Proposed diagnostic volumetric bone mineral density thresholds for osteoporosis and osteopenia at the cervicothoracic spine in correlation to the lumbar spine

Simulated low-dose multi-detector computed tomography: spatial effects on surrogate parameters of bone strength at the proximal femur

This study investigated simulated tube current reduction and sparse sampling for low-dose computed tomography (CT) regarding volumetric bone mineral density (vBMD) and cortical bone thickness (Ct.Th) of the proximal femur. Sparse sampling with dose reductions of up to 90% may still allow extraction of bone strength parameters with clinically acceptable accuracy.

INTRODUCTION

We aimed to investigate effects of CT with simulated lowered tube current and sparse sampling on trabecular and cortical vBMD as well as Ct.Th of the entire proximal femur, its subregions, and with detailed spatial assessments.

METHODS

Clinical routine multi-detector CT (MDCT) scans covering the hips from 40 patients were used for simulations of low-dose imaging with 50% and 10% of the original tube current (D50, D10) or projections (P50, P10) combined with statistical iterative reconstruction (SIR), which were then compared against original data with full dose (D100 P100) regarding trabecular vBMD, cortical vBMD, and Ct.Th. An automated framework for multi-parametric assessments was used. Relative errors by comparing measures from original data and simulated low-dose data, regression analyses, Bland-Altman analyses, and statistical parametric mapping (SPM, to assess the spatial distribution of accuracy) were computed.

RESULTS

Sparse sampling enabled drastic reductions of radiation exposure (down to 10% of original imaging) while still producing determinants of bone strength with clinically acceptable relative changes. Lower biases according to Bland-Altman analyses were observed for sparse sampling compared to imaging with virtually lowered tube currents (D10 P100 versus D100 P10) regarding trabecular vBMD, cortical vBMD, as well as Ct.Th. Better accuracy across the whole proximal femur for D100 P50 than for D50 P100 and for D100 P10 than for D10 P100 was observed.

CONCLUSIONS

Sparse sampling with SIR may enable drastic reductions of radiation exposure (up to 90% of original doses) for opportunistically measuring image-based surrogate parameters of bone strength.

Cervicothoracic volumetric bone mineral density assessed by opportunistic QCT may be a reliable marker for osteoporosis in adults

This study aimed to validate the correlation between volumetric bone mineral density in the cervicothoracic and lumbar spine using measurements from opportunistic CT scans. The bone density assessment proved feasible, allowing us to propose optimal cut-off values for diagnosing osteoporosis and predicting vertebral fractures in the cervical and thoracic spine.

OBJECTIVES

To investigate the performance of cervicothoracic volumetric bone mineral density (vBMD), obtained through opportunistic quantitative computed tomography (QCT), in discriminating patients with/without osteoporosis and with/without vertebral fractures (VFs), using lumbar vBMD as the reference.

METHODS

Three hundred twenty-five patients (65.3 ± 19.2 years, 140 women) with routine non-contrast or contrast-enhanced multi-detector CT (MDCT) scans were included. Trabecular vBMD was automatically extracted from each vertebra using a convolutional neural network (CNN)-based framework (SpineQ software v1.0) with asynchronous calibration and contrast phase correction. The correlations of vBMD between each vertebra spanning C2-T12 and the averaged lumbar spine (L1-L3, or L4 and L5) vBMD values were analyzed, considering fracture status and degeneration. Vertebra-specific linear regression equations were used to approximate lumbar vBMD at the cervicothoracic spine.

RESULTS

Cervicothoracic vBMD correlated well with lumbar vBMD (r = 0.79), with significant improvement after excluding degenerated vertebrae (p < 0.05; r = 0.89), except for C7-T3 and T9. Cervical (AUC = 0.94) and thoracic vBMD (AUC = 0.97) showed strong discriminatory ability for osteoporosis (vBMD < 80 mg/cm3). Excluding degenerated vertebrae at the cervical spine increased the AUC to 0.97. Cervical and thoracic vBMD (AUC = 0.74, AUC = 0.72) were comparable to lumbar vBMD (AUC = 0.72) in differentiating patients with and without prevalent VFs. Trabecular vBMD < 190 mg/cm3 for the cervical spine and < 100 mg/cm3 for the thoracic spine were potential indicators of osteoporosis, similar to < 80 mg/cm3 at the lumbar spine.

CONCLUSION

Cervicothoracic vBMD may allow for determination of osteoporosis and prediction of VFs.

