A deep neural network for parametric image reconstruction on a large axial field-of-view PET

Purpose The PET scanners with long axial field of view (AFOV) having ~ 20 times higher sensitivity than conventional scanners provide new opportunities for enhanced parametric imaging but suffer from the dramatically increased volume and complexity of dynamic data. This study reconstructed a high-qu...

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Veröffentlicht in:	European journal of nuclear medicine and molecular imaging 2023-02, Vol.50 (3), p.701-714
Hauptverfasser:	Li, Y., Hu, J., Sari, H., Xue, S., Ma, R., Kandarpa, S., Visvikis, D., Rominger, A., Liu, H., Shi, K.
Format:	Artikel
Sprache:	eng
Schlagworte:	Advanced Image Analyses (Radiomics and Artificial Intelligence) Algorithms Artificial Intelligence Artificial neural networks Cardiology Computed tomography Deep learning Field of view Fluorine isotopes Fluorodeoxyglucose F18 Humans Image processing Image Processing, Computer-Assisted - methods Image quality Image reconstruction Imaging Lesions Machine learning Medical imaging Medicine Medicine & Public Health Neural networks Neural Networks, Computer Noise prediction Nuclear Medicine Oncology Original Article Orthopedics Positron emission Positron emission tomography Positron Emission Tomography Computed Tomography Positron-Emission Tomography - methods Radiology Scanners Signal to noise ratio Time dependence Tumors
Online-Zugang:	Volltext
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