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Three-Dimensional Reconstruction for the Whole Lung with Early Multiple Pulmonary Nodules
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LOTUS: Latent Outpainting Diffusion Model for Three-Dimensional Ultrasound Stitching.

Xing Yao1, Runxuan Yu1, Nick DiSanto1

  • 1Vanderbilt University.

Proceedings of Machine Learning Research
|July 9, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces LOTUS, a new AI model for improving 3D ultrasound (3DUS) image stitching. LOTUS enhances the field-of-view (FOV) by transforming images, leading to more accurate 3D reconstructions.

Keywords:
Latent Diffusion ModelOutpaintingRegistrationUltrasound

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • 3D ultrasound (3DUS) stitching enlarges field-of-view (FOV) by registering overlapping images.
  • Standard registration methods struggle with 3DUS due to sector-shaped FOV and local minima.
  • Existing methods face challenges in accurately stitching 3D ultrasound data.

Purpose of the Study:

  • To develop a novel method for 3D ultrasound field-of-view (FOV) outpainting.
  • To address limitations of standard registration algorithms in 3DUS stitching.
  • To improve the accuracy and efficiency of extending 3DUS FOV.

Main Methods:

  • Proposed LOTUS, a Latent Diffusion Model (LDM) tailored for 3DUS FOV outpainting.
  • Encoded 3DUS data into a compact latent space for processing.
  • Performed outpainting at test time to extend sector-shaped FOV to rectangular shape.

Main Results:

  • LOTUS effectively mitigated local minima issues inherent in the original FOV shape.
  • Demonstrated significant improvements in registration accuracy compared to existing models.
  • Showcased enhanced efficiency in the 3DUS outpainting process.

Conclusions:

  • LOTUS offers a robust and efficient solution for 3D ultrasound image stitching.
  • The novel LDM approach successfully extends the FOV, improving registration accuracy.
  • This method facilitates better 3DUS data utilization by overcoming FOV limitations.