Regional analysis of age-related local bone loss in the spine of a healthy population using 3D voxel-based modeling

Preoperative Prediction of New Vertebral Fractures after Vertebral Augmentation with a Radiomics Nomogram

The occurrence of new vertebral fractures (NVFs) after vertebral augmentation (VA) procedures is common in patients with osteoporotic vertebral compression fractures (OVCFs), leading to painful experiences and financial burdens. We aim to develop a radiomics nomogram for the preoperative prediction of NVFs after VA. Data from center 1 (training set: n = 153; internal validation set: n = 66) and center 2 (external validation set: n = 44) were retrospectively collected. Radiomics features were extracted from MRI images and radiomics scores (radscores) were constructed for each level-specific vertebra based on least absolute shrinkage and selection operator (LASSO). The radiomics nomogram, integrating radiomics signature with presence of intravertebral cleft and number of previous vertebral fractures, was developed by multivariable logistic regression analysis. The predictive performance of the vertebrae was level-specific based on radscores and was generally superior to clinical variables. RadscoreL2 had the optimal discrimination (AUC ≥ 0.751). The nomogram provided good predictive performance (AUC ≥ 0.834), favorable calibration, and large clinical net benefits in each set. It was used successfully to categorize patients into high- or low-risk subgroups. As a noninvasive preoperative prediction tool, the MRI-based radiomics nomogram holds great promise for individualized prediction of NVFs following VA.

Artificial intelligence-based radiomics on computed tomography of lumbar spine in subjects with fragility vertebral fractures

PURPOSE

There is emerging evidence that radiomics analyses can improve detection of skeletal fragility. In this cross-sectional study, we evaluated radiomics features (RFs) on computed tomography (CT) images of the lumbar spine in subjects with or without fragility vertebral fractures (VFs).

METHODS

Two-hundred-forty consecutive individuals (mean age 60.4 ± 15.4, 130 males) were evaluated by radiomics analyses on opportunistic lumbar spine CT. VFs were diagnosed in 58 subjects by morphometric approach on CT or XR-ray spine (D4-L4) images. DXA measurement of bone mineral density (BMD) was performed on 17 subjects with VFs.

RESULTS

Twenty RFs were used to develop the machine learning model reaching 0.839 and 0.789 of AUROC in the train and test datasets, respectively. After correction for age, VFs were significantly associated with RFs obtained from non-fractured vertebrae indicating altered trabecular microarchitecture, such as low-gray level zone emphasis (LGLZE) [odds ratio (OR) 1.675, 95% confidence interval (CI) 1.215-2.310], gray level non-uniformity (GLN) (OR 1.403, 95% CI 1.023-1.924) and neighboring gray-tone difference matrix (NGTDM) contrast (OR 0.692, 95% CI 0.493-0.971). Noteworthy, no significant differences in LGLZE (p = 0.94), GLN (p = 0.40) and NGDTM contrast (p = 0.54) were found between fractured subjects with BMD T score < - 2.5 SD and those in whom VFs developed in absence of densitometric diagnosis of osteoporosis.

CONCLUSIONS

Artificial intelligence-based analyses on spine CT images identified RFs associated with fragility VFs. Future studies are needed to test the predictive value of RFs on opportunistic CT scans in identifying subjects with primary and secondary osteoporosis at high risk of fracture.

Focal osteoporosis defect is associated with vertebral compression fracture prevalence in a bone mineral density-independent manner

Introduction

Focal osteoporosis defect has shown a high association with the bone fragility and osteoporotic fracture prevalence. However, no routine computed tomography (CT)‐based vertebral focal osteoporosis defect measurement and its association with vertebral compression fracture (VCF) were discussed yet. This study aimed to develop a routine CT‐based measurement method for focal osteoporosis defect quantification, and to assess its association with the VCF prevalence.