The influence of lumbar vertebra and cage related factors on cage-endplate contact after lumbar interbody fusion: An in-vitro experimental study

Lumbar interbody fusion (LIF) using interbody cages is an established treatment for lumbar degenerative disc disease, but fusion results are known to be affected by risk factors such as bone mineral density (BMD), endplate geometry and cage position. At present, direct measurement of endplate-cage contact variables that affect LIF have not been fully identified. The aim of this study was to use cadaveric experiments to investigate the dependency between BMD, endplate geometry, cage parameters like type, orientation, position, and contact variables like stress and area. One vertebral body specimen from each of the five lumbar positions was harvested from five male donors. The lower half of each vertebra was potted and placed in a material testing machine (Instron 8874). A spinal cage was clamped to the machine then lowered to bring it into contact against the superior endplate. A lockable ball-joint was used to rotate the cage such that its inferior surface was congruent with the local endplate surface. A pressure sensor (Tekscan) was placed between the cage and endplate to record contact area and the peak and average contact pressures. Axial compression of 400 N was performed for five positions using a straight cage, and in one anterior position using a curved cage. The linear mixed model was utilised to perform data analyses for experimental results with statistical significance set at p < 0.05. The results indicated two trends toward significance for contact area, one for volumetric BMD (vBMD) of the vertebra (p = 0.081), and another for predicted contact area (p = 0.057). Peak contact pressure correlated significantly with vBMD (p = 0.041), and there was a trend between average contact pressure and lateral position of cage (p = 0.051). In addition, predicted contact area correlated significantly with cage orientation (p < 0.001). These results indicated that high vBMD of vertebra and a medially positioned cage led to higher contact pressures. Logically, low vBMD of vertebra and transverse cage orientation increased the contact area between the cage and endplate. In conclusion, the study identified significant influence of vBMD of vertebra, cage position and orientation on cage-endplate contact which may help to inform cage selection and design for LIF.

Assessing Thoracic Vertebral Bone Mineral Density (T8-T10) for Osteoporosis Diagnosis During CT Lung Cancer Screening in Older Adults

Introduction

Osteoporosis diagnosis often utilizes quantitative computed tomography (QCT). This study explored the validity of applying lumbar bone mineral density (LBMD) standards to thoracic vertebrae (T8-T10) for osteoporosis detection during CT lung cancer screenings. This study investigated the utility of thoracic BMD (BMD-T8-T10) for detecting osteoporosis in older persons during CT lung cancer screening.

Methods

We studied 701 participants who underwent QCT scans for both LBMD and BMD-T8-T10. Osteoporosis was diagnosed using ACR criteria based on LBMD. We determined BMD-T8-T10 thresholds via a receiver operating characteristic (ROC) curve and translated BMD-T8+T9+T10 to LBMD (TTBMD) using linear regression. Kappa test was used to evaluate the accuracy of BMD-T8-T10 thresholds and TTBMD in diagnosing osteoporosis.

Results

Raw BMD-T8-T10 poorly identified osteoporosis (kappa = 0.51). ROC curve analysis identified BMD-T8-T10 thresholds for osteopenia (138 mg/cm3) and osteoporosis (97 mg/cm3) with areas under the curve of 0.97 and 0.99, respectively. We normalized BMD-T8-T10 to TTBMD based on the formula: TTBMD = 0.9 × BMD-T8-T10 - 2.56. These thresholds (kappa = 0.74) and TTBMD performed well in detecting osteoporosis/osteopenia (kappa = 0.74).

Conclusion

Both calculating BMD-T8-T10 threshold (138.0 mg/cm3 for osteopenia and 97 mg/cm3 for osteoporosis) and normalizing BMD-T8-T10 to LBMD demonstrated good performance in identifying osteoporosis in older adults during CT lung cancer screening.

Effect of fully automatic classification model from different tube voltage images on bone density screening: A self-controlled study

PURPOSE

To develop two bone status prediction models combining deep learning and radiomics based on standard-dose chest computed tomography (SDCT) and low-dose chest computed tomography (LDCT), and to evaluate the effect of tube voltage on reproducibility of radiomics features and predictive efficacy of these models.

METHODS

A total of 1508 patients were enrolled in this retrospective study. LDCT was conducted using 80 kVp, tube current ranging from 100 to 475 mA. On the other hand, SDCT was performed using 120 kVp, tube current ranging from 100 to 520 mA. We developed an automatic thoracic vertebral cancellous bone (TVCB) segmentation model. Subsequently, 1184 features were extracted and two classifiers were developed based on LDCT and SDCT images. Based on the diagnostic results of quantitative computed tomography examination, the first-level classifier was initially developed to distinguish normal or abnormal BMD (including osteoporosis and osteopenia), while the second-level classifier was employed to identify osteoporosis or osteopenia. The Dice coefficient was used to evaluate the performance of the automated segmentation model. The Concordance Correlation Coefficients (CCC) of radiomics features were calculated between LDCT and SDCT, and the performance of these models was evaluated.

