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<p>2D/3D medical image registration is crucial for image-guided surgery and medical research. Single-view methods often lack depth information, while multiview approaches typically rely on large-scale training datasets, which may not always be available in clinical scenarios. This paper explores the feasibility of multiview registration with insufficient data. We propose an innovative multiview 2D/3D image registration model with three key components: a points of interest (POIs) tracking network based on style transfer (POITT), a triangulation layer, and shape alignment based on branch and bound (SA-BnB). POITT constructs a POIs tracking network on Digital Reconstructed Radiographs images (DRRs) and performs points matching on X-ray images via style transfer. The triangulation layer establishes high-precision spatial mapping relationships between X-ray images and the patient. SA-BnB calculates the optimal transformation matrix between the CT volume and the patient. We collected clinical X-ray and CT data from 8 patients and generated 300 pairs of simulated DRRs from two views for each patient to validate the proposed model. The average fiducial registration error was reduced to 6 mm (less than 10 mm is considered successful) with a 96.67<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2025_10884_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="15" /> </InlineMediaObject> <EquationSource Format="TEX">\(\%\)</EquationSource> </InlineEquation> average registration success rate on DRRs. The average SSIM reached 0.77 between X-ray images and the aligned DRRs. All experimental results demonstrate significant advantages over traditional 2D/3D registration. This study may provide a reliable solution for minimally invasive pelvic surgery navigation. The key code is available at https://github.com/TUYaYa1/Points-of-Interest-tracking-network.</p>

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Multiview 2D/3D image registration in minimally invasive pelvic surgery navigation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月18日

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  • Fujiao Ju,
  • Ya Wang,
  • Jingxin Zhao,
  • Mingjie Dong

摘要

2D/3D medical image registration is crucial for image-guided surgery and medical research. Single-view methods often lack depth information, while multiview approaches typically rely on large-scale training datasets, which may not always be available in clinical scenarios. This paper explores the feasibility of multiview registration with insufficient data. We propose an innovative multiview 2D/3D image registration model with three key components: a points of interest (POIs) tracking network based on style transfer (POITT), a triangulation layer, and shape alignment based on branch and bound (SA-BnB). POITT constructs a POIs tracking network on Digital Reconstructed Radiographs images (DRRs) and performs points matching on X-ray images via style transfer. The triangulation layer establishes high-precision spatial mapping relationships between X-ray images and the patient. SA-BnB calculates the optimal transformation matrix between the CT volume and the patient. We collected clinical X-ray and CT data from 8 patients and generated 300 pairs of simulated DRRs from two views for each patient to validate the proposed model. The average fiducial registration error was reduced to 6 mm (less than 10 mm is considered successful) with a 96.67 \(\%\) average registration success rate on DRRs. The average SSIM reached 0.77 between X-ray images and the aligned DRRs. All experimental results demonstrate significant advantages over traditional 2D/3D registration. This study may provide a reliable solution for minimally invasive pelvic surgery navigation. The key code is available at https://github.com/TUYaYa1/Points-of-Interest-tracking-network.