Saderi Moujan, Patel Jaykumar H, Sheagren Calder D, Csőre Judit, Roy Trisha L, Wright Graham A
Department of Medical Biophysics, University of Toronto, Toronto, Canada.
Physical Sciences, Sunnybrook Research Institute, Toronto, Canada.
Int J Comput Assist Radiol Surg. 2025 Apr;20(4):753-763. doi: 10.1007/s11548-024-03302-z. Epub 2025 Jan 5.
During endovascular revascularization interventions for peripheral arterial disease, the standard modality of X-ray fluoroscopy (XRF) used for image guidance is limited in visualizing distal segments of infrapopliteal vessels. To enhance visualization of arteries, an image registration technique was developed to align pre-acquired computed tomography (CT) angiography images and to create fusion images highlighting arteries of interest.
X-ray image metadata capturing the position of the X-ray gantry initializes a multiscale iterative optimization process, which uses a local-variance masked normalized cross-correlation loss to rigidly align a digitally reconstructed radiograph (DRR) of the CT dataset with the target X-ray, using the edges of the fibula and tibia as the basis for alignment. A precomputed library of DRRs is used to improve run-time, and the six-degree-of-freedom optimization problem of rigid registration is divided into three smaller sub-problems to improve convergence. The method was tested on a dataset of paired cone-beam CT (CBCT) and XRF images of ex vivo limbs, and registration accuracy at the midline of the artery was evaluated.
On a dataset of CBCTs from 4 different limbs and a total of 17 XRF images, successful registration was achieved in 13 cases, with the remainder suffering from input image quality issues. The method produced average misalignments of less than 1 mm in horizontal projection distance along the artery midline, with an average run-time of 16 s.
The sub-mm spatial accuracy of artery overlays is sufficient for the clinical use case of identifying guidewire deviations from the path of the artery, for early detection of guidewire-induced perforations. The semiautomatic workflow and average run-time of the algorithm make it feasible for integration into clinical workflows.
在针对外周动脉疾病的血管内血运重建干预过程中，用于图像引导的标准X射线荧光透视（XRF）模式在可视化腘下血管的远端节段方面存在局限性。为了增强动脉的可视化效果，开发了一种图像配准技术，用于对齐预先获取的计算机断层扫描（CT）血管造影图像，并创建突出显示感兴趣动脉的融合图像。
捕获X射线机架位置的X射线图像元数据初始化了一个多尺度迭代优化过程，该过程使用局部方差掩码归一化互相关损失，以腓骨和胫骨的边缘为对齐基础，将CT数据集的数字重建射线照片（DRR）与目标X射线进行刚性对齐。使用预先计算的DRR库来提高运行时效率，并将刚性配准的六自由度优化问题分解为三个较小的子问题以提高收敛性。该方法在离体肢体的成对锥束CT（CBCT）和XRF图像数据集上进行了测试，并评估了动脉中线处的配准精度。
在来自4个不同肢体的CBCT数据集和总共17张XRF图像上，13例实现了成功配准，其余病例存在输入图像质量问题。该方法在沿动脉中线的水平投影距离上产生的平均错位小于1毫米，平均运行时间为16秒。
动脉叠加的亚毫米空间精度足以用于识别导丝与动脉路径偏差的临床用例，以便早期检测导丝引起的穿孔。该算法的半自动工作流程和平均运行时间使其能够集成到临床工作流程中。
