论文标题
快速猫：快速锥束CT（CBCT）模拟
FastCAT: Fast Cone Beam CT (CBCT) Simulation
论文作者
论文摘要
FastCAT应用使用预估计的蒙特卡洛（MC）CBCT幻影特异性散射和检测器响应函数，以减少Megavoltage（MV）和Kilovltage（KV）CBCT成像的模拟时间。预算的X射线束能光谱，检测器光扩散功能和能量沉积，以及幻影散射核与GPU射线缩放结合，以产生CBCT体积。使用EGSNRC模拟MV X射线梁光谱，以2.5和6 MeV电子束入射在各种目标材料上，并分析具有钨阳极的X射线管。在TOPAS扩展的Geant4中对探测器进行了建模，并在闪烁体中包括光传输。对两个MV检测器进行了建模，标准Varian AS1200 GOS检测器和一个新型的CWO高侦探量子效率检测器。还对KV CSI检测器进行了建模。在TOPA中创建了两个直径16厘米的幻影：CATPHAN 515对比幻影和拟人化头幻影。 CATPHAN PHANTOM包含六种不同组织类型的直径为1-5 mm的插入物。 FastCAT模拟保留了对测量值和MC模拟的高忠诚：MTF曲线分别在杆体和GOS探测器的测量值的3.5％和1.2％之内。对于所有平均平方误差小于16 HU和1.6的组织，在FastCat Catphan 515模拟中，HU值和CNR在95％的置信区间内，HU值和CNR比较分别为1.6。图像大小为1024x1024x10素的CATPHAN 515模块的快速猫模拟在GPU上需要61秒，而等效的TOPAS MC估计需要超过0.3 CPU年。可以在https://github.com/jerichooconnell/fastcats.git上找到FastCat应用程序。
The fastCAT application uses pre-calculated Monte Carlo (MC) CBCT phantom-specific scatter and detector response functions to reduce simulation time for megavoltage (MV) and kilovoltage (kV) CBCT imaging. Pre-calculated x-ray beam energy spectra, detector optical spread functions and energy deposition, and phantom scatter kernels are combined with GPU raytracing to produce CBCT volumes. MV x-ray beam spectra are simulated with EGSnrc for 2.5 and 6 MeV electron beams incident on a variety of target materials and kV x-ray beam spectra are calculated analytically for an x-ray tube with a tungsten anode. Detectors were modelled in Geant4 extended by Topas and included optical transport in the scintillators. Two MV detectors were modelled, a standard Varian AS1200 GOS detector and a novel CWO high detective quantum efficiency detector. A kV CsI detector was also modelled. Energy dependent scatter kernels were created in Topas for two 16-cm diameter phantoms: A Catphan 515 contrast phantom and an anthropomorphic head phantom. The Catphan phantom contained inserts of 1-5 mm in diameter of six different tissue types. FastCAT simulations retain high fidelity to measurements and MC simulations: MTF curves were within 3.5% and 1.2% of measured values for the CWO and GOS detectors, respectively. HU values and CNR in a fastCAT Catphan 515 simulation were seen to be within 95 % confidence intervals of an equivalent MC simulation for all of the tissues with root mean squared errors less than 16 HU and 1.6 in HU values and CNR comparisons, respectively. A fastCAT simulation of the Catphan 515 module with an image size of 1024x1024x10 voxels took 61 seconds on a GPU while the equivalent Topas MC was estimated to take more than 0.3 CPU years. The fastCAT application can be found at https://github.com/jerichooconnell/fastCATs.git.