Materials and Methods

A total of 205 cases who underwent routine CT scanning, were retrospectively reviewed and enrolled into either the VCF or the control group. The focal bone mineral content loss (focal BMC loss), measured as the cumulated demineralization within bone void space, was proposed for focal osteoporosis defect quantification. Its scan‐rescan reproducibility and its correlation with trabecular bone mineral density (BMD) and apparent microarchitecture parameters were evaluated. The association between focal BMC loss and the prevalence of VCF was studied by logistic regression.

Results

The measurement of focal BMC loss showed high reproducibility (RMSSD = 0.011 mm, LSC = 0.030 mm, ICC = 0.97), and good correlation with focal bone volume fraction (r = 0.79, P < 0.001), trabecular bone separation (r = 0.76, P < 0.001), but poor correlation with trabecular BMD (r = 0.37, P < 0.001). The focal BMC loss was significantly higher in the fracture group than the control (1.03 ± 0.13 vs. 0.93 ± 0.11 mm; P < 0.001), and was associated with prevalent VCF (1.87, 95% CI = 1.31–2.65, P < 0.001) independent of trabecular BMD level.

Discussion

As a surrogate measure of focal osteoporosis defect, focal BMC Loss independently associated with the VCF prevalence. It suggests that focal osteoporosis defect is a common manifestation that positively contributed to compression fracture risk and can be quantified with routine CT using focal BMC Loss.

Level-Specific Volumetric BMD Threshold Values for the Prediction of Incident Vertebral Fractures Using Opportunistic QCT: A Case-Control Study

PURPOSE

To establish and evaluate the diagnostic accuracy of volumetric bone mineral density (vBMD) threshold values at different spinal levels, derived from opportunistic quantitative computed tomography (QCT), for the prediction of incident vertebral fractures (VF).

MATERIALS AND METHODS

In this case-control study, 35 incident VF cases (23 women, 12 men; mean age: 67 years) and 70 sex- and age-matched controls were included, based on routine multi detector CT (MDCT) scans of the thoracolumbar spine. Trabecular vBMD was measured from routine baseline CT scans of the thoracolumbar spine using an automated pipeline including vertebral segmentation, asynchronous calibration for HU-to-vBMD conversion, and correction of intravenous contrast medium (https://anduin.bonescreen.de). Threshold values at T1-L5 were calculated for the optimal operating point according to the Youden index and for fixed sensitivities (60 - 85%) in receiver operating characteristic (ROC) curves.

RESULTS

vBMD at each single level of the thoracolumbar spine was significantly associated with incident VFs (odds ratio per SD decrease [OR], 95% confidence interval [CI] at T1-T4: 3.28, 1.66-6.49; at T5-T8: 3.28, 1.72-6.26; at T9-T12: 3.37, 1.78-6.36; and at L1-L4: 3.98, 1.97-8.06), independent of adjustment for age, sex, and prevalent VF. AUC showed no significant difference between vertebral levels and was highest at the thoracolumbar junction (AUC = 0.75, 95%-CI = 0.63 - 0.85 for T11-L2). Optimal threshold values increased from lumbar (L1-L4: 52.0 mg/cm³) to upper thoracic spine (T1-T4: 69.3 mg/cm³). At T11-L2, T12-L3 and L1-L4, a threshold of 80.0 mg/cm³ showed sensitivities of 85 - 88%, and specificities of 41 - 49%. To achieve comparable sensitivity (85%) at more superior spinal levels, resulting thresholds were higher: 114.1 mg/cm³ (T1-T4), 92.0 mg/cm³ (T5-T8), 88.2 mg/cm³ (T9-T12).

CONCLUSIONS

At all levels of the thoracolumbar spine, lower vBMD was associated with incident VFs in an elderly, predominantly oncologic patient population. Automated opportunistic osteoporosis screening of vBMD along the entire thoracolumbar spine allows for risk assessment of imminent VFs. We propose level-specific vBMD threshold at the thoracolumbar spine to identify individuals at high fracture risk.

