Abstract
AbstractThis review focuses on positron emission tomography (PET) imaging algorithms and traces the evolution of PET image reconstruction methods. First, we provide an overview of conventional PET image reconstruction methods from filtered backprojection through to recent iterative PET image reconstruction algorithms, and then review deep learning methods for PET data up to the latest innovations within three main categories. The first category involves post-processing methods for PET image denoising. The second category comprises direct image reconstruction methods that learn mappings from sinograms to the reconstructed images in an end-to-end manner. The third category comprises iterative reconstruction methods that combine conventional iterative image reconstruction with neural-network enhancement. We discuss future perspectives on PET imaging and deep learning technology.
Funder
Japan Society for Promotion of Science
Nakatani Foundation for Advancement of Measuring Technologies in Biomedical Engineering
Publisher
Springer Science and Business Media LLC
Subject
Radiology, Nuclear Medicine and imaging,Physical Therapy, Sports Therapy and Rehabilitation,General Medicine,Radiation
Reference250 articles.
1. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–44.
2. Goodfellow IJ, Bengio Y, Courville A. Deep learning. Cambridge, MA, USA: MIT Press; 2016 http://www.deeplearningbook.org.
3. Schmidhuber J. Deep learning in neural networks: An overview. Neural Netw. 2015;61:85–117.
4. Suzuki K. Overview of deep learning in medical imaging. Radiol Phys Technol. 2017;10(3):257–73.
5. Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, et al. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60–88.