Abstract
PurposeTo create an accurate 3D reconstruction of the vascular trees, it is necessary to know the exact geometrical parameters of the angiographic imaging system. Many previous studies used vascular structures to estimate the system’s exact geometry. However, utilizing interventional devices and their relative features may be less challenging, as they are unique in different views. We present a semi-automatic self-calibration approach considering the markers attached to the interventional instruments to estimate the accurate geometry of a biplane X-ray angiography system for neuroradiologic use.MethodsA novel approach is proposed to detect and segment the markers using machine learning classification, a combination of support vector machine and boosted tree. Then, these markers are considered as reference points to optimize the acquisition geometry iteratively.ResultsThe method is evaluated on four clinical datasets and three pairs of phantom angiograms. The mean and standard deviation of backprojection error for the catheter or guidewire before and after self-calibration are 7.13pm 6.47 mm and 0.10pm 0.06 mm, respectively. The mean and standard deviation of the 3D root-mean-square error (RMSE) for some markers in the phantom reduced from 0.51pm 0.11 to 0.31pm 0.08 mm.ConclusionA semi-automatic approach to estimate the accurate geometry of the C-arm system was presented. Results show the reduction in the 2D backprojection error as well as the 3D RMSE after using our proposed self-calibration technique. This approach is essential for 3D reconstruction of the vascular trees or post-processing techniques of angiography systems that rely on accurate geometry parameters.
Highlights
Biplane angiography has found increasing use in minimally invasive endovascular interventions to treat different types of aneurysms via coiling or stent placement
The classifier most likely recognizes other marker-like structures such as the tip of the catheter or guidewire, and the location where the catheter or guidewire bends as marker areas leading to false positives (FP)
To better evaluate how optimizing the geometrical parameters affects the 3D reconstruction, we provide the results of backprojection for some known true correspondences in both views
Summary
Biplane angiography has found increasing use in minimally invasive endovascular interventions to treat different types of aneurysms via coiling or stent placement. X-ray projection images lack 3D information of the vascular structures. This can be compensated for with the 3D reconstruction of vessels from a series (or a pair) of digital X-ray images. To reliably reconstruct vascular structures, the exact geometry of the system, including the rotation and translation parameters that relate the two projection views, is needed for each configuration of the C-arm. DICOM image files contain angiographic system parameters, projection matrices directly derived from those parameters may not International Journal of Computer Assisted Radiology and Surgery (2022) 17:1355–1366 accurately describe the spatial relationship between the two views. The recorded gantry parameters may not exactly define the orientations of the C-arm Some main reasons are [1]: 1. The recorded gantry parameters may not exactly define the orientations of the C-arm
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