3D reconstruction of renal vascular tree from micro-CT scans of corrosive endocasts

Kidney surgeries present significant complexity due to the kidneys’ susceptibility to ischemic injury and bleeding. Ensuring precise reconstruction of the renal vascular tree (RVT) is critical for patient safety and surgical outcomes. The analysis encompassed 33 corrosive endocasts as well as 12 3D artificial models, generated, 3D printed, and subjected to micro-CT, to provide both visual comparison and quantitative evaluation of the reconstruction method viability. The efficiency and accuracy of two RVT reconstruction methods, adapting circle rolling and convolution, were compared. The study also investigated the potential enhancement of combining these two methods. Results show that the convolution method offers high accuracy and short computation times. The accuracy of the reconstruction quantitatively evaluated on 3D artificial models provided promising results, i.e. dice coefficient equal to 0.8609 (std=0.0017), and accuracy equal to 0.99677 (std=0.00092). The combined method exhibited the fastest execution time among the three while, the sphere rolling method demonstrated the slowest performance. Moreover, the sphere rolling method duration for the larger data exceeded the feasible time limit. Future work should focus on optimizing the convolution method.Clinical relevance: Kidney surgeries carry inherent risks, including the potential for ischemic injury or bleeding due to the kidneys’ sensitivity to vascular damage during surgical procedures. Precise reconstruction of the renal vascular tree is essential to mitigate these risks.