Simard Mikaël, Bouchard Hugo
Université de Montréal, Département de physique, Montréal, Québec, Canada.
Centre de recherche du Centre hospitalier de l'Université de Montréal, Montréal, Québec, Canada.
J Med Imaging (Bellingham). 2022 Jul;9(4):044003. doi: 10.1117/1.JMI.9.4.044003. Epub 2022 Jul 27.
: We propose a one-step tissue characterization method for spectral photon-counting computed tomography (SPCCT) using eigentissue decomposition (ETD), tailored for highly accurate human tissue characterization in radiotherapy. : The approach combines a Poisson likelihood, a spatial prior, and a quantitative prior constraining eigentissue fractions based on expected values for tabulated tissues. There are two regularization parameters: for the quantitative prior, and for the spatial prior. The approach is validated in a realistic simulation environment for SPCCT. The impact of and is evaluated on a virtual phantom. The framework is tested on a virtual patient and compared with two sinogram-based two-step methods [using respectively filtered backprojection (FBP) and an iterative method for the second step] and a post-reconstruction approach with the same quantitative prior. All methods use ETD. : Optimal performance with respect to bias or RMSE is achieved with different combinations of and on the cylindrical phantom. Evaluated in tissues of the virtual patient, the one-step framework outperforms two-step and post-reconstruction approaches to quantify proton-stopping power (SPR). The mean absolute bias on the SPR is 0.6% (two-step FBP), 0.6% (two-step iterative), 0.6% (post-reconstruction), and 0.2% (one-step optimized for low bias). Following the same order, the RMSE on the SPR is 13.3%, 2.5%, 3.2%, and 1.5%. : Accurate and precise characterization with ETD can be achieved with noisy SPCCT data without the need to rely on post-reconstruction methods. The one-step framework is more accurate and precise than two-step methods for human tissue characterization.
我们提出了一种用于光谱光子计数计算机断层扫描（SPCCT）的一步式组织表征方法，该方法使用本征组织分解（ETD），专为放射治疗中高精度的人体组织表征而设计。该方法结合了泊松似然、空间先验和基于表格化组织期望值约束本征组织分数的定量先验。有两个正则化参数：一个用于定量先验，另一个用于空间先验。该方法在SPCCT的真实模拟环境中得到验证。在虚拟体模上评估了这两个参数的影响。该框架在虚拟患者上进行了测试，并与两种基于正弦图的两步法（第二步分别使用滤波反投影（FBP）和迭代法）以及具有相同定量先验的重建后方法进行了比较。所有方法均使用ETD。在圆柱形体模上，通过这两个参数的不同组合实现了关于偏差或均方根误差（RMSE）的最佳性能。在虚拟患者的组织中进行评估时，一步式框架在量化质子阻止本领（SPR）方面优于两步法和重建后方法。SPR上的平均绝对偏差分别为0.6%（两步FBP法）、0.6%（两步迭代法）、0.6%（重建后方法）和0.2%（针对低偏差优化的一步法）。按照相同顺序，SPR上的RMSE分别为13.3%、2.5%、3.2%和1.5%。使用有噪声的SPCCT数据无需依赖重建后方法即可通过ETD实现准确而精确的表征。对于人体组织表征，一步式框架比两步法更准确、更精确。
