Multidetector Computed Tomography Imaging: Effect of Sparse Sampling and Iterative Reconstruction on Trabecular Bone Microstructure

A diagnostic approach integrated multimodal radiomics with machine learning models based on lumbar spine CT and X-ray for osteoporosis

INTRODUCTION

The aim of this analysis is to construct a combined model that integrates radiomics, clinical risk factors, and machine learning algorithms to diagnose osteoporosis in patients and explore its potential in clinical applications.

MATERIALS AND METHODS

A retrospective analysis was conducted on 616 lumbar spine. Radiomics features were extracted from the computed tomography (CT) scans and anteroposterior and lateral X-ray images of the lumbar spine. Logistic regression (LR), support vector machine (SVM), and random forest (RF) algorithms were used to construct radiomics models. The receiver operating characteristic curve (ROC) was employed to select the best-performing model. Clinical risk factors were identified through univariate logistic regression analysis (ULRA) and multivariate logistic regression analysis (MLRA) and utilized to develop a clinical model. A combined model was then created by merging radiomics and clinical risk factors. The performance of the models was evaluated using ROC curve analysis, and the clinical value of the models was assessed using decision curve analysis (DCA).

RESULTS

A total of 4858 radiomics features were extracted. Among the radiomics models, the SVM model demonstrated the optimal diagnostic capabilities and accuracy, with an area under the curve (AUC) of 0.958 (0.9405-0.9762) in the training cohort and 0.907 (0.8648-0.9492) in the test cohort. Furthermore, the combined model exhibited an AUC of 0.959 (0.9412-0.9763) in the training cohort and 0.910 (0.8690-0.9506) in the test cohort.

CONCLUSION

The combined model displayed outstanding ability in diagnosing osteoporosis, providing a safe and efficient method for clinical decision-making.

Bone quality in endocrine diseases: determinants and clinical relevance

PURPOSE

Bone is one of the main targets of hormones and endocrine diseases are frequent causes of secondary osteoporosis and fractures in real-world clinical practice. However, diagnosis of skeletal fragility and prediction of fractures in this setting could be a challenge, since the skeletal alterations induced by endocrine disorders are not generally captured by dual-energy X-ray absorptiometry (DXA) measurement of bone mineral density (BMD), that is the gold standard for diagnosis of osteoporosis in the general population. The aim of this paper is to review the existing evidence related to bone quality features in endocrine diseases, proposing assessment with new techniques in the future.

METHODS

A comprehensive search within electronic databases was performed to collect reports of bone quality in primary hyperparathyroidism, hypoparathyroidism, hyperthyroidism, hypercortisolism, growth hormone deficiency, acromegaly, male hypogonadism and diabetes mellitus.

RESULTS

Using invasive and non-invasive techniques, such as high-resolution peripheral quantitative computed tomography or DXA measurement of trabecular bone score (TBS), several studies consistently reported altered bone quality as predominant determinant of fragility fractures in subjects affected by chronic endocrine disorders.

CONCLUSIONS

Assessment of skeletal fragility in endocrine diseases might take advantage from the use of techniques to detect perturbation in bone architecture with the aim of best identifying patients at high risk of fractures.

Features of lumbar spine texture extracted from routine MRI correlate with bone mineral density and can potentially differentiate patients with and without fragility fractures in the spine

The use of routine magnetic resonance imaging (MRI) to potentially assess skeletal fragility has been widely studied in osteoporosis. The aim of this study was to evaluate bone texture attributes (TA) from routine lumbar spine (LS) MRI and their correlation with vertebral fragility fractures (VFF) and bone mineral density (BMD). Sixty-four post-menopausal women were submitted to LS densitometry, total spine radiographs, and routine T2-weighted LS MRI. Twenty-two TA were extracted with the platform IBEX from L3 vertebra. The statistical difference was evaluated using ANOVA and Duncan's post-test. Correlation analyses were performed using Spearman's coefficient. Statistical significance was considered when P<0.05. The results did not show a significant difference in BMD between the women with and without fractures. Two bone TA (cluster tendency and variance) were significantly lower in the fracture group. Cluster tendency with VFF in osteopenia was 1.54±1.37 and in osteoporosis was 1.11±58. Cluster tendency without VFF in osteopenia was 2.23±1.38 and in osteoporosis was 1.88±1.14). Variance with VFF in osteopenia was 1.44±1.37 and in osteoporosis was 1.13±59. Variance without VFF in osteopenia was 2.34±1.38 and in osteoporosis was 1.89±1.14. There was a significant correlation between BMD and cluster prominence (r=0.409), cluster tendency (r=0.345), correlation (r=0.570), entropy (r=0.364), information measure corr1 (r=0.378), inverse variance (r=0.449), sum entropy (r=0.320), variance (r=0.338), sum average (r=-0.274), and sum variance (r=-0.266). Our results demonstrated the potential use of TA extracted from routine MRI as a biomarker to assess osteoporosis and identify the tendency of skeletal fragility vertebral fractures.

