Surface-based registration between CT and US for image-guided percutaneous renal access - a feasibility study

Purpose: As a crucial step in accessing the kidney in several minimally invasive interventions, percutaneous renal access (PRA) practicality and safety may be improved through the fusion of computed tomography (CT) and ultrasound (US) data. This work aims to assess the potential of a surface-based registration technique and establish an optimal US acquisition protocol to fuse 2D US and CT data for image-guided PRA. Methods: Ten porcine kidney phantoms with fiducial markers were imaged using CT and 3D US. Both images were manually segmented and aligned. In a virtual environment, 2D contours were extracted by slicing the 3D US kidney surfaces and using usual PRA US-guided views, while the 3D CT kidney surfaces were transformed to simulate positional variability. Surface-based registration was performed using two methods of the iterative closest point algorithm (point-to-point, ICP1; and point-to-plane, ICP2), while four acquisition variants were studied: i) use of single-plane (transverse, SPT; or longitudinal, SPL) vs. bi-plane views (BP); ii) use of different kidney’s coverage ranges acquired by a probe’s sweep; iii) influence of sweep movements; and iv) influence of the spacing between consecutive slices acquired for a specific coverage range. Results: view showed the best performance (TRE = 2.26 mm) when ICP2 method, a wide kidney coverage range (20º, with slices spaced by 5º), and a large sweep along the central longitudinal view were used, showing a statistically similar performance (p = .097) to a full 3D US surface registration (TRE = 2.28 mm). Conclusions: An optimal 2D US acquisition protocol was evaluated. Surface-based registration, using multiple slices and specific sweep movements and views, is here suggested as a valid strategy for intraoperative image fusion using CT and US data, having the potential to be applied to other image modalities and/or interventions.This work has been funded by FEDER funds, through the Competitiveness Factors Operational Programme (COMPETE), and by National funds, through the Foundation for Science and Technology (FCT), under the scope of the project POCI-01-0145-FEDER-007038; and by the project NORTE-01- 0145-FEDER-000013, supported by the Northern Portugal Regional Operational Programme (NORTE 2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (FEDER). João Gomes-Fonseca was funded by FCT under the Ph.D. grant PD/BDE/113597/2015. The funders had no role in study design, data collection, and analysis, decision to publish, or preparation of the manuscrip