A GPU tool for efficient, accurate, and realistic simulation of cone beam CT projections
A GPU tool for efficient, accurate, and realistic simulation of cone beam CT projections
复制标题
DOI：
10.1118/1.4766436
复制
发表时间：
2012-12-01
期刊：
影响因子：
3.8
通讯作者：
Jiang, Steve B.
中科院分区：
文献类型：
--
作者：
Jia, Xun;Yan, Hao;Jiang, Steve B.
目的：X射线投影图像的模拟在锥形束CT（CBCT）相关研究项目中起着重要作用，例如重建算法或扫描仪的设计。投影图像包含主信号、散射信号和噪声。对所有这些组件执行精确和现实的计算在计算上是苛刻的。在这项工作中，作者开发了一个软件包的图形处理单元（GPU），称为gDRR，在CBCT的X射线投影图像的准确和有效的计算在临床上现实的conditions.Methods：主要信号计算三线性射线跟踪算法。然后执行蒙特卡罗（MC）模拟，产生主信号和散射信号，两者都具有噪声。专门设计用于泊松噪声去除的去噪过程被应用以获得平滑的散射信号。然后，通过组合MC主信号和射线跟踪主信号之间的差以及MC模拟散射信号和去噪散射信号之间的差来获得噪声分量。最后，校准步骤通过根据指定的mAs水平缩放其幅度来将计算出的噪声信号转换为现实的噪声信号。gDRR的计算包括许多真实特征，例如，蝴蝶结滤波器、多能光谱和检测器响应。该实现是微调的GPU平台，以产生高的计算效率。结果：对于一个典型的CBCT投影与多能谱，使用射线跟踪算法的主要信号的计算时间是1.2-2.3秒，而MC模拟需要28.1-95.3秒，这取决于体素的大小。所有其他步骤的计算时间可以忽略不计。射线追踪的原始信号与MC模拟结果的原始部分吻合较好。使用gDRR的MC模拟散射信号与EGSnrc结果一致，相对差异为3.8%。进行噪声校准过程以相对于真实的CBCT扫描仪校准gDRR。所计算的投影是准确和真实的，使得可以使用模拟投影再现射束硬化伪影和散射伪影。从模拟投影重建的CBCT图像中的噪声幅度也同意在相应的mAs level.Conclusions测量图像中的噪声幅度：GPU计算工具，gDRR，已开发的CBCT的X射线投影与现实配置的准确和有效的模拟。(C)2012年美国医学物理学家协会。[http：//dx.doi.org/10.1118/1.4766436]
Purpose: Simulation of x-ray projection images plays an important role in cone beam CT (CBCT) related research projects, such as the design of reconstruction algorithms or scanners. A projection image contains primary signal, scatter signal, and noise. It is computationally demanding to perform accurate and realistic computations for all of these components. In this work, the authors develop a package on graphics processing unit (GPU), called gDRR, for the accurate and efficient computations of x-ray projection images in CBCT under clinically realistic conditions.Methods: The primary signal is computed by a trilinear ray-tracing algorithm. A Monte Carlo (MC) simulation is then performed, yielding the primary signal and the scatter signal, both with noise. A denoising process specifically designed for Poisson noise removal is applied to obtain a smooth scatter signal. The noise component is then obtained by combining the difference between the MC primary and the ray-tracing primary signals, and the difference between the MC simulated scatter and the denoised scatter signals. Finally, a calibration step converts the calculated noise signal into a realistic one by scaling its amplitude according to a specified mAs level. The computations of gDRR include a number of realistic features, e.g., a bowtie filter, a polyenergetic spectrum, and detector response. The implementation is fine-tuned for a GPU platform to yield high computational efficiency.Results: For a typical CBCT projection with a polyenergetic spectrum, the calculation time for the primary signal using the ray-tracing algorithms is 1.2-2.3 s, while the MC simulations take 28.1-95.3 s, depending on the voxel size. Computation time for all other steps is negligible. The ray-tracing primary signal matches well with the primary part of the MC simulation result. The MC simulated scatter signal using gDRR is in agreement with EGSnrc results with a relative difference of 3.8%. A noise calibration process is conducted to calibrate gDRR against a real CBCT scanner. The calculated projections are accurate and realistic, such that beam-hardening artifacts and scatter artifacts can be reproduced using the simulated projections. The noise amplitudes in the CBCT images reconstructed from the simulated projections also agree with those in the measured images at corresponding mAs levels.Conclusions: A GPU computational tool, gDRR, has been developed for the accurate and efficient simulations of x-ray projections of CBCT with realistic configurations. (C) 2012 American Association of Physicists in Medicine. [http://dx.doi.org/10.1118/1.4766436]