Molecular Imaging Center, Chang Gung Memorial Hospital, Linkou, Taiwan and Health Aging Research Center, Chang Gung University, Taoyuan, Taiwan.
Med Phys. 2013 Oct;40(10):102505. doi: 10.1118/1.4819822.
The aim of this study is to develop and evaluate a novel direct reconstruction method to improve the signal-to-noise ratio (SNR) of parametric images in dynamic positron-emission tomography (PET), especially for applications in myocardial perfusion studies.
Simulation studies were used to test the performance in SNR and computational efficiency for different methods. The NCAT phantom was used to generate simulated dynamic data. Noise realization was performed in the sinogram domain and repeated for 30 times with four different noise levels by varying the injection dose (ID) from standard ID to 1/8 of it. The parametric images were calculated by (1) three direct methods that compute the kinetic parameters from the sinogram and (2) an indirect method, which computes the kinetic parameter with pixel-by-pixel curve fitting in image space using weighted least-squares. The first direct reconstruction maximizes the likelihood function using trust-region-reflective (TRR) algorithm. The second approach uses tabulated parameter sets to generate precomputed time-activity curves for maximizing the likelihood functions. The third approach, as a newly proposed method, assumes separable complete data to derive the M-step for maximizing the likelihood.
The proposed method with the separable complete data performs similarly to the other two direct reconstruction methods in terms of the SNR, providing a 5%-10% improvement as compared to the indirect parametric reconstruction under the standard ID. The improvement of SNR becomes more obvious as the noise level increases, reaching more than 30% improvement under 1/8 ID. Advantage of the proposed method lies in the computation efficiency by shortening the time requirement to 25% of the indirect approach and 3%-6% of other direct reconstruction methods.
With results provided from this simulation study, direct reconstruction of myocardial blood flow shows a high potential for improving the parametric image quality for clinical use.
本研究旨在开发和评估一种新的直接重建方法，以提高动态正电子发射断层扫描（PET）中参数图像的信噪比（SNR），特别是在心肌灌注研究中的应用。
模拟研究用于测试不同方法的 SNR 和计算效率性能。使用 NCAT 体模生成模拟动态数据。在谱线域中进行噪声实现，并通过将注射剂量（ID）从标准 ID 变化为其 1/8 来重复 30 次，以四种不同的噪声水平进行重复。通过（1）从谱线计算动力学参数的三种直接方法和（2）间接方法，使用加权最小二乘法在图像空间中逐像素进行曲线拟合来计算参数图像。第一种直接重建方法使用信任区域反射（TRR）算法最大化似然函数。第二种方法使用参数表生成预计算的时活性曲线，以最大化似然函数。第三种方法，作为一种新提出的方法，假设完全可分数据，以便推导出用于最大化似然的 M 步骤。
在所提出的方法中，具有可分完整数据的方法在 SNR 方面与其他两种直接重建方法表现相似，与标准 ID 下的间接参数重建相比，提供了 5%-10%的改进。随着噪声水平的增加，SNR 的提高变得更加明显，在 1/8 ID 下达到 30%以上的提高。该方法的优势在于计算效率，将时间要求缩短到间接方法的 25%和其他直接重建方法的 3%-6%。
基于本模拟研究的结果，心肌血流的直接重建显示出了提高临床应用中参数图像质量的巨大潜力。
