标题： 放射科医生循环自我训练用于可推广的CT金属伪影减少 显示英文标题

标题： Radiologist-in-the-Loop Self-Training for Generalizable CT Metal Artifact Reduction

摘要： 在计算机断层扫描（CT）图像中的金属伪影会显著降低图像质量并阻碍准确的诊断。 监督式金属伪影减少（MAR）方法，使用模拟数据集进行训练，在真实临床CT图像上的表现往往不佳，这是由于领域差距较大。 尽管最先进的半监督方法使用由先验网络生成的伪真实数据来缓解这个问题，但它们对固定先验的依赖限制了这些伪真实数据的质量和数量，引入了确认偏差并降低了临床适用性。 为了解决这些限制，我们提出了一种新颖的放射科医生反馈循环的自训练框架用于MAR，称为RISE-MAR，该框架可以将放射科医生的反馈整合到半监督学习过程中，逐步提高伪真实数据的质量和数量，从而在真实临床CT图像上实现更好的泛化能力。 为了保证质量，我们引入了一个临床质量评估模型，该模型模拟放射科医生的评估，有效地选择高质量的伪真实数据用于半监督训练。 为了保证数量，我们的自训练框架迭代生成额外的高质量伪真实数据，扩展临床数据集，并进一步提高模型的泛化能力。 在多个临床数据集上的大量实验结果表明，我们的RISE-MAR在泛化性能方面优于最先进的方法，推动了MAR模型在实际应用中的发展。 代码可在 https://github.com/Masaaki-75/rise-mar 获取。 显示英文摘要

摘要： Metal artifacts in computed tomography (CT) images can significantly degrade image quality and impede accurate diagnosis. Supervised metal artifact reduction (MAR) methods, trained using simulated datasets, often struggle to perform well on real clinical CT images due to a substantial domain gap. Although state-of-the-art semi-supervised methods use pseudo ground-truths generated by a prior network to mitigate this issue, their reliance on a fixed prior limits both the quality and quantity of these pseudo ground-truths, introducing confirmation bias and reducing clinical applicability. To address these limitations, we propose a novel Radiologist-In-the-loop SElf-training framework for MAR, termed RISE-MAR, which can integrate radiologists' feedback into the semi-supervised learning process, progressively improving the quality and quantity of pseudo ground-truths for enhanced generalization on real clinical CT images. For quality assurance, we introduce a clinical quality assessor model that emulates radiologist evaluations, effectively selecting high-quality pseudo ground-truths for semi-supervised training. For quantity assurance, our self-training framework iteratively generates additional high-quality pseudo ground-truths, expanding the clinical dataset and further improving model generalization. Extensive experimental results on multiple clinical datasets demonstrate the superior generalization performance of our RISE-MAR over state-of-the-art methods, advancing the development of MAR models for practical application. Code is available at https://github.com/Masaaki-75/rise-mar.