论文标题
使用马尔可夫链蒙特卡洛（Monte Carlo）使用拟人化幻象的理想观察者计算
Ideal-observer computation with anthropomorphic phantoms using Markov Chain Monte Carlo
论文作者
论文摘要
在医学成像中，人们广泛认识到，应根据临床任务的性能进行客观评估图像质量。为了评估信号检测任务中的性能，理想的观察者（IO）是最佳的，但在临床现实的环境中计算也很具有挑战性。马尔可夫链蒙特卡洛（MCMC）的策略已经证明了使用解剖数据库的预计投影计算IO的能力。为了评估临床现实情况下的图像质量，应测量观察者的性能以进行现实的患者分布。这意味着解剖数据库也应源自现实的人群。在本手稿中，我们建议推进基于MCMC的方法以实现这些目标。然后，我们使用所提出的方法研究解剖数据库大小对IO计算的影响，用于检测模拟心肌灌注SPECT图像中灌注缺陷的任务。我们的初步结果提供了证据，表明解剖数据库的大小会影响IO的计算。
In medical imaging, it is widely recognized that image quality should be objectively evaluated based on performance in clinical tasks. To evaluate performance in signal-detection tasks, the ideal observer (IO) is optimal but also challenging to compute in clinically realistic settings. Markov Chain Monte Carlo (MCMC)-based strategies have demonstrated the ability to compute the IO using pre-computed projections of an anatomical database. To evaluate image quality in clinically realistic scenarios, the observer performance should be measured for realistic patient distribution. This implies that the anatomical database should also be derived from a realistic population. In this manuscript, we propose to advance the MCMC-based approach to achieve these goals. We then use the proposed approach to study the effect of anatomical database size on IO computation for the task of detecting perfusion defects in simulated myocardial perfusion SPECT images. Our preliminary results provide evidence that the size of the anatomical database affects the computation of IO.