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.

Preoperative prediction of residual back pain after vertebral augmentation for osteoporotic vertebral compression fractures: Initial application of a radiomics score based nomogram

BACKGROUND

Most patients with osteoporotic vertebral compression fracture (OVCF) obtain pain relief after vertebral augmentation, but some will experience residual back pain (RBP) after surgery. Although several risk factors of RBP have been reported, it is still difficult to estimate the risk of RBP preoperatively. Radiomics is helpful for disease diagnosis and outcome prediction by establishing complementary relationships between human-recognizable and computer-extracted features. However, musculoskeletal radiomics investigations are less frequently reported.

OBJECTIVE

This study aims to establish a radiomics score (rad-score) based nomogram for the preoperative prediction of RBP in OVCF patients.

METHODS

The training cohort of 731 OVCF patients was used for nomogram development, and the validation cohort was utilized for performance test. RBP was determined as the score of visual analogue scale ≥ 4 at both 3 and 30 days following surgery. After normalization, the RBP-related radiomics features were selected to create rad-scores. These rad-scores, along with the RBP predictors initially identified by univariate analyses, were included in the multivariate analysis to establish a nomogram for the assessment of the RBP risk in OVCF patients preoperatively.

RESULTS

A total of 81 patients (11.2%) developed RBP postoperatively. We finally selected 8 radiomics features from 1316 features extracted from each segmented image to determine the rad-score. Multivariate analysis revealed that the rad-score plus bone mineral density, intravertebral cleft, and thoracolumbar fascia injury were independent factors of RBP. Our nomograms based on these factors demonstrated good discrimination, calibration, and clinical utility in both training and validation cohorts. Furthermore, it achieved better performance than the rad-score itself, as well as the nomogram only incorporating regular features.

CONCLUSION

We developed and validated a nomogram incorporating the rad-score and regular features for preoperative prediction of the RBP risk in OVCF patients, which contributed to improved surgical outcomes and patient satisfaction.

Radiomics analysis based on lumbar spine CT to detect osteoporosis

OBJECTIVES

Undiagnosed osteoporosis may lead to severe complications after spinal surgery. This study aimed to construct and validate a radiomic signature based on CT scans to screen for lumbar spine osteoporosis.

METHODS

Using a stratified random sample method, 386 vertebral bodies were randomly divided into a training set (n = 270) and a test set (n = 116). A total of 1040 radiomics features were automatically retracted from lumbar spine CT scans using the 3D slicer pyradiomics module, and a radiomic signature was created. The sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) of the Hounsfield and radiomics signature models were calculated. The AUCs of the two models were compared using the DeLong test. Their clinical usefulness was assessed using a decision curve analysis.

RESULTS

Twelve features were chosen to establish the radiomic signature. The AUCs of the radiomics signature and Hounsfield models were 0.96 and 0.88 in the training set and 0.92 and 0.84 in the test set, respectively. According to the DeLong test, the AUCs of the two models were significantly different (p < 0.05). The radiomics signature model indicated a higher overall net benefit than the Hounsfield model, as determined by decision curve analysis.

CONCLUSIONS

The CT-based radiomic signature can differentiate patients with/without osteoporosis prior to lumbar spinal surgery. Without additional medical cost and radiation exposure, the radiomics method may provide valuable information facilitating surgical decision-making.

KEY POINTS

• The goal of the study was to evaluate the efficacy of a radiomics signature model based on routine preoperative lumbar spine CT scans in screening osteoporosis. • The radiomics signature model demonstrated excellent prediction performance in both the training and test sets. • This radiomics method may provide valuable information and facilitate surgical decision-making without additional medical costs and radiation exposure.

