3D Reconstruction of Coronary Vessel Trees from Biplanar X-Ray Images Using a Geometric Approach

• URL: http://arxiv.org/abs/2509.13358v1
• Date: Mon, 15 Sep 2025 08:23:01 GMT
• Title: 3D Reconstruction of Coronary Vessel Trees from Biplanar X-Ray Images Using a Geometric Approach
• Authors: Ethan Koland, Lin Xi, Nadeev Wijesuriya, YingLiang Ma,
• Abstract summary: We propose a framework for reconstructing 3D vessel trees from biplanar X-ray images.<n>The proposed framework consists of three main components: image segmentation, motion phase matching, and 3D reconstruction.
• Score: 0.685068326729525
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: X-ray angiography is widely used in cardiac interventions to visualize coronary vessels, assess integrity, detect stenoses and guide treatment. We propose a framework for reconstructing 3D vessel trees from biplanar X-ray images which are extracted from two X-ray videos captured at different C-arm angles. The proposed framework consists of three main components: image segmentation, motion phase matching, and 3D reconstruction. An automatic video segmentation method for X-ray angiography to enable semantic segmentation for image segmentation and motion phase matching. The goal of the motion phase matching is to identify a pair of X-ray images that correspond to a similar respiratory and cardiac motion phase to reduce errors in 3D reconstruction. This is achieved by tracking a stationary object such as a catheter or lead within the X-ray video. The semantic segmentation approach assigns different labels to different object classes enabling accurate differentiation between blood vessels, balloons, and catheters. Once a suitable image pair is selected, key anatomical landmarks (vessel branching points and endpoints) are matched between the two views using a heuristic method that minimizes reconstruction errors. This is followed by a novel geometric reconstruction algorithm to generate the 3D vessel tree. The algorithm computes the 3D vessel centrelines by determining the intersection of two 3D surfaces. Compared to traditional methods based on epipolar constraints, the proposed approach simplifies there construction workflow and improves overall accuracy. We trained and validated our segmentation method on 62 X-ray angiography video sequences. On the test set, our method achieved a segmentation accuracy of 0.703. The 3D reconstruction framework was validated by measuring the reconstruction error of key anatomical landmarks, achieving a reprojection errors of 0.62mm +/- 0.38mm.

