BACKGROUND:Repeat CT scanning is ubiquitous in many clinical situations, e.g. to follow disease progression, to evaluate treatment efficacy, and to monitor interventional CT procedures. However, it incurs in cumulative radiation to the patient which can be significantly reduced by using a region ofinterest (ROI) and the existing baseline scan. OBJECTIVE:To obtain a high-quality reconstruction of a ROI with a significantly reduced X-ray radiation dosage that accounts for deformations. METHODS:We present a new method for deformable registration and image reconstruction inside an ROI in repeat CT scans with a highly reduced X-ray radiation dose based on sparse scanning. Our method uses the existing baseline scan data, a user-defined ROI, and a new sparse repeat scan to compute a high-quality repeat scan ROI image with a significantly reduced radiation dose. Our method first performs rigid registration between the densely scanned baseline and the sparsely scanned repeat CT scans followed by deformable registration with a low-order parametric model, both in 3D Radon space and without reconstructing the repeat scan image. It then reconstructs the repeat scan ROI without computing the entire repeat scan image. RESULTS:Our experimental results on clinical lung and liver CT scans yield a mean × 14 computation speedup and a × 7.6-12.5 radiation dose reduction, with a minor image quality loss of 0.0157 in the NRMSE metric. CONCLUSION:Our method is considerably faster than existing methods, thereby enabling intraoperative online repeat scanning that it is accurate and accounts for position, deformation, and structure changes at a fraction of the radiation dose required by existing methods.

中文翻译：

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稀疏重复 CT 扫描中的可变形配准和感兴趣区域图像重建。

背景：重复 CT 扫描在许多临床情况中无处不在，例如跟踪疾病进展、评估治疗效果和监测介入性 CT 程序。然而，它会给患者带来累积辐射，这可以通过使用感兴趣区域 (ROI) 和现有基线扫描显着减少。目标：获得高质量的 ROI 重建，显着降低 X 射线辐射剂量，从而解释变形。方法：我们提出了一种新方法，用于在基于稀疏扫描的重复 CT 扫描中在 ROI 内进行可变形配准和图像重建，其中 X 射线辐射剂量大大降低。我们的方法使用现有的基线扫描数据，用户定义的 ROI，以及新的稀疏重复扫描，以计算出辐射剂量显着降低的高质量重复扫描 ROI 图像。我们的方法首先在密集扫描的基线和稀疏扫描的重复 CT 扫描之间执行刚性配准，然后在 3D Radon 空间和不重建重复扫描图像的情况下，使用低阶参数模型进行可变形配准。然后它在不计算整个重复扫描图像的情况下重建重复扫描 ROI。结果：我们对临床肺和肝脏 CT 扫描的实验结果产生了平均 × 14 的计算加速和 × 7.6-12.5 的辐射剂量降低，在 NRMSE 指标中图像质量损失为 0.0157。结论：我们的方法比现有方法快得多，