3D Freehand Ultrasound Reconstruction of the Carotid Artery

Yimeng Dou; Maxwell J Kiernan; Zhiwei Zhang; Carol Mitchell; Amber Possell; Matthew Lee; Tomy Varghese

doi:10.1016/j.ultrasmedbio.2026.03.005

3D Freehand Ultrasound Reconstruction of the Carotid Artery

Ultrasound Med Biol. 2026 May 4:S0301-5629(26)00098-0. doi: 10.1016/j.ultrasmedbio.2026.03.005. Online ahead of print.

Authors

Yimeng Dou¹, Maxwell J Kiernan², Zhiwei Zhang³, Carol Mitchell⁴, Amber Possell⁵, Matthew Lee⁶, Tomy Varghese³

Affiliations

¹ Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI, USA; Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA. Electronic address: ydou8@wisc.edu.
² Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI, USA.
³ Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI, USA; Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
⁴ Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
⁵ Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
⁶ Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.

PMID: 42086379
DOI: 10.1016/j.ultrasmedbio.2026.03.005

Abstract

Objective: Sensorless alignment of two-dimensional (2D) freehand ultrasound scans for three-dimensional US (3DUS) reconstruction offers significant advantages due to its ease of use. Prior approaches have used transducers with motion sensors, which are cumbersome and inconvenient in a clinical setting, linear wobblers, or motorized 2D scanning which suffer from a small field of view (FOV) and low volume acquisition rates.

Method: Freehand transverse B-mode data loops from 20 human volunteers (10 males, 10 females) were used for 3DUS reconstruction Our two-stream Physics inspired Learning-based Prediction of Pose Information (PLPPI) model explicitly integrates and utilizes speckle decorrelation as an inductive bias (temporal information) along with spatial information for alignment using 2D convolutions. A correlation layer then synergizes spatiotemporal cues for freehand frame alignment. A residual neural network (ResNet) predicted the spatial location of the input frames.

Results: PLPPI outperformed baseline deep learning networks (DLN), i.e. 2D CNN, ConvLSTM, and DC²-Net, with a 13% improvement in global pixel reconstruction error, 59.36% improvement in final drift, and 35.74% in final drift rate over the next best DLN, while requiring significantly less Graphics Processing Unit (GPU) memory.

Conclusion: Our model has fewer parameters, requiring less GPU memory to train for freehand 3DUS reconstruction along with a major reduction in computation time (106% speedup and 131% reduction in GPU memory usage) compared to baseline DLN.

Keywords: Carotid Artery; Freehand ultrasound; Reconstruction; Sensorless; Three-dimensional; Trackerless.