Erasmus MC, University Medical Center, Rotterdam, The Netherlands.
Maastricht University Medical Center, Maastricht, The Netherlands.
Int J Comput Assist Radiol Surg. 2024 Jan;19(1):147-150. doi: 10.1007/s11548-023-02999-8. Epub 2023 Jul 17.
Our aim is to automatically align digital subtraction angiography (DSA) series, recorded before and after endovascular thrombectomy. Such alignment may enable quantification of procedural success.
Firstly, we examine the inherent limitations for image registration, caused by the projective characteristics of DSA imaging, in a representative set of image pairs from thrombectomy procedures. Secondly, we develop and assess various image registration methods (SIFT, ORB). We assess these methods using manually annotated point correspondences for thrombectomy image pairs.
Linear transformations that account for scale differences are effective in aligning DSA sequences. Two anatomical landmarks can be reliably identified for registration using a U-net. Point-based registration using SIFT and ORB proves to be most effective for DSA registration and are applicable to recordings for all patient sub-types. Image-based techniques are less effective and did not refine the results of the best point-based registration method.
We developed and assessed an automated image registration approach for cerebral DSA sequences, recorded before and after endovascular thrombectomy. Accurate results were obtained for approximately 85% of our image pairs.
我们的目的是自动对齐血管内血栓切除术前后记录的数字减影血管造影（DSA）系列。这种对齐方式可能能够量化手术的成功程度。
首先，我们在一组有代表性的血栓切除术图像对中检查由于 DSA 成像的投影特性而导致的图像配准的固有局限性。其次，我们开发并评估了各种图像配准方法（SIFT、ORB）。我们使用手动注释的血栓切除术图像对的点对应关系来评估这些方法。
线性变换可以有效地对齐 DSA 序列，这些变换考虑到了尺度差异。使用 U-net 可以可靠地识别用于注册的两个解剖学标记点。使用 SIFT 和 ORB 的基于点的配准对于 DSA 配准最有效，并且适用于所有患者亚型的记录。基于图像的技术效果较差，并且没有改进最佳基于点的配准方法的结果。
我们开发并评估了一种用于血管内血栓切除术前后记录的脑 DSA 序列的自动图像配准方法。我们的图像对中约有 85%的结果是准确的。
