Effects of monthly intravenous ibandronate on bone mineral density and microstructure in patients with primary osteoporosis after teriparatide treatment: The MONUMENT study

Non-invasive quantification of bone (re)modeling dynamics in adults with osteogenesis imperfecta treated with setrusumab using timelapse high-resolution peripheral-quantitative computed tomography

Timelapse imaging using high-resolution peripheral quantitative computed tomography has emerged as a non-invasive method to quantify bone (re)modeling. However, there is no consensus on how to perform the procedure. As part of the ASTEROID phase-2b multicenter trial, we used 29 same-day repeated scans from adults with OI to identify a method that minimized measurement error. We evaluated input image type, registration method, segmentation mask, and for grayscale images various values for the voxel density difference considered formed or resorbed, minimum formation/resorption cluster size, and Gaussian smoothing sigma. We verified the accuracy of our method and then used it on longitudinal scans (baseline, 6, 12, 18, and 24 mo) from 78 participants to assess bone formation and resorption induced by an anabolic (setrusumab) and anti-catabolic (zoledronic acid) treatments as part of the ASTEROID trial. Regardless of image registration method, binary input images resulted in large errors ~13% and ~8% for first- and second-generation scanners, respectively. For the grayscale input images, errors were smaller for 3D compared to matched angle registration. For both scanner generations, a density threshold of 200 mgHA/cm3 combined with Gaussian noise reduction resulted in errors <1%. We verified the method was accurate by showing that similar regions of bone formation and resorption were identified when comparing each scan from the same-day repeated scans with a scan from another timepoint. Timelapse analysis revealed a dose-dependent increase in bone formation and resorption with setrusumab treatment. Zoledronic acid altered bone changes in favor of formation, although no changes reached statistical significance. This study identifies a timelapse method that minimizes measurement error, which can be used in future studies to improve the uniformity of results. This non-invasive imaging biomarker revealed dose dependent bone (re)modeling outcomes from 1 year of setrusumab treatment in adults with OI.

Automated, calibration-free quantification of cortical bone porosity and geometry in postmenopausal osteoporosis from ultrashort echo time MRI and deep learning

BACKGROUND

Assessment of cortical bone porosity and geometry by imaging in vivo can provide useful information about bone quality that is independent of bone mineral density (BMD). Ultrashort echo time (UTE) MRI techniques of measuring cortical bone porosity and geometry have been extensively validated in preclinical studies and have recently been shown to detect impaired bone quality in vivo in patients with osteoporosis. However, these techniques rely on laborious image segmentation, which is clinically impractical. Additionally, UTE MRI porosity techniques typically require long scan times or external calibration samples and elaborate physics processing, which limit their translatability. To this end, the UTE MRI-derived Suppression Ratio has been proposed as a simple-to-calculate, reference-free biomarker of porosity which can be acquired in clinically feasible acquisition times.

PURPOSE

To explore whether a deep learning method can automate cortical bone segmentation and the corresponding analysis of cortical bone imaging biomarkers, and to investigate the Suppression Ratio as a fast, simple, and reference-free biomarker of cortical bone porosity.

METHODS

In this retrospective study, a deep learning 2D U-Net was trained to segment the tibial cortex from 48 individual image sets comprised of 46 slices each, corresponding to 2208 training slices. Network performance was validated through an external test dataset comprised of 28 scans from 3 groups: (1) 10 healthy, young participants, (2) 9 postmenopausal, non-osteoporotic women, and (3) 9 postmenopausal, osteoporotic women. The accuracy of automated porosity and geometry quantifications were assessed with the coefficient of determination and the intraclass correlation coefficient (ICC). Furthermore, automated MRI biomarkers were compared between groups and to dual energy X-ray absorptiometry (DXA)- and peripheral quantitative CT (pQCT)-derived BMD. Additionally, the Suppression Ratio was compared to UTE porosity techniques based on calibration samples.

RESULTS

The deep learning model provided accurate labeling (Dice score 0.93, intersection-over-union 0.88) and similar results to manual segmentation in quantifying cortical porosity (R2 ≥ 0.97, ICC ≥ 0.98) and geometry (R2 ≥ 0.82, ICC ≥ 0.75) parameters in vivo. Furthermore, the Suppression Ratio was validated compared to established porosity protocols (R2 ≥ 0.78). Automated parameters detected age- and osteoporosis-related impairments in cortical bone porosity (P ≤ .002) and geometry (P values ranging from <0.001 to 0.08). Finally, automated porosity markers showed strong, inverse Pearson's correlations with BMD measured by pQCT (|R| ≥ 0.88) and DXA (|R| ≥ 0.76) in postmenopausal women, confirming that lower mineral density corresponds to greater porosity.