Related papers

• 3D Path Planning for Robot-assisted Vertebroplasty from Arbitrary Bi-plane X-ray via Differentiable Rendering [10.03537569638041]
  We introduce a differentiable rendering-based framework for 3D transpedicular path planning utilizing bi-planar 2D X-rays.<n>Our method integrates differentiable rendering with a vertebral atlas generated through a Statistical Shape Model.<n>Our framework facilitates versatile, CT-free 3D path planning for robot-assisted vertebroplasty.
  arXiv  Detail & Related papers  (2025-12-05T15:26:13Z)
• RayEmb: Arbitrary Landmark Detection in X-Ray Images Using Ray Embedding Subspace [0.7937206070844555]
  Intra-operative 2D-3D registration of X-ray images with pre-operatively acquired CT scans is a crucial procedure in orthopedic surgeries. We propose a novel method to address this issue by detecting arbitrary landmark points in X-ray images.
  arXiv  Detail & Related papers  (2024-10-10T17:36:21Z)
• X-Ray to CT Rigid Registration Using Scene Coordinate Regression [1.1687067206676627]
  This paper proposes a fully automatic registration method that is robust to extreme viewpoints. It is based on a fully convolutional neural network (CNN) that regresses the overlapping coordinates for a given X-ray image. The proposed method achieved an average mean target registration error (mTRE) of 3.79 mm in the 50th percentile of the simulated test dataset and projected mTRE of 9.65 mm in the 50th percentile of real fluoroscopic images for pelvis registration.
  arXiv  Detail & Related papers  (2023-11-25T17:48:46Z)
• Accurate Fine-Grained Segmentation of Human Anatomy in Radiographs via Volumetric Pseudo-Labeling [66.75096111651062]
  We created a large-scale dataset of 10,021 thoracic CTs with 157 labels. We applied an ensemble of 3D anatomy segmentation models to extract anatomical pseudo-labels. Our resulting segmentation models demonstrated remarkable performance on CXR.
  arXiv  Detail & Related papers  (2023-06-06T18:01:08Z)
• Deep learning network to correct axial and coronal eye motion in 3D OCT retinal imaging [65.47834983591957]
  We propose deep learning based neural networks to correct axial and coronal motion artifacts in OCT based on a single scan. The experimental result shows that the proposed method can effectively correct motion artifacts and achieve smaller error than other methods.
  arXiv  Detail & Related papers  (2023-05-27T03:55:19Z)
• Geometry-Aware Attenuation Learning for Sparse-View CBCT Reconstruction [53.93674177236367]
  Cone Beam Computed Tomography (CBCT) plays a vital role in clinical imaging. Traditional methods typically require hundreds of 2D X-ray projections to reconstruct a high-quality 3D CBCT image. This has led to a growing interest in sparse-view CBCT reconstruction to reduce radiation doses. We introduce a novel geometry-aware encoder-decoder framework to solve this problem.
  arXiv  Detail & Related papers  (2023-03-26T14:38:42Z)
• DeepRecon: Joint 2D Cardiac Segmentation and 3D Volume Reconstruction via A Structure-Specific Generative Method [12.26150675728958]
  We propose an end-to-end latent-space-based framework, DeepRecon, that generates multiple clinically essential outcomes. Our method identifies the optimal latent representation of the cine image that contains accurate semantic information for cardiac structures. In particular, our model jointly generates synthetic images with accurate semantic information and segmentation of the cardiac structures.
  arXiv  Detail & Related papers  (2022-06-14T20:46:11Z)
• A unified 3D framework for Organs at Risk Localization and Segmentation for Radiation Therapy Planning [56.52933974838905]
  Current medical workflow requires manual delineation of organs-at-risk (OAR) In this work, we aim to introduce a unified 3D pipeline for OAR localization-segmentation. Our proposed framework fully enables the exploitation of 3D context information inherent in medical imaging.
  arXiv  Detail & Related papers  (2022-03-01T17:08:41Z)
• SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detection [76.01333073259677]
  We propose the use of Space-aware Memory Queues for In-painting and Detecting anomalies from radiography images (abbreviated as SQUID) We show that SQUID can taxonomize the ingrained anatomical structures into recurrent patterns; and in the inference, it can identify anomalies (unseen/modified patterns) in the image.
  arXiv  Detail & Related papers  (2021-11-26T13:47:34Z)
• IGCN: Image-to-graph Convolutional Network for 2D/3D Deformable Registration [1.2246649738388387]
  We propose an image-to-graph convolutional network that achieves deformable registration of a 3D organ mesh for a single-viewpoint 2D projection image. We show shape prediction considering relationships among multiple organs can be used to predict respiratory motion and deformation from radiographs with clinically acceptable accuracy.
  arXiv  Detail & Related papers  (2021-10-31T12:48:37Z)
• Deep Learning compatible Differentiable X-ray Projections for Inverse Rendering [8.926091372824942]
  We propose a differentiable by deriving the distance travelled by a ray inside mesh structures to generate a distance map. We show its application by solving the inverse problem, namely reconstructing 3D models from real 2D fluoroscopy images of the pelvis.
  arXiv  Detail & Related papers  (2021-02-04T22:06:05Z)
• XraySyn: Realistic View Synthesis From a Single Radiograph Through CT Priors [118.27130593216096]
  A radiograph visualizes the internal anatomy of a patient through the use of X-ray, which projects 3D information onto a 2D plane. To the best of our knowledge, this is the first work on radiograph view synthesis. We show that by gaining an understanding of radiography in 3D space, our method can be applied to radiograph bone extraction and suppression without groundtruth bone labels.
  arXiv  Detail & Related papers  (2020-12-04T05:08:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.