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Effect of fabric on the accuracy of computed tomography-based finite element analyses of the vertebra.

ABSTRACT: Quantitative computed tomography (QCT)-based finite element (FE) models of the vertebra are widely used in studying spine biomechanics and mechanobiology, but their accuracy has not been fully established. Although the models typically assign material properties based only on local bone mineral density (BMD), the mechanical behavior of trabecular bone also depends on fabric. The goal of this study was to determine the effect of incorporating measurements of fabric on the accuracy of FE predictions of vertebral deformation. Accuracy was assessed by using displacement fields measured via digital volume correlation-applied to time-lapse microcomputed tomography (μCT)-as the gold standard. Two QCT-based FE models were generated from human L1 vertebrae (n = 11): the entire vertebral body and a cuboid-shaped portion of the trabecular centrum [dimensions: (20-30) × (15-20) × (15-20) mm³]. For axial compression boundary conditions, there was no difference (p = 0.40) in the accuracy of the FE-computed displacements for models using material properties based on local values of BMD versus those using material properties based on local values of fabric and volume fraction. However, when using BMD-based material properties, errors were higher for the vertebral-body models (8.4-50.1%) than cuboid models (1.5-19.6%), suggesting that these properties are inaccurate in the peripheral regions of the centrum. Errors also increased when assuming that the cuboid region experienced uniaxial loading during axial compression of the vertebra. These findings indicate that a BMD-based constitutive model is not sufficient for the peripheral region of the vertebral body when seeking accurate QCT-based FE modeling of the vertebra.

SUBMITTER: Wu Y

PROVIDER: S-EPMC7062572 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundOsteoporotic vertebral compression fractures (OVCFs) are the most common type of fragility fracture. Distinguishing between OVCFs and other types of vertebra diseases, such as old fractures (OFs), Schmorl's node (SN), Kummell's disease (KD), and previous surgery (PS), is critical for subsequent surgery and treatment. Combining with advanced deep learning (DL) technologies, this study plans to develop a DL-driven diagnostic system for diagnosing multi-type vertebra diseases.MethodsWe established a large-scale dataset based on the computed tomography (CT) images of 1,051 patients with OVCFs from Luhe Hospital and used data of 46 patients from Xuanwu Hospital as alternative hospital validation dataset. Each patient underwent one examination. The dataset contained 11,417 CT slices and 19,718 manually annotated vertebrae with diseases. A two-stage DL-based system was developed to diagnose five vertebra diseases. The proposed system consisted of a vertebra detection module (VDModule) and a vertebra classification module (VCModule).ResultsThe training and testing dataset for the VDModule consisted of 9,135 and 3,212 vertebrae, respectively. The VDModule using the ResNet18-based Faster region-based convolutional neural network (R-CNN) model achieved an area under the curve (AUC), false-positive (FP) rate, and false-negative (FN) rate of 0.982, 1.52%, and 1.33%, respectively, in the testing dataset. The training dataset for VCModule consisted of 14,584 and 47,604 diseased and normal vertebrae, respectively. The testing dataset consisted of 4,489 and 15,122 diseased and normal vertebrae, respectively. The ResNet50-based VCModule achieved an average sensitivity and specificity of 0.919 and 0.995, respectively, in diagnosing four kinds of vertebra diseases except for SN in the testing dataset. In the alternative hospital validation dataset, the ResNet50-based VCModule achieved an average sensitivity and specificity of 0.891 and 0.989, respectively, in diagnosing four kinds of vertebra diseases except for SN.ConclusionsOur proposed DL system can accurately diagnose four vertebra diseases and has strong potential to facilitate the accurate and rapid diagnosis of vertebral diseases.

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Effect of fabric on the accuracy of computed tomography-based finite element analyses of the vertebra.

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