Radiomics and stacking regression model for measuring bone mineral density using abdominal computed tomography

BACKGROUND

Measurement of bone mineral density (BMD) is the most important method to diagnose osteoporosis. However, current BMD measurement is always performed after a fracture has occurred.

PURPOSE

To explore whether a radiomic model based on abdominal computed tomography (CT) can predict the BMD of lumbar vertebrae.

MATERIAL AND METHODS

A total of 245 patients who underwent both dual-energy X-ray absorptiometry (DXA) and abdominal CT examination (training cohort, n = 196; validation cohort, n = 49) were included in our retrospective study. In total, 1218 image features were extracted from abdominal CT images for each patient. Combined with clinical information, three steps including least absolute shrinkage and selection operator (LASSO) regression were used to select key features. A two-tier stacking regression model with multi-algorithm fusion was used for BMD prediction, which can integrate the advantages of linear model and non-linear model. The prediction results of this model were compared with those using a single regressor. The degree-of-freedom adjusted coefficient of determination (Adjusted-R²), root mean square error (RMSE), and mean absolute error (MAE) were used to evaluate the regression performance.

RESULTS

Compared with other regression methods, the two-tier stacking regression model has a higher regression performance, with Adjusted-R², RMSE, and MAE of 0.830, 0.077, and 0.06, respectively. Pearson correlation analysis and Bland-Altman analysis showed that the BMD predicted by the model had a high correlation with the DXA results (r = 0.932, difference = -0.01 ± 0.1412 mg/cm²).

CONCLUSION

Using radiomics, the BMD of lumbar vertebrae could be predicted from abdominal CT images.

Impact of dose reduction and iterative model reconstruction on multi-detector CT imaging of the brain in patients with suspected ischemic stroke

Non-contrast cerebral computed tomography (CT) is frequently performed as a first-line diagnostic approach in patients with suspected ischemic stroke. The purpose of this study was to evaluate the performance of hybrid and model-based iterative image reconstruction for standard-dose (SD) and low-dose (LD) non-contrast cerebral imaging by multi-detector CT (MDCT). We retrospectively analyzed 131 patients with suspected ischemic stroke (mean age: 74.2 ± 14.3 years, 67 females) who underwent initial MDCT with a SD protocol (300 mAs) as well as follow-up MDCT after a maximum of 10 days with a LD protocol (200 mAs). Ischemic demarcation was detected in 26 patients for initial and in 64 patients for follow-up imaging, with diffusion-weighted magnetic resonance imaging (MRI) confirming ischemia in all of those patients. The non-contrast cerebral MDCT images were reconstructed using hybrid (Philips “iDose4”) and model-based iterative (Philips “IMR3”) reconstruction algorithms. Two readers assessed overall image quality, anatomic detail, differentiation of gray matter (GM)/white matter (WM), and conspicuity of ischemic demarcation, if any. Quantitative assessment included signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) calculations for WM, GM, and demarcated areas. Ischemic demarcation was detected in all MDCT images of affected patients by both readers, irrespective of the reconstruction method used. For LD imaging, anatomic detail and GM/WM differentiation was significantly better when using the model-based iterative compared to the hybrid reconstruction method. Furthermore, CNR of GM/WM as well as the SNR of WM and GM of healthy brain tissue were significantly higher for LD images with model-based iterative reconstruction when compared to SD or LD images reconstructed with the hybrid algorithm. For patients with ischemic demarcation, there was a significant difference between images using hybrid versus model-based iterative reconstruction for CNR of ischemic/contralateral unaffected areas (mean ± standard deviation: SD_IMR: 4.4 ± 3.1, SD_iDose: 3.5 ± 2.3, P < 0.0001; LD_IMR: 4.6 ± 2.9, LD_iDose: 3.2 ± 2.1, P < 0.0001).  In conclusion, model-based iterative reconstruction provides higher CNR and SNR without significant loss of image quality for non-enhanced cerebral MDCT.

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.

Radiomics Based on Lumbar Spine Magnetic Resonance Imaging to Detect Osteoporosis

RATIONALE AND OBJECTIVES

Signal intensity of the lumbar spine in magnetic resonance imaging (MRI) correlates to bone mineral density (BMD). This study aims to explore a lumbar spine magnetic resonance imaging based on the radiomics model for detecting osteoporosis.