A computed tomography vertebral segmentation dataset with anatomical variations and multi-vendor scanner data

With the advent of deep learning algorithms, fully automated radiological image analysis is within reach. In spine imaging, several atlas- and shape-based as well as deep learning segmentation algorithms have been proposed, allowing for subsequent automated analysis of morphology and pathology. The first “Large Scale Vertebrae Segmentation Challenge” (VerSe 2019) showed that these perform well on normal anatomy, but fail in variants not frequently present in the training dataset. Building on that experience, we report on the largely increased VerSe 2020 dataset and results from the second iteration of the VerSe challenge (MICCAI 2020, Lima, Peru). VerSe 2020 comprises annotated spine computed tomography (CT) images from 300 subjects with 4142 fully visualized and annotated vertebrae, collected across multiple centres from four different scanner manufacturers, enriched with cases that exhibit anatomical variants such as enumeration abnormalities (n = 77) and transitional vertebrae (n = 161). Metadata includes vertebral labelling information, voxel-level segmentation masks obtained with a human-machine hybrid algorithm and anatomical ratings, to enable the development and benchmarking of robust and accurate segmentation algorithms.

Measurement(s)	vertebra
Technology Type(s)	computed tomography
Factor Type(s)	imaging centre • scanner manufacturer
Sample Characteristic - Organism	Homo sapiens

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.14716968

Physiologically decreased F-18 fluorodeoxyglucose uptake in the lower vertebrae associated with daily drinking habit in Japanese men with alcohol flushing reaction

Alcohol flushing reaction (AFR) is known as one of the risks for esophageal squamous cell cancer, and scientists have been elucidating this issue. However, little attention has been given to relevant imaging features. This study aims to investigate whether physiological ¹⁸F-fluorodeoxyglucose (FDG) uptake patterns in vertebrae are associated with drinking habits or AFR. Japanese male patients who underwent FDG positron emission computed tomography for evaluation of their known or suspected malignancies or inflammatory diseases were asked about their drinking habits and AFR. Altogether, 192 patients, 139 every-day drinkers and 53 non-drinkers were evaluated. Comparing the FDG uptake between that in the thoracic region and that in the lumbar region, vertebral uptake was visually classified into four patterns: Ld, dominant in lumbar region; TL, almost equal in both regions; BL, slightly higher in thoracic region (borderline pattern); Td, dominant in thoracic region. The uptake patterns were evaluated according to drinking habit (every-day drinker or non-drinker), AFR (flusher or non-flusher), and the combination of these two factors (habit/reaction: every-day drinker/flusher, every-day drinker/non-flusher, non-drinker/flusher, or non-drinker/non-flusher). There were 95 flushers (51 every-day drinkers and 44 non-drinkers) and 97 non-flushers (88 every-day drinkers and 9 non-drinkers). Ld, TL, BL, and Td patterns were observed in 0, 109 (56.8%), 31 (16.1%), and 52 (27.1%) patients, respectively. Td and BL patterns were more frequently observed in every-day drinkers compared with non-drinkers (p = 0.0467). Though the uptake patterns did not differ between flushers and non-flushers (p = 0.116), the Td pattern was more frequently observed in every-day drinkers/flushers (51%) compared with every-day drinkers/non-flushers (20.5%), non-drinkers/flushers (13.6%), and non-drinkers/non-flushers (22.2%) (p = 0.0014). The Td pattern was observed in patients with various diseases, with higher frequency in esophageal cancer, head and neck cancer, and lung cancer compared with other diseases. In conclusion, drinking habits and AFR were related to the vertebral uptake pattern with decreased uptake in the lumbar region in Japanese male patients.

Prediction of incident vertebral fractures in routine MDCT: Comparison of global texture features, 3D finite element parameters and volumetric BMD

PURPOSE

In this case-control study, we evaluated different quantitative parameters derived from routine multi-detector computed tomography (MDCT) scans with respect to their ability to predict incident osteoporotic vertebral fractures of the thoracolumbar spine.

