Direct estimation of human trabecular bone stiffness using cone beam computed tomography

A convolutional neural network-based method for the generation of super-resolution 3D models from clinical CT images

BACKGROUND AND OBJECTIVE

The accurate evaluation of bone mechanical properties is essential for predicting fracture risk based on clinical computed tomography (CT) images. However, blurring and noise in clinical CT images can compromise the accuracy of these predictions, leading to incorrect diagnoses. Although previous studies have explored enhancing trabecular bone CT images to super-resolution (SR), none of these studies have examined the possibility of using clinical CT images from different instruments, typically of lower resolution, as a basis for analysis. Additionally, previous studies rely on 2D SR images, which may not be sufficient for accurate mechanical property evaluation, due to the complex nature of the 3D trabecular bone structures. The objective of this study was to address these limitations.

METHODS

A workflow was developed that utilizes convolutional neural networks to generate SR 3D models across different clinical CT instruments. The morphological and finite-element-derived mechanical properties of these SR models were compared with ground truth models obtained from micro-CT scans.

RESULTS

A significant improvement in analysis accuracy was demonstrated, where the new SR models increased the accuracy by up to 700 % compared with the low-resolution data, i.e. clinical CT images. Additionally, we found that the mixture of different CT image datasets may improve the SR model performance.

CONCLUSIONS

SR images, generated by convolutional neural networks, outperformed clinical CT images in the determination of morphological and mechanical properties. The developed workflow could be implemented for fracture risk prediction, potentially leading to improved diagnoses and subsequent clinical decision making.

High-Resolution Cone-Beam Computed Tomography is a Fast and Promising Technique to Quantify Bone Microstructure and Mechanics of the Distal Radius

Obtaining high-resolution scans of bones and joints for clinical applications is challenging. HR-pQCT is considered the best technology to acquire high-resolution images of the peripheral skeleton in vivo, but a breakthrough for widespread clinical applications is still lacking. Recently, we showed on trapezia that CBCT is a promising alternative providing a larger FOV at a shorter scanning time. The goals of this study were to evaluate the accuracy of CBCT in quantifying trabecular bone microstructural and predicted mechanical parameters of the distal radius, the most often investigated skeletal site with HR-pQCT, and to compare it with HR-pQCT. Nineteen radii were scanned with four scanners: (1) HR-pQCT (XtremeCT, Scanco Medical AG, @ (voxel size) 82 μm), (2) HR-pQCT (XtremeCT-II, Scanco, @60.7 μm), (3) CBCT (NewTom 5G, Cefla, @75 μm) reconstructed and segmented using in-house developed software and (4) microCT (VivaCT40, Scanco, @19 μm-gold standard). The following parameters were evaluated: predicted stiffness, strength, bone volume fraction (BV/TV) and trabecular thickness (Tb.Th), separation (Tb.Sp) and number (Tb.N). The overall accuracy of CBCT with in-house optimized algorithms in quantifying bone microstructural parameters was comparable (R² = 0.79) to XtremeCT (R² = 0.76) and slightly worse than XtremeCT-II (R² = 0.86) which were both processed with the standard manufacturer's technique. CBCT had higher accuracy for BV/TV and Tb.Th but lower for Tb.Sp and Tb.N compared to XtremeCT. Regarding the mechanical parameters, all scanners had high accuracy (R² [Formula: see text] 0.96). While HR-pQCT is optimized for research, the fast scanning time and good accuracy renders CBCT a promising technique for high-resolution clinical scanning.

A comparative study of trabecular bone micro-structural measurements using different CT modalities

