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Objectives To compare opportunistic quantitative CT (QCT) with dual energy X-ray absorptiometry (DXA) in their ability to predict incident vertebral fractures. Methods We included 84 patients aged 50 years and older, who had routine CT including the lumbar spine and DXA within a 12-month period (baseline) as well as follow-up imaging after at least 12 months or who sustained an incident vertebral fracture documented earlier. Patients with bone disorders aside from osteoporosis were excluded. Fracture status and trabecular bone mineral density (BMD) were retrospectively evaluated in baseline CT and fracture status was reassessed at follow-up. BMD QCT was assessed by opportunistic QCT with asynchronous calibration of multiple MDCT scanners. Results Sixteen patients had incident vertebral fractures showing lower mean BMD QCT than patients without fracture ( p = 0.001). For the risk of incident vertebral fractures, the hazard ratio increased per SD in BMD QCT (4.07; 95% CI, 1.98–8.38), as well as after adjusting for age, sex, and prevalent fractures (2.54; 95% CI, 1.09–5.90). For DXA, a statistically significant increase in relative hazard per SD decrease in T -score was only observed after age and sex adjustment (1.57; 95% CI, 1.04–2.38). The predictability of incident vertebral fractures was good by BMD QCT (AUC = 0.76; 95% CI, 0.64–0.89) and non-significant by T -scores. Asynchronously calibrated CT scanners showed good long-term stability (linear drift ranging from − 0.55 to − 2.29 HU per year). Conclusions Opportunistic screening of mainly neurosurgical and oncologic patients in CT performed for indications other than densitometry allows for better risk assessment of imminent vertebral fractures than dedicated DXA. Key Points • Opportunistic QCT predicts osteoporotic vertebral fractures better than DXA reference standard in mainly neurosurgical and oncologic patients. • More than every second patient (56%) with an incident vertebral fracture was misdiagnosed not having osteoporosis according to DXA. • Standard ACR QCT-cutoff values for osteoporosis (< 80 mg/cm 3 ) and osteopenia (≤ 120 mg/cm 3 ) can also be applied scanner independently in calibrated opportunistic QCT.

The objective of this study was to analyze regional variations of magnetic resonance (MR) relaxation times (T1ρ and T2) in hip joint cartilage of healthy volunteers and subjects with femoral acetabular impingement (FAI). Morphological and quantitative images of the hip joints of 12 healthy volunteers and 9 FAI patients were obtained using a 3 T MR scanner. Both femoral and acetabular cartilage layers in each joint were semi-automatically segmented on sagittal 3D high-resolution spoiled gradient echo (SPGR) images. These segmented regions of interest (ROIs) were automatically divided radially into twelve equal sub-regions (300 intervals) based on the fitted center of the femur head. The mean value of T1ρ/T2 was calculated in each subregion after superimposing the divided cartilage contours on the MR relaxation (T1ρ/T2) maps to quantify the relaxation times. T1ρ and T2 relaxation times of the femoral cartilage were significantly higher in FAI subjects compared to healthy controls (39.9 ± 3.3 msec in FAI vs. 35.4 ± 2.3 msec in controls for T1ρ (P = 0.0020); 33.9 ± 3.1 msec in FAI vs. 31.1 ± 1.7 msec in controls for T2 (P = 0.0160)). Sub-regional analysis showed significantly different T1ρ and T2 relaxation times in the anterior-superior region (R9) of the hip joint cartilage between subjects with FAI and healthy subjects, suggesting possible regional differences in cartilage matrix composition between these two groups. Receiver operating characteristic (ROC) analysis showed that subregional analysis in femoral cartilage was more sensitive in discriminating FAI joint cartilage from that of healthy joints than global analysis of the whole region (T1ρ: area under the curve (AUC) = 0.981, P = 0.0001 for R9 sub-region; AUC = 0.901, P = 0.002 for whole region; T2: AUC = 0.976, P = 0.0005 for R9 sub-region; AUC = 0.808, P = 0.0124 for whole region). The results of this study demonstrated regional variations in hip cartilage composition using MR relaxation times (T1ρ and T2) and suggested that analysis based on local regions was more sensitive than global measures in subjects with and without FAI.