METHODS

16 patients who received baseline and follow-up contrast-enhanced MDCT and were diagnosed with an incident osteoporotic vertebral fracture at follow-up, and 16 age-, sex-, and follow-up-time-matched controls were included in the study. Vertebrae were labelled and segmented using a fully automated pipeline. Volumetric bone mineral density (vBMD), finite element analysis (FEA)-based failure load (FL) and failure displacement (FD), as well as 24 texture features were extracted from L1 - L3 and averaged. Odds ratios (OR) with 95% confidence intervals (CI), expressed per standard deviation decrease, receiver operating characteristic (ROC) area under the curve (AUC), as well as logistic regression models, including all analyzed parameters as independent variables, were used to assess the prediction of incident vertebral fractures.

RESULTS

The texture feature Correlation (AUC = 0.754, p = 0.014; OR = 2.76, CI = 1.16-6.58) and vBMD (AUC = 0.750, p = 0.016; OR = 2.67, CI = 1.12-6.37) classified incident vertebral fractures best, while the best FEA-based parameter FL showed an AUC = 0.719 (p = 0.035). Correlation was the only significant predictor of incident fractures in the logistic regression analysis of all parameters (p = 0.022).

CONCLUSION

MDCT-derived FEA parameters and texture features, averaged from L1 - L3, showed only a moderate, but no statistically significant improvement of incident vertebral fracture prediction beyond BMD, supporting the hypothesis that vertebral-specific parameters may be superior for fracture risk assessment.

Differentiation Between Osteoporotic And Neoplastic Vertebral Fractures: State Of The Art And Future Perspectives

Vertebral fractures are a common condition, occurring in the context of osteoporosis and malignancy. These entities affect a group of patients in the same age range; clinical features may be indistinct and symptoms non-existing, and thus present challenges to diagnosis. In this article, we review the use and accuracy of different imaging modalities available to characterize vertebral fracture etiology, from well-established classical techniques, to the role of new and advanced imaging techniques, and the prospective use of artificial intelligence. We also address the role of imaging on treatment. In the context of osteoporosis, the importance of opportunistic diagnosis is highlighted. In the near future, the use of automated computer-aided diagnostic algorithms applied to different imaging techniques may be really useful to aid on diagnosis.

MDCT-Based Finite Element Analyses: Are Measurements at the Lumbar Spine Associated with the Biomechanical Strength of Functional Spinal Units of Incidental Osteoporotic Fractures along the Thoracolumbar Spine?

Assessment of osteoporosis-associated fracture risk during clinical routine is based on the evaluation of clinical risk factors and T-scores, as derived from measurements of areal bone mineral density (aBMD). However, these parameters are limited in their ability to identify patients at high fracture risk. Finite element models (FEMs) have shown to improve bone strength prediction beyond aBMD. This study aims to investigate whether FEM measurements at the lumbar spine can predict the biomechanical strength of functional spinal units (FSUs) with incidental osteoporotic vertebral fractures (VFs) along the thoracolumbar spine. Multi-detector computed tomography (MDCT) data of 11 patients (5 females and 6 males, median age: 67 years) who underwent MDCT twice (median interval between baseline and follow-up MDCT: 18 months) and sustained an incidental osteoporotic VF between baseline and follow-up scanning were used. Based on baseline MDCT data, two FSUs consisting of vertebral bodies and intervertebral discs (IVDs) were modeled: one standardly capturing L1-IVD-L2-IVD-L3 (FSU_L1-L3) and one modeling the incidentally fractured vertebral body at the center of the FSU (FSU_F). Furthermore, volumetric BMD (vBMD) derived from MDCT, FEM-based displacement, and FEM-based load of the single vertebrae L1 to L3 were determined. Statistically significant correlations (adjusted for a BMD ratio of fracture/L1-L3 segments) were revealed between the FSU_F and mean load of L1 to L3 (r = 0.814, p = 0.004) and the mean vBMD of L1 to L3 (r = 0.745, p = 0.013), whereas there was no statistically significant association between the FSU_F and FSU_L1-L3 or between FSU_F and the mean displacement of L1 to L3 (p > 0.05). In conclusion, FEM measurements of single vertebrae at the lumbar spine may be able to predict the biomechanical strength of incidentally fractured vertebral segments along the thoracolumbar spine, while FSUs seem to predict only segment-specific fracture risk.

