Surgical planning assistance in keyhole and percutaneous surgery: A systematic review

Scorza, Davide; Hadji, Sara El; Cortés, Camilo; Bertelsen, Álvaro; Cardinale, Francesco; Baselli, Giuseppe; Essert, Caroline; Momi, Elena De

doi:10.1016/j.media.2020.101820

Cited by 15 publications

(1 citation statement)

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“…Beyond a purely morphological angiography, the separation of arteries and veins (A/V) can be extremely advantageous in several clinical procedures, with a major impact on surgical planning and navigation (Kruger et al 2018, Tomasi et al 2020). For example, in keyhole neurosurgery procedures such as StereoElectroEncephaloGraphy (SEEG), hemorrhage is one of the most commonly occurring complication, which makes recognition and safeguarding of arteries a major requirement (Nowell et al 2015, Gilard et al 2016, Gonzalez-Martınez et al 2016, Mullin et al 2016, Minkin et al 2017, Sparks et al 2017, Neumann et al 2019, Vakharia et al 2018, Scorza et al 2021. Additionally, in procedures such as arteriovenous malformations (AVMs) embolization, clinicians would benefit from the selective visualization of the feeding arteries and the venous drainage network to guarantee major safety and efficacy of the surgical resection (Kaminogo et al 2002, Chen et al 2017, Prada et al 2018.…”

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confidence: 99%

Validation of SART 3.5D algorithm for cerebrovascular dynamics and artery versus vein classification in presurgical 3D digital subtraction angiographies

et al. 2022

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Classification of arteries and veins in cerebral angiograms can increase the safety of neurosurgical procedures, such as StereoElectroEncephaloGraphy, and aid the diagnosis of vascular pathologies, as arterovenous malformations. We propose a new method for vessel classification using the contrast medium dynamics in standard Digital Subtraction Angiography (DSA). After 3D DSA and angiogram segmentation, contrast enhanced projections are processed to suppress soft tissue and bone structures attenuation effect and further enhance the contrast medium flow. For each voxel labelled as vessel, a Time Intensity Curve (TIC) is obtained as a linear combination of temporal basis functions whose weights are addressed by Simultaneous Algebraic Reconstruction Technique (SART 3.5D), expanded to include dynamics. Each TIC is classified by comparing the areas under the curve in the arterial and venous phases. Clustering is applied to optimize the classification thresholds. On a dataset of 60 patients, a median value of sensitivity (90%), specificity (91%), and accuracy (92%) were obtained with respect to manually annotated arterial and venous voxels up to branching order 4-5. Qualitative results are also presented about CM arrival time mapping and its distribution in arteries and veins respectively. In conclusion, this study shows a valuable impact, at no protocol extra-cost or invasiveness, concerning surgical planning related to the enhancement of arteries as major organs at risk. Also, it opens a new scope on the pathophysiology of cerebrovascular dynamics and its anatomical relationships.

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