Literature DB >> 21934192

Monte Carlo simulations of clinical PET and SPECT scans: impact of the input data on the simulated images.

S Stute1, T Carlier, K Cristina, C Noblet, A Martineau, B Hutton, L Barnden, I Buvat.   

Abstract

Monte Carlo simulations of emission tomography have proven useful to assist detector design and optimize acquisition and processing protocols. The more realistic the simulations, the more straightforward the extrapolation of conclusions to clinical situations. In emission tomography, accurate numerical models of tomographs have been described and well validated under specific operating conditions (collimator, radionuclide, acquisition parameters, count rates, etc). When using these models under these operating conditions, the realism of simulations mostly depends on the activity distribution used as an input for the simulations. It has been proposed to derive the input activity distribution directly from reconstructed clinical images, so as to properly model the heterogeneity of the activity distribution between and within organs. However, reconstructed patient images include noise and have limited spatial resolution. In this study, we analyse the properties of the simulated images as a function of the properties of the reconstructed images used to define the input activity distributions in (18)F-FDG PET and (131)I SPECT simulations. The propagation through the simulation/reconstruction process of the noise and spatial resolution in the input activity distribution was studied using simulations. We found that the noise properties of the images reconstructed from the simulated data were almost independent of the noise in the input activity distribution. The spatial resolution in the images reconstructed from the simulations was slightly poorer than that in the input activity distribution. However, using high-noise but high-resolution patient images as an input activity distribution yielded reconstructed images that could not be distinguished from clinical images. These findings were confirmed by simulated highly realistic (131)I SPECT and (18)F-FDG PET images from patient data. In conclusion, we demonstrated that (131)I SPECT and (18)F-FDG PET images indistinguishable from real scans can be simulated using activity maps with spatial resolution higher than that used in routine clinical applications.

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Year:  2011        PMID: 21934192     DOI: 10.1088/0031-9155/56/19/017

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  5 in total

1.  Bayesian Analysis of a One Compartment Kinetic Model Used in Medical Imaging.

Authors:  Peter Malave; Arkadiusz Sitek
Journal:  J Appl Stat       Date:  2015       Impact factor: 1.404

2.  Segmentation of PET images for computer-aided functional quantification of tuberculosis in small animal models.

Authors:  Brent Foster; Ulas Bagci; Bappaditya Dey; Brian Luna; William Bishai; Sanjay Jain; Daniel J Mollura
Journal:  IEEE Trans Biomed Eng       Date:  2013-11-05       Impact factor: 4.538

3.  Monte Carlo simulation of digital photon counting PET.

Authors:  Julien Salvadori; Joey Labour; Freddy Odille; Pierre-Yves Marie; Jean-Noël Badel; Laëtitia Imbert; David Sarrut
Journal:  EJNMMI Phys       Date:  2020-04-25

4.  Assessment of acquisition protocols for routine imaging of Y-90 using PET/CT.

Authors:  Thomas Carlier; Thomas Eugène; Caroline Bodet-Milin; Etienne Garin; Catherine Ansquer; Caroline Rousseau; Ludovic Ferrer; Jacques Barbet; Frédéric Schoenahl; Françoise Kraeber-Bodéré
Journal:  EJNMMI Res       Date:  2013-02-17       Impact factor: 3.138

5.  Functional Brain Imaging Synthesis Based on Image Decomposition and Kernel Modeling: Application to Neurodegenerative Diseases.

Authors:  Francisco J Martinez-Murcia; Juan M Górriz; Javier Ramírez; Ignacio A Illán; Fermín Segovia; Diego Castillo-Barnes; Diego Salas-Gonzalez
Journal:  Front Neuroinform       Date:  2017-11-14       Impact factor: 4.081

  5 in total

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