Partial volume correction strategies for quantitative FDG PET in oncology

Purpose: Quantitative accuracy of positron emission tomography (PET) is affected by partial volume effects resulting in increased underestimation of the standardized uptake value (SUV) with decreasing tumour volume. The purpose of the present study was to assess accuracy and precision of different partial volume correction (PVC) methods. Methods: Three methods for PVC were evaluated: (1) inclusion of the point spread function (PSF) within the reconstruction, (2) iterative deconvolution of PET images and (3) calculation of spill-in and spill-out factors based on tumour masks. Simulations were based on a mathematical phantom with tumours of different sizes and shapes. Phantom experiments were performed in 2-D mode using the National Electrical Manufacturers Association (NEMA) NU2 image quality phantom containing six differently sized spheres. Clinical studies (2-D mode) included a test-retest study consisting of 10 patients with stage IIIB and IV non-small cell lung cancer and a response monitoring study consisting of 15 female breast cancer patients. In all studies tumour or sphere volumes of interest (VOI) were generated using VOI based on adaptive relative thresholds. Results: Simulations and experiments provided similar results. All methods were able to accurately recover true SUV within 10% for spheres equal to and larger than 1 ml. Reconstruction-based recovery, however, provided up to twofold better precision than image-based methods. Cl

Extraction of epileptogenic foci from PET and SPECT images by fuzzy modeling and data fusion

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Tomographie conique de 6 MV : simulation et évaluation GATE v6.2 Monte-Carlo d’un imageur portal avec application spécifique en radiothérapie ORL

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SU-C500-03: GGEMS Brachy: fully GPU Geant4 based efficient Monte Carlo simulation for brachytherapy applications

International audiencePurpose: In brachytherapy, dosimetric plans are routinely calculated with the TG43 formalism which considers the patient as a simple water box. However, an accurate modeling of the physical processes considering patient heterogeneity using Monte Carlo (MC) methods is currently too time‐consuming and computationally demanding to be routinely used. As solution we implemented an accurate and fast MC simulation on graphics processing unit (GPU) for brachytherapy (HDR and LDR) applications. Methods: Based on Geant4 a MC simulation framework was developed on GPU. This framework was extended to include a hybrid GPU navigator, allowing navigation within a voxelized phantom derived from CT imaging including an analytical based structure for accurately modeling the 125I seeds (Source Tech Medical STM1251). In addition, dose scoring based on TLE including uncertainty calculations was incorporated. The implemented full GPU based modeling was compared with a classical CPU MC simulation based on GATE/GEANT4, as well as previously proposed GPU approaches based on the use of phasespace files for the seeds and their positioning based on simple replacement of voxels within the CT volumes. Results: Energy distribution from the seed and dose mapping including uncertainty were compared showing a high agreement (differences <1%). Preliminary results have shown that the GPU implementation is faster by at least two orders of magnitude compared to the GATE mono‐CPU version. A comparison between dosimetric plans based on TG43 and a full MC simulation using the GPU code for LDR prostate brachytherapy led to 40%-100% differences depending on the level of tissue heterogeneity. Conclusion: We propose a full GPU MC simulation based on Geant4, with a hybrid navigator dedicated for brachytherapy applications. Our evaluation shows large dose differences compared to the simplistic TG43 formalism in LDR and very fast execution times compatible with clinical practice

PET image denoising using a synergistic multiresolution analysis of structural (MRI/CT) and functional datasets

PET allows the imaging of functional properties of the living tissue, whereas other modalities (CT, MRI) provide structural information at significantly higher resolution and better image quality. Con-straints for injected radioactivity, technologic limitations of current instrumentation, and inherent spatial uncertainties on the decaying process affect the quality of PET images. In this article we illustrate how structural information of matched anatomic images can be used in amultiresolutionmodel to enhance the signal-to-noise ratio ofPET images.Themodel statesaflexible relationbetween function andstructure in thebrainandreplaceshigh-resolution informationof PET images with appropriately scaled MRI or CT local detail. The method can be naturally extended to other functional imaging mo-dalities (SPECT, functional MRI). Methods: The methodology is based on the multiresolution property of the wavelet transform (WT). First, the coregistered structural image (MRI/CT) is down