Accuracy and precision of volumetric bone mineral density assessment using dual-source dual-energy versus quantitative CT: a phantom study

A vision transformer-convolutional neural network framework for decision-transparent dual-energy X-ray absorptiometry recommendations using chest low-dose CT

OBJECTIVE

This study introduces an ensemble framework that integrates Vision Transformer (ViT) and Convolutional Neural Networks (CNN) models to leverage their complementary strengths, generating visualized and decision-transparent recommendations for dual-energy X-ray absorptiometry (DXA) scans from chest low-dose computed tomography (LDCT).

METHODS

The framework was developed using data from 321 individuals and validated with an independent test cohort of 186 individuals. It addresses two classification tasks: (1) distinguishing normal from abnormal bone mineral density (BMD) and (2) differentiating osteoporosis from non-osteoporosis. Three field-of-view (FOV) settings-fitFOV (entire vertebra), halfFOV (vertebral body only), and largeFOV (fitFOV + 20 %)-were analyzed to assess their impact on model performance. Model predictions were weighted and combined to enhance classification accuracy, and visualizations were generated to improve decision transparency. DXA scans were recommended for individuals classified as having abnormal BMD or osteoporosis.

RESULTS

The ensemble framework significantly outperformed individual models in both classification tasks (McNemar test, p < 0.001). In the development cohort, it achieved 91.6 % accuracy for task 1 with largeFOV (area under the receiver operating characteristic curve [AUROC]: 0.97) and 86.0 % accuracy for task 2 with fitFOV (AUROC: 0.94). In the test cohort, it demonstrated 86.6 % accuracy for task 1 (AUROC: 0.93) and 76.9 % accuracy for task 2 (AUROC: 0.99). DXA recommendation accuracy was 91.6 % and 87.1 % in the development and test cohorts, respectively, with notably high accuracy for osteoporosis detection (98.7 % and 100 %).

CONCLUSIONS

This combined ViT-CNN framework effectively assesses bone status from LDCT images, particularly when utilizing fitFOV and largeFOV settings. By visualizing classification confidence and vertebral abnormalities, the proposed framework enhances decision transparency and supports clinicians in making informed DXA recommendations following opportunistic osteoporosis screening.

A three-material model for dual-layer detector spectral computed tomography measurements of marrow adipose tissue and bone mineral density

Single-energy QCT (SEQCT) scans to measure volumetric BMD (vBMD) are susceptible to errors caused by variability in the amount of marrow adipose tissue (MAT). We developed a three-material model that uses dual-layer spectral CT (DLCT) technology to measure bone matrix (BM), yellow marrow (YM), and red marrow (RM) and compared the results with measurements of proton density fat fraction (PDFF) by MRI and vBMD by SEQCT. Hounsfield units (HU) were measured in the L1-3 vertebrae on 50 and 150 keV mono-energy images in a training set of 100 Chinese adults. The densities of YM and RM in the three-material model were adjusted so that the mean and SD of the YM volume as a fraction of total marrow volume matched historical bone histology data. A validation set of 125 adults was scanned, and the findings were compared with measurements of L1-3 MRI PDFF and SEQCT vBMD. We evaluated the sensitivity, specificity, and area under the ROC curve (AUROC) for DLCT vBMD measurements to predict osteoporosis and investigated the relationship between SEQCT vBMD, DLCT vBMD, and YM volume fraction. The mean (range) of the YM volume as a fraction of total marrow volume averaged 0.471 (0.190-0.674) and 0.480 (0.258-0.760) in men and women. The corresponding results for MRI PDFF were 0.487 (0.224-0.675) and 0.477 (0.238-0.745). The coefficient of determination was r 2 = 0.696 (p < .0001; SEE = 0.059). A L1-3 DLCT vBMD of 100 mg/cm3 gave a sensitivity of 100.0% and a specificity of 94.3% for predicting osteoporosis (AUROC = 0.986). A multiple linear regression model to predict L1-3 SEQCT vBMD from DLCT vBMD and the YM fraction gave a coefficient of determination of r 2 = 0.989 (p < .0001; SEE = 5.2 mg/cm3). In conclusion, we developed a three-material model for analyzing DLCT scans that correlates with MRI measurements of MAT PDFF and offers a potentially improved method of using CT to measure vBMD.