RESULTS

Our automated segmentation model achieved a Dice coefficient of 0.98 ± 0.01 and 0.97 ± 0.02 in LDCT and SDCT, respectively. Alterations in tube voltage decreased the reproducibility of the extracted radiomic features, with 85.05 % of the radiomic features exhibiting low reproducibility (CCC < 0.75). The area under the curve (AUC) using LDCT-based and SDCT-based models was 0.97 ± 0.01 and 0.94 ± 0.02, respectively. Nonetheless, cross-validation with independent test sets of different tube voltage scans suggests that variations in tube voltage can impair the diagnostic efficacy of the model. Consequently, radiomics models are not universally applicable to images of varying tube voltages. In clinical settings, ensuring consistency between the tube voltage of the image used for model development and that of the acquired patient image is critical.

CONCLUSIONS

Automatic bone status prediction models, utilizing either LDCT or SDCT images, enable accurate assessment of bone status. Tube voltage impacts reproducibility of features and predictive efficacy of models. It is necessary to account for tube voltage variation during the image acquisition.

Fully automated deep learning system for osteoporosis screening using chest computed tomography images

Background

Osteoporosis, a disease stemming from bone metabolism irregularities, affects approximately 200 million people worldwide. Timely detection of osteoporosis is pivotal in grappling with this public health challenge. Deep learning (DL), emerging as a promising methodology in the field of medical imaging, holds considerable potential for the assessment of bone mineral density (BMD). This study aimed to propose an automated DL framework for BMD assessment that integrates localization, segmentation, and ternary classification using various dominant convolutional neural networks (CNNs).

Methods

In this retrospective study, a cohort of 2,274 patients underwent chest computed tomography (CT) was enrolled from January 2022 to June 2023 for the development of the integrated DL system. The study unfolded in 2 phases. Initially, 1,025 patients were selected based on specific criteria to develop an automated segmentation model, utilizing 2 VB-Net networks. Subsequently, a distinct cohort of 902 patients was employed for the development and testing of classification models for BMD assessment. Then, 3 distinct DL network architectures, specifically DenseNet, ResNet-18, and ResNet-50, were applied to formulate the 3-classification BMD assessment model. The performance of both phases was evaluated using an independent test set consisting of 347 individuals. Segmentation performance was evaluated using the Dice similarity coefficient; classification performance was appraised using the receiver operating characteristic (ROC) curve. Furthermore, metrics such as the area under the curve (AUC), accuracy, and precision were meticulously calculated.

Results

In the first stage, the automatic segmentation model demonstrated excellent segmentation performance, with mean Dice surpassing 0.93 in the independent test set. In the second stage, both the DenseNet and ResNet-18 demonstrated excellent diagnostic performance in detecting bone status. For osteoporosis, and osteopenia, the AUCs were as follows: DenseNet achieved 0.94 [95% confidence interval (CI): 0.91-0.97], and 0.91 (95% CI: 0.87-0.94), respectively; ResNet-18 attained 0.96 (95% CI: 0.92-0.98), and 0.91 (95% CI: 0.87-0.94), respectively. However, the ResNet-50 model exhibited suboptimal diagnostic performance for osteopenia, with an AUC value of only 0.76 (95% CI: 0.69-0.80). Alterations in tube voltage had a more pronounced impact on the performance of the DenseNet. In the independent test set with tube voltage at 100 kVp images, the accuracy and precision of DenseNet decreased on average by approximately 14.29% and 18.82%, respectively, whereas the accuracy and precision of ResNet-18 decreased by about 8.33% and 7.14%, respectively.

Conclusions

The state-of-the-art DL framework model offers an effective and efficient approach for opportunistic osteoporosis screening using chest CT, without incurring additional costs or radiation exposure.

Bone mineral density in lower thoracic vertebra for osteoporosis diagnosis in older adults during CT lung cancer screening

BACKGROUND

Quantitative computed tomography (QCT)-based lumbar bone mineral density (LBMD) has been used to diagnose osteoporosis. This study explored the value of lower thoracic BMD (TBMD) in diagnosing osteoporosis in older adults during CT lung cancer screening.

METHODS

This study included 751 subjects who underwent QCT scans with both LBMD and TBMD. 141 of them was selected for a validation. Osteoporosis was diagnosed based on LBMD using the ACR criteria (gold standard). TBMD thresholds were obtained using receiver operating characteristic curve. TBMD was also translated into LBMD (TTBMD) and osteoporosis was defined based on TTBMD using ACR criteria. The performance of TBMD and TTBMD in identifying osteoporosis was determined by Kappa test. The associations between TBMD- and TTBMD-based osteoporosis and fracture were tested in 227 subjects with followed up status of spine fracture.

