Dark-field radiography of the human chest is a promising novel imaging technique with the potential of becoming a valuable tool for the early diagnosis of chronic obstructive pulmonary disease and other diseases of the lung. The large field-of-view needed for clinical purposes could recently be achieved by a scanning system. While this approach overcomes the limited availability of large area grating structures, it also results in a prolonged image acquisition time, leading to concomitant motion artifacts caused by intrathoracic movements (e.g. the heartbeat). Here we report on a motion artifact reduction algorithm for a dark-field X-ray scanning system, and its successful evaluation in a simulated chest phantom and human in vivo chest X-ray dark-field data. By partitioning the acquired data into virtual scans with shortened acquisition time, such motion artifacts may be reduced or even fully avoided. Our results demonstrate that motion artifacts (e.g. induced by cardiac motion or diaphragmatic movements) can effectively be reduced, thus significantly improving the image quality of dark-field chest radiographs.

中文翻译：

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人体胸部暗场放射摄影中运动伪影的校正


人体胸部暗场放射成像是一种有前途的新型成像技术，有可能成为慢性阻塞性肺病和其他肺部疾病的早期诊断的有价值的工具。临床用途所需的大视野最近可以通过扫描系统实现。虽然这种方法克服了大面积光栅结构的有限可用性，但它也导致图像采集时间延长，导致胸腔内运动（例如心跳）引起的伴随运动伪影。在这里，我们报告了一种用于暗场 X 射线扫描系统的运动伪影减少算法，及其在模拟胸部模型和人体体内胸部 X 射线暗场数据中的成功评估。通过将采集的数据划分为虚拟扫描并缩短采集时间，可以减少甚至完全避免这种运动伪影。我们的结果表明，运动伪影（例如由心脏运动或膈肌运动引起的）可以有效减少，从而显着提高暗场胸部X光片的图像质量。