CONCLUSION

This study demonstrated feasibility of a simple, automated, and ionizing-radiation-free protocol for quantifying cortical bone porosity and geometry in vivo from UTE MRI and deep learning.

3D Image Registration Marginally Improves the Precision of HR-pQCT Measurements Compared to Cross-Sectional-Area Registration in Adults With Osteogenesis Imperfecta

Repositioning error in longitudinal high-resolution peripheral-quantitative computed tomography (HR-pQCT) imaging can lead to different bone volumes being assessed over time. To identify the same bone volumes at each time point, image registration is used. While cross-sectional area image registration corrects axial misalignment, 3D registration additionally corrects rotations. Other registration methods involving matched angle analysis (MA) or boundary transformations (3D-TB) can be used to limit interpolation error in 3D-registering micro-finite-element data. We investigated the effect of different image registration methods on short-term in vivo precision in adults with osteogenesis imperfecta, a collagen-related genetic disorder resulting in low bone mass, impaired quality, and increased fragility. The radii and tibiae of 29 participants were imaged twice on the same day with full repositioning. We compared the precision error of different image registration methods for density, microstructural, and micro-finite-element outcomes with data stratified based on anatomical site, motion status, and scanner generation. Regardless of the stratification, we found that image registration improved precision for total and trabecular bone mineral densities, trabecular and cortical bone mineral contents, area measurements, trabecular bone volume fraction, separation, and heterogeneity, as well as cortical thickness and perimeter. 3D registration marginally outperformed cross-sectional area registration for some outcomes, such as trabecular bone volume fraction and separation. Similarly, precision of micro-finite-element outcomes was improved after image registration, with 3D-TB and MA methods providing greatest improvements. Our regression model confirmed the beneficial effect of image registration on HR-pQCT precision errors, whereas motion had a detrimental effect on precision even after image registration. Collectively, our results indicate that 3D registration is recommended for longitudinal HR-pQCT imaging in adults with osteogenesis imperfecta. Since our precision errors are similar to those of healthy adults, these results can likely be extended to other populations, although future studies are needed to confirm this. © 2022 American Society for Bone and Mineral Research (ASBMR).

Enhanced osteogenic differentiation of alendronate-conjugated nanodiamonds for potential osteoporosis treatment

Background

Alendronate (Alen) is promising material used for bone-targeted drug delivery due to its high bone affinity and therapeutic effects on bone diseases. In addition, Alen can enhance the osteogenic differentiation of osteoblastic cell. Recently, nanodiamonds (NDs) with hardness, non-toxicity, and excellent biocompatibility are employed as promising materials for carrier systems and osteogenic differentiation. Therefore, we prepared Alen-conjugated NDs (Alen-NDs) and evaluated their osteogenic differentiation performances.

Methods

Alen-NDs were synthesized using DMTMM as a coupling reagent. Morphological change of Mouse calvaria-derived preosteoblast (MC3T3-E1) treated with Alen-NDs was observed using the confocal microscope. The osteogenic differentiation was confirmed by cell proliferation, alkaline phosphatase (ALP), calcium deposition, and real-time polymerase chain reaction assay.

Results

Alen-NDs were prepared to evaluate their effect on the proliferation and differentiation of osteoblastic MC3T3-E1 cells. The Alen-NDs had a size of about 100 nm, and no cytotoxicity at less than 100 μg/mL of concentration. The treatment of NDs and Alen-NDs reduced the proliferation rate of MC3T3-E1 cells without cell death. Confocal microscopy images confirmed that the treatment of NDs and Alen-NDs changed the cellular morphology from a fibroblastic shape to a cuboidal shape. Flow cytometry, alkaline phosphatase (ALP) activity, calcium deposition, and real-time polymerase chain reaction (RT-PCR) confirmed the higher differentiation of MC3T3-E1 cells treated by Alen-NDs, compared to the groups treated by osteogenic medium and NDs. The higher concentration of Alen-ND treated in MC3T3-E1 resulted in a higher differentiation level.

Conclusions

Alen-NDs can be used as potential therapeutic agents for osteoporosis treatment by inducing osteogenic differentiation.