Prediction of Incidental Osteoporotic Fractures at Vertebral-Specific Level Using 3D Non-Linear Finite Element Parameters Derived from Routine Abdominal MDCT

To investigate whether finite element (FE) analysis of the spine in routine thoracic/abdominal multi-detector computed tomography (MDCT) can predict incidental osteoporotic fractures at vertebral-specific level; Baseline routine thoracic/abdominal MDCT scans of 16 subjects (8(m), mean age: 66.1 ± 8.2 years and 8(f), mean age: 64.3 ± 9.5 years) who sustained incidental osteoporotic vertebral fractures as confirmed in follow-up MDCTs were included in the current study. Thoracic and lumbar vertebrae (T5-L5) were automatically segmented, and bone mineral density (BMD), finite element (FE)-based failure-load, and failure-displacement were determined. These values of individual vertebrae were normalized globally (g), by dividing the absolute value with the average of L1-3 and locally by dividing the absolute value with the average of T5-12 and L1-5 for thoracic and lumbar vertebrae, respectively. Mean-BMD of L1-3 was determined as reference. Receiver operating characteristics (ROC) and area under the curve (AUC) were calculated for different normalized FE (Kload, Kdisplacement,K(load)g, and K(displacement)g) and BMD (KBMD, and K(BMD)g) ratio parameter combinations for identifying incidental fractures. Kload, K(load)g, KBMD, and K(BMD)g showed significantly higher discriminative power compared to standard mean BMD of L1-3 (BMDStandard) (AUC = 0.67 for Kload; 0.64 for K(load)g; 0.64 for KBMD; 0.61 for K(BMD)g vs. 0.54 for BMDStandard). The combination of Kload, Kdisplacement, and KBMD increased the AUC further up to 0.77 (p < 0.001). The combination of FE with BMD measurements derived from routine thoracic/abdominal MDCT allowed an improved prediction of incidental fractures at vertebral-specific level.

Gender-, Age- and Region-Specific Characterization of Vertebral Bone Microstructure Through Automated Segmentation and 3D Texture Analysis of Routine Abdominal CT

PURPOSE

To identify long-term reproducible texture features (TFs) of spinal computed tomography (CT), and characterize variations with regard to gender, age and vertebral level using our automated quantification framework.

METHODS

We performed texture analysis (TA) on baseline and follow-up CT (follow-up duration: 30-90 days) of 21 subjects (8 females, 13 males, age at baseline 61.2 ± 9.2 years) to determine long-term reproducibility. TFs with a long-term reproducibility error Δ_rel<5% were further analyzed for an association with age and vertebral level in a cohort of 376 patients (129 females, 247 males, age 62.5 ± 9.2 years). Automated analysis comprised labeling and segmentation of vertebrae into subregions using a convolutional neural network, calculation of volumetric bone mineral density (vBMD) with asynchronous calibration and TF extraction. Variance_global measures the spread of the gray-level distribution in an image while Entropy reflects the uniformity of gray-levels. Short-run emphasis (SRE), Long-run emphasis (LRE), Run-length non-uniformity (RLN) and Run percentage (RP) contain information on consecutive voxels of a particular grey-level, or grey-level range, in a particular direction. Long runs (LRE) represent coarse texture while short runs (SRE) represent fine texture. RLN reflects similarities in the length of runs while RP reflects distribution and homogeneity of runs with a specific direction.