RESULTS

The performance of TBMD in identifying osteoporosis was low (kappa = 0.66) if using the ACR criteria. Two thresholds of TBMD for identifying osteopenia (128 mg/cm3) and osteoporosis (91 mg/cm3) were obtained with areas under the curve of 0.97 and 0.99, respectively. The performance of the identification of osteoporosis/osteopenia using the two thresholds or TTBMD both had good agreement with the gold standard (kappa = 0.78, 0.86). Similar results were observed in validation population. Osteoporosis identified using the thresholds (adjusted hazard ratio (HR) = 18.72, 95% confidence interval (CI): 5.13-68.36) or TTBMD (adjusted HR = 10.28, 95% CI: 4.22-25.08) were also associated with fractures.

CONCLUSION

Calculating the threshold of TBMD or normalizing TBMD to LBMD are both useful in identifying osteoporosis in older adults during CT lung cancer screening.

Interobserver variability in the determination of bone mineral density in Hounsfield units from differently configured fields of measurement in the cancellous bone of vertebral bodies from elderly body donors

Background

Due to the absence of suitable diagnostic procedures, osteoporosis (OP) is frequently detected late or not at all. Many elderly persons undergo computed tomographies (CT). The routine determination of Hounsfield units (HU) in bone as a part of these examinations could close a gap here.

Methods

Spines were extracted from 22 body donors, fixed in a PVC water phantom, and subjected to a high-resolution CT investigation. Cancellous bone was examined and its bone mineral density measured in HU from cervical vertebra 3 to lumbar vertebra 5 (484 vertebral bodies). On sagittal sections, a circular and a rectangular region of interest (ROI) were defined in mid-vertebral cancellous bone, positioned manually, and the measurements were performed by three experienced radiologists. Bone mineral density (BMD), measured in mg/cm3, was used to determine the presence of OP.

Results

All of the spines were osteoporotic. In the presence of a BMD below 60 mg/cm3 and HU values below 63.36 in lumbar vertebrae, there were significantly more vertebral body fractures in the thoracic and thoracolumbar spine. No difference was observed between the manually positioned circular and rectangular regions of interest (ROI) on the sagittal CT section (p > 0.05). Similar HU counts were obtained by the individual examiners (p > 0.05). The following formula was used to determine QCT values on a non-contrasted CT of the spine: QCT = 0.6 × HU + 13.7.

Conclusions

Measurement of the density of cancellous bone in HU can be used to determine BMD for estimating demineralization. Quantitative BMD values in mg/cm3, which can be calculated from the HU data, concur well with QCT values.

Sex differences and age-related changes in vertebral body volume and volumetric bone mineral density at the thoracolumbar spine using opportunistic QCT

Objectives

To quantitatively investigate the age- and sex-related longitudinal changes in trabecular volumetric bone mineral density (vBMD) and vertebral body volume at the thoracolumbar spine in adults.

Methods

We retrospectively included 168 adults (mean age 58.7 ± 9.8 years, 51 women) who received ≥7 MDCT scans over a period of ≥6.5 years (mean follow-up 9.0 ± 2.1 years) for clinical reasons. Level-wise vBMD and vertebral body volume were extracted from 22720 thoracolumbar vertebrae using a convolutional neural network (CNN)-based framework with asynchronous calibration and correction of the contrast media phase. Human readers conducted semiquantitative assessment of fracture status and bony degenerations.

Results

In the 40-60 years age group, women had a significantly higher trabecular vBMD than men at all thoracolumbar levels (p<0.05 to p<0.001). Conversely, men, on average, had larger vertebrae with lower vBMD. This sex difference in vBMD did not persist in the 60-80 years age group. While the lumbar (T12-L5) vBMD slopes in women only showed a non-significant trend of accelerated decline with age, vertebrae T1-11 displayed a distinct pattern, with women demonstrating a significantly accelerated decline compared to men (p<0.01 to p<0.0001). Between baseline and last follow-up examinations, the vertebral body volume slightly increased in women (T1-12: 1.1 ± 1.0 cm3; L1-5: 1.0 ± 1.4 cm3) and men (T1-12: 1.2 ± 1.3 cm3; L1-5: 1.5 ± 1.6 cm3). After excluding vertebrae with bony degenerations, the residual increase was only small in women (T1-12: 0.6 ± 0.6 cm3; L1-5: 0.7 ± 0.7 cm3) and men (T1-12: 0.7 ± 0.6 cm3; L1-5: 1.2 ± 0.8 cm3). In non-degenerated vertebrae, the mean change in volume was <5% of the respective vertebral body volumes.

Conclusion

Sex differences in thoracolumbar vBMD were apparent before menopause, and disappeared after menopause, likely attributable to an accelerated and more profound vBMD decline in women at the thoracic spine. In patients without advanced spine degeneration, the overall volumetric changes in the vertebral body appeared subtle.

