Ultrasound Volume Reconstruction From Freehand Scans Without Tracking

Guo, Hengtao; Chao, Hanqing; Xu, Sheng; Wood, Bradford J.; Wang, Jing; Yan, Pingkun

doi:10.1109/tbme.2022.3206596

Cited by 10 publications

(1 citation statement)

References 37 publications

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“…Xie et al [10] proposed a pyramid warping layer that exploits the optical flow features in addition to the ultrasound features in order to reconstruct the volume. To enable a smooth 3D reconstruction, a case-wise correlation loss based on 3D CNN and Pearson correlation coefficient was proposed in [10,12]. Qi et al [13] leverages past and future frames to estimate the relative transformation between each pair of the sequence; they used the consistency loss proposed in [14].…”

Section: Introductionmentioning

confidence: 99%

Trackerless Volume Reconstruction from Intraoperative Ultrasound Images

El hadramy,

Verde,

Beaudet

et al. 2023

Lecture Notes in Computer Science

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This paper proposes a method for trackerless ultrasound volume reconstruction in the context of minimally invasive surgery. It is based on a Siamese architecture, including a recurrent neural network that leverages the ultrasound image features and the optical flow to estimate the relative position of frames. Our method does not use any additional sensor and was evaluated on ex vivo porcine data. It achieves translation and orientation errors of 0.449 ± 0.189 mm and 1.3 ± 1.5 degrees respectively for the relative pose estimation. In addition, despite the predominant non-linearity motion in our context, our method achieves a good reconstruction with final and average drift rates of 23.11% and 28.71% respectively. To the best of our knowledge, this is the first work to address volume reconstruction in the context of intravascular ultrasound. Source code of this work is publicly available at https://github.com/Sidaty1/IVUS Trakerless Volume Reconstruction.

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