Evidence map›Paper›PMID 37814880›Full record

ArticleNan fang yi ke da xue xue bao = Journal of Southern Medical University2023

[A 3D/2D registration method based on reconstruction of orthogonal-view Xray images].

J Mi, Y Zhou, Q Feng

Abstract readEnglish Abstract
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Article in Nan fang yi ke da xue xue bao = Journal of Southern Medical University, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

J MiSchool of Biomedical Engineering, Southern Medical University//Guangdong Provincial Key Laboratory of Medical Image Processing//Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China.
Y ZhouSchool of Biomedical Engineering, Southern Medical University//Guangdong Provincial Key Laboratory of Medical Image Processing//Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China.
Q FengSchool of Biomedical Engineering, Southern Medical University//Guangdong Provincial Key Laboratory of Medical Image Processing//Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo establish a 3D/2D registration method for preoperative CT and intra-operative X-ray images in imageguided spine surgery.

methodsWe propose a 3D/2D registration algorithm based on 3D image reconstruction. The algorithm performs 3D image reconstruction of 2D orthogonal view X-ray images, thus converting the problem into 3D/3D registration. By constructing an end-to-end framework that combines the two tasks of reconstruction and registration, the geodesic distance is measured in the 3D manifold space to complete the registration.

resultsWe conducted experiments on the public dataset CTSpine1k. The tests on two test sets with different initial registration errors showed that for data with small initial errors, the proposed algorithm achieved a rotation estimation error of 0.115±0.095° and a translation estimation error of 0.144±0.124 mm; for data with larger initial errors, a rotation estimation error of 0.792±0.659° and a translation estimation error of 0.867±0.701 mm were achieved.

conclusionThe proposed method can achieve robust and accurate 3D/2D registration at a speed that meets real-time requirements to improve the performance of spine surgery navigation.

Indexed as

AlgorithmsImaging, Three-DimensionalX-Rays3D/2D registrationdeep learningreconstructionsurgery navigation

Identifiers

PMID37814880
PMCPMC10563102

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.