Dataset Information

Quantifying Subresolution 3D Morphology of Bone with Clinical Computed Tomography.

ABSTRACT: The aim of this study was to quantify sub-resolution trabecular bone morphometrics, which are also related to osteoarthritis (OA), from clinical resolution cone beam computed tomography (CBCT). Samples (n?=?53) were harvested from human tibiae (N?=?4) and femora (N?=?7). Grey-level co-occurrence matrix (GLCM) texture and histogram-based parameters were calculated from CBCT imaged trabecular bone data, and compared with the morphometric parameters quantified from micro-computed tomography. As a reference for OA severity, histological sections were subjected to OARSI histopathological grading. GLCM and histogram parameters were correlated to bone morphometrics and OARSI individually. Furthermore, a statistical model of combined GLCM/histogram parameters was generated to estimate the bone morphometrics. Several individual histogram and GLCM parameters had strong associations with various bone morphometrics (|r|?>?0.7). The most prominent correlation was observed between the histogram mean and bone volume fraction (r?=?0.907). The statistical model combining GLCM and histogram-parameters resulted in even better association with bone volume fraction determined from CBCT data (adjusted R2 change?=?0.047). Histopathology showed mainly moderate associations with bone morphometrics (|r|?>?0.4). In conclusion, we demonstrated that GLCM- and histogram-based parameters from CBCT imaged trabecular bone (ex vivo) are associated with sub-resolution morphometrics. Our results suggest that sub-resolution morphometrics can be estimated from clinical CBCT images, associations becoming even stronger when combining histogram and GLCM-based parameters.

SUBMITTER: Karhula SS

PROVIDER: S-EPMC6949315 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Quantifying Subresolution 3D Morphology of Bone with Clinical Computed Tomography.

Karhula S S SS Finnilä M A J MAJ Rytky S J O SJO Cooper D M DM Thevenot J J Valkealahti M M Pritzker K P H KPH Haapea M M Joukainen A A Lehenkari P P Kröger H H Korhonen R K RK Nieminen H J HJ Saarakkala S S

Annals of biomedical engineering 20191003 2

The aim of this study was to quantify sub-resolution trabecular bone morphometrics, which are also related to osteoarthritis (OA), from clinical resolution cone beam computed tomography (CBCT). Samples (n = 53) were harvested from human tibiae (N = 4) and femora (N = 7). Grey-level co-occurrence matrix (GLCM) texture and histogram-based parameters were calculated from CBCT imaged trabecular bone data, and compared with the morphometric parameters quantified from micro-computed tomography. As a r ...[more]

PMID: 31583552

Similar Datasets

Project description:Angiogenesis is a critical process essential for optimal bone healing. Several in vitro and in vivo systems have been previously used to elucidate some of the mechanisms involved in the process of angiogenesis, and at the same time, to test potential therapeutic agents and bioactive factors that play important roles in neovascularization. Computed tomography (CT) is a noninvasive imaging technique that has recently allowed investigators to obtain a diverse range of high-resolution, three-dimensional characterization of structures, such as bone formation within bony defects. Unfortunately, to date, angiogenesis evaluation relies primarily on histology, or ex vivo imaging and few studies have utilized CT to qualitatively and quantitatively study the vascular response during bone repair. In the current study a clinical CT-based technique was used to evaluate the effects of rhBMP-2 eluting graft treatment on soft tissue vascular architecture surrounding a large segmental bone defect model in the minipig mandible. The objective of this study was to demonstrate the efficacy of contrast-enhanced, clinical 64-slice CT technology in extracting quantitative metrics of vascular architecture over a 12-week period. The results of this study show that the presence of rhBMP-2 had a positive effect on vessel volume from 4 to 12 weeks, which was explained by a concurrent increase in vessel number, which was also significantly higher at 4 weeks for the rhBMP-2 treatment. More importantly, analysis of vessel architecture showed no changes throughout the duration of the study, indicating therapeutic safety. This study validates CT analysis as a relevant imaging method for quantitative and qualitative analysis of morphological characteristics of vascular tissue around a bone healing site. Also important, the study shows that CT technology can be used in large animal models and potentially be translated into clinical models for the development of improved methods to evaluate tissue healing and vascular adaptation processes over the course of therapy. This methodology has demonstrated sensitivity to tracking spatial and temporal changes in vascularization and has the potential to be applied to studying changes in other high-contrast tissues as well. Impact Statement Tissue engineering solutions depend on the surrounding tissue response to support regeneration. The inflammatory environment and surrounding vascular supply are critical to determining if therapies will survive, engraftment occurs, and native physiology is restored. This study for the first time evaluates the blood vessel network changes in surrounding soft tissue to a bone defect site in a large animal model, using clinically available computed tomography tools and model changes in vessel number, size, and architecture. While this study focuses on rhBMP2 delivery impacting surrounding vasculature, this validated method can be extended to studying the vascular network changes in other tissues as well.

