Department of Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, WI, 53233, USA.
GE Healthcare, Waukesha, WI, 53188, USA.
Med Phys. 2019 Jan;46(1):140-151. doi: 10.1002/mp.13286. Epub 2018 Dec 4.
Identifying an appropriate tube current setting can be challenging when using iterative reconstruction due to the varying relationship between spatial resolution, contrast, noise, and dose across different algorithms. This study developed and investigated the application of a generalized detectability index ( ) to determine the noise parameter to input to existing automated exposure control (AEC) systems to provide consistent image quality (IQ) across different reconstruction approaches.
This study proposes a task-based automated exposure control (AEC) method using a generalized detectability index ( ). The proposed method leverages existing AEC methods that are based on a prescribed noise level. The generalized metric is calculated using lookup tables of task-based modulation transfer function (MTF) and noise power spectrum (NPS). To generate the lookup tables, the American College of Radiology CT accreditation phantom was scanned on a multidetector CT scanner (Revolution CT, GE Healthcare) at 120 kV and tube current varied manually from 20 to 240 mAs. Images were reconstructed using a reference reconstruction algorithm and four levels of an in-house iterative reconstruction algorithm with different regularization strengths (IR1-IR4). The task-based MTF and NPS were estimated from the measured images to create lookup tables of scaling factors that convert between and noise standard deviation. The performance of the proposed -AEC method in providing a desired IQ level over a range of iterative reconstruction algorithms was evaluated using the American College of Radiology (ACR) phantom with elliptical shell and using a human reader evaluation on anthropomorphic phantom images.
The study of the ACR phantom with elliptical shell demonstrated reasonable agreement between the predicted by the lookup table and measured in the images, with a mean absolute error of 15% across all dose levels and maximum error of 45% at the lowest dose level with the elliptical shell. For the anthropomorphic phantom study, the mean reader scores for images resulting from the -AEC method were 3.3 (reference image), 3.5 (IR1), 3.6 (IR2), 3.5 (IR3), and 2.2 (IR4). When using the -AEC method, the observers' IQ scores for the reference reconstruction were statistical equivalent to the scores for IR1, IR2, and IR3 iterative reconstructions (P > 0.35). The -AEC method achieved this equivalent IQ at lower dose for the IR scans compared to the reference scans.
A novel AEC method, based on a generalized detectability index, was investigated. The proposed method can be used with some existing AEC systems to derive the tube current profile for iterative reconstruction algorithms. The results provide preliminary evidence that the proposed -AEC can produce similar IQ across different iterative reconstruction approaches at different dose levels.
在使用迭代重建时，由于不同算法之间空间分辨率、对比度、噪声和剂量之间的关系不同，因此确定合适的管电流设置可能具有挑战性。本研究开发并研究了一种广义可检测性指数（ ）的应用，以确定输入到现有的自动曝光控制（AEC）系统中的噪声参数，以在不同的重建方法中提供一致的图像质量（IQ）。
本研究提出了一种基于广义可检测性指数（ ）的基于任务的自动曝光控制（AEC）方法。该方法利用基于规定噪声水平的现有 AEC 方法。广义 度量值是使用基于任务的调制传递函数（MTF）和噪声功率谱（NPS）的查找表计算得出的。为了生成查找表，使用美国放射学院 CT 认证体模在多探测器 CT 扫描仪（Revolution CT，GE Healthcare）上以 120kV 进行扫描，并手动将管电流从 20mA 到 240mA 进行调节。使用参考重建算法和具有不同正则化强度（IR1-IR4）的内部迭代重建算法对图像进行重建。从测量的图像中估计基于任务的 MTF 和 NPS，以创建转换 和噪声标准偏差之间的比例因子的查找表。使用美国放射学院（ACR）具有椭圆形壳的体模和人体模图像的人类读者评估来评估在一系列迭代重建算法中提供所需 IQ 水平的建议 -AEC 方法的性能。
对具有椭圆形壳的 ACR 体模的研究表明，查找表预测的 与图像中测量的 之间存在合理的一致性，在所有剂量水平下的平均绝对误差为 15%，在最低剂量水平下的最大误差为 45%。对于人体模研究，使用 -AEC 方法的图像的平均读者评分分别为 3.3（参考图像）、3.5（IR1）、3.6（IR2）、3.5（IR3）和 2.2（IR4）。当使用 -AEC 方法时，观察者对参考重建的 IQ 评分与 IR1、IR2 和 IR3 迭代重建的评分相当（P>0.35）。与参考扫描相比，IR 扫描的 -AEC 方法在较低剂量下实现了相同的 IQ。
研究了一种基于广义可检测性指数的新型 AEC 方法。该方法可以与一些现有的 AEC 系统一起使用，为迭代重建算法推导管电流曲线。结果初步证明，所提出的 -AEC 可以在不同的剂量水平下在不同的迭代重建方法中产生相似的 IQ。