Summary 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. Electronic supplementary material The online version of this article (10.1007/s00198-019-04910-1) contains supplementary material, which is available to authorized users.

High-resolution peripheral quantitative computed tomography (HR-pQCT) has recently been introduced as a clinical research tool for in vivo assessment of bone quality. The utility of this technique to address important skeletal health questions requires translation to standardized multi-center data pools. Our goal was to evaluate the feasibility of pooling data in multi-center HR-pQCT imaging trials. Reproducibility imaging experiments were performed using structure and composition-realistic phantoms constructed from cadaveric radii. Single-center precision was determined by repeat scanning over short (<72hrs), intermediate (3–5mo), and long-term intervals (28mo). Multi-center precision was determined by imaging the phantoms at nine different HR-pQCT centers. Least significant change (LSC) and root mean squared coefficient of variation (RMSCV) for each interval and across centers was calculated for bone density, geometry, microstructure, and biomechanical parameters. Single-center short-term RMSCVs were <1% for all parameters except Ct.Th (1.1%), Ct.Th.SD (2.6%), Tb.Sp.SD (1.8%), and porosity measures (6–8%). Intermediate-term RMSCVs were generally not statistically different from short-term values. Long-term variability was significantly greater for all density measures (0.7–2.0%; p < 0.05 vs. short-term) and several structure measures: Ct.Th (3.4%; p < 0.01 vs. short-term), Ct.Po (15.4%; p < 0.01 vs. short-term), and Tb.Th (2.2%; p < 0.01 vs. short-term). Multi-center RMSCVs were also significantly higher than short-term values: 2–4% for density and µFE measures (p < 0.0001), 2.6–5.3% for morphometric measures (p < 0.001), while Ct.Po was 16.2% (p < 0.001). In the absence of subject motion, multi-center precision errors for HR-pQCT parameters were generally less than 5%. Phantom-based multi-center precision was comparable to previously reported in vivo single-center precision errors, although this was approximately 2–5 times worse than ex vivo short-term precision. The data generated from this study will contribute to the future design and validation of standardized procedures that are broadly translatable to multi-center study designs.

Robust localisation and identification of vertebrae is essential for automated spine analysis. The contribution of this work to the task is two-fold: (1) Inspired by the human expert, we hypothesise that a sagittal and coronal reformation of the spine contain sufficient information for labelling the vertebrae. Thereby, we propose a butterfly-shaped network architecture (termed Btrfly Net) that efficiently combines the information across reformations. (2) Underpinning the Btrfly net, we present an energy-based adversarial training regime that encodes local spine structure as an anatomical prior into the network, thereby enabling it to achieve state-of-art performance in all standard metrics on a benchmark dataset of 302 scans without any post-processing during inference.

We hypothesized that machine learning analysis based on texture information from the preoperative MRI can predict IDH mutational status in newly diagnosed WHO grade II and III gliomas. This retrospective study included in total 79 consecutive patients with a newly diagnosed WHO grade II or III glioma. Local binary pattern texture features were generated from preoperative B0 and fractional anisotropy (FA) diffusion tensor imaging. Using a training set of 59 patients, a single hidden layer neural network was then trained on the texture features to predict IDH status. The model was validated based on the prediction accuracy calculated in a previously unseen set of 20 gliomas. Prediction accuracy of the generated model was 92% (54/59 cases; AUC = 0.921) in the training and 95% (19/20; AUC = 0.952) in the validation cohort. The ten most important features were comprised of tumor size and both B0 and FA texture information, underlining the joint contribution of imaging data to classification. Machine learning analysis of DTI texture information and tumor size reliably predicts IDH status in preoperative MRI of gliomas. Such information may increasingly support individualized surgical strategies, supplement pathological analysis and highlight the potential of radiogenomics.