RESULTS

Six of the 24 extracted TFs had Δ_rel<5% (Variance_global, Entropy, SRE, LRE, RLN, RP), and were analyzed further in 4716 thoracolumbar vertebrae. Five TFs (Variance_global,SRE,LRE, RLN,RP) showed a significant difference between genders (p<0.001), potentially being caused by a finer and more directional vertebral trabecular microstructure in females compared to males. Variance_global and Entropy showed a significant increase from the thoracic to the lumbar spine (p<0.001), indicating a higher degree and earlier initiation of trabecular microstructure deterioration at lower spinal levels. The four higher-order TFs showed significant variations between spine regions without a clear directional gradient (p ≤ 0.001-0.012). No TF showed a clear age dependence. vBMD differed significantly between genders, age groups and spine regions (p ≤ 0.001-0.002).

CONCLUSION

Long-term reproducible CT-based TFs of the thoracolumbar spine were established and characterized in a predominantly older adult study population. The gender-, age- and vertebral-level-specific values may serve as foundation for osteoporosis diagnostics and facilitate future studies investigating vertebral microstructure.

A Vertebral Segmentation Dataset with Fracture Grading

Published under a CC BY 4.0 license. Supplemental material is available for this article.

Labeling Vertebrae with Two-dimensional Reformations of Multidetector CT Images: An Adversarial Approach for Incorporating Prior Knowledge of Spine Anatomy

Purpose

To use and test a labeling algorithm that operates on two-dimensional reformations, rather than three-dimensional data to locate and identify vertebrae.

Materials and Methods

The authors improved the Btrfly Net, a fully convolutional network architecture described by Sekuboyina et al, which works on sagittal and coronal maximum intensity projections (MIPs) and augmented it with two additional components: spine localization and adversarial a priori learning. Furthermore, two variants of adversarial training schemes that incorporated the anatomic a priori knowledge into the Btrfly Net were explored. The superiority of the proposed approach for labeling vertebrae on three datasets was investigated: a public benchmarking dataset of 302 CT scans and two in-house datasets with a total of 238 CT scans. The Wilcoxon signed rank test was employed to compute the statistical significance of the improvement in performance observed with various architectural components in the authors' approach.

Results

On the public dataset, the authors' approach using the described Btrfly Net with energy-based prior encoding (Btrfly_pe-eb) network performed as well as current state-of-the-art methods, achieving a statistically significant (P < .001) vertebrae identification rate of 88.5% ± 0.2 (standard deviation) and localization distances of less than 7 mm. On the in-house datasets that had a higher interscan data variability, an identification rate of 85.1% ± 1.2 was obtained.

Conclusion

An identification performance comparable to existing three-dimensional approaches was achieved when labeling vertebrae on two-dimensional MIPs. The performance was further improved using the proposed adversarial training regimen that effectively enforced local spine a priori knowledge during training. Spine localization increased the generalizability of our approach by homogenizing the content in the MIPs.Supplemental material is available for this article.© RSNA, 2020.

X-ray-based quantitative osteoporosis imaging at the spine

Osteoporosis is a metabolic bone disease with a high prevalence that affects the population worldwide, particularly the elderly. It is often due to fractures associated with bone fragility that the diagnosis of osteoporosis becomes clinically evident. However, early diagnosis would be necessary to initiate therapy and to prevent occurrence of further fractures, thus reducing morbidity and mortality. X-ray-based imaging plays a key role for fracture risk assessment and monitoring of osteoporosis. Whereas over decades dual-energy X-ray absorptiometry (DXA) has been the main method used and still reflects the reference standard, another modality reemerges with quantitative computed tomography (QCT) because of its three-dimensional advantages and the opportunistic exploitation of routine CT scans. Against this background, this article intends to review and evaluate recent advances in the field of X-ray-based quantitative imaging of osteoporosis at the spine. First, standard DXA with the recent addition of trabecular bone score (TBS) is presented. Secondly, standard QCT, dual-energy BMD quantification, and opportunistic BMD screening in non-dedicated CT exams are discussed. Lastly, finite element analysis and microstructural parameter analysis are reviewed.

