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Development and Initial Validation of Two Simulation Workflows Using GATE for a Total-Body PET/CT Scanner

成像体模 扫描仪 计算机科学 工作流程 迭代重建 软件 DICOM 蒙特卡罗方法 人工智能 核医学 数据库 数学 医学 统计 程序设计语言
作者
Christian Pommranz,Ezzat Elmoujarkach,Jorge Cabello,Wenhong Lan,M. Rafecas,Julia G. Mannheim,A. Santangelo,Christian la Fougère,Bernd J. Pichler,Fabian Schmidt
标识
DOI:10.1109/nssmicrtsd49126.2023.10338368
摘要

Monte Carlo simulation studies potentially support understanding and characterizing new total-body PET/CT scanners such as the Biograph Vision Quadra (Siemens Healthineers), which enable a broad field of novel applications. This work aims to develop and validate two simulation workflows using the Geant4 Application for Emission Tomography (GATE). Workflow 1 is designed to model the response of the Biograph Vision Quadra as closely as possible by using a custom readout and digitizer modeling tool with an integrated coincidence sorter. Further, the same image reconstruction is performed as for real scan data with the e7 tools (Siemens Healthineers). Workflow 2 is developed for increased flexibility based on open-source software and the Customizable and Advanced Software for Tomographic Reconstruction (CASToR) code for image reconstruction. Phantom simulation studies were performed to validate the workflows against measurement data. Using the normalization matrix of the real scanner with an adapted calibration factor, Workflow 1 demonstrated an excellent quantification accuracy with differences of less than 1% for three different phantoms. Image quality analysis using an IEC phantom simulation with 60 s scan time showed good agreements of contrast recovery coefficients and lung residual error with experimental data. The anthropomorphic XCAT phantom was used for patient-like simulations. The successful NEMA-NU validation of the simulation workflows will enable the investigation of scenarios which practically cannot be tested on patients. Further, the generation of training and validation data sets including simulation ground truth data for machine learning algorithms will be enabled.

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