Opportunistic screening for osteoporosis using enhanced images based on dual-energy computed tomography material decomposition: a comparison with quantitative computed tomography

Background

Many patients with malignant tumors require chemotherapy and radiation therapy, which can result in a decline in physical function and potentially influence bone mineral density (BMD). Furthermore, these treatments necessitate enhanced computed tomography (CT) scans for determining disease staging or treatment outcomes, and opportunistic screening with available imaging data is beneficial for patients at high risk for osteoporosis if existing imaging data can be used. The study aimed to investigate the feasibility of opportunistic screening for osteoporosis using enhanced CT based on a dual-energy CT (DECT) material decomposition technique.

Methods

We prospectively enrolled 346 consecutive patients who underwent abdominal unenhanced and triphasic contrast-enhanced CT (arterial, portal venous, and delayed phases) between June 2021 and June 2022. The BMD, and the density of hydroxyapatite (HAP) on HAP-iodine images and calcium (Ca) on Ca-iodine images were measured on the L1-L3 vertebral bodies. The iodine intake was recorded. Pearson analysis was conducted to assess the correlation between iodine intake and the density values in three phases and the correlation between BMD and the densities of HAP and Ca. Furthermore, linear regression was employed for quantitative evaluation. Bland-Altman analysis was used to evaluate the agreement between calculated BMD derived from DECT (BMD-DECT) and reference BMD derived from quantitative CT (BMD-QCT). Receiver operating characteristic (ROC) analysis was applied to assess the diagnostic efficacy.

Results

The HAP and Ca density of the L1-L3 vertebral bodies did not differ significantly among the three phases of contrast-enhanced CT (F=0.001-0.049; P>0.05). Significant positive correlations were found between HAP, Ca densities, and BMD (HAP-BMD: r=0.9472, R2=0.8973; Ca-BMD: r=0.9470, R2=0.8968; all P<0.001). Bland-Altman plots showed high agreement between BMD-DECT and BMD-QCT. The area under the curve (AUC) using HAP and Ca measurements was 0.963 [95% confidence interval (CI): 0.937-0.980] and 0.964 (95% CI: 0.939-0.981), respectively, for diagnosing osteoporosis and was 0.951 (95% CI: 0.917-0.973) and 0.950 (95% CI: 0.916-0.973), respectively, for diagnosing osteopenia.

Conclusions

The HAP and Ca density measurements generated through the material decomposition technique in DECT have good diagnostic performances in assessing BMD, which offers a new perspective for opportunistic screening of osteoporosis on contrast-enhanced CT.

Differentiation of benign and malignant vertebral fractures using a convolutional neural network to extract CT-based texture features

PURPOSE

To assess the diagnostic performance of three-dimensional (3D) CT-based texture features (TFs) using a convolutional neural network (CNN)-based framework to differentiate benign (osteoporotic) and malignant vertebral fractures (VFs).

METHODS

A total of 409 patients who underwent routine thoracolumbar spine CT at two institutions were included. VFs were categorized as benign or malignant using either biopsy or imaging follow-up of at least three months as standard of reference. Automated detection, labelling, and segmentation of the vertebrae were performed using a CNN-based framework ( https://anduin.bonescreen.de ). Eight TFs were extracted: Varianceglobal, Skewnessglobal, energy, entropy, short-run emphasis (SRE), long-run emphasis (LRE), run-length non-uniformity (RLN), and run percentage (RP). Multivariate regression models adjusted for age and sex were used to compare TFs between benign and malignant VFs.

RESULTS

Skewnessglobal showed a significant difference between the two groups when analyzing fractured vertebrae from T1 to L6 (benign fracture group: 0.70 [0.64-0.76]; malignant fracture group: 0.59 [0.56-0.63]; and p = 0.017), suggesting a higher skewness in benign VFs compared to malignant VFs.

CONCLUSION

Three-dimensional CT-based global TF skewness assessed using a CNN-based framework showed significant difference between benign and malignant thoracolumbar VFs and may therefore contribute to the clinical diagnostic work-up of patients with VFs.

Long-term reproducibility of opportunistically assessed vertebral bone mineral density and texture features in routine clinical multi-detector computed tomography using an automated segmentation framework

Background

To investigate reproducibility of texture features and volumetric bone mineral density (vBMD) extracted from trabecular bone in the thoracolumbar spine in routine clinical multi-detector computed tomography (MDCT) data in a single scanner environment.