MATERIALS AND METHODS

A total of 109 patients, who underwent both dual-energy X-ray absorptiometry (DEXA) and MRI of the lumbar spine, were recruited. Among these patients, 38 patients were normal, 32 patients had osteopenia, and 39 patients had osteoporosis, according to the DEXA results. A total of 396 × 2 radiomic features were extracted from the T1WI and T2WI images of the segmentation images in the lumbar magnetic resonance imaging. The correlated radiomic features were selected to establish the radiomic classification model. Then, the classification models (based on T1WI, T2WI, and T1WI+T2WI) of normal vs. osteopenia, normal vs. osteoporosis, and osteopenia vs. osteoporosis were established. The performance of the classification models was evaluated through the estimated area under the receiver operating characteristic curve.

RESULTS

The area under the receiver operating characteristic curves based on T1WI, T2WI, and T1WI+T2WI were 0.772, 0.772, and 0.810, respectively, for the models of normal vs. osteopenia, 0.724, 0.682, and 0.797, respectively, for the models of normal vs. osteoporosis, and 0.730, 0.734, and 0.769, respectively, for the models of osteopenia vs. osteoporosis.

CONCLUSION

Radiomic models established based on lumbar spine MRI can be used to detect osteoporosis.

Simulation Study of Low-Dose Sparse-Sampling CT with Deep Learning-Based Reconstruction: Usefulness for Evaluation of Ovarian Cancer Metastasis

The usefulness of sparse-sampling CT with deep learning-based reconstruction for detection of metastasis of malignant ovarian tumors was evaluated. We obtained contrast-enhanced CT images (n = 141) of ovarian cancers from a public database, whose images were randomly divided into 71 training, 20 validation, and 50 test cases. Sparse-sampling CT images were calculated slice-by-slice by software simulation. Two deep-learning models for deep learning-based reconstruction were evaluated: Residual Encoder-Decoder Convolutional Neural Network (RED-CNN) and deeper U-net. For 50 test cases, we evaluated the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) as quantitative measures. Two radiologists independently performed a qualitative evaluation for the following points: entire CT image quality; visibility of the iliac artery; and visibility of peritoneal dissemination, liver metastasis, and lymph node metastasis. Wilcoxon signed-rank test and McNemar test were used to compare image quality and metastasis detectability between the two models, respectively. The mean PSNR and SSIM performed better with deeper U-net over RED-CNN. For all items of the visual evaluation, deeper U-net scored significantly better than RED-CNN. The metastasis detectability with deeper U-net was more than 95%. Sparse-sampling CT with deep learning-based reconstruction proved useful in detecting metastasis of malignant ovarian tumors and might contribute to reducing overall CT-radiation exposure.

Low-dose and sparse sampling MDCT-based femoral bone strength prediction using finite element analysis

This study aims to evaluate the impact of dose reduction through tube current and sparse sampling on multi-detector computed tomography (MDCT)-based femoral bone strength prediction using finite element (FE) analysis. FE-predicted femoral failure load obtained from MDCT scan data was not significantly affected by 50% dose reductions through sparse sampling. Further decrease in dose through sparse sampling (25% of original projections) and virtually reduced tube current (50% and 25% of the original dose) showed significant effects on the FE-predicted failure load results.

PURPOSE

To investigate the effect of virtually reduced tube current and sparse sampling on multi-detector computed tomography (MDCT)-based femoral bone strength prediction using finite element (FE) analysis.

METHODS

Routine MDCT data covering the proximal femur of 21 subjects (17 males; 4 females; mean age, 71.0 ± 8.8 years) without any bone diseases aside from osteoporosis were included in this study. Fifty percent and 75% dose reductions were achieved by virtually reducing tube current and by applying a sparse sampling strategy from the raw image data. Images were then reconstructed with a statistically iterative reconstruction algorithm. FE analysis was performed on all reconstructed images and the failure load was calculated. The root mean square coefficient of variation (RMSCV) and coefficient of correlation (R²) were calculated to determine the variation in the FE-predicted failure load data for dose reductions, using original-dose MDCT scan as the standard of reference.

RESULTS

Fifty percent dose reduction through sparse sampling showed lower RMSCV and higher correlations when compared with virtually reduced tube current method (RMSCV = 5.70%, R² = 0.96 vs. RMSCV = 20.78%, R² = 0.79). Seventy-five percent dose reduction achieved through both methods (RMSCV = 22.38%, R² = 0.80 for sparse sampling; RMSCV = 24.58%, R² = 0.73 for reduced tube current) could not predict the failure load accurately.