A study of dynamic contrast-enhanced MR imaging features and influence factors of pelvic bone marrow in adult females

Perfusion of the pelvic bone marrow is reduced in the postmenopausal group and with age. Quantitative dynamic contrast-enhanced MRI could reflect the blood supply characteristics and hemodynamic changes of the pelvic bone marrow. These results contribute to the description of osteoporosis in the postmenopausal females and the elderly.

INTRODUCTION

To investigate the effect of menstrual status and age on the perfusion of pelvic bone marrow in adult females using quantitative dynamic contrast-enhanced MRI (DCE-MRI).

METHODS

In total, 96 adult females who underwent DCE-MRI between September 2017 and December 2017 were included. All the subjects' quantitative DCE-MRI parameters of pelvic bone marrow were measured and retrospectively analyzed, including K^trans (volume transfer constant), K_ep (efflux rate constant), and V_e (interstitial volume). According to their menstrual status, the subjects were divided into a premenopausal group (n = 39) and a postmenopausal group (n = 57), and the two groups were then divided into four subgroups according to age. The intraobserver reliability was assessed by the intraclass correlation coefficient (ICC). The parameters were compared between different menstrual status groups and age subgroups by Mann-Whitney test, and Spearman correlation analysis was used to evaluate the correlation between the age and the quantitative parameters.

RESULTS

The ICCs of the K^trans, K_ep, and V_e values were 0.989, 0.974, and 0.920, respectively. K^trans, K_ep, and V_e of the premenopausal group were significantly higher than those of the postmenopausal group (P < 0.05). The overall age was negatively correlated with K^trans, K_ep, and V_e (r = - 0.590, - 0.357, and - 0.381, respectively, P < 0.05). In the premenopausal group, K^trans and V_e were significantly higher in subgroup 1 (≤ 40 years) compared with subgroup 2 (> 40 years) (P < 0.05), and age showed a negative correlation with K^trans and V_e (r = - 0.344 and - 0.334, respectively, P < 0.05). In the postmenopausal group, K^trans and K_ep were significantly higher in subgroup 3 (≤ 60 years) compared with subgroup 4 (> 60 years) (P < 0.05), and age showed a negative correlation with K^trans and K_ep (r = - 0.460 and - 0.303, respectively, P < 0.05).

CONCLUSION

Menstrual status and age have significant effects on the perfusion of the pelvic bone marrow microenvironment in adult females and that the microenvironment of the pelvic bone marrow displays different changes at different age stages. Quantitative DCE-MRI has contributed to the interpretation of the pelvic bone marrow perfusion status.

Opportunistic osteoporosis screening in multi-detector CT images via local classification of textures

Our study proposed an automatic pipeline for opportunistic osteoporosis screening using 3D texture features and regional vBMD using multi-detector CT images. A combination of different local and global texture features outperformed the global vBMD and showed high discriminative power to identify patients with vertebral fractures.

INTRODUCTION

Many patients at risk for osteoporosis undergo computed tomography (CT) scans, usable for opportunistic (non-dedicated) screening. We compared the performance of global volumetric bone mineral density (vBMD) with a random forest classifier based on regional vBMD and 3D texture features to separate patients with and without osteoporotic fractures.

METHODS

In total, 154 patients (mean age 64 ± 8.5, male; n = 103) were included in this retrospective single-center analysis, who underwent contrast-enhanced CT for other reasons than osteoporosis screening. Patients were dichotomized regarding prevalent vertebral osteoporotic fractures (noFX, n = 101; FX, n = 53). Vertebral bodies were automatically segmented, and trabecular vBMD was calculated with a dedicated phantom. For 3D texture analysis, we extracted gray-level co-occurrence matrix Haralick features (HAR), histogram of gradients (HoG), local binary patterns (LBP), and wavelets (WL). Fractured vertebrae were excluded for texture-feature and vBMD data extraction. The performance to identify patients with prevalent osteoporotic vertebral fractures was evaluated in a fourfold cross-validation.

