Decreased bone mineral density (BMD) impairs screw purchase in trabecular bone and can cause screw loosening following spinal instrumentation. Existing computed tomography (CT) scans could be used for opportunistic osteoporosis screening for decreased BMD. Purpose of this case-control study was to investigate the association of opportunistically assessed BMD with the outcome after spinal surgery with semi-rigid instrumentation for lumbar degenerative instability.

Background: Bone marrow fat (BMF) fraction quantification in vertebral bodies is used as a novel imaging biomarker to assess and characterize chronic lower back pain. However, manual segmentation of vertebral bodies is time consuming and laborious. Purpose: (1) Develop a deep learning pipeline for segmentation of vertebral bodies using quantitative water-fat MRI. (2) Compare BMF measurements between manual and automatic segmentation methods to assess performance. Materials and Methods: In this retrospective study, MR images using a 3D spoiled gradient-recalled echo (SPGR) sequence with Iterative Decomposition of water and fat with Echo Asymmetry and Least-squares estimation (IDEAL) reconstruction algorithm were obtained in 57 subjects (28 women, 29 men, mean age, 47.2 ± 12.6 years). An artificial network was trained for 100 epochs on a total of 165 lumbar vertebrae manually segmented from 31 subjects. Performance was assessed by analyzing the receiver operating characteristic curve, precision-recall, F1 scores, specificity, sensitivity, and similarity metrics. Bland-Altman analysis was used to assess performance of BMF fraction quantification using the predicted segmentations. Results: The deep learning segmentation method achieved an AUC of 0.92 (CI 95%: 0.9186, 0.9195) on a testing dataset (n = 24 subjects) on classification of pixels as vertebrae. A sensitivity of 0.99 and specificity of 0.80 were achieved for a testing dataset, and a mean Dice similarity coefficient of 0.849 ± 0.091. Comparing manual and automatic segmentations on fat fraction maps of lumbar vertebrae (n = 124 vertebral bodies) using Bland-Altman analysis resulted in a bias of only -0.605% (CI 95% = -0.847 to -0.363%) and agreement limits of -3.275% and +2.065%. Automatic segmentation was also feasible in 16 ± 1 s. Conclusion: Our results have demonstrated the feasibility of automated segmentation of vertebral bodies using deep learning models on water-fat MR (Dixon) images to define vertebral regions of interest with high specificity. These regions of interest can then be used to quantify BMF with comparable results as manual segmentation, providing a framework for completely automated investigation of vertebral changes in CLBP.

Unloading associated with spaceflight results in bone loss and increased fracture risk. Bone morphogenetic protein 2 (BMP2) is known to enhance bone formation, in part, through molecular pathways associated with mechanical loading; however, the effects of BMP2 during spaceflight remain unclear. Here, we investigated the systemic effects of BMP2 on mice sustaining a femoral fracture followed by housing in spaceflight (International Space Station or ISS) or on Earth. We hypothesized that in spaceflight, the systemic effects of BMP2 on weight-bearing bones would be blunted compared to that observed on Earth. Nine-week-old male mice were divided into four groups: 1) Saline+Earth; 2) BMP+Earth; 3) Saline+ISS; and 4) BMP+ISS (n = 10 mice/group, but only n = 5 mice/group were reserved for micro-computed tomography analyses). All mice underwent femoral defect surgery and were followed for approximately 4 weeks. We found a significant reduction in trabecular separation within the lumbar vertebrae after administering BMP2 at the fracture site of mice housed on Earth. In contrast, BMP2 treatment led to a significant increase in trabecular separation concomitant with a reduction in trabecular number within spaceflown tibiae. Although these and other lines of evidence support our hypothesis, the small sample size associated with rodent spaceflight studies limits interpretations. That said, it appears that a locally applied single dose of BMP2 at the femoral fracture site can have a systemic impact on distant bones, affecting bone quantity in several skeletal sites. Moreover, our results suggest that BMP2 treatment works through a pathway involving mechanical loading in which the best outcomes during its treatment on Earth occurred in the weight-bearing bones and in spaceflight occurred in bones subjected to higher muscle contraction.

Objective: To study the impact of dose reduction in MDCT images through tube current reduction or sparse sampling on the vertebral bone strength prediction using finite element (FE) analysis for fracture risk assessment. Methods: Routine MDCT data covering lumbar vertebrae of 12 subjects (six male; six female; 74.70 ± 9.13 years old) were included in this study. Sparsely sampled and virtually reduced tube current-based MDCT images were computed using statistical iterative reconstruction (SIR) with reduced dose levels at 50, 25, and 10% of the tube current and original projections, respectively. Subject-specific static non-linear FE analyses were performed on vertebra models (L1, L2, and L3) 3-D-reconstructed from those dose-reduced MDCT images to predict bone strength. Coefficient of correlation (R2), Bland-Altman plots, and root mean square coefficient of variation (RMSCV) were calculated to find the variation in the FE-predicted strength at different dose levels, using high-intensity dose-based strength as the reference. Results: FE-predicted failure loads were not significantly affected by up to 90% dose reduction through sparse sampling (R2 = 0.93, RMSCV = 8.6% for 50%; R2 = 0.89, RMSCV = 11.90% for 75%; R2 = 0.86, RMSCV = 11.30% for 90%) and up to 50% dose reduction through tube current reduction method (R2 = 0.96, RMSCV = 12.06%). However, further reduction in dose with the tube current reduction method affected the ability to predict the failure load accurately (R2 = 0.88, RMSCV = 22.04% for 75%; R2 = 0.43, RMSCV = 54.18% for 90%). Conclusion: Results from this study suggest that a 50% radiation dose reduction through reduced tube current and a 90% radiation dose reduction through sparse sampling can be used to predict vertebral bone strength. Our findings suggest that the sparse sampling-based method performs better than the tube current-reduction method in generating images required for FE-based bone strength prediction models.