Low-count whole-body PET with deep learning in a multicenter and externally validated study

More widespread use of positron emission tomography (PET) imaging is limited by its high cost and radiation dose. Reductions in PET scan time or radiotracer dosage typically degrade diagnostic image quality (DIQ). Deep-learning-based reconstruction may improve DIQ, but such methods have not been cli...

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Bibliographic Details
Published in:NPJ digital medicine 2021-08, Vol.4 (1), p.127-11, Article 127
Main Authors: Chaudhari, Akshay S., Mittra, Erik, Davidzon, Guido A., Gulaka, Praveen, Gandhi, Harsh, Brown, Adam, Zhang, Tao, Srinivas, Shyam, Gong, Enhao, Zaharchuk, Greg, Jadvar, Hossein
Format: Article
Language:English
Subjects:
692/308/575
692/699/67/2321
692/700/1421/1771
Biomedicine
Biotechnology
Deep learning
Digital technology
Medicine
Medicine & Public Health
Radiation
Radiation protection
Scanners
Tomography
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Summary:More widespread use of positron emission tomography (PET) imaging is limited by its high cost and radiation dose. Reductions in PET scan time or radiotracer dosage typically degrade diagnostic image quality (DIQ). Deep-learning-based reconstruction may improve DIQ, but such methods have not been clinically evaluated in a realistic multicenter, multivendor environment. In this study, we evaluated the performance and generalizability of a deep-learning-based image-quality enhancement algorithm applied to fourfold reduced-count whole-body PET in a realistic clinical oncologic imaging environment with multiple blinded readers, institutions, and scanner types. We demonstrate that the low-count-enhanced scans were noninferior to the standard scans in DIQ ( p  
ISSN:2398-6352
2398-6352
DOI:10.1038/s41746-021-00497-2