Single-Subject Deep-Learning Image Reconstruction With a Neural Optimization Transfer Algorithm for PET-Enabled Dual-Energy CT Imaging

• Published in: IEEE transactions on image processing Vol. 33; pp. 4075 - 4089
• Main Authors: Li, Siqi, Zhu, Yansong, Spencer, Benjamin A., Wang, Guobao
• Format: Journal Article
• Language: English
• Published: United States IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; Computed tomography; Computer simulation; convolutional neural-network; Deep Learning; Humans; Image Processing, Computer-Assisted - methods; Image quality; Image reconstruction; Imaging; Kernel; kernel methods; Machine learning; Medical imaging; Neural Networks, Computer; Optimization; PET-enabled dual-energy CT; PET/CT; Phantoms, Imaging; Positron emission; Positron emission tomography; Positron Emission Tomography Computed Tomography - methods; Radiation dosage; Tomography; Tomography, X-Ray Computed - methods; Upgrading; X-ray imaging

Combining dual-energy computed tomography (DECT) with positron emission tomography (PET) offers many potential clinical applications but typically requires expensive hardware upgrades or increases radiation doses on PET/CT scanners due to an extra X-ray CT scan. The recent PET-enabled DECT method al...