260 citations · 1.5k across the 65 of their papers we have counts for
6 papers · 1 filter
Issues and Challenges in Applications of Artificial Intelligence to Nuclear Medicine -- The Bethesda Report (AI Summit 2022)
Arman Rahmim, Tyler J. Bradshaw, Irène Buvat +10
The SNMMI Artificial Intelligence (SNMMI-AI) Summit, organized by the SNMMI AI Task Force, took place in Bethesda, MD on March 21-22, 2022. It brought together various community me…
Clinically Translatable Direct Patlak Reconstruction from Dynamic PET with Motion Correction Using Convolutional Neural Network
Nuobei Xie, Kuang Gong, Ning Guo +5
Patlak model is widely used in 18F-FDG dynamic positron emission tomography (PET) imaging, where the estimated parametric images reveal important biochemical and physiology informa…
Self-supervised Dynamic CT Perfusion Image Denoising with Deep Neural Networks
Dufan Wu, Hui Ren, Quanzheng Li
Dynamic computed tomography perfusion (CTP) imaging is a promising approach for acute ischemic stroke diagnosis and evaluation. Hemodynamic parametric maps of cerebral parenchyma a…
MR-Based PET Attenuation Correction using a Combined Ultrashort Echo Time/Multi-Echo Dixon Acquisition
Paul Kyu Han, Debra E. Horng, Kuang Gong +7
We propose a magnetic resonance (MR)-based method for estimation of continuous linear attenuation coefficients (LAC) in positron emission tomography (PET) using a physical compartm…
Penalized-likelihood PET Image Reconstruction Using 3D Structural Convolutional Sparse Coding
Nuobei Xie, Kuang Gong, Ning Guo +4
Positron emission tomography (PET) is widely used for clinical diagnosis. As PET suffers from low resolution and high noise, numerous efforts try to incorporate anatomical priors i…
Attenuation correction for brain PET imaging using deep neural network based on dixon and ZTE MR images
Kuang Gong, Jaewon Yang, Kyungsang Kim +3
Positron Emission Tomography (PET) is a functional imaging modality widely used in neuroscience studies. To obtain meaningful quantitative results from PET images, attenuation corr…