Microrobots (MRs) have attracted growing interest Q1 Q2 6 for their potentialities in diagnosis and noninvasive intervention in 7 hard-to-reach body areas. The safe operation of biomedical MRs 8 requires fine control capabilities, which strongly depend on precise 9 and robust feedback about their position over time. Ultrasound 10 acoustic phase analysis (US-APA) may allow for a reliable feedback 11 strategy for MR imaging and tracking in tissue. In this article, we 12 combine task-specific magnetic actuation and related US-APA mo-13 tion tracking to achieve closed-loop navigation of a magnetic MR, 14 rolling on the boundary of a lumen in a tissue-mimicking phantom. 15 A C-arm system attached to a robotic platform is used to precisely 16 position the magnetic actuation source and US-APA detection unit 17 within the workspace, thus enabling MR visual-servoing. In the 18 first place, the proposed approach allows to perform supervised lo-19 calization of the MR without any a-priori knowledge of its position. 20 After localization, a robust real-time tracking enables closed-loop 21 MR teleoperation in the phantom lumina over a travel distance of 22 80 mm (145 body lengths), both in static and counter flow, thus 23 achieving an average position tracking error of 368 micron (0.67 24 body lengths). For the first time, our results validate US-APA as 25 a reliable feedback strategy for visual-servoing control of MRs in Q3 26 simulated in-body environment. Q4 27 Index Terms-Acoustic phase analysis (APA), closed-loop 28 control, magnetic actuation, medical microrobots (MRs), 29 ultrasound (US) imaging, visual-servoing. 30 I. INTRODUCTION 31 M icrorobots (MRS) for biomedical applications hold the 32 potential to revolutionize diagnosis and therapy, thanks33
Transcarotid Artery Revascularization (TCAR) is typically performed by manual catheter insertion and implies radiation exposure for both the patient and the surgeon. Taking advantage from robotics and artificial intelligence (AI), this letter presents a robotic ultrasound (RUS) platform for improving the procedure. To this purpose, ultrasound (US) imaging is considered both in the pre-operative stage for procedure planning and in the intra-operative stage to track a catheter. 3D vascular volumes can be precisely reconstructed from sequences of 2D images exploiting robotic probe manipulation and AI-based image analysis. The method proved a median reconstruction error lower than 1 mm. Pre-operative information are mapped to the intra-operative scenario thanks to a US-based registration routine. The automatic probe alignment on the target vessel demonstrated to be as precise as 0.84°. The reconstructed 3D model can be exploited to automatically generate a catheter trajectory based on user inputs. Such trajectory enabled automatic insertion of a magnetic catheter steered by an external permanent magnet actuated by the RUS platform. Our results demonstrate a catheter tip target reaching error of 3.3 mm. We believe that these results can open the way for the introduction of robotics and AI in TCAR procedures enabling precise and automatic small-scale intravascular devices control.
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