RESULTS

The random forest classifier showed a high discriminatory power (AUC = 0.88). Parameters of all vertebral levels significantly contributed to this classification. Importantly, the AUC of the proposed algorithm was significantly higher than that of volumetric global BMD alone (AUC = 0.64).

CONCLUSION

The presented classifier combining 3D texture features and regional vBMD including the complete thoracolumbar spine showed high discriminatory power to identify patients with vertebral fractures and had a better diagnostic performance than vBMD alone.

Cortical Bone Mapping: Measurement and Statistical Analysis of Localised Skeletal Changes

PURPOSE OF REVIEW

Cortical bone mapping (CBM) is a technique for measuring localised skeletal changes from computed tomography (CT) images. It can provide measurements with accuracy surpassing the underlying imaging resolution. CBM can detect changes in several properties of the cortex, with no prior assumptions about the likely location of said changes. This paper summarises the theory behind CBM, discusses its strengths and limitations, and reviews some studies in which it has been applied.

RECENT FINDINGS

CBM has revealed associations between fracture risk and cortical properties in specific regions of the proximal femur which present feasible therapeutic targets. Analyses of several pharmaceutical and exercise interventions quantify effects that are distinct both in location and in the nature of the micro-architectural changes. CBM has illuminated age-related changes in the proximal femur and has recently been applied to other bones, as well as to the assessment of cartilage. The CBM processing pipeline is designed primarily for large cohort studies. Its main impact thus far has not been in the realm of clinical practice, but rather to improve our fundamental understanding of localised bone structure and changes.

Anatomical Variation of Age-Related Changes in Vertebral Bone Marrow Composition Using Chemical Shift Encoding-Based Water-Fat Magnetic Resonance Imaging

Assessment of vertebral bone marrow composition has been proposed as imaging biomarker for osteoporosis, hematopoietic, and metabolic disorders. We investigated the anatomical variation of age-related changes of vertebral proton density fat fraction (PDFF) using chemical shift encoding-based water-fat magnetic resonance imaging (MRI). 156 healthy subjects were recruited (age range 20-29 years: 12/30 males/females; 30-39: 15/9; 40-49: 4/14; 50-59: 9/27; 60-69: 5/19; 70-79: 4/8). An eight-echo 3D spoiled gradient-echo sequence at 3T MRI was used for chemical shift-encoding based water-fat separation at the lumbar spine. Vertebral bodies of L1-L4 were manually segmented to extract PDFF values at each vertebral level. PDFF averaged over L1-L4 was significantly (p < 0.05) higher in males than females in the twenties (32.0 ± 8.0 vs. 27.2 ± 6.0%) and thirties (35.3 ± 6.7 vs. 27.3 ± 6.2%). With increasing age, females showed an accelerated fatty conversion of the bone marrow compared to men with no significant (p > 0.05) mean PDFF differences in the forties (32.4 ± 8.4 vs. 34.5 ± 6.8%) and fifties (42.0 ± 6.1 vs. 40.5 ± 9.7%). The accelerated conversion process continued resulting in greater mean PDFF values in females than males in the sixties (40.2 ± 6.9 vs. 48.8 ± 7.7%; p = 0.033) and seventies (43.9 ± 7.6 vs. 50.5 ± 8.2%; p = 0.208), though the latter did not reach statistical significance. Relative age-related PDFF change from the twenties to the seventies increased from 16.7% (L1) to 51.4% (L4) in males and 76.8% (L1) to 85.7% (L4) in females. An accelerated fatty conversion of bone marrow was observed in females with increasing age particularly evident after menopause. Relative age-related PDFF changes showed an anatomical variation with most pronounced changes at lower lumbar vertebral levels in both sexes.