8 papers
Unsupervised Adaptation from FDG to PSMA PET/CT for 3D Lesion Detection under Label Shift
Xiaofeng Liu, Menghua Xia, Yanis Chemli +3
In this work, we propose an unsupervised domain adaptation (UDA) framework for 3D volumetric lesion detection that adapts a detector trained on labeled FDG PET/CT to unlabeled PSMA…
DECADE: A Temporally-Consistent Unsupervised Diffusion Model for Enhanced Rb-82 Dynamic Cardiac PET Image Denoising
Yinchi Zhou, Liang Guo, Huidong Xie +14
Rb-82 dynamic cardiac PET imaging is widely used for the clinical diagnosis of coronary artery disease (CAD), but its short half-life results in high noise levels that degrade dyna…
On hallucinations in AI-generated content for nuclear medicine imaging (the DREAM report)
Menghua Xia, Reimund Bayerlein, Yanis Chemli +7
Artificial intelligence-generated content (AIGC) has shown remarkable performance in nuclear medicine imaging (NMI), offering cost-effective software solutions for tasks such as im…
Anatomically and Metabolically Informed Diffusion for Unified Denoising and Segmentation in Low-Count PET Imaging
Menghua Xia, Kuan-Yin Ko, Der-Shiun Wang +12
Positron emission tomography (PET) image denoising, along with lesion and organ segmentation, are critical steps in PET-aided diagnosis. However, existing methods typically treat t…
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…
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…