A Unified Platform and Quality Assurance Framework for 3D Ultrasound Reconstruction with Robotic, Optical, and Electromagnetic Tracking

Abstract

Three-dimensional (3D) Ultrasound (US) can facilitate diagnosis, treatment planning, and image-guided therapy. However, current studies rarely provide a comprehensive evaluation of volumetric accuracy and reproducibility, highlighting the need for robust Quality Assurance (QA) frameworks, particularly for tracked 3D US reconstruction using freehand or robotic acquisition. This study presents a QA framework for 3D US reconstruction and a flexible open source platform for tracked US research. A custom phantom containing geometric inclusions with varying symmetry properties enables straightforward evaluation of optical, electromagnetic, and robotic kinematic tracking for 3D US at different scanning speeds and insonation angles. A standardised pipeline performs real-time segmentation and 3D reconstruction of geometric targets (DSC = 0.97, FPS = 46) without GPU acceleration, followed by automated registration and comparison with ground-truth geometries. Applying this framework showed that our robotic 3D US achieves state-of-the-art reconstruction performance (DSC-3D = 0.94 +- 0.01, HD95 = 1.17 +- 0.12), approaching the spatial resolution limit imposed by the transducer. This work establishes a flexible experimental platform and a reproducible validation methodology for 3D US reconstruction. The proposed framework enables robust cross-platform comparisons and improved reporting practices, supporting the safe and effective clinical translation of 3D ultrasound in diagnostic and image-guided therapy applications.

Figures (6)

• Figure 1: Technical diagram showing hardware devices (grey) and software modules (blue) included as part of the experimental system used for real-time 3D US reconstruction. The OpenIGTLink protocol was used to exchange data between modules (dashed arrows). The data flow within modules (thin solid arrows) and connections between hardware devices are also shown (thick solid arrows).
• Figure 2: Experimental setup for experiments with US-QA-3D phantom, showing the robotic arm, ultrasound transducer, tracking assembly, water tank with geometric phantom, optical cameras, and electromagnetic field generator. The co-robotic arm was used for navigation. An EM sensor and an optical tracking frame enable simultaneous multimodal tracking alongside the robot's kinematics. The reference frames for the robot flange ($\textit{XYZ}_\textit{flange}$), transducer ($\textit{XYZ}_\textit{trasnducer}$), and phantom ($\textit{XYZ}_\textit{phantom}$) are shown. The US-QA-3D phantom with geometric inclusions is shown in oblique, top, and side views.
• Figure 3: Thresholding-based segmentation of objects in the US-QA-3D phantom. Comparison shows a) B-mode images, b) B-mode images with overlaid prediction mask, and c) B-mode images with overlaid ground-truth reference mask.
• Figure 4: Dual 3D view of live real-time volumetric reconstruction of robotic-tracked US in the US-QA-3D phantom. a) Close-up of voxel-based volume reconstruction showing the tracked US frame and reconstructed geometry. b) Robot arm and transducer assembly during the scan. c) B-mode image from the US machine showing a cross-section of the triangular prism inclusion, which appears anechoic compared to the background. d) B-mode image with segmentation overlay from the thresholding algorithm. For a video of the live 3D reconstruction, see Supplementary Video.
• Figure 5: 3D reconstruction of objects in US-QA-3D phantom for different tracking modalities (robotic, optical, and electromagnetic). a) Heat maps show the surface deviation error compared with co-registered reference objects. b) Mean DSC across shapes at different scanning speeds. c) Mean DSC at varying insonation angles. Error bars show the standard deviation across three repeats. *These experiments required multi-sweep (compound) scanning.