PURPOSE: This study aimed to develop a deep learning (DL) model for brain region parcellation using CT data from PET/CT scans to enable accurate amyloid quantification in 18 F-FBB PET/CT without relying on high-resolution MRI.
PURPOSE: The common approach for organ segmentation in hybrid imaging relies on coregistered CT (CTAC) images. This method, however, presents several limitations in real clinical workflows where mismatch between PET and CT images are very common. Mor...
INTRODUCTION: Multiplexed PET imaging revolutionized clinical decision-making by simultaneously capturing various radiotracer data in a single scan, enhancing diagnostic accuracy and patient comfort. Through a transformer-based deep learning, this st...
PURPOSE: The aim of this study was to generate deep learning-based regions of interest (ROIs) from equilibrium radionuclide angiography datasets for left ventricular ejection fraction (LVEF) measurement.
PURPOSE: We aimed to develop deep learning (DL)-based attenuation correction models for Tl-201 myocardial perfusion SPECT (MPS) images and evaluate their clinical feasibility.
Port-site metastasis is an extremely rare complication following minimally invasive oncologic surgery for prostate cancer. We present the case of a 74-year-old man who underwent robot-assisted laparoscopic radical prostatectomy followed by salvage ra...
PURPOSE: The generalizability and trustworthiness of deep learning (DL)-based algorithms depend on the size and heterogeneity of training datasets. However, because of patient privacy concerns and ethical and legal issues, sharing medical images betw...
PURPOSE: The availability of automated, accurate, and robust gross tumor volume (GTV) segmentation algorithms is critical for the management of head and neck cancer (HNC) patients. In this work, we evaluated 3 state-of-the-art deep learning algorithm...
PURPOSE: We aimed to evaluate the performance of a deep learning system for differential diagnosis of lung cancer with conventional CT and FDG PET/CT using transfer learning (TL) and metadata.