CONCLUSION

Our simulations indicate that up to 50% reduction in radiation dose through sparse sampling can be used for FE-based prediction of femoral failure load. Sparse-sampled MDCT may allow fracture risk prediction and treatment monitoring in osteoporosis with less radiation exposure in the future.

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.

Finite Element Analysis-Based Vertebral Bone Strength Prediction Using MDCT Data: How Low Can We Go?

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.

Vertebral Bone Marrow Heterogeneity Using Texture Analysis of Chemical Shift Encoding-Based MRI: Variations in Age, Sex, and Anatomical Location

Objective: Vertebral bone marrow composition has been extensively studied in the past and shown potential as imaging biomarker for osteoporosis, hematopoietic, and metabolic disorders. However, beyond quantitative assessment of bone marrow fat, little is known about its heterogeneity. Therefore, we investigated bone marrow heterogeneity of the lumbar spine using texture analysis of chemical-shift-encoding (CSE-MRI) based proton density fat fraction (PDFF) maps and its association with age, sex, and anatomical location. Methods: One hundred and fifty-six healthy subjects were scanned (age range: 20-29 years, 12/30 males/females; 30-39, 15/9; 40-49, 5/13; 50-59, 9/27; ≥60: 9/27). A sagittal 8-echo 3D spoiled-gradient-echo sequence at 3T was used for CSE-MRI-based water-fat separation at the lumbar spine. Manual segmentation of vertebral bodies L1-4 was performed. Mean PDFF and texture features (global: variance, skewness, kurtosis; second-order: energy, entropy, contrast, homogeneity, correlation, sum-average, variance, dissimilarity) were extracted at each vertebral level and compared between age groups, sex, and anatomical location. Results: Mean PDFF significantly increased from L1 to L4 (35.89 ± 11.66 to 39.52 ± 11.18%, p = 0.017) and with age (females: 27.19 ± 6.01 to 49.34 ± 7.75%, p < 0.001; males: 31.97 ± 7.96 to 41.83 ± 7.03 %, p = 0.025), but showed no difference between females and males after adjustment for age and BMI (37.13 ± 11.63 vs. 37.17 ± 8.67%; p = 0.199). Bone marrow heterogeneity assessed by texture analysis, in contrast to PDFF, was significantly higher in females compared to males after adjustment for age and BMI (namely contrast and dissimilarity; p < 0.031), demonstrated age-dependent differences, in particular in females (p < 0.05), but showed no statistically significant dependence on vertebral location. Conclusion: Vertebral bone marrow heterogeneity, assessed by texture analysis of PDFF maps, is primarily dependent on sex and age but not on anatomical location. Future studies are needed to investigate bone marrow heterogeneity with regard to aging and disease.

Texture analysis of vertebral bone marrow using chemical shift encoding-based water-fat MRI: a feasibility study

This feasibility study investigated the spatial heterogeneity of the lumbar vertebral bone marrow using chemical shift encoding-based water-fat MRI. Acquired texture features like contrast and dissimilarity allowed for differentiation of pre- and postmenopausal women and may serve as imaging biomarkers in the future.

INTRODUCTION

While the vertebral bone marrow fat using chemical shift encoding water-fat magnetic resonance imaging (MRI) has been extensively studied, its spatial heterogeneity has not been analyzed yet. Therefore, this feasibility study investigated the spatial heterogeneity of the lumbar vertebral bone marrow by using texture analysis in proton density fat fraction (PDFF) maps.

METHODS

Forty-one healthy pre- and postmenopausal women were recruited for this study (premenopausal (n = 15) 30 ± 7 years, postmenopausal (n = 26) 65 ± 7 years). An eight-echo 3D spoiled gradient echo sequence was used for chemical shift encoding-based water-fat separation at the lumbar spine. Vertebral bodies L1 to L5 were manually segmented. Mean PDFF values and texture features were extracted at each vertebral level, namely variance, skewness, and kurtosis, using statistical moments and second-order features (energy, contrast, correlation, homogeneity, dissimilarity, entropy, variance, and sum average). Parameters were compared between pre- and postmenopausal women and vertebral levels.

