Machine learning-based approaches outperform competing methods in most
disciplines relevant to diagnostic radiology. Interventional radiology,
however, has not yet benefited substantially from the advent of deep learning,
in particular because of two reasons: 1) Most images acquired during the
procedure are never archived and are thus not available for learning, and 2)
even if they were available, annotations would be a severe challenge due to the
vast amounts of data. When considering fluoroscopy-guided procedures, an
interesting alternative to true interventional fluoroscopy is in silico
simulation of the procedure from 3D diagnostic CT. In this case, labeling is
comparably easy and potentially readily available, yet, the appropriateness of
resulting synthetic data is dependent on the forward model. In this work, we
propose DeepDRR, a framework for fast and realistic simulation of fluoroscopy
and digital radiography from CT scans, tightly integrated with the software
platforms native to deep learning. We use machine learning for material
decomposition and scatter estimation in 3D and 2D, respectively, combined with
analytic forward projection and noise injection to achieve the required
performance. On the example of anatomical landmark detection in X-ray images of
the pelvis, we demonstrate that machine learning models trained on DeepDRRs
generalize to unseen clinically acquired data without the need for re-training
or domain adaptation. Our results are promising and promote the establishment
of machine learning in fluoroscopy-guided procedures.

本研究提出了一种基于机器学习的用于快速、真实模拟 CT 扫描的衍射、数字放射成像模拟的框架，并通过在盆骨的影像检测中的应用展示了模型无需重新训练和领域适应即可推广到未知的临床数据的良好表现。