Methods

Patients who underwent two routine clinical thoraco-abdominal MDCT exams at a single scanner with a time interval of 6 to 26 months (n=203, 131 males; time interval mean, 13 months; median, 12 months) were included in this observational study. Exclusion criteria were metabolic and hematological disorders, bone metastases, use of bone-active medications, and history of osteoporotic vertebral fractures (VFs) or prior diagnosis of osteoporosis. A convolutional neural network (CNN)-based framework was used for automated spine labeling and segmentation (T5-L5), asynchronous Hounsfield unit (HU)-to-BMD calibration, and correction for the intravenous contrast medium phase. Vertebral vBMD and six texture features [varianceglobal, entropy, short-run emphasis (SRE), long-run emphasis (LRE), run-length non-uniformity (RLN), and run percentage (RP)] were extracted for mid- (T5-T8) and lower thoracic (T9-T12), and lumbar vertebrae (L1-L5), respectively. Relative annual changes were calculated in texture features and vBMD for each vertebral level and sorted by sex, and changes were checked for statistical significance (P<0.05) using paired t-tests. Root mean square coefficient of variation (RMSCV) and root mean square error (RMSE) were calculated as measures of variability.

Results

SRE, LRE, RLN, and RP exhibited substantial reproducibility with RMSCV-values below 2%, for both sexes and at all spine levels, while vBMD was less reproducible (RMSCV =11.9-16.2%). Entropy showed highest variability (RMSCV =4.34-7.69%) due to statistically significant increases [range, mean ± standard deviation: (4.40±5.78)% to (8.36±8.66)%, P<0.001]. RMSCV of varianceglobal ranged from 1.60% to 3.03%.

Conclusions

Opportunistic assessment of texture features in a single scanner environment using the presented CNN-based framework yields substantial reproducibility, outperforming vBMD reproducibility. Lowest scan-rescan variability was found for higher-order texture features. Further studies are warranted to determine, whether microarchitectural changes to the trabecular bone may be assessed through texture features.

Automated Opportunistic Trabecular Volumetric Bone Mineral Density Extraction Outperforms Manual Measurements for the Prediction of Vertebral Fractures in Routine CT

Opportunistic osteoporosis screening using multidetector CT-scans (MDCT) and convolutional neural network (CNN)-derived segmentations of the spine to generate volumetric bone mineral density (vBMD) bears the potential to improve incidental osteoporotic vertebral fracture (VF) prediction. However, the performance compared to the established manual opportunistic vBMD measures remains unclear. Hence, we investigated patients with a routine MDCT of the spine who had developed a new osteoporotic incidental VF and frequency matched to patients without incidental VFs as assessed on follow-up MDCT images after 1.5 years. Automated vBMD was generated using CNN-generated segmentation masks and asynchronous calibration. Additionally, manual vBMD was sampled by two radiologists. Automated vBMD measurements in patients with incidental VFs at 1.5-years follow-up (n = 53) were significantly lower compared to patients without incidental VFs (n = 104) (83.6 ± 29.4 mg/cm³ vs. 102.1 ± 27.7 mg/cm³, p < 0.001). This comparison was not significant for manually assessed vBMD (99.2 ± 37.6 mg/cm³ vs. 107.9 ± 33.9 mg/cm³, p = 0.30). When adjusting for age and sex, both automated and manual vBMD measurements were significantly associated with incidental VFs at 1.5-year follow-up, however, the associations were stronger for automated measurements (β = -0.32; 95% confidence interval (CI): -20.10, 4.35; p < 0.001) compared to manual measurements (β = -0.15; 95% CI: -11.16, 5.16; p < 0.03). In conclusion, automated opportunistic measurements are feasible and can be useful for bone mineral density assessment in clinical routine.

The role of bone metastases on the mechanical competence of human vertebrae

Spine is the most common site for bone metastases. The evaluation of the mechanical competence and failure location in metastatic vertebrae is a biomechanical and clinical challenge. Little is known about the failure behaviour of vertebrae with metastatic lesions. The aim of this study was to use combined micro-Computed Tomography (microCT) and time-lapsed mechanical testing to reveal the failure location in metastatic vertebrae. Fifteen spine segments, each including a metastatic and a radiologically healthy vertebra, were tested in compression up to failure within a microCT. Volumetric strains were measured using Digital Volume Correlation. The images of undeformed and deformed specimens were overlapped to identify the failure location. Vertebrae with lytic metastases experienced the largest average compressive strains (median ± standard deviation: -8506 ± 4748microstrain), followed by the vertebrae with mixed metastases (-7035 ± 15605microstrain), the radiologically healthy vertebrae (-5743 ± 5697microstrain), and the vertebrae with blastic metastases (-3150 ± 4641microstrain). Strain peaks were localised within and nearby the lytic lesions or around the blastic tissue. Failure between the endplate and the metastasis was identified in vertebrae with lytic metastases, whereas failure localised around the metastasis in vertebrae with blastic lesions. This study showed for the first time the role of metastases on the vertebral internal deformations. While lytic lesions lead to failure of the metastatic vertebra, vertebrae with blastic metastases are more likely to induce failure in the adjacent vertebrae. Nevertheless, every metastatic lesion affects the vertebral deformation differently, making it essential to assess how the lesion affects the bone microstructure. These results suggest that the properties of the lesion (type, size, location within the vertebral body) should be considered when developing clinical tools to predict the risk of fracture in patients with metastatic lesions.

