Deep Learning Based Approach to Quantification of PET Tracer Uptake in Small Tumors
Dal Toso L, Pfaehler E, Boellaard R, Schnabel JA, Marsden PK (2019)
Publication Type: Conference contribution
Publication year: 2019
Journal
Publisher: Springer
Book Volume: 11905 LNCS
Pages Range: 181-192
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Shenzhen, CHN
ISBN: 9783030338428
DOI: 10.1007/978-3-030-33843-5_17
Abstract
In Positron Emission Tomography (PET), quantification of tumor radiotracer uptake is mainly performed using standardised uptake value and related methods. However, the accuracy of these metrics is limited by the poor spatial resolution and noise properties of PET images. Therefore, there is a great need for new methods that allow for accurate and reproducible quantification of tumor radiotracer uptake, particularly for small regions. In this work, we propose a deep learning approach to improve quantification of PET tracer uptake in small tumors using a 3D convolutional neural network. The network was trained on simulated images that present 3D shapes with typical tumor tracer uptake distributions (‘ground truth distributions’), and the corresponding set of simulated PET images. The network was tested on unseen simulated PET images and was shown to robustly estimate the original radiotracer uptake, yielding improved images both in terms of shape and activity distribution. The same network was successful when applied to 3D tumors acquired from physical phantom PET scans.
Involved external institutions
King’s College London
United Kingdom (GB)
University of Groningen / Rijksuniversiteit Groningen
Netherlands (NL)
United Kingdom (GB)
University of Groningen / Rijksuniversiteit Groningen
Netherlands (NL)
How to cite
APA:
Dal Toso, L., Pfaehler, E., Boellaard, R., Schnabel, J.A., & Marsden, P.K. (2019). Deep Learning Based Approach to Quantification of PET Tracer Uptake in Small Tumors. In Florian Knoll, Andreas Maier, Daniel Rueckert, Jong Chul Ye (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 181-192). Shenzhen, CHN: Springer.
MLA:
Dal Toso, Laura, et al. "Deep Learning Based Approach to Quantification of PET Tracer Uptake in Small Tumors." Proceedings of the 2nd International Workshop on Machine Learning for Medical Image Reconstruction, MLMIR 2019 held in Conjunction with 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, Shenzhen, CHN Ed. Florian Knoll, Andreas Maier, Daniel Rueckert, Jong Chul Ye, Springer, 2019. 181-192.
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