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<p>Vitreoretinal surgery, requiring precise microscale tissue manipulation, is well-suited for robotic assistance. Image registration enhances surgeons’ visual perception by aligning high-resolution preoperative OCT images with the intraoperative environment, improving visibility of anatomical features not seen in microscope images. However, optical distortions from the cornea, lens, eye curvature, and scanning patterns challenge the use of diagnostic data in robotic navigation. This study introduces a novel technique for curvature-corrected retinal registration, integrating diagnostic OCT with instrument-integrated OCT. The pipeline comprises feature extraction, curvature correction, initial alignment, and fine registration. Experiments using an artificial model eye and ex vivo porcine eye validate the method. Curvature correction achieves accuracy comparable to existing methods, with deviations of 17 <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\upmu\)</EquationSource> </InlineEquation>m for the model eye and 460 <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\upmu\)</EquationSource> </InlineEquation>m for the porcine eye. Post-registration, the fiducial marker error reduces to 103 <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(\upmu\)</EquationSource> </InlineEquation>m for the model eye and 318 <InlineEquation ID="IEq4"> <EquationSource Format="TEX">\(\upmu\)</EquationSource> </InlineEquation>m for the porcine eye. Our method provides intraoperative diagnostic context, enabling reliable topological assistance in retinal robotic systems.</p>

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Curvature-corrected retinal registration of diagnostic OCT with instrument-integrated OCT

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月28日

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  • Marius Briel,
  • Ludwig Haide,
  • Michael Meyling,
  • Benjamin Saalfrank,
  • Philipp Matten,
  • Nicola Piccinelli,
  • Gernot Kronreif,
  • Maria Francesca Spadea,
  • Eleonora Tagliabue,
  • Franziska Mathis-Ullrich

摘要

Vitreoretinal surgery, requiring precise microscale tissue manipulation, is well-suited for robotic assistance. Image registration enhances surgeons’ visual perception by aligning high-resolution preoperative OCT images with the intraoperative environment, improving visibility of anatomical features not seen in microscope images. However, optical distortions from the cornea, lens, eye curvature, and scanning patterns challenge the use of diagnostic data in robotic navigation. This study introduces a novel technique for curvature-corrected retinal registration, integrating diagnostic OCT with instrument-integrated OCT. The pipeline comprises feature extraction, curvature correction, initial alignment, and fine registration. Experiments using an artificial model eye and ex vivo porcine eye validate the method. Curvature correction achieves accuracy comparable to existing methods, with deviations of 17 \(\upmu\) m for the model eye and 460 \(\upmu\) m for the porcine eye. Post-registration, the fiducial marker error reduces to 103 \(\upmu\) m for the model eye and 318 \(\upmu\) m for the porcine eye. Our method provides intraoperative diagnostic context, enabling reliable topological assistance in retinal robotic systems.