Total variation and point spread function priors for MLEM reconstruction in Compton camera imaging
| العنوان: | Total variation and point spread function priors for MLEM reconstruction in Compton camera imaging |
|---|---|
| المؤلفون: | Jean Michel Létang, David Sarrut, Voichita Maxim, Yue-Meng Feng, Ane Etxebeste |
| المساهمون: | Imagerie Tomographique et Radiothérapie, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), ANR-15-CE09-0009,3DCLEAN,Tri-Dimensionnel Nano-laboratoire catalytique environnemental(2015), ANR-11-LABX-0063,PRIMES,Physique, Radiobiologie, Imagerie Médicale et Simulation(2011) |
| المصدر: | 2018 IEEE Nuclear Science Symposium and Medical Imaging Conference Proceedings (NSS/MIC) 2018 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) 2018 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), Nov 2018, Sydney, Australia. ⟨10.1109/NSSMIC.2018.8824767⟩ |
| بيانات النشر: | IEEE, 2018. |
| سنة النشر: | 2018 |
| مصطلحات موضوعية: | Point spread function, maximum a posteriori, Tomographic reconstruction, Computer science, Statistical noise, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, point spread function, 01 natural sciences, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, total variation, 0302 clinical medicine, Kernel (image processing), gamma ray, 0103 physical sciences, Prior probability, Maximum a posteriori estimation, A priori and a posteriori, Deconvolution, maximum likelihood, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, 010303 astronomy & astrophysics, Algorithm, Compton camera imaging |
| الوصف: | International audience; The Compton camera is a gamma ray imaging device already employed in astronomy and still in investigation for clinical domain. A key point in the imaging process is the to-mographic reconstruction step. When the acquisition parameters and the a priori information are correctly accounted for, iterative algorithms are able to produce accurate images by compensating for measurement uncertainties and statistical noise. In this work we focus on the list-mode maximum likelihood expectation maximization (LM-MLEM) algorithm with smoothness a priori information expressed by the total variation norm. This type of regularization is particularly well suited for low-dose acquisitions, as it is the case in the applications foreseen for the camera. We show that the TV a priori strongly improves the images when data are acquired in ideal conditions. For realistic data, this a priori is not sufficient and deconvolution with a pre-calculated image-space kernel should also be considered. |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1f8201a99526912ad1dbf70dfaa77b18Test https://doi.org/10.1109/nssmic.2018.8824767Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....1f8201a99526912ad1dbf70dfaa77b18 |
| قاعدة البيانات: | OpenAIRE |
| الوصف غير متاح. |