94 citations · 176 across the 17 of their papers we have counts for
8 papers · 1 filter
S3PET: Semi-supervised Standard-dose PET Image Reconstruction via Dose-aware Token Swap
Jiaqi Cui, Pinxian Zeng, Yuanyuan Xu +3
To acquire high-quality positron emission tomography (PET) images while reducing the radiation tracer dose, numerous efforts have been devoted to reconstructing standard-dose PET (…
MCAD: Multi-modal Conditioned Adversarial Diffusion Model for High-Quality PET Image Reconstruction
Jiaqi Cui, Xinyi Zeng, Pinxian Zeng +4
Radiation hazards associated with standard-dose positron emission tomography (SPET) images remain a concern, whereas the quality of low-dose PET (LPET) images fails to meet clinica…
Image2Points:A 3D Point-based Context Clusters GAN for High-Quality PET Image Reconstruction
Jiaqi Cui, Yan Wang, Lu Wen +4
To obtain high-quality Positron emission tomography (PET) images while minimizing radiation exposure, numerous methods have been proposed to reconstruct standard-dose PET (SPET) im…
UMedNeRF: Uncertainty-aware Single View Volumetric Rendering for Medical Neural Radiance Fields
Jing Hu, Qinrui Fan, Shu Hu +3
In the field of clinical medicine, computed tomography (CT) is an effective medical imaging modality for the diagnosis of various pathologies. Compared with X-ray images, CT images…
TriDo-Former: A Triple-Domain Transformer for Direct PET Reconstruction from Low-Dose Sinograms
Jiaqi Cui, Pinxian Zeng, Xinyi Zeng +5
To obtain high-quality positron emission tomography (PET) images while minimizing radiation exposure, various methods have been proposed for reconstructing standard-dose PET (SPET)…
DiffDP: Radiotherapy Dose Prediction via a Diffusion Model
Zhenghao Feng, Lu Wen, Peng Wang +4
Currently, deep learning (DL) has achieved the automatic prediction of dose distribution in radiotherapy planning, enhancing its efficiency and quality. However, existing methods s…
Deep Learning based Multi-modal Computing with Feature Disentanglement for MRI Image Synthesis
Yuchen Fei, Bo Zhan, Mei Hong +3
Purpose: Different Magnetic resonance imaging (MRI) modalities of the same anatomical structure are required to present different pathological information from the physical level f…
DSU-net: Dense SegU-net for automatic head-and-neck tumor segmentation in MR images
Pin Tang, Chen Zu, Mei Hong +7
Precise and accurate segmentation of the most common head-and-neck tumor, nasopharyngeal carcinoma (NPC), in MRI sheds light on treatment and regulatory decisions making. However,…