Search Results for author: Jianan Cui

Found 6 papers, 0 papers with code

STPDnet: Spatial-temporal convolutional primal dual network for dynamic PET image reconstruction

no code implementations8 Mar 2023 Rui Hu, Jianan Cui, Chengjin Yu, YunMei Chen, Huafeng Liu

Dynamic positron emission tomography (dPET) image reconstruction is extremely challenging due to the limited counts received in individual frame.

Image Reconstruction

LMPDNet: TOF-PET list-mode image reconstruction using model-based deep learning method

no code implementations21 Feb 2023 Chenxu Li, Rui Hu, Jianan Cui, Huafeng Liu

Additionally, we compare the spatial and temporal consumption of list-mode data and sinogram data in model-based deep learning methods, demonstrating the superiority of list-mode data in model-based TOF-PET reconstruction.

Image Reconstruction

A Noise-level-aware Framework for PET Image Denoising

no code implementations15 Mar 2022 Ye Li, Jianan Cui, Junyu Chen, Guodong Zeng, Scott Wollenweber, Floris Jansen, Se-In Jang, Kyungsang Kim, Kuang Gong, Quanzheng Li

Our hypothesis is that by explicitly providing the local relative noise level of the input image to a deep convolutional neural network (DCNN), the DCNN can outperform itself trained on image appearance only.

Image Denoising SSIM

Super Resolution of Arterial Spin Labeling MR Imaging Using Unsupervised Multi-Scale Generative Adversarial Network

no code implementations14 Sep 2020 Jianan Cui, Kuang Gong, Paul Han, Huafeng Liu, Quanzheng Li

After the network was trained, the super-resolution (SR) image was generated by supplying the upsampled LR ASL image and corresponding T1-weighted image to the generator of the last layer.

SSIM Super-Resolution

Clinically Translatable Direct Patlak Reconstruction from Dynamic PET with Motion Correction Using Convolutional Neural Network

no code implementations13 Sep 2020 Nuobei Xie, Kuang Gong, Ning Guo, Zhixing Qin, Jianan Cui, Zhifang Wu, Huafeng Liu, Quanzheng Li

Patlak model is widely used in 18F-FDG dynamic positron emission tomography (PET) imaging, where the estimated parametric images reveal important biochemical and physiology information.


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