论文标题
检测心血管MRIS的呼吸运动伪像，以确保高质量分割
Detecting respiratory motion artefacts for cardiovascular MRIs to ensure high-quality segmentation
论文作者
论文摘要
尽管机器学习方法在其训练领域表现良好，但它们通常在现实世界中往往会失败。在心血管磁共振成像（CMR）中，呼吸运动代表了采集质量以及随后的分析和最终诊断的主要挑战。我们提出了一个工作流程，该工作流程预测CMRXMotion Challenge 2022中CMR中呼吸运动的严重程度评分。这是技术人员在获取过程中立即提供有关CMR质量的反馈的重要工具，因为可以直接重新获得质量质量的图像，而患者仍在病人中可用。因此，我们的方法确保获得的CMR在用于进一步诊断之前达到了特定的质量标准。因此，在严重运动人工制品的情况下，它可以有效地进行适当诊断的有效基础。结合我们的细分模型，这可以通过提供完整的管道来保证适当的质量评估和心血管扫描的真实分段来帮助心脏病专家和技术人员的日常工作。代码库可在https://github.com/meclabtuda/qa_med_data/tree/dev_qa_cmrxmotion获得。
While machine learning approaches perform well on their training domain, they generally tend to fail in a real-world application. In cardiovascular magnetic resonance imaging (CMR), respiratory motion represents a major challenge in terms of acquisition quality and therefore subsequent analysis and final diagnosis. We present a workflow which predicts a severity score for respiratory motion in CMR for the CMRxMotion challenge 2022. This is an important tool for technicians to immediately provide feedback on the CMR quality during acquisition, as poor-quality images can directly be re-acquired while the patient is still available in the vicinity. Thus, our method ensures that the acquired CMR holds up to a specific quality standard before it is used for further diagnosis. Therefore, it enables an efficient base for proper diagnosis without having time and cost-intensive re-acquisitions in cases of severe motion artefacts. Combined with our segmentation model, this can help cardiologists and technicians in their daily routine by providing a complete pipeline to guarantee proper quality assessment and genuine segmentations for cardiovascular scans. The code base is available at https://github.com/MECLabTUDA/QA_med_data/tree/dev_QA_CMRxMotion.
