Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstr. 55, 45147, Essen, Germany.
Department of Thoracic and Cardiovascular Surgery, West German Heart and Vascular Centre Essen, University Hospital Essen, 45147, Essen, Germany.
Eur Radiol. 2024 Jan;34(1):411-421. doi: 10.1007/s00330-023-09971-9. Epub 2023 Aug 8.
Cardiac computed tomography (CT) is essential in diagnosing coronary heart disease. However, a disadvantage is the associated radiation exposure to the patient which depends in part on the scan range. This study aimed to develop a deep neural network to optimize the delimitation of scan ranges in CT localizers to reduce the radiation dose.
On a retrospective training cohort of 1507 CT localizers randomly selected from calcium scoring and angiography scans and acquired between 2010 and 2017, optimized scan ranges were delimited by two radiologists in consensus. A neural network was trained to reproduce the scan ranges and was tested on two randomly selected and independent validation cohorts: an internal cohort of 233 CT localizers (January 2018-June 2020) and an external cohort from a nearby hospital of 298 CT localizers (July 2020-December 2020). Localizers where a bypass surgery was visible were excluded. The effective radiation dose to the patient was simulated using a Monte Carlo simulation. Scan ranges of radiographers, radiologists, and the network were compared using an equivalence test; likewise, the reduction in effective dose was tested using a superior test.
The network replicated the radiologists' scan ranges with a Dice score of 96.5 ± 0.02 (p < 0.001, indicating equivalence). The generated scan ranges resulted in an effective dose reduction of 10.0% (p = 0.002) in the internal cohort and 12.6% (p < 0.001) in the external cohort compared to the scan ranges delimited by radiographers in clinical routine.
Automatic delimitation of the scan range can result in a radiation dose reduction to the patient.
Fully automated delimitation of the scan range using a deep neural network enables a significant reduction in radiation exposure during CT coronary angiography compared to manual examination planning. It can also reduce the workload of the radiographers.
• Scan range delimitation for coronary computed tomography angiography could be performed with high accuracy by a deep neural network. • Automated scan ranges showed a high agreement of 96.5% with the scan ranges of radiologists. • Using a Monte Carlo simulation, automated scan ranges reduced the effective dose to the patient by up to 12.6% (0.9 mSv) compared to the scan ranges of radiographers in clinical routine.
心脏计算机断层扫描（CT）在诊断冠心病方面至关重要。然而，其缺点之一是患者会受到辐射，而辐射量部分取决于扫描范围。本研究旨在开发一种深度学习神经网络，以优化 CT 定位器的扫描范围，从而降低辐射剂量。
在 2010 年至 2017 年间，从钙评分和血管造影扫描中随机选择了 1507 例 CT 定位器的回顾性训练队列，由两名放射科医生共同确定优化的扫描范围。然后，使用神经网络来复制扫描范围，并在两个随机选择的独立验证队列上进行测试：内部队列包含 233 例 CT 定位器（2018 年 1 月至 2020 年 6 月），外部队列包含来自附近医院的 298 例 CT 定位器（2020 年 7 月至 2020 年 12 月）。排除可见旁路手术的定位器。使用蒙特卡罗模拟模拟患者的有效辐射剂量。使用等效性检验比较放射技师、放射科医生和网络的扫描范围；同样，使用优效性检验测试有效剂量的降低。
该网络复制了放射科医生的扫描范围，Dice 评分达到 96.5±0.02（p<0.001，表明等效性）。与临床常规中由放射技师确定的扫描范围相比，生成的扫描范围使内部队列的有效剂量降低了 10.0%（p=0.002），使外部队列的有效剂量降低了 12.6%（p<0.001）。
自动确定扫描范围可降低患者的辐射剂量。
与手动检查规划相比，使用深度神经网络自动确定冠状动脉 CT 血管造影的扫描范围可显著降低辐射暴露。它还可以减轻放射技师的工作量。
• 深度神经网络可以非常准确地对冠状动脉计算机断层扫描血管造影进行扫描范围界定。• 自动化扫描范围与放射科医生的扫描范围具有 96.5%的高度一致性。• 使用蒙特卡罗模拟，与临床常规中由放射技师确定的扫描范围相比，自动扫描范围可将患者的有效剂量降低多达 12.6%（0.9 mSv）。