Development of a system to assess the two- and three-dimensional bone mineral density of the lumbar vertebrae from clinical quantitative CT images

This study developed a system to quantify the lumbar spine's bone mineral density (BMD) in two and three dimensions for osteoporosis screening using quantitative CT images. Measuring the two-dimensional BMD could reproduce the BMD measurement performed in dual-energy X-ray absorptiometry, and an accurate diagnosis of osteoporosis was possible.

PURPOSE

To date, the assessment of bone mineral density (BMD) using CT images has been made in three dimensions, leading to errors in detecting osteoporosis based on the two-dimensional assessments of BMD using dual-energy X-ray absorptiometry (DXA-BMD). Herein, we aimed to develop a system that measures two- and three-dimensional lumbar BMD from quantitative CT images and validated the accuracy of the system in diagnosing osteoporosis with regard to the DXA classification.

METHODS

Fifty-nine pairs of spinal CT and DXA images were analyzed. First, the three-dimensional BMD was measured at the axial slice of the L1 vertebra on CT images (L1-vBMD). Then, the L1-L4 vertebrae were segmented from the CT images to measure the three-dimensional BMD at the trabecular region of the L1-L4 vertebral bodies (CT-vBMD). Lastly, the segmented vertebrae were projected onto the coronal plane to measure the two-dimensional BMD (CT-aBMD). Each parameter was correlated with DXA-BMD, and the receiver operating characteristic (ROC) curve to diagnose osteoporosis was assessed.

RESULTS

The correlation coefficients of DXA-BMD with L1-vBMD, CT-vBMD, and CT-aBMD were 0.364, 0.456, and 0.911, respectively (all p < 0.01). In the ROC curve analysis to diagnose osteoporosis, the area under the curve for CT-aBMD (0.941) was significantly higher than those for L1-vBMD (0.582) and CT-vBMD (0.657) (both p < 0.01).

CONCLUSION

Compared with L1-vBMD and CT-vBMD, CT-aBMD could accurately predict DXA-BMD and detect patients with osteoporosis. Given that our method can quantify BMD in both two and three dimensions, it could be used to screen for osteoporosis from quantitative CT images.

Automated Opportunistic Osteoporosis Screening in Routine Computed Tomography of the Spine: Comparison With Dedicated Quantitative CT

Opportunistic osteoporosis screening in nondedicated routine computed tomography (CT) is of increasing importance. The purpose of this study was to compare lumbar volumetric bone mineral density (vBMD) assessed by a convolutional neural network (CNN)-based framework in routine CT to vBMD from dedicated quantitative CT (QCT), and to evaluate the ability of vBMD and surrogate measurements of Hounsfield units (HU) to distinguish between patients with and without osteoporotic vertebral fractures (VFs). A total of 144 patients (median age: 70.7 years, 93 females) with clinical routine CT (eight different CT scanners, 120 kVp or 140 kVp, with and without intravenous contrast medium) and dedicated QCT acquired within ≤30 days were included. Vertebral measurements included (i) vBMD from the CNN-based approach including automated vertebral body labeling, segmentation, and correction of the contrast media phase for routine CT data (vBMD_OPP), (ii) vBMD from dedicated QCT (vBMD_QCT), and (iii) noncalibrated HU from vertebral bodies of routine CT data as previously proposed for immanent opportunistic osteoporosis screening based on CT attenuation. The intraclass correlation coefficient (ICC) for vBMD_QCT versus vBMD_OPP indicated better agreement (ICC = 0.913) than the ICC for vBMD_QCT versus noncalibrated HU (ICC = 0.704). Bland-Altman analysis showed data points from 137 patients (95.1%) within the limits of agreement (LOA) of -23.2 to 25.0 mg/cm³ for vBMD_QCT versus vBMD_OPP. Osteoporosis (vBMD <80 mg/cm³ ) was detected in 89 patients (vBMD_QCT) and 88 patients (vBMD_OPP), whereas no patient crossed the diagnostic thresholds from normal vBMD to osteoporosis or vice versa. In a subcohort of 88 patients (thoracolumbar spine covered by imaging for VF reading), 69 patients showed one or more prevalent VFs, and the performance for discrimination between patients with and without VFs was best for vBMD_OPP (area under the curve [AUC] = 0.862; 95% confidence interval [CI], 0.771-0.953). In conclusion, automated opportunistic osteoporosis screening in routine CT of various scanner setups is feasible and may demonstrate high diagnostic accuracy for prevalent VFs. © 2022 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Patient-Specific Finite Element Modeling of the Whole Lumbar Spine Using Clinical Routine Multi-Detector Computed Tomography (MDCT) Data-A Pilot Study

