Multi-observation PET image analysis for patient follow-up quantitation and therapy assessment.: Multi observation PET image fusion for patient follow-up quantitation and therapy response
| Autor: | Mathieu Hatt, Christian Roux, Dimitris Visvikis, Simon David |
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| Přispěvatelé: | Laboratoire de Traitement de l'Information Medicale (LaTIM), Université européenne de Bretagne - European University of Brittany (UEB)-Télécom Bretagne-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Mines-Télécom [Paris] (IMT) |
| Rok vydání: | 2011 |
| Předmět: |
Male
Remote patient monitoring patient monitoring image fusion Pattern Recognition Automated 030218 nuclear medicine & medical imaging 0302 clinical medicine Neoplasms MESH: Pattern Recognition Automated Medicine MESH: Neoplasms Radiological and Ultrasound Technology medicine.diagnostic_test MESH: Follow-Up Studies MESH: Positron-Emission Tomography MESH: Reproducibility of Results Positron emission tomography MESH: Stochastic Processes 030220 oncology & carcinogenesis oncology Pattern recognition (psychology) unsupervised segmentation Female Algorithms MESH: Radiopharmaceuticals MESH: Algorithms [SDV.IB.MN]Life Sciences [q-bio]/Bioengineering/Nuclear medicine Sensitivity and Specificity Article Bayesian classification Image (mathematics) 03 medical and health sciences Naive Bayes classifier Therapy assessment Expectation–maximization algorithm Humans Radiology Nuclear Medicine and imaging Stochastic Processes Image fusion MESH: Humans business.industry therapeutic response Reproducibility of Results Pattern recognition MESH: Sensitivity and Specificity MESH: Male PET Positron-Emission Tomography Artificial intelligence Radiopharmaceuticals Nuclear medicine business MESH: Female Follow-Up Studies |
| Zdroj: | Physics in Medicine and Biology Physics in Medicine and Biology, IOP Publishing, 2011, 56 (18), pp.5771-5788. ⟨10.1088/0031-9155/56/18/001⟩ Physics in Medicine and Biology, IOP Publishing, 2011, 56 (18), pp.5771-88. ⟨10.1088/0031-9155/56/18/001⟩ |
| ISSN: | 1361-6560 0031-9155 |
| DOI: | 10.1088/0031-9155/56/18/001 |
| Popis: | International audience; In positron emission tomography (PET) imaging, an early therapeutic response is usually characterized by variations of semi-quantitative parameters restricted to maximum SUV measured in PET scans during the treatment. Such measurements do not reflect overall tumor volume and radiotracer uptake variations. The proposed approach is based on multi-observation image analysis for merging several PET acquisitions to assess tumor metabolic volume and uptake variations. The fusion algorithm is based on iterative estimation using a stochastic expectation maximization (SEM) algorithm. The proposed method was applied to simulated and clinical follow-up PET images. We compared the multi-observation fusion performance to threshold-based methods, proposed for the assessment of the therapeutic response based on functional volumes. On simulated datasets the adaptive threshold applied independently on both images led to higher errors than the ASEM fusion and on clinical datasets it failed to provide coherent measurements for four patients out of seven due to aberrant delineations. The ASEM method demonstrated improved and more robust estimation of the evaluation leading to more pertinent measurements. Future work will consist in extending the methodology and applying it to clinical multi-tracer datasets in order to evaluate its potential impact on the biological tumor volume definition for radiotherapy applications. |
| Databáze: | OpenAIRE |
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