Deep Learning Based Approach to Quantification of PET Tracer Uptake in Small Tumors
| Autor: | Dal Toso, Laura, Pfaehler, Elisabeth, Boellaard, Ronald, Schnabel, Julia A., Marsden, Paul K., Knoll, Florian, Maier, Andreas, Rueckert, Daniel, Ye, Jong Chul |
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| Přispěvatelé: | Knoll, Florian, Maier, Andreas, Rueckert, Daniel, Ye, Jong Chul, CCA - Imaging and biomarkers, Amsterdam Neuroscience - Brain Imaging, Radiology and nuclear medicine, ACS - Heart failure & arrhythmias |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Ground truth
medicine.diagnostic_test business.industry Computer science Deep learning Convolutional neural network Imaging phantom 030218 nuclear medicine & medical imaging 03 medical and health sciences PET 0302 clinical medicine Positron emission tomography Quantification medicine Tracer uptake Artificial intelligence Reconstruction Pet tracer business Small tumors Image resolution 030217 neurology & neurosurgery Biomedical engineering |
| Zdroj: | 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 F Knoll, A Maier, D Rueckert & J C Ye (eds), Machine Learning for Medical Image Reconstruction-2nd International Workshop, MLMIR 2019, held in Conjunction with MICCAI 2019, Proceedings . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11905 LNCS, Springer, pp. 181-192, 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, China, 17/10/2019 . https://doi.org/10.1007/978-3-030-33843-5_17 Lecture Notes in Computer Science Lecture Notes in Computer Science-Machine Learning for Medical Image Reconstruction Machine Learning for Medical Image Reconstruction ISBN: 9783030338428 MLMIR@MICCAI Machine Learning for Medical Image Reconstruction-Second International Workshop, MLMIR 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings Machine Learning for Medical Image Reconstruction-2nd International Workshop, MLMIR 2019, held in Conjunction with MICCAI 2019, Proceedings, 181-192 STARTPAGE=181;ENDPAGE=192;TITLE=Machine Learning for Medical Image Reconstruction-2nd International Workshop, MLMIR 2019, held in Conjunction with MICCAI 2019, Proceedings 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 F Knoll, A Maier, D Rueckert & J C Ye (eds), Machine Learning for Medical Image Reconstruction-2nd International Workshop, MLMIR 2019, held in Conjunction with MICCAI 2019, Proceedings . vol. 11905, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11905 LNCS, Switzerland, pp. 181-192 . https://doi.org/10.1007/978-3-030-33843-5_17 |
| ISSN: | 0302-9743 1611-3349 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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