Racine Damien, Ba Alexandre H, Ott Julien G, Bochud François O, Verdun Francis R
Institute of Radiation Physics, Lausanne University Hospital, 1 Rue du Grand-Pré, 1007 Lausanne, Switzerland; University Joseph Fourier Grenoble I, 621 Avenue Centrale, 38041 Saint-Martin-d'Hères, France.
Institute of Radiation Physics, Lausanne University Hospital, 1 Rue du Grand-Pré, 1007 Lausanne, Switzerland.
Phys Med. 2016 Jan;32(1):76-83. doi: 10.1016/j.ejmp.2015.09.011. Epub 2015 Oct 26.
Iterative algorithms introduce new challenges in the field of image quality assessment. The purpose of this study is to use a mathematical model to evaluate objectively the low contrast detectability in CT.
A QRM 401 phantom containing 5 and 8 mm diameter spheres with a contrast level of 10 and 20 HU was used. The images were acquired at 120 kV with CTDIvol equal to 5, 10, 15, 20 mGy and reconstructed using the filtered back-projection (FBP), adaptive statistical iterative reconstruction 50% (ASIR 50%) and model-based iterative reconstruction (MBIR) algorithms. The model observer used is the Channelized Hotelling Observer (CHO). The channels are dense difference of Gaussian channels (D-DOG). The CHO performances were compared to the outcomes of six human observers having performed four alternative forced choice (4-AFC) tests.
For the same CTDIvol level and according to CHO model, the MBIR algorithm gives the higher detectability index. The outcomes of human observers and results of CHO are highly correlated whatever the dose levels, the signals considered and the algorithms used when some noise is added to the CHO model. The Pearson coefficient between the human observers and the CHO is 0.93 for FBP and 0.98 for MBIR.
The human observers' performances can be predicted by the CHO model. This opens the way for proposing, in parallel to the standard dose report, the level of low contrast detectability expected. The introduction of iterative reconstruction requires such an approach to ensure that dose reduction does not impair diagnostics.
迭代算法给图像质量评估领域带来了新的挑战。本研究的目的是使用数学模型客观评估CT中的低对比度可探测性。
使用了一个QRM 401体模，其中包含直径为5毫米和8毫米的球体，对比度水平分别为10 HU和20 HU。在120 kV下采集图像，CTDIvol分别等于5、10、15、20 mGy，并使用滤波反投影（FBP）、50%自适应统计迭代重建（ASIR 50%）和基于模型的迭代重建（MBIR）算法进行重建。所使用的模型观察者是通道化霍特林观察者（CHO）。通道为高斯通道的密集差分（D-DOG）。将CHO的性能与六名进行了四项二择一迫选（4-AFC）测试的人类观察者的结果进行比较。
对于相同的CTDIvol水平，根据CHO模型，MBIR算法给出了更高的可探测性指数。无论剂量水平、所考虑的信号以及在CHO模型中添加一些噪声时所使用的算法如何，人类观察者的结果与CHO的结果都高度相关。FBP算法下人类观察者与CHO之间的皮尔逊系数为0.93，MBIR算法下为0.98。
CHO模型可以预测人类观察者的性能。这为在标准剂量报告的同时提出预期的低对比度可探测性水平开辟了道路。迭代重建的引入需要这样一种方法来确保剂量降低不会损害诊断。
