• Greg Zaharchuk.
• Department of Radiology, Stanford University, Stanford, CA, USA. gregz@stanford.edu.
• Eur. J. Nucl. Med. Mol. Imaging. 2019 Dec 1; 46 (13): 2700-2707.

IntroductionRecently there have been significant advances in the field of machine learning and artificial intelligence (AI) centered around imaging-based applications such as computer vision. In particular, the tremendous power of deep learning algorithms, primarily based on convolutional neural network strategies, is becoming increasingly apparent and has already had direct impact on the fields of radiology and nuclear medicine. While most early applications of computer vision to radiological imaging have focused on classification of images into disease categories, it is also possible to use these methods to improve image quality. Hybrid imaging approaches, such as PET/MRI and PET/CT, are ideal for applying these methods.MethodsThis review will give an overview of the application of AI to improve image quality for PET imaging directly and how the additional use of anatomic information from CT and MRI can lead to further benefits. For PET, these performance gains can be used to shorten imaging scan times, with improvement in patient comfort and motion artifacts, or to push towards lower radiotracer doses. It also opens the possibilities for dual tracer studies, more frequent follow-up examinations, and new imaging indications. How to assess quality and the potential effects of bias in training and testing sets will be discussed.ConclusionHarnessing the power of these new technologies to extract maximal information from hybrid PET imaging will open up new vistas for both research and clinical applications with associated benefits in patient care.

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