stuart Crozier, Feng Liu, Hongfu Sun
In addition, experiments on patients with intracranial hemorrhage and multiple sclerosis were also performed to test the generalization of the novel neural networks.
no code implementations • 1 Jun 2021 • Xuanyu Zhu, Yang Gao, Feng Liu, Stuart Crozier, Hongfu Sun
Method: A recently proposed deep learning-based QSM method, namely xQSM, is investigated to assess the accuracy of dipole inversion on reduced brain coverages.
1 code implementation • 17 Mar 2021 • Yang Gao, Martijn Cloos, Feng Liu, Stuart Crozier, G. Bruce Pike, Hongfu Sun
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.
1 code implementation • 14 Apr 2020 • Yang Gao, Xuanyu Zhu, Stuart Crozier, Feng Liu, Hongfu Sun
Quantitative susceptibility mapping (QSM) is a valuable magnetic resonance imaging (MRI) contrast mechanism that has demonstrated broad clinical applications.
Image and Video Processing