38 citations · 65 across the 13 of their papers we have counts for
5 papers · 1 filter
Fed-NDIF: A Noise-Embedded Federated Diffusion Model For Low-Count Whole-Body PET Denoising
Yinchi Zhou, Huidong Xie, Menghua Xia +12
Low-count positron emission tomography (LCPET) imaging can reduce patients' exposure to radiation but often suffers from increased image noise and reduced lesion detectability, nec…
PET Image Denoising via Text-Guided Diffusion: Integrating Anatomical Priors through Text Prompts
Boxiao Yu, Savas Ozdemir, Jiong Wu +4
Low-dose Positron Emission Tomography (PET) imaging presents a significant challenge due to increased noise and reduced image quality, which can compromise its diagnostic accuracy…
Dose-aware Diffusion Model for 3D PET Image Denoising: Multi-institutional Validation with Reader Study and Real Low-dose Data
Huidong Xie, Weijie Gan, Reimund Bayerlein +24
Reducing scan times, radiation dose, and enhancing image quality for lower-performance scanners, are critical in low-dose PET imaging. Deep learning techniques have been investigat…
LeqMod: Adaptable Lesion-Quantification-Consistent Modulation for Deep Learning Low-Count PET Image Denoising
Menghua Xia, Huidong Xie, Qiong Liu +9
Deep learning-based positron emission tomography (PET) image denoising offers the potential to reduce radiation exposure and scanning time by transforming low-count images into hig…
FedFTN: Personalized Federated Learning with Deep Feature Transformation Network for Multi-institutional Low-count PET Denoising
Bo Zhou, Huidong Xie, Qiong Liu +10
Low-count PET is an efficient way to reduce radiation exposure and acquisition time, but the reconstructed images often suffer from low signal-to-noise ratio (SNR), thus affecting…