Photon attenuation and scatter are the two main physical factors affecting the diagnostic quality of SPECT in its applications in brain imaging. In this work, we present a novel Bayesian Optimization approach for Attenuation Correction (BOAC) in SPECT brain imaging. BOAC utilizes a prior model parametrizing the head geometry and exploits High Performance Computing (HPC) to reconstruct attenuation corrected images without requiring prior anatomical information from complementary CT scans. BOAC is demonstrated in SPECT brain imaging using noisy and attenuated sinograms, simulated from numerical phantoms. The quality of the tomographic images obtained with the proposed method are compared to those obtained without attenuation correction by employing the appropriate image quality metrics. The quantitative results show the capacity of BOAC to provide images exhibiting higher contrast and less background artifacts as compared to the non-attenuation corrected MLEM images.
翻译:光衰减和散射是影响SPECT在脑成像应用中的诊断质量的两个主要物理因素。在这项工作中,我们介绍了一种新型的Bayesian优化方法,用于SPECT脑成像中的减速校正(BOAC)。BOC使用先前的模型,对头部几何进行模拟,并利用高性能计算法(HPC)重建减速校正图像,而无需事先从补充的CT扫描中获得解剖信息。BOCAC在SPECT脑成像中显示的是用从数字幻影模拟的噪音和减速的罪理图。用拟议方法获得的图象质量与通过使用适当的图像质量衡量法在不减速的情况下获得的图象质量进行了比较。定量结果显示,BOCC有能力提供与非加速校正的MLEM图像相比,显示更高对比度和较少背景文物的图像。