Task-Based Optimization for Gated Myocardial ECT

描述(由申请人提供)：在此次5年竞争续签的第二次重新提交中，我们仔细地解决了评审者提出的一系列新问题，包括关于MAP-EM算法的更多细节、使用模拟4D门控SPECT和PET数据的优化方法、建议的4D观察者模型的验证方法以及临床评估研究。在目前的资助期间，我们开发了用于门控心肌灌注(GMP)SPECT的改进的4D图像重建方法，通过在不降低图像噪声特性的情况下，在每个心脏周期使用更多的帧来提供更好的时间分辨率。这有可能改善局部心肌壁运动异常的可视化。我们开发了一种方法，使用已知的心脏材料属性和心脏的平均运动模型作为初始估计，从GMP SPECT数据确定心肌的运动矢量场。此外，我们已经开发了基于任务的方法，使用CHO和ROC方法来优化和评估这些新技术，使用来自4D NCAT模体的模拟数据，真实地模拟在临床数据中发现的解剖和生理变化。基于本次研究的可喜结果，我们建议继续研究4D图像重建方法，以进一步提高4D GMP ECT图像的质量和定量精度。我们建议将研究扩展到使用RB-82的GMP PET。类似于目前对GMP SPECT的研究，我们建议使用真实的计算机模拟数据对GMP PET的数据采集和4D图像重建方法进行初步优化和评估。我们实验室开发的混合蒙特卡罗技术将用于从4D NCAT模体的种群中高效地生成GMP PET数据，并对PET系统的几何结构和探测器配置进行准确的建模。然后，我们将通过在临床系统上使用动态心脏物理模型进行实验来验证数据采集和优化4D图像重建方法。在GE Healthcare的支持下，我们建议在本研究中使用的GE SPECT/CT和PET/CT临床系统中实现列表模式数据采集功能。使用CHO和ROC方法的基于任务的评估方法将被应用于评估从模体数据获得的GMP SPECT和PET图像在检测节段性室壁运动异常方面的改进。最后，我们建议使用临床GMP SPECT和使用LISTMODE数据采集获得的PET数据来启动对4D图像重建方法的评估。我们将使用模拟数据比较CHO和人类观察员的评估结果。我们还将使用训练有素的医生对模拟研究和临床研究的评估结果进行比较。这些结果将允许评估数学观察者在4D GMP SPECT和PET成像技术的基于任务的评估中的效用。

DESCRIPTION (provided by applicant): In this second resubmission of a 5-year competing renewal, we have carefully addressed a new set of concerns raised by the reviewers including more details on the MAP-EM algorithm, the optimization methods using simulated 4D gated SPECT and PET data, validation method for the proposed 4D observer model and the clinical evaluation study. During the current grant, we have developed improved 4D image reconstruction methods for gated myocardial perfusion (GMP) SPECT that provide better time resolution through the use of more frames per cardiac cycle without degrading the noise characteristics of the images. This has the potential to provide improved visualization of regional myocardial wall motion abnormalities. We have developed methods to determine the motion vector fields of the myocardium from the GMP SPECT data using known cardiac material properties and an average motion model of the heart as the initial estimate. Also, we have developed task-based methods using the CHO and ROC methodology to optimize and evaluate these new techniques using simulated data from populations of 4D NCAT phantoms that realistically model anatomical and physiological variations found in clinical data. Based on encouraging results from this study, we propose to continue our investigation into 4D image reconstruction methods to further improve the quality and quantitative accuracy of 4D GMP ECT images. We propose to extend the investigation to GMP PET using Rb-82. Similar to the current study on GMP SPECT, we propose to initially optimize and evaluate the data acquisition and 4D image reconstruction methods for GMP PET using realistic computer simulated data. A hybrid Monte Carlo technique developed in our laboratory will be used to provide efficient generation of GMP PET data from populations of the 4D NCAT phantoms with accurate modeling of the PET system geometries and detector configuration. We will then validate the data acquisition and optimize the 4D image reconstruction methods through experiments on clinical systems using a dynamic cardiac physical phantom. With the support of GE Healthcare, we propose to implement listmode data acquisition capabilities in both the GE SPECT/CT and PET/CT clinical systems used in this study. Task-based evaluation methods using CHO and ROC methodology will be applied to evaluate improvements in detecting regional wall motion abnormalities in GMP SPECT and PET images obtained from the phantom data. Finally, we propose to initiate evaluation of the 4D image reconstruction methods using clinical GMP SPECT and PET data acquired using the listmode data acquisition. We will compare evaluation results from the CHO and human observers using simulated data. We will also compare evaluation results from the simulation study and clinical study using trained physicians. These results will allow assessment of the utility of mathematical observers in task-based evaluation of 4D GMP SPECT and PET imaging techniques.