Ravi Arpitha, Bernhardt Philipp, Hoffmann Mathis, Kordon Florian, Bayer Siming, Achenbach Stephan, Maier Andreas
Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), 91058, Erlangen, Germany.
Siemens Healthineers AG, 91301, Forchheim, Germany.
Sci Rep. 2025 Apr 30;15(1):15186. doi: 10.1038/s41598-025-99651-z.
Optimal selection of X-ray imaging parameters is crucial in coronary angiography and structural cardiac procedures to ensure optimal image quality and minimize radiation exposure. These anatomydependent parameters are organized into customizable organ programs, but manual selection of the programs increases workload and complexity. Our research introduces a deep learning algorithm that autonomously detects three target anatomies:the left coronary artery (LCA), right coronary artery (RCA), and left ventricle (LV),based on singleX-ray frames without vessel structure and enables adjustment of imaging parameters by choosing the appropriate organ program. We compared three deep-learning architectures: ResNet-50 for image data, a Multilayer Perceptron (MLP) for angulation data, and a multimodal approach combining both. The dataset for training and validation included 275 radiographic sequences from clinical examinations, incorporating coronary angiography, left ventriculography, and corresponding C-arm angulation, using only the first non-contrast frame of the sequence for the possibility of adapting the system before the actual contrast injection. The dataset was acquired from multiple sites, ensuring variation in acquisition and patient statistics. An independent test set of 146 sequences was used for evaluation. The multimodal model outperformed the others, achieving an average F1 score of 0.82 and an AUC of 0.87, matching expert evaluations. The model effectively classified cardiac anatomies based on pre-contrast angiographic frames without visible coronary or ventricular structures. The proposed deep learning model accurately predicts cardiac anatomy for cine acquisitions, enabling the potential for quick and automatic selection of imaging parameters to optimize image quality and reduce radiation exposure. This model has the potential to streamline clinical workflows, improve diagnostic accuracy, and enhance safety for both patients and operators.
在冠状动脉造影和心脏结构手术中，优化X射线成像参数对于确保最佳图像质量和最小化辐射暴露至关重要。这些依赖解剖结构的参数被组织成可定制的器官程序，但手动选择程序会增加工作量和复杂性。我们的研究引入了一种深度学习算法，该算法基于无血管结构的单X射线帧自主检测三个目标解剖结构：左冠状动脉（LCA）、右冠状动脉（RCA）和左心室（LV），并通过选择合适的器官程序来调整成像参数。我们比较了三种深度学习架构：用于图像数据的ResNet-50、用于角度数据的多层感知器（MLP）以及结合两者的多模态方法。用于训练和验证的数据集包括来自临床检查的275个射线照相序列，涵盖冠状动脉造影、左心室造影以及相应的C形臂角度，仅使用序列的第一个非对比帧，以便在实际注射造影剂之前使系统适应。该数据集从多个站点获取，确保采集和患者统计数据的多样性。使用146个序列的独立测试集进行评估。多模态模型表现优于其他模型，平均F1分数达到0.82，AUC为0.87，与专家评估结果相当。该模型基于无可见冠状动脉或心室结构的造影前血管造影帧有效地对心脏解剖结构进行分类。所提出的深度学习模型能够准确预测电影采集的心脏解剖结构，具有快速自动选择成像参数以优化图像质量和减少辐射暴露的潜力。该模型有可能简化临床工作流程，提高诊断准确性，并增强患者和操作人员的安全性。
