Geometrically variable three‐dimensional ultrasound for mechanically assisted image‐guided therapy of focal liver cancer tumors

Abstract

Purpose Image‐guided focal ablation procedures are first‐line therapy options in the treatment of liver cancer tumors that provide advantageous reductions in patient recovery times and complication rates relative to open surgery. However, extensive physician training is required and image guidance variabilities during freehand therapy applicator placement limit the sufficiency of ablation volumes and the overall potential of these procedures. We propose the use of three‐dimensional ultrasound (3D US) to provide guidance and localization of therapy applicators, augmenting current ablation therapies without the need for specialized procedure suites. We have developed a novel scanning mechanism for geometrically variable 3D US images, a mechanical tracking system, and a needle applicator insertion workflow using a custom needle applicator guide for targeted image‐guided procedures. Methods A three‐motor scanner was designed to use any commercially available US probe to generate accurate, consistent, and geometrically variable 3D US images. The designed scanner was mounted on a counterbalanced stabilizing and mechanical tracking system for determining the US probe orientation, which was assessed using optical tracking. Further exploiting the utility of the motorized scanner, an image‐guidance workflow was developed that moved the probe to any identified target within an acquired 3D US image. The complete 3D US guidance system was used to perform mock targeted interventional procedures on a phantom by selecting a target in a 3D US image, navigating to the target, and performing needle insertion using a custom 3D‐printed needle applicator guide. Registered postinsertion 3D US images and cone‐beam computed tomography (CBCT) images were used to evaluate tip targeting errors when using the motors, tracking system, or mixed navigation approaches. Two 3D US image geometries were investigated to assess the accuracy of a small‐footprint tilt approach and a large field‐of‐view hybrid approach for a total of 48 targeted needle insertions. 3D US image quality was evaluated in a healthy volunteer and compared to a commercially available matrix array US probe. Results A mean positioning error of 1.85 ± 1.33 mm was observed when performing compound joint manipulations with the mechanical tracking system. A combined approach for navigation that incorporated the motorized movement and the in‐plane tracking system corrections performed the best with a mean tip error of 3.77 ± 2.27 mm and 4.27 ± 2.47 mm based on 3D US and CBCT images, respectively. No significant differences were observed between hybrid and tilt image acquisition geometries with all mean registration errors ≤1.2 mm. 3D US volunteer images resulted in clear reconstruction of clinically relevant anatomy. Conclusions A mechanically tracked system with geometrically variable 3D US provides a utility that enables enhanced applicator guidance, placement verification, and improved clinical workflow during focal liver tumor ablation procedures. Evaluations of the tracking accu...

Feasibility of fusing three‐dimensional transabdominal and transrectal ultrasound images for comprehensive intraoperative visualization of gynecologic brachytherapy applicators

“…The 3D TAUS phantom images were acquired using a similar system to that developed by Gillies et al 40 . for liver interventional procedures (Figure 2(b)).…”

“…The 3D TAUS phantom images were acquired using a similar system to that developed by Gillies et al 40 for liver interventional procedures (Figure 2(b)). The scanner was positioned and held by hand during the 8-s acquisition.…”

Spatially tracked whole‐breast three‐dimensional ultrasound system toward point‐of‐care breast cancer screening in high‐risk women with dense breasts

“…To perform tracking validation, a custom-designed optically tracked stylus (Figure 2b) was mounted in place of the 3DUS scanner with its tip positioned at the location of the probe's face and centerline. [54][55][56] The wrist joint assembly's three rotational DOF (J 4-6 ) were first evaluated by sampling the optically and spatially tracked 3D coordinate position at various positions, while manipulating only the wrist joint. 54 The acquired optically and spatially tracked data (3D coordinate positions) were plotted in 3D coordinate space then fit to a sphere using MATLAB R2018b (MathWorks, Natick, Massachusetts, USA) to compute the wrist joint assembly's spherical radius.…”

“…[54][55][56] The wrist joint assembly's three rotational DOF (J 4-6 ) were first evaluated by sampling the optically and spatially tracked 3D coordinate position at various positions, while manipulating only the wrist joint. 54 The acquired optically and spatially tracked data (3D coordinate positions) were plotted in 3D coordinate space then fit to a sphere using MATLAB R2018b (MathWorks, Natick, Massachusetts, USA) to compute the wrist joint assembly's spherical radius. The assembly's spherical radius, corresponding to the stylus tip position, was determined in both optically and spatially tracked sampling methods, and the mean, standard deviation (SD), and percent error (%) to the actual machined wrist joint radius were calculated.…”

“…Similarly, d(q 1 , q 2 ) is the Euclidean distance between the corresponding point q 1 , and the second point q 2 , as determined by the optically tracked system. 54 The tracking error was calculated three times (N = 3) per joint with the two recorded 3D positions obtained at variable locations (distances) within each joint's range of motion, approximately between [40,140] • for the translational joints J 1-3 , and between [−90, 90] • for the rotational joints J 4-6 on the wrist joint's spherical assembly. After all independent joint absolute tracking errors were assessed and deemed sufficiently accurate (i.e., E d < 1.0 mm), compound motions exercising all joints (J 1-6 ) were tested simultaneously across the intended, clinically accessible workspace for bedside imaging,and the mean error (SD) was computed in each workspace case, relative to the home position of the manipulator.…”