Abstract：
Objective.Digitally reconstructed radiography (DRR) plays an important role in the registration of intraoperative x-ray and preoperative CT images. However, existing DRR algorithms often neglect the critical isocentric fixed angle irradiation (IFAI) principle in C-arm imaging, resulting in inaccurate simulation of x-ray images. This limitation degrades registration algorithms relying on DRR image libraries or employing DRR images (DRRs) to train neural network models. To address this issue, we propose a novel IFAI-based DRR method that accurately captures the true projection transformation during x-ray imaging of the human body.Approach.By strictly adhering to the IFAI principle and utilizing known parameters from intraoperative x-ray images paired with CT scans, our method successfully simulates the real projection transformation and generates DRRs that closely resemble actual x-ray images.Main result.Experimental results validate the effectiveness of our IFAI-based DRR method by successfully registering intraoperative x-ray images with preoperative CT images from multiple patients who underwent thoracic endovascular aortic procedures.Significance. The proposed IFAI-based DRR method enhances the quality of DRR images, significantly accelerates the construction of DRR image libraries, and thereby improves the performance of x-ray and CT image registration. Additionally, the method has the generality of registering CT and x-ray images generated by large C-arm devices.
摘要：
目的：数字重建射线照相（DRR）在术中X射线和术前CT图像的配准中起着重要作用。然而,现有的DRR算法往往忽略了C臂成像中的临界等中心固定角度照射(IFAI)原理,导致X射线图像模拟不准确。这种限制降低了依赖于DRR图像库或采用DRR图像(DRR)来训练神经网络模型的配准算法。为了解决这个问题，我们提出了一种新颖的基于IFAI的DRR方法，该方法可以在人体X射线成像过程中准确捕获真实的投影变换。
方法。通过严格遵守IFAI原则，并利用术中X射线图像与CT扫描配对的已知参数，我们的方法成功地模拟了真实的投影变换，并生成了与实际X射线图像非常相似的DRR。
主要结果。实验结果通过成功地将术中X射线图像与术前CT图像配准，验证了我们基于IFAI的DRR方法的有效性。
意义。提出的基于IFAI的DRR方法提高了DRR图像的质量，大大加快了DRR图像库的建设，从而提高了X射线和CT图像配准的性能。此外,该方法具有配准大型C形臂设备产生的CT和X射线图像的通用性。
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