Osteoporosis, characterized by reduced bone mineral density and micro-architectural degeneration, significantly enhances fracture-risk. There are several viable methods for trabecular bone micro-imaging, which widely vary in terms of technology, reconstruction principle, spatial resolution, and acquisition time. We have performed an excised cadaveric bone specimen study to evaluate different computed tomography (CT)-imaging modalities for trabecular bone micro-structural analysis. Excised cadaveric bone specimens from the distal radius were scanned using micro-CT and fourin vivoCT imaging modalities: high-resolution peripheral quantitative computed tomography (HR-pQCT), dental cone beam CT (CBCT), whole-body multi-row detector CT (MDCT), and extremity CBCT. A new algorithm was developed to optimize soft thresholding parameters for individualin vivoCT modalities for computing quantitative bone volume fraction maps. Finally, agreement of trabecular bone micro-structural measures, derived from differentin vivoCT imaging, with reference measures from micro-CT imaging was examined. Observed values of most trabecular measures, including trabecular bone volume, network area, transverse and plate-rod micro-structure, thickness, and spacing, forin vivoCT modalities were higher than their micro-CT-based reference values. In general, HR-pQCT-based trabecular bone measures were closer to their reference values as compared to otherin vivoCT modalities. Despite large differences in observed values of measures among modalities, high linear correlation (rε [0.94 0.99]) was found between micro-CT andin vivoCT-derived measures of trabecular bone volume, transverse and plate micro-structural volume, and network area. All HR-pQCT-derived trabecular measures, except the erosion index, showed high correlation (rε [0.91 0.99]). The plate-width measure showed a higher correlation (rε [0.72 0.91]) amongin vivoand micro-CT modalities than its counterpart binary plate-rod characterization-based measure erosion index (rε [0.65 0.81]). Although a strong correlation was observed between micro-structural measures fromin vivoand micro-CT imaging, large shifts in their values forin vivomodalities warrant proper scanner calibration prior to adopting in multi-site and longitudinal studies.

Alternative cone-beam CT method for the analysis of mandibular condylar bone in patients with degenerative joint disease

OBJECTIVE

The aim of this study was to investigate the use of 3D microstructural bone analysis in patients with degenerative joint disorder (DJD) to enhance the diagnostic capacity of cone beam computed tomography (CBCT) in the evaluation of bone tissue.

METHODS

147 TMJ CBCT images of 88 participants were assessed with regard to DJD in the mandibular condyle. We divided each condyle into 3 groups (0, 1, 2) according to diagnosis of DJD: 0 indicates normal condyles (control individuals), 1 indicates mild erosive osteoarthritic change (EOC) and 2 indicates severe EOC. 3D fractal dimension (FD) was calculated on CBCT images of mandibular condyle and were compared with the radiographic diagnosis of patients.

RESULTS

ANOVA test showed that there was statistically significant difference in FD values among each groups. The average FD value of group 0 was 1.971, group 1 was 1.918 and group 2 was 1.863. Lower FD values and more severe degenerative changes were seen in patient group 2. To evaluate the reliability of fractal analysis (FA) method, receiver operating characteristic (ROC) curve analysis was performed. Area under the curve (AUC) was 0.717 (p < 0.001).

CONCLUSION

This study provides a preliminary conclusion that fractal analysis may be a helpful tool to enhance the diagnostic capacity of CBCT in the evaluation of DJD.

Computer-based automatic classification of trabecular bone pattern can assist radiographic bone quality assessment at dental implant site

OBJECTIVE:

To develop and validate an automated classification method that determines the trabecular bone pattern at implant site based on three-dimensional bone morphometric parameters derived from CBCT images.

METHODS:

25 human cadaver mandibles were scanned using CBCT clinical scanning protocol. Volumes-of-interest comprising only the trabecular bone of the posterior regions were selected and segmented for three-dimensional morphometric parameters calculation. Three experts rated all bone regions into one of the three trabecular pattern classes (sparse, intermediate and dense) to generate a reference classification. Morphometric parameters were used to automatically classify the trabecular pattern with linear discriminant analysis statistical model. The discriminatory power of each morphometric parameter for automatic classification was indicated and the accuracy compared to the reference classification. Repeated-measures analysis of variances were used to statistically compare morphometric indices between the three classes. Finally, the outcome of the automatic classification was evaluated against a subjective classification performed independently by four different observers.

RESULTS:

The overall correct classification was 83% for quantity-, 86% for structure-related parameters and 84% for the parameters combined. Cross-validation showed a 79% model prediction accuracy. Bone volume fraction (BV/TV) had the most discriminatory power in the automatic classification. Trabecular bone patterns could be distinguished based on most morphometric parameters, except for trabecular thickness (Tb.Th) and degree of anisotropy (DA). The interobserver agreement between the subjective observers was fair (0.25), while the test-retest agreement was moderate (0.46). In comparison with the reference standard, the overall agreement was moderate (0.44).

CONCLUSION:

Automatic classification performed better than subjective classification with a prediction model comprising structure- and quantity-related morphometric parameters.

ADVANCES IN KNOWLEDGE:

Computer-aided trabecular bone pattern assessment based on morphometric parameters could assist objectivity in clinical bone quality classification.