1. Automatic 3D analysis of the ex-vivo porcine lumbar interbody fusion based on X-ray micro computed tomography data.
- Author
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Laznovsky J, Brinek A, Zikmund T, Boudova M, Vojtova L, Dorazilova J, Trunec M, Stastny P, Krticka M, Planka L, Ira D, Faldyna M, and Kaiser J
- Subjects
- Animals, Humans, Lumbar Vertebrae diagnostic imaging, Lumbar Vertebrae surgery, Lumbosacral Region, Swine, X-Ray Microtomography, X-Rays, Spinal Diseases, Spinal Fusion
- Abstract
Spinal fusion is a surgical procedure used to join two or more vertebrae to prevent movement between them. This surgical procedure is considered in patients suffering from a wide range of degenerative spinal diseases or vertebral fractures. The success rate of spinal fusion is frequently evaluated subjectively using X-ray computed tomography. The pig was chosen as an animal model for spinal fusion, since its spinal structure is similar to the human spine. Our paper presents an automatic approach for pig's spinal fusion evaluation in 3D. The proposed approach is based on the determination of the vertebral fused area, which reflects the fusion quality. The approach was applied and tested on microCT (μCT) data of fused porcine vertebrae ex-vivo. In our study, three types of implants were used to perform spinal fusion: the iliac crest bone graft used as the gold standard, and two types of novel scaffold implants based on the polymer/ceramic porous foam involving either growth factors or polyphosphates. The evaluation worked automatically for all three types of used implants, and the fusion quality was determined quantitatively. The calculation is based on the detection of the fused area and area of facies intervertebralis, so the percentual representation of the vertebral joint can be determined. Since this approach is versatile and is described in detail as a guide for image processing the data of vertebrae fusion, this methodology has the potential to establish a standard approach for evaluating the fusion quality in ex-vivo samples that can be tested on clinical data., (Copyright © 2022 Elsevier Ltd. All rights reserved.)
- Published
- 2022
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