Browsing by Subject "High resolution peripheral quantitative computed tomography"

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    A Comprehensive Set of Ultrashort Echo Time Magnetic Resonance Imaging Biomarkers to Assess Cortical Bone Health: A Feasibility Study at Clinical Field Strength
    (Elsevier, 2024) Jacobson, Andrea M.; Zhao, Xuandong; Sommer, Stefan; Sadik, Farhan; Warden, Stuart J.; Newman, Christopher; Siegmund, Thomas; Allen, Matthew R.; Surowiec, Rachel K.; Radiology and Imaging Sciences, School of Medicine
    Introduction: Conventional bone imaging methods primarily use X-ray techniques to assess bone mineral density (BMD), focusing exclusively on the mineral phase. This approach lacks information about the organic phase and bone water content, resulting in an incomplete evaluation of bone health. Recent research highlights the potential of ultrashort echo time magnetic resonance imaging (UTE MRI) to measure cortical porosity and estimate BMD based on signal intensity. UTE MRI also provides insights into bone water distribution and matrix organization, enabling a comprehensive bone assessment with a single imaging technique. Our study aimed to establish quantifiable UTE MRI-based biomarkers at clinical field strength to estimate BMD and microarchitecture while quantifying bound water content and matrix organization. Methods: Femoral bones from 11 cadaveric specimens (n = 4 males 67-92 yrs of age, n = 7 females 70-95 yrs of age) underwent dual-echo UTE MRI (3.0 T, 0.45 mm resolution) with different echo times and high resolution peripheral quantitative computed tomography (HR-pQCT) imaging (60.7 μm voxel size). Following registration, a 4.5 mm HR-pQCT region of interest was divided into four quadrants and used across the multi-modal images. Statistical analysis involved Pearson correlation between UTE MRI porosity index and a signal-intensity technique used to estimate BMD with corresponding HR-pQCT measures. UTE MRI was used to calculate T1 relaxation time and a novel bound water index (BWI), compared across subregions using repeated measures ANOVA. Results: The UTE MRI-derived porosity index and signal-intensity-based estimated BMD correlated with the HR-pQCT variables (porosity: r = 0.73, p = 0.006; BMD: r = 0.79, p = 0.002). However, these correlations varied in strength when we examined each of the four quadrants (subregions, r = 0.11-0.71). T1 relaxometry and the BWI exhibited variations across the four subregions, though these differences were not statistically significant. Notably, we observed a strong negative correlation between T1 relaxation time and the BWI (r = -0.87, p = 0.0006). Conclusion: UTE MRI shows promise for being an innocuous method for estimating cortical porosity and BMD parameters while also giving insight into bone hydration and matrix organization. This method offers the potential to equip clinicians with a more comprehensive array of imaging biomarkers to assess bone health without the need for invasive or ionizing procedures.
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    Integrating deep learning and machine learning for improved CKD-related cortical bone assessment in HRpQCT images: A pilot study
    (Elsevier, 2024-12-26) Lee, Youngjun; Bandara, Wikum R.; Park, Sangjun; Lee, Miran; Seo, Choongboem; Yang, Sunwoo; Lim, Kenneth J.; Moe, Sharon M.; Warden, Stuart J.; Surowiec, Rachel K.; Medicine, School of Medicine
    High resolution peripheral quantitative computed tomography (HRpQCT) offers detailed bone geometry and microarchitecture assessment, including cortical porosity, but assessing chronic kidney disease (CKD) bone images remains challenging. This proof-of-concept study merges deep learning and machine learning to 1) improve automatic segmentation, particularly in cases with severe cortical porosity and trabeculated endosteal surfaces, and 2) maximize image information using machine learning feature extraction to classify CKD-related skeletal abnormalities, surpassing conventional DXA and CT measures. We included 30 individuals (20 non-CKD, 10 stage 3 to 5D CKD) who underwent HRpQCT of the distal and diaphyseal radius and tibia and contributed data to develop and validate four different AI models for each anatomical site. Manually annotated cortical bone was used to train each segmentation deep-learning model. Textural features were extracted via Gray-Level Co-occurrence Matrix (GLCM) and classified as CKD or non-CKD using XGBoost with each segmentation model. For comparison, manufacturer-supplied segmentation was used to extract cortical geometry, microarchitecture, and finite element analysis (FEA) outcomes. Model performance was confirmed using the test dataset and a separate independent validation cohort which included HRpQCT imaging from 42 additional individuals (18 non-CKD, 24 CKD stage 5D). For segmentation, the diaphyseal location showed strong performance on test datasets, with Mean IoUs of 0.96 and 0.95, and accuracies of 0.97 for both radius and tibia sites in CKD. Model 4 developed from the diaphyseal tibia region excelled in classifying test and independent validation datasets, achieving F1 scores of 0.99 and 0.96, AUCs of 0.99 and 0.94, sensitivities of 0.99, and specificities of 0.99 and 0.92. No single parameter, including BMD and cortical porosity, among conventional CT outcomes consistently differentiated CKD from non-CKD across all anatomical sites. Integrating HRpQCT with deep and machine learning, this innovative approach enables precise automatic segmentation of severely deteriorated endocortical surfaces and enhances sensitivity to CKD-related cortical bone changes compared to standard DXA and HRpQCT outcomes.