Three-dimensional Reconstruction of the Lumbar Spine with Submillimeter Accuracy Using Biplanar X-ray Images

• URL: http://arxiv.org/abs/2503.14573v1
• Date: Tue, 18 Mar 2025 15:00:39 GMT
• Title: Three-dimensional Reconstruction of the Lumbar Spine with Submillimeter Accuracy Using Biplanar X-ray Images
• Authors: Wanxin Yu, Zhemin Zhu, Cong Wang, Yihang Bao, Chunjie Xia, Rongshan Cheng, Yan Yu, Tsung-Yuan Tsai,
• Abstract summary: The proposed method achieved a 3D reconstruction accuracy of 0.80 mm, representing a significant improvement over the mainstream approaches.<n>This study will contribute to the clinical diagnosis of lumbar in weight-bearing positions.
• Score: 4.252036643472159
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Three-dimensional reconstruction of the spine under weight-bearing conditions from biplanar X-ray images is of great importance for the clinical assessment of spinal diseases. However, the current fully automated reconstruction methods have low accuracy and fail to meet the clinical application standards. This study developed and validated a fully automated method for high-accuracy 3D reconstruction of the lumbar spine from biplanar X-ray images. The method involves lumbar decomposition and landmark detection from the raw X-ray images, followed by a deformable model and landmark-weighted 2D-3D registration approach. The reconstruction accuracy was validated by the gold standard obtained through the registration of CT-segmented vertebral models with the biplanar X-ray images. The proposed method achieved a 3D reconstruction accuracy of 0.80 mm, representing a significant improvement over the mainstream approaches. This study will contribute to the clinical diagnosis of lumbar in weight-bearing positions.

Related papers

• Landmark-Free Preoperative-to-Intraoperative Registration in Laparoscopic Liver Resection [50.388465935739376]
  Liver registration by overlaying preoperative 3D models onto intraoperative 2D frames can assist surgeons in perceiving the spatial anatomy of the liver clearly for a higher surgical success rate. Existing registration methods rely heavily on anatomical landmark-based, which encounter two major limitations. We propose a landmark-free preoperative-to-intraoperative registration framework utilizing effective self-supervised learning.
  arXiv  Detail & Related papers  (2025-04-21T14:55:57Z)
• IXGS-Intraoperative 3D Reconstruction from Sparse, Arbitrarily Posed Real X-rays [1.2721397985664153]
  We extend the $R2$-Gaussian splatting framework to reconstruct consistent 3D volumes under challenging conditions. We introduce an anatomy-guided radiographic standardization step using style transfer, improving visual consistency across views.
  arXiv  Detail & Related papers  (2025-04-20T18:28:13Z)
• 3D Acetabular Surface Reconstruction from 2D Pre-operative X-ray Images using SRVF Elastic Registration and Deformation Graph [15.26681988459618]
  This paper proposes a novel framework that integrates square-root velocity function (SRVF)-based elastic shape registration technique. It reconstructs the 3D articular surface of the acetabulum by fusing multiple views of 2D pre-operative pelvic X-ray images and a hemispherical surface model.
  arXiv  Detail & Related papers  (2025-03-28T06:47:32Z)
• Reconstruction of 3D lumbar spine models from incomplete segmentations using landmark detection [0.4194295877935868]
  We present a novel method to reconstruct complete 3D lumbar spine models from incomplete 3D vertebral bodies.<n>Our method achieves the registration of the entire lumbar spine, spanning segments L1 to L5, in just 0.14 seconds.
  arXiv  Detail & Related papers  (2024-12-06T14:23:42Z)
• 3D Spine Shape Estimation from Single 2D DXA [49.53978253009771]
  We propose an automated framework to estimate the 3D spine shape from 2D DXA scans.<n>We achieve this by explicitly predicting the sagittal view of the spine from the DXA scan.
  arXiv  Detail & Related papers  (2024-12-02T13:58:26Z)
• 3DDX: Bone Surface Reconstruction from a Single Standard-Geometry Radiograph via Dual-Face Depth Estimation [7.832005676209272]
  3D reconstruction from a single radiograph, so-called 2D-3D reconstruction, offers the possibility of various clinical applications. We propose a novel approach that simultaneously learns multiple depth maps derived from the X-ray image to computed tomography registration.
  arXiv  Detail & Related papers  (2024-09-25T07:48:57Z)
• EAR: Edge-Aware Reconstruction of 3-D vertebrae structures from bi-planar X-ray images [19.902946440205966]
  We propose a new Edge-Aware Reconstruction network (EAR) to focus on the performance improvement of the edge information and vertebrae shapes. By using the auto-encoder architecture as the backbone, the edge attention module and frequency enhancement module are proposed to strengthen the perception of the edge reconstruction. The proposed method is evaluated using three publicly accessible datasets and compared with four state-of-the-art models.
  arXiv  Detail & Related papers  (2024-07-30T16:19:14Z)
• Domain adaptation strategies for 3D reconstruction of the lumbar spine using real fluoroscopy data [9.21828361691977]
  This study tackles key obstacles in adopting surgical navigation in orthopedic surgeries. It shows an approach for generating 3D anatomical models of the spine from only a few fluoroscopic images. It achieved an 84% F1 score, matching the accuracy of our previous synthetic data-based research.
  arXiv  Detail & Related papers  (2024-01-29T10:22:45Z)
• 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)
• Context-Aware Transformers For Spinal Cancer Detection and Radiological Grading [70.04389979779195]
  This paper proposes a novel transformer-based model architecture for medical imaging problems involving analysis of vertebrae. It considers two applications of such models in MR images: (a) detection of spinal metastases and the related conditions of vertebral fractures and metastatic cord compression. We show that by considering the context of vertebral bodies in the image, SCT improves the accuracy for several gradings compared to previously published model.
  arXiv  Detail & Related papers  (2022-06-27T10:31:03Z)
• Tattoo tomography: Freehand 3D photoacoustic image reconstruction with an optical pattern [49.240017254888336]
  Photoacoustic tomography (PAT) is a novel imaging technique that can resolve both morphological and functional tissue properties. A current drawback is the limited field-of-view provided by the conventionally applied 2D probes. We present a novel approach to 3D reconstruction of PAT data that does not require an external tracking system.
  arXiv  Detail & Related papers  (2020-11-10T09:27:56Z)
• Probabilistic 3D surface reconstruction from sparse MRI information [58.14653650521129]
  We present a novel probabilistic deep learning approach for concurrent 3D surface reconstruction from sparse 2D MR image data and aleatoric uncertainty prediction. Our method is capable of reconstructing large surface meshes from three quasi-orthogonal MR imaging slices from limited training sets.
  arXiv  Detail & Related papers  (2020-10-05T14:18:52Z)
• A Convolutional Approach to Vertebrae Detection and Labelling in Whole Spine MRI [70.04389979779195]
  We propose a novel convolutional method for the detection and identification of vertebrae in whole spine MRIs. This involves using a learnt vector field to group detected vertebrae corners together into individual vertebral bodies. We demonstrate the clinical applicability of this method, using it for automated scoliosis detection in both lumbar and whole spine MR scans.
  arXiv  Detail & Related papers  (2020-07-06T09:37:12Z)

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.