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

Lewis Howell; Manisha Waterston; Tze Min Wah; James H. Chandler; James R. McLaughlan

cs.CVMar 20, 2026

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

Authors: Lewis Howell, Manisha Waterston, Tze Min Wah, James H. Chandler, James R. McLaughlan

Organizations: School of Computer Science, University of Leeds, Leeds, LS2 9JT · School of Electronic and Electrical Engineering, University of Leeds, Leeds, LS2 9JT · St James’s University Hospital, Leeds Teaching Hospitals NHS Trust, Leeds, LS9 7TF · Leeds Institute of Medical Research, University of Leeds, St James’ University Hospital, Leeds, LS9 7TF

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 & tables

Figure 1: Technical diagram showing hardware devices (grey) and software modules (blue) included as part of the experimental system used for real-time Three-dimensional Ultrasound 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 Ultrasound Quality Assurance in 3-Dimensions 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 Ultrasound Quality Assurance in 3-Dimensions phantom with geometric inclusions is shown in oblique, top, and side views.

	Metric	Value
System Timing	Synchronisation uncertainty	< 0.5 ms
	End-to-end latency	90.2 ± 7.4 ms
	Volume update rate	0.2 s
Calibration and registration	Image-to-transducer RMS error	0.81 ± 0.15 mm
Calibration and registration	Cross-modality RMS registration error	0.19 ± 0.07 mm
Tracking Accuracy	Robot kinematic tracking accuracy	< 0.03 mm

Table 1: System performance, including timing and synchronisation, calibration and registration, and tracking accuracy.

Figure 3: Thresholding-based segmentation of objects in the Ultrasound Quality Assurance in 3-Dimensions 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 Three-dimensional view of live real-time volumetric reconstruction of robotic-tracked Ultrasound in the Ultrasound Quality Assurance in 3-Dimensions phantom. a) Close-up of voxel-based volume reconstruction showing the tracked Ultrasound frame and reconstructed geometry. b) Robot arm and transducer assembly during the scan. c) B-mode image from the Ultrasound 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 Three-dimensional reconstruction, see Supplementary Video.

		Robotic	Optical	EM
Segmentation metrics	DSC-3D	0.94 ± 0.01	0.93 ± 0.01	0.86 ± 0.02
	HD (mm)	1.79 ± 0.37	1.93 ± 0.73	3.87 ± 0.87
	HD95 (mm)	1.17 ± 0.12	1.19 ± 0.12	2.43 ± 0.44
Shape metrics	Volume error (%)	11.80 ± 1.73	11.90 ± 1.47	20.05 ± 4.77
	Surface Area error (%)	8.90 ± 2.33	10.41 ± 6.76	18.10 ± 8.54
	Sphere roundness	0.98 ± 0.01	0.98 ± 0.01	0.95 ± 0.01

Table 2: Baseline scans of the Ultrasound Quality Assurance in 3-Dimensions phantom, using simultaneous tracking with robotic kinematic, optical and electromagnetic (EM) tracking. Fitting metrics provide the following shape measurements: sphere radius (R); ellipsoid semi-major axis (R1) and semi-minor axis (R2) radii; cylinder radius (R) and height (H); and triangular prism length (L) and height (H). All metrics are given as mean

\pm

standard deviation. The error relative to the ground truth is shown in brackets for the fitting metrics.

Figure 5: Three-dimensional reconstruction of objects in Ultrasound Quality Assurance in 3-Dimensions 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.

Figure 6: Ultrasound Quality Assurance in 3-Dimensions phantom construction showing a) fabrication of silicone mould of reference object in Three-dimensional-printed container, b) silicone mould with agar inclusion (before trimming the sprue), c-f) agar sphere, ellipsoid, cylinder, and triangular prism reference objects.

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Institute of Biomedical Manufacturing and Life Quality Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China · Department of Ultrasound in Medicine, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China · Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China

Ultrasound Imaging Object Pose Estimation

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Freehand 3D ultrasound (US) reconstruction promises volumetric imaging with the flexibility of standard 2D probes, yet existing tracking paradigms face a restrictive trilemma: marker-based systems demand prohibitive costs, inside-out methods require intrusive sensor attachment, and sensorless approaches suffer from severe cumulative drift. To overcome these limitations, we present MLRecon, a robust markerless 3D US reconstruction framework delivering drift-resilient 6D probe pose tracking using a single commodity RGB-D camera. Leveraging the generalization power of vision foundation models, our pipeline enables continuous markerless tracking of the probe, augmented by a vision-guided divergence detector that autonomously monitors tracking integrity and triggers failure recovery to ensure uninterrupted scanning. Crucially, we further propose a dual-stage pose refinement network that explicitly disentangles high-frequency jitter from low-frequency bias, effectively denoising the trajectory while maintaining the kinematic fidelity of operator maneuvers. Experiments demonstrate that MLRecon significantly outperforms competing sensorless and sensor-aided methods, achieving average position errors as low as 0.88 mm on complex trajectories and yielding high-quality 3D reconstructions with sub-millimeter mean surface accuracy. This establishes a new benchmark for low-cost, accessible volumetric US imaging in resource-limited clinical settings.

Abstract

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