Postoperative 3D spine reconstruction by navigating partitioning manifolds. Issue 3 (4th February 2016)

• Record Type: Journal Article
• Authors: Kadoury, Samuel; Labelle, Hubert; Parent, Stefan
• Is Part Of: Medical physics. Volume 43:Issue 3(2016)
• Page Start: 1045
• Page End: 1056
• Publication Date: 2016-02-04
• Subjects: bone -- image reconstruction -- manifolds -- medical image processing -- surgery; X‐ray imaging -- Reconstruction; Surgical instruments, devices or methods, e.g. tourniquets -- Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general -- Other arrangements or adaptations of exhaust conduits -- Engines with prolonged expansion; 3D post‐operative spine reconstruction -- adolescent idiopathic scoliosis -- manifold learning -- Markov random fields -- biplanar radiographs; Manifolds -- Three dimensional image processing -- Medical image reconstruction -- Computer modeling -- X‐ray imaging -- Radiography -- Anatomy -- Testing procedures; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4940792
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: : The postoperative evaluation of scoliosis patients undergoing corrective treatment is an important task to assess the strategy of the spinal surgery. Using accurate 3D geometric models of the patient's spine is essential to measure longitudinal changes in the patient's anatomy. On the other hand, reconstructing the spine in 3D from postoperative radiographs is a challenging problem due to the presence of instrumentation (metallic rods and screws) occluding vertebrae on the spine. Methods: : This paper describes the reconstruction problem by searching for the optimal model within a manifold space of articulated spines learned from a training dataset of pathological cases who underwent surgery. The manifold structure is implemented based on a multilevel manifold ensemble to structure the data, incorporating connections between nodes within a single manifold, in addition to connections between different multilevel manifolds, representing subregions with similar characteristics. Results: : The reconstruction pipeline was evaluated on x‐ray datasets from both preoperative patients and patients with spinal surgery. By comparing the method to ground‐truth models, a 3D reconstruction accuracy of 2.24 ± 0.90 mm was obtained from 30 postoperative scoliotic patients, while handling patients with highly deformed spines. Conclusions: : This paper illustrates how this manifold model can accurately identify similar spine models by navigating in the low‐dimensional space, as well as computing nonlinear charts within local neighborhoods of the embedded space during the testing phase. This technique allows postoperative follow‐ups of spinal surgery using personalized 3D spine models and assess surgical strategies for spinal deformities.