Search Results for author: Hongyu An

Found 15 papers, 4 papers with code

A Self-supervised Diffusion Bridge for MRI Reconstruction

no code implementations6 Jan 2025 Harry Gao, Weijie Gan, Yuyang Hu, Hongyu An, Ulugbek S. Kamilov

Diffusion bridges (DBs) are a class of diffusion models that enable faster sampling by interpolating between two paired image distributions.

Denoising MRI Reconstruction

A Generalizable 3D Diffusion Framework for Low-Dose and Few-View Cardiac SPECT

no code implementations21 Dec 2024 Huidong Xie, Weijie Gan, Wei Ji, Xiongchao Chen, Alaa Alashi, Stephanie L. Thorn, Bo Zhou, Qiong Liu, Menghua Xia, Xueqi Guo, Yi-Hwa Liu, Hongyu An, Ulugbek S. Kamilov, Ge Wang, Albert J. Sinusas, Chi Liu

This work introduced DiffSPECT-3D, a diffusion framework for 3D cardiac SPECT imaging that effectively adapts to different acquisition settings without requiring further network re-training or fine-tuning.

Spatio-Temporal Distortion Aware Omnidirectional Video Super-Resolution

1 code implementation15 Oct 2024 Hongyu An, Xinfeng Zhang, Li Zhang, Ruiqin Xiong

Omnidirectional video (ODV) can provide an immersive experience and is widely utilized in the field of virtual reality and augmented reality.

Video Super-Resolution

Preliminary Results of Neuromorphic Controller Design and a Parkinson's Disease Dataset Building for Closed-Loop Deep Brain Stimulation

no code implementations25 Jul 2024 Ananna Biswas, Hongyu An

Additionally, to address the data scarcity of Parkinson's Disease symptoms, we built Parkinson's Disease datasets that include the raw neural activities from the subthalamic nucleus at beta oscillations, which are typical physiological biomarkers for Parkinson's Disease.

DDPET-3D: Dose-aware Diffusion Model for 3D Ultra Low-dose PET Imaging

no code implementations7 Nov 2023 Huidong Xie, Weijie Gan, Bo Zhou, Xiongchao Chen, Qiong Liu, Xueqi Guo, Liang Guo, Hongyu An, Ulugbek S. Kamilov, Ge Wang, Chi Liu

We extensively evaluated DDPET-3D on 100 patients with 6 different low-dose levels (a total of 600 testing studies), and demonstrated superior performance over previous diffusion models for 3D imaging problems as well as previous noise-aware medical image denoising models.

Image Denoising Medical Image Denoising

A Plug-and-Play Image Registration Network

no code implementations6 Oct 2023 Junhao Hu, Weijie Gan, Zhixin Sun, Hongyu An, Ulugbek S. Kamilov

A traditional DL approach to DIR is based on training a convolutional neural network (CNN) to estimate the registration field between two input images.

Image Registration

Self-Supervised Deep Equilibrium Models for Inverse Problems with Theoretical Guarantees

no code implementations7 Oct 2022 Weijie Gan, Chunwei Ying, Parna Eshraghi, Tongyao Wang, Cihat Eldeniz, Yuyang Hu, Jiaming Liu, Yasheng Chen, Hongyu An, Ulugbek S. Kamilov

Our numerical results on in-vivo MRI data show that SelfDEQ leads to state-of-the-art performance using only undersampled and noisy training data.

Image Reconstruction

Image Reconstruction for MRI using Deep CNN Priors Trained without Groundtruth

no code implementations10 Apr 2022 Weijie Gan, Cihat Eldeniz, Jiaming Liu, Sihao Chen, Hongyu An, Ulugbek S. Kamilov

We propose a new plug-and-play priors (PnP) based MR image reconstruction method that systematically enforces data consistency while also exploiting deep-learning priors.

Image Reconstruction

Deformation-Compensated Learning for Image Reconstruction without Ground Truth

1 code implementation12 Jul 2021 Weijie Gan, Yu Sun, Cihat Eldeniz, Jiaming Liu, Hongyu An, Ulugbek S. Kamilov

Deep neural networks for medical image reconstruction are traditionally trained using high-quality ground-truth images as training targets.

Image Reconstruction Object

Deep Image Reconstruction using Unregistered Measurements without Groundtruth

no code implementations29 Sep 2020 Weijie Gan, Yu Sun, Cihat Eldeniz, Jiaming Liu, Hongyu An, Ulugbek S. Kamilov

One of the key limitations in conventional deep learning based image reconstruction is the need for registered pairs of training images containing a set of high-quality groundtruth images.

Image Reconstruction

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