Search Results for author: Hongfu Sun

Found 14 papers, 6 papers with code

IR2QSM: Quantitative Susceptibility Mapping via Deep Neural Networks with Iterative Reverse Concatenations and Recurrent Modules

no code implementations18 Jun 2024 Min Li, Chen Chen, Zhuang Xiong, Ying Liu, Pengfei Rong, Shanshan Shan, Feng Liu, Hongfu Sun, Yang Gao

Quantitative susceptibility mapping (QSM) is an MRI phase-based post-processing technique to extract the distribution of tissue susceptibilities, demonstrating significant potential in studying neurological diseases.

Bi-level Guided Diffusion Models for Zero-Shot Medical Imaging Inverse Problems

no code implementations4 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.

Computed Tomography (CT)

QSMDiff: Unsupervised 3D Diffusion Models for Quantitative Susceptibility Mapping

no code implementations21 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.

Image Denoising Image Generation +1

Fast Controllable Diffusion Models for Undersampled MRI Reconstruction

1 code implementation20 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.

MRI Reconstruction

Plug-and-Play Latent Feature Editing for Orientation-Adaptive Quantitative Susceptibility Mapping Neural Networks

1 code implementation14 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.

Multi-scale MRI reconstruction via dilated ensemble networks

no code implementations7 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.

MRI Reconstruction

Quantitative Susceptibility Mapping through Model-based Deep Image Prior (MoDIP)

no code implementations18 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.

Image Reconstruction

Affine Transformation Edited and Refined Deep Neural Network for Quantitative Susceptibility Mapping

no code implementations25 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.

BFRnet: A deep learning-based MR background field removal method for QSM of the brain containing significant pathological susceptibility sources

1 code implementation6 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.

Instant tissue field and magnetic susceptibility mapping from MR raw phase using Laplacian enabled deep neural networks

2 code implementations15 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.

Deep grey matter quantitative susceptibility mapping from small spatial coverages using deep learning

no code implementations1 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.

Accelerating Quantitative Susceptibility Mapping using Compressed Sensing and Deep Neural Network

1 code implementation17 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.


xQSM-Quantitative Susceptibility Mapping with Octave Convolutional Neural Networks

1 code implementation14 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

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