RESULTS

PDFF was significantly higher in post- than in premenopausal women (49.37 ± 8.14% versus 27.76 ± 7.30%, p < 0.05). Furthermore, PDFF increased from L1 to L5 (L1 37.93 ± 12.85%, L2 38.81 ± 12.77%, L3 40.23 ± 12.72%, L4 42.80 ± 13.27%, L5 45.21 ± 14.55%, p < 0.05). Bone marrow heterogeneity based on texture analysis was significantly (p < 0.05) increased in postmenopausal women. Contrast and dissimilarity performed best in differentiating pre- and postmenopausal women (AUC = 0.97 and 0.96, respectively), not significantly different compared with PDFF (AUC = 0.97).

CONCLUSION

Conclusively, an increased bone marrow heterogeneity could be observed in postmenopausal women. In the future, texture parameters might provide additional information to detect and monitor vertebral bone marrow alterations due to aging or hormonal changes beyond conventional anatomic imaging.

Multi-detector CT imaging: impact of virtual tube current reduction and sparse sampling on detection of vertebral fractures

PURPOSE

To systematically evaluate the effects of virtual tube current reduction and sparse sampling on image quality and vertebral fracture diagnostics in multi-detector computed tomography (MDCT).

MATERIALS AND METHODS

In routine MDCT scans of 35 patients (80.0% females, 70.6 ± 14.2 years, 65.7% showing vertebral fractures), reduced radiation doses were retrospectively simulated by virtually lowering tube currents and applying sparse sampling, considering 50%, 25%, and 10% of the original tube current and projections, respectively. Two readers evaluated items of image quality and presence of vertebral fractures. Readout between the evaluations in the original images and those with virtually lowered tube currents or sparse sampling were compared.

RESULTS

A significant difference was revealed between the evaluations of image quality between MDCT with virtually lowered tube current and sparse-sampled MDCT (p < 0.001). Sparse-sampled data with only 25% of original projections still showed good to very good overall image quality and contrast of vertebrae as well as minimal artifacts. There were no missed fractures in sparse-sampled MDCT with 50% reduction of projections, and clinically acceptable determination of fracture age was possible in MDCT with 75% reduction of projections, in contrast to MDCT with 50% or 75% virtual tube current reduction, respectively.

CONCLUSION

Sparse-sampled MDCT provides adequate image quality and diagnostic accuracy for vertebral fracture detection with 50% of original projections in contrast to corresponding MDCT with lowered tube current. Thus, sparse sampling is a promising technique for dose reductions in MDCT that could be introduced in future generations of scanners.

KEY POINTS

• MDCT with a reduction of projection numbers of 50% still showed high diagnostic accuracy without any missed vertebral fractures. • Clinically acceptable determination of vertebral fracture age was possible in MDCT with a reduction of projection numbers of 75%. • With sparse sampling, higher reductions in radiation exposure can be achieved without compromised image or diagnostic quality in routine MDCT of the spine as compared to MDCT with reduced tube currents.

Bone mineral density assessment using iterative reconstruction compared with quantitative computed tomography as the standard of reference

This study examines the influence of iterative reconstruction on bone mineral density (BMD) measurement by comparison with standard quantitative computed tomography (QCT; reference) and two other protocols based on filtered back projection. Ten human cadaver specimens of the lumbar spine with a hydroxyapatite calibration phantom underneath, were scanned with 4 protocols: 1. standard QCT, 2. volume scan with FBP, 3. helical scan with FBP, and 4. helical scan with IR (Adaptive Iterative Dose Reduction 3D (AIDR3D)). Radiation doses were recorded as CT dose index (CTDIvol) and BMD, signal-to-noise and contrast-to-noise ratio were calculated. Mean hydroxyapatite concentration (HOA) did not differ significantly between protocols, ranging from 98.58 ± 31.09 mg cm3 (protocol 4) to 100.47 ± 30.82 mg cm3 (protocol 2). Paired sample correlations of HOA values for protocol 4 and protocols 1, 2 and 3 were nearly perfect with coefficients of 0.980, 0.979 and 0.982, respectively (p < 0.004). CTDIvol were 7.50, 5.00, 6.82 (±2.03) and 1.72 (±0.50) mGy for protocols 1, 2, 3 and 4 respectively. Objective image quality was highest for protocol 4. The use of IR for BMD assessment significantly lowers radiation exposure compared to standard QCT and protocols with FBP while not degrading BMD measurement.