(1) Background: To study the feasibility of developing finite element (FE) models of the whole lumbar spine using clinical routine multi-detector computed tomography (MDCT) scans to predict failure load (FL) and range of motion (ROM) parameters. (2) Methods: MDCT scans of 12 subjects (6 healthy controls (HC), mean age ± standard deviation (SD): 62.16 ± 10.24 years, and 6 osteoporotic patients (OP), mean age ± SD: 65.83 ± 11.19 years) were included in the current study. Comprehensive FE models of the lumbar spine (5 vertebrae + 4 intervertebral discs (IVDs) + ligaments) were generated (L1−L5) and simulated. The coefficients of correlation (ρ) were calculated to investigate the relationship between FE-based FL and ROM parameters and bone mineral density (BMD) values of L1−L3 derived from MDCT (BMDQCT-L1-3). Finally, Mann−Whitney U tests were performed to analyze differences in FL and ROM parameters between HC and OP cohorts. (3) Results: Mean FE-based FL value of the HC cohort was significantly higher than that of the OP cohort (1471.50 ± 275.69 N (HC) vs. 763.33 ± 166.70 N (OP), p < 0.01). A strong correlation of 0.8 (p < 0.01) was observed between FE-based FL and BMDQCT-L1-L3 values. However, no significant differences were observed between ROM parameters of HC and OP cohorts (p = 0.69 for flexion; p = 0.69 for extension; p = 0.47 for lateral bending; p = 0.13 for twisting). In addition, no statistically significant correlations were observed between ROM parameters and BMDQCT- L1-3. (4) Conclusions: Clinical routine MDCT data can be used for patient-specific FE modeling of the whole lumbar spine. ROM parameters do not seem to be significantly altered between HC and OP. In contrast, FE-derived FL may help identify patients with increased osteoporotic fracture risk in the future.

Finite Element Analysis of Osteoporotic and Osteoblastic Vertebrae and Its Association With the Proton Density Fat Fraction From Chemical Shift Encoding-Based Water-Fat MRI - A Preliminary Study

PURPOSE

Osteoporosis is prevalent and entails alterations of vertebral bone and marrow. Yet, the spine is also a common site of metastatic spread. Parameters that can be non-invasively measured and could capture these alterations are the volumetric bone mineral density (vBMD), proton density fat fraction (PDFF) as an estimate of relative fat content, and failure displacement and load from finite element analysis (FEA) for assessment of bone strength. This study's purpose was to investigate if osteoporotic and osteoblastic metastatic changes in lumbar vertebrae can be differentiated based on the abovementioned parameters (vBMD, PDFF, and measures from FEA), and how these parameters correlate with each other.

MATERIALS AND METHODS

Seven patients (3 females, median age: 77.5 years) who received 3-Tesla magnetic resonance imaging (MRI) and multi-detector computed tomography (CT) of the lumbar spine and were diagnosed with either osteoporosis (4 patients) or diffuse osteoblastic metastases (3 patients) were included. Chemical shift encoding-based water-fat MRI (CSE-MRI) was used to extract the PDFF, while vBMD was extracted after automated vertebral body segmentation using CT. Segmentation masks were used for FEA-based failure displacement and failure load calculations. Failure displacement, failure load, and PDFF were compared between patients with osteoporotic vertebrae versus patients with osteoblastic metastases, considering non-fractured vertebrae (L1-L4). Associations between those parameters were assessed using Spearman correlation.

RESULTS

Median vBMD was 59.3 mg/cm³ in osteoporotic patients. Median PDFF was lower in the metastatic compared to the osteoporotic patients (11.9% vs. 43.8%, p=0.032). Median failure displacement and failure load were significantly higher in metastatic compared to osteoporotic patients (0.874 mm vs. 0.348 mm, 29,589 N vs. 3,095 N, p=0.034 each). A strong correlation was noted between PDFF and failure displacement (rho -0.679, p=0.094). A very strong correlation was noted between PDFF and failure load (rho -0.893, p=0.007).

CONCLUSION

PDFF as well as failure displacement and load allowed to distinguish osteoporotic from diffuse osteoblastic vertebrae. Our findings further show strong associations between PDFF and failure displacement and load, thus may indicate complimentary pathophysiological associations derived from two non-invasive techniques (CSE-MRI and CT) that inherently measure different properties of vertebral bone and marrow.