Project description:PurposeComputed tomography (CT)-derived ventilation methods compute respiratory induced volume changes as a surrogate for pulmonary ventilation. Currently, there are no known methods to derive perfusion information from noncontrast CT. We introduce a novel CT-Perfusion (CT-P) method for computing the magnitude mass changes apparent on dynamic noncontrast CT as a surrogate for pulmonary perfusion.MethodsCT-Perfusion is based on a mass conservation model which describes the unknown mass change as a linear combination of spatially corresponding inhale and exhale HU estimated voxel densities. CT-P requires a deformable image registration (DIR) between the inhale/exhale lung CT pair, a preprocessing lung volume segmentation, and an estimate for the Jacobian of the DIR transformation. Given this information, the CT-P image, which provides the magnitude mass change for each voxel within the lung volume, is formulated as the solution to a constrained linear least squares problem defined by a series of subregional mean magnitude mass change measurements. Similar to previous robust CT-ventilation methods, the amount of uncertainty in a subregional sample mean measurement is related to measurement resolution and can be characterized with respect to a tolerance parameter τ . Spatial Spearman correlation between single photon emission CT perfusion (SPECT-P) and the proposed CT-P method was assessed in two patient cohorts via a parameter sweep of τ . The first cohort was comprised of 15 patients diagnosed with pulmonary embolism (PE) who had SPECT-P and 4DCT imaging acquired within 24 h of PE diagnosis. The second cohort was comprised of 15 nonsmall cell lung cancer patients who had SPECT-P and 4DCT images acquired prior to radiotherapy. For each test case, CT-P images were computed for 30 different uncertainty parameter values, uniformly sampled from the range [0.01, 0.125], and the Spearman correlation between the SPECT-P and the resulting CT-P images were computed.ResultsThe median correlations between CT-P and SPECT-P taken over all 30 test cases ranged between 0.49 and 0.57 across the parameter sweep. For the optimal tolerance τ = 0.0385, the CT-P and SPECT-P correlations across all 30 test cases ranged between 0.02 and 0.82. A one-sample sign test was applied separately to the PE and lung cancer cohorts. A low Spearmen correlation of 15% was set as the null median value and two-sided alternative was tested. The PE patients showed a median correlation of 0.57 (IQR = 0.305). One-sample sign test was statistically significant with 96.5 % confidence interval: 0.20-0.63, P < 0.00001. Lung cancer patients had a median correlation of 0.57(IQR = 0.230). Again, a one-sample sign test for median was statistically significant with 96.5 percent confidence interval: 0.45-0.71, P < 0.00001.ConclusionCT-Perfusion is the first mechanistic model designed to quantify magnitude blood mass changes on noncontrast dynamic CT as a surrogate for pulmonary perfusion. While the reported correlations with SPECT-P are promising, further investigation is required to determine the optimal CT acquisition protocol and numerical method implementation for CT-P imaging.

Project description:Knowledge of the lung vessel morphology in healthy subjects is necessary to improve our understanding about the functional network of the lung and to recognize pathologic deviations beyond the normal inter-subject variation. Established values of normal lung morphology have been derived from necropsy material of only very few subjects. In order to determine morphologic readouts from a large number of healthy subjects, computed tomography pulmonary angiography (CTPA) datasets, negative for pulmonary embolism, and other thoracic pathologies, were analyzed using a fully-automatic, in-house developed artery/vein separation algorithm. The number, volume, and tortuosity of the vessels in a diameter range between 2 and 10 mm were determined. Visual inspection of all datasets was used to exclude subjects with poor image quality or inadequate artery/vein separation from the analysis. Validation of the algorithm was performed manually by a radiologist on randomly selected subjects. In 123 subjects (men/women: 55/68), aged 59 ± 17 years, the median overlap between visual inspection and fully-automatic segmentation was 94.6% (69.2-99.9%). The median number of vessel segments in the ranges of 8-10, 6-8, 4-6, and 2-4 mm diameter was 9, 34, 134, and 797, respectively. Number of vessel segments divided by the subject's lung volume was 206 vessels/L with arteries and veins contributing almost equally. In women this vessel density was about 15% higher than in men. Median arterial and venous volumes were 1.52 and 1.54% of the lung volume, respectively. Tortuosity was best described with the sum-of-angles metric and was 142.1 rad/m (138.3-144.5 rad/m). In conclusion, our fully-automatic artery/vein separation algorithm provided reliable measures of pulmonary arteries and veins with respect to age and gender. There was a large variation between subjects in all readouts. No relevant dependence on age, gender, or vessel type was observed. These data may provide reference values for morphometric analysis of lung vessels.

Dataset Information

Quantifying Subresolution 3D Morphology of Bone with Clinical Computed Tomography.

Publications

Quantifying Subresolution 3D Morphology of Bone with Clinical Computed Tomography.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets