38 citations · 65 across the 13 of their papers we have counts for
14 papers
Voxel-wise Bayesian Estimation for Multi-Population Positronium Lifetime Imaging
Berkin Uluutku, Narendra Rathod, Giulianno Gasparato +4
Positronium lifetime imaging (PLI) provides local annihilation environment data beyond conventional activity imaging. Existing approaches, however, often estimate lifetime paramete…
Patient-specific AI for generation of 3D dosimetry imaging from two 2D-planar measurements
Alejandro Lopez-Montes, Robert Seifert, Astrid Delker +6
In this work we explored the use of patient specific reinforced learning to generate 3D activity maps from two 2D planar images (anterior and posterior). The solution of this probl…
First Positronium Lifetime Imaging with Scandium-44 on a Long Axial Field-of-view PET/CT
Lorenzo Mercolli, William M. Steinberger, Pascal V. Grundler +12
Purpose: 44Sc has been successfully produced, synthesized, labeled and first-in-human studies were conducted some years ago. The decay properties of 44Sc, together with being close…
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…
Developing a PET/CT Foundation Model for Cross-Modal Anatomical and Functional Imaging
Yujin Oh, Robert Seifert, Yihan Cao +15
In oncology, Positron Emission Tomography-Computed Tomography (PET/CT) is widely used in cancer diagnosis, staging, and treatment monitoring, as it combines anatomical details from…
Positronium Imaging: History, Current Status, and Future Perspectives
Paweł Moskal, Aleksander Bilewicz, Manish Das +11
Positronium imaging was recently proposed to image the properties of positronium atoms in the patient body. Positronium properties depend on the size of intramolecular voids and ox…
Semi-Supervised Learning for Dose Prediction in Targeted Radionuclide: A Synthetic Data Study
Jing Zhang, Alexandre Bousse, Chi-Hieu Pham +2
Targeted Radionuclide Therapy (TRT) is a modern strategy in radiation oncology that aims to administer a potent radiation dose specifically to cancer cells using cancer-targeting r…
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…
Evaluation of Deep Learning-based Scatter Correction on a Long-axial Field-of-view PET scanner
Baptiste Laurent, Alexandre Bousse, Thibaut Merlin +3
Objective: Long-axial field-of-view (LAFOV) positron emission tomography (PET) systems allow higher sensitivity, with an increased number of detected lines of response induced by a…
Positronium Lifetime Imaging with the Biograph Vision Quadra using 124I
Lorenzo Mercolli, William M. Steinberger, Narendra Rathod +7
Purpose: Measuring the ortho-positronium (oPs) lifetime in human tissue bears the potential of adding clinically relevant information about the tissue microenvironment to conventio…
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…
A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising with Neural Network Approaches
Alexandre Bousse, Venkata Sai Sundar Kandarpa, Kuangyu Shi +4
Low-dose emission tomography (ET) plays a crucial role in medical imaging, enabling the acquisition of functional information for various biological processes while minimizing the…
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…