Automatic exposure control (AEC) for CT based on neural network-driven patient-specific real-time assessment of dose distributions and minimization of the effective dose

现代诊断CT系统包括各种措施，以保持病人的剂量在最低限度。特别重要的是管电流调制（TCM）技术，该技术自动适应每次投影的管电流，以最小化给定图像质量的管电流时间积（mas -积）。这也可以看作是最大化给定mas产品的图像质量。TCM根据x射线管的角度位置和扫描的z轴位置执行调制。与TCM相关的措施是自动选择平均管电流和最佳管电压。这三种剂量减少方法也被称为自动暴露控制（AEC）。然而，到目前为止，AEC并没有最大限度地减少患者的实际剂量，从而减少患者的实际风险。相反，它将其替代品最小化。中药的替代品是mas产品。用于自动选择管电压的替代值是CTDI值或剂量长度积（DLP）。直接最小化加权器官总剂量值，从而使患者风险最小化，目前是不可实现的，因为A)蒙特卡罗剂量计算算法的计算时间非常长，b)缺乏对辐射敏感和风险相关器官的可靠分割。因此，我们计划使用人工神经网络来解决上述问题，并实现一种新的AEC，可以直接减少患者的剂量和风险，而不是使用替代参数。第一个神经网络将患者的地形图转换为CT体积的粗略估计。在只有一个地形图可用的情况下，将使用表高度信息进行估计。第二个神经网络将分割相关器官。我们可以在这里部分使用之前DFG项目（KA 1678/20, LE 2763/2, MA 4898/5）的先前工作。第三种网络将使用进一步的扫描参数（表增量、螺距值、旋转时间、准直、管电压等）来计算每次投影的预期剂量分布。再加上对器官的分割，可以计算出每次投射的有效剂量（或风险）或患者。然后，最小化算法将找到在给定图像质量下最小化患者风险或在给定患者风险下最大化图像质量的最佳管电流曲线。为了评估我们的深度AEC算法，将收集诊断CT数据。通过在原始数据中添加噪声，然后进行另一次重建，将数据回顾性地转换为所需的管电流曲线。然后，经验丰富的放射科医生将进行一项盲法研究，他们将在没有AEC的情况下，使用目前的传统AEC和我们新的深度AEC算法，阅读图像。

Modern diagnostic CT systems comprise a variety of measures to keep patient dose at a minimum. Of particular importance is the tube current modulation (TCM) technique that automatically adapts the tube current for each projection in a way to minimize the tube current time product (mAs-product) for a given image quality. This can also be regarded as maximizing the image quality for a given mAs-product. TCM performs a modulation depending on the angular position of the x-ray tube and depending on the z-position of the scan. Measures related to TCM are the automated choice of the mean tube current and of the optimal tube voltage. These three dose reduction methods are also known under the term automatic exposure control (AEC).As of today, however, the AEC does not minimize the actual patient dose and thereby the actual patient risk. It rather minimizes surrogates thereof. The surrogate of TCM is the mAs-product. The surrogate used to automatically select the tube voltage is the CTDI value or the dose length product (DLP). A direct minimization of the weighted summed organ dose values and thereby the patient risk is currently not practicable due to a) the very high computation times of the Monte Carlo dose calculation algorithms and b) due to the lack of a reliable segmentation of the radiation sensitive and risk-relevant organs.We therefore plan to use artificial neural networks to solve the above-mentioned problems and to realize a new AEC which is capable of directly minimizing patient dose and risk instead of using surrogate parameters. A first neural net will convert the patient topogram(s) into a coarse estimate of the CT volume. In cases where only a single topogram is available information of the table height will be used for this estimation. A second neural net will segment the relevant organs. We can here partially use prior work of a previous DFG project (KA 1678/20, LE 2763/2, MA 4898/5). A third network will use further scan parameters (table increment, pitch value, rotation time, collimation, tube voltage, …) to compute the expected dose distribution per projection. This, together with the segmentation of the organs, shall be used to compute the effective dose (or risk) or the patient per projection. A minimization algorithm will then find the optimal tube current curve that minimizes patient risk at a given image quality or that maximizes image quality at a given patient risk.To evaluate our deep AEC algorithm diagnostic CT data will be collected. The data will be retrospectively converted to the desired tube current curve by adding noise to the rawdata followed by another reconstruction. Experienced radiologists will then perform a blinded study where they read images produced without AEC, with the conventional AEC as of today, and with our new deep AEC algorithm.