Improved 3D tumour definition and quantification of uptake in simulated lung tumours using deep learning
| Autor: | Laura Dal Toso, Zacharias Chalampalakis, Irène Buvat, Claude Comtat, Gary Cook, Vicky Goh, Julia A Schnabel, Paul K Marsden |
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| Přispěvatelé: | King‘s College London, LaBoratoire d'Imagerie biOmédicale MultimodAle Paris-Saclay (BIOMAPS), Service Hospitalier Frédéric Joliot (SHFJ), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Imagerie Translationnelle en Oncologie (LITO ), Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Helmholtz Zentrum München = German Research Center for Environmental Health, Technische Universität München = Technical University of Munich (TUM), Buvat, Irène |
| Rok vydání: | 2021 |
| Předmět: |
Lung Neoplasms
Radiological and Ultrasound Technology Phantoms Imaging [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging PET imaging Paper PET CNN quantification ddc Deep Learning [SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging Positron-Emission Tomography Image Processing Computer-Assisted Humans Radiology Nuclear Medicine and imaging Analytical simulator |
| Zdroj: | Physics in Medicine and Biology Physics in Medicine and Biology, 2022, 67 (9), pp.095013. ⟨10.1088/1361-6560/ac65d6⟩ |
| ISSN: | 0031-9155 1361-6560 |
| DOI: | 10.1088/1361-6560/ac65d6⟩ |
| Popis: | Objective. In clinical positron emission tomography (PET) imaging, quantification of radiotracer uptake in tumours is often performed using semi-quantitative measurements such as the standardised uptake value (SUV). For small objects, the accuracy of SUV estimates is limited by the noise properties of PET images and the partial volume effect. There is need for methods that provide more accurate and reproducible quantification of radiotracer uptake. Approach. In this work, we present a deep learning approach with the aim of improving quantification of lung tumour radiotracer uptake and tumour shape definition. A set of simulated tumours, assigned with ‘ground truth’ radiotracer distributions, are used to generate realistic PET raw data which are then reconstructed into PET images. In this work, the ground truth images are generated by placing simulated tumours characterised by different sizes and activity distributions in the left lung of an anthropomorphic phantom. These images are then used as input to an analytical simulator to simulate realistic raw PET data. The PET images reconstructed from the simulated raw data and the corresponding ground truth images are used to train a 3D convolutional neural network. Results. When tested on an unseen set of reconstructed PET phantom images, the network yields improved estimates of the corresponding ground truth. The same network is then applied to reconstructed PET data generated with different point spread functions. Overall the network is able to recover better defined tumour shapes and improved estimates of tumour maximum and median activities. Significance. Our results suggest that the proposed approach, trained on data simulated with one scanner geometry, has the potential to restore PET data acquired with different scanners. |
| Databáze: | OpenAIRE |
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