Deep Learning-enhanced Opportunistic Osteoporosis Screening in Ultralow-Voltage (80 kV) Chest CT: A Preliminary Study

RATIONALE AND OBJECTIVES

To explore the feasibility of deep learning (DL)-enhanced, fully automated bone mineral density (BMD) measurement using the ultralow-voltage 80 kV chest CT scans performed for lung cancer screening.

MATERIALS AND METHODS

This study involved 987 patients who underwent 80 kV chest and 120 kV lumbar CT from January to July 2024. Patients were collected from six CT scanners and divided into the training, validation, and test sets 1 and 2 (561: 177: 112: 137). Four convolutional neural networks (CNNs) were employed for automated segmentation (3D VB-Net and SCN), region of interest extraction (3D VB-Net), and BMD calculation (DenseNet and ResNet) of the target vertebrae (T12-L2). The BMD values of T12-L2 were obtained using 80 and 120 kV quantitative CT (QCT), the latter serving as the standard reference. Linear regression and Bland-Altman analyses were used to compare BMD values between 120 kV QCT and 80 kV CNNs, and between 120 kV QCT and 80 kV QCT. Receiver operating characteristic curve analysis was used to assess the diagnostic performance of the 80 kV CNNs and 80 kV QCT for osteoporosis and low BMD from normal BMD.

RESULTS

Linear regression and Bland-ltman analyses revealed a stronger correlation (R2=0.991-0.998 and 0.990-0.991, P<0.001) and better agreement (mean error, -1.36 to 1.62 and 1.72 to 2.27 mg/cm3; 95% limits of agreement, -9.73 to 7.01 and -5.71 to 10.19mg/cm3) for BMD between 120 kV QCT and 80 kV CNNs than between 120 kV QCT and 80 kV QCT. The areas under the curve of the 80 kV CNNs and 80 kV QCT in detecting osteoporosis and low BMD were 0.997-1.000 and 0.997-0.998, and 0.998-1.000 and 0.997, respectively.

CONCLUSION

The DL method could achieve fully automated BMD calculation for opportunistic osteoporosis screening with high accuracy using ultralow-voltage 80 kV chest CT performed for lung cancer screening.

Radiation Diagnostics of the Maxillofacial Region and Skeleton Bone Density in the Case of Vitamin D Insufficiency: A Pilot Study

(1) Background: A decrease in bone mineral density has been noted not only in at-risk patients (e.g., postmenopausal women) but also in young and middle-aged individuals due to changes in lifestyle. The aim of the study was to find a possible correlation for dual-energy X-ray absorptiometry (DXA) and quantitative computed tomography (QCT) with cone beam computed tomography (CBCT) of the jaws. (2) Methods: A total of 24 patients (14 women and 10 men aged 25 to 50 years) with partial secondary tooth loss and vitamin D insufficiency underwent cone beam computed tomography of the jaws and skeletal mineral density assessment using DXA (n = 12) and QCT (n = 12). (3) Results: When conducting CBCT of the jaws, a predominance of bone tissue type D3 (350-850 Hu) on the upper jaw (p > 0.05 (F = 0.68) and D2 (850-1350 Hu) on the lower jaw (p > 0.05 (F = 1) was revealed. According to the results of QCT densitometry of the skeleton, signs of osteopenia were found in four patients (with vitamin D3 deficiency) (33%) according to DXA; signs of osteopenia were found in six patients (with severe deficiency and deficiency of vitamin D3) (50%). The difference between QCT and DXA was not significant (p > 0.05) for each group. The significant strong correlation between CBCT and DXA or QCT was not found (p > 0.05). (4) Conclusions: Primary changes in bone density can be detected earlier in the dental system using cone beam computed tomography of the jaws. At the same time, the question of using a specific densitometry method-DXA or QCT-remains open, as their results correlating with CBCT optical density was not approved.

Biomechanical perspectives on image-based hip fracture risk assessment: advances and challenges

Hip fractures pose a significant health challenge, particularly in aging populations, leading to substantial morbidity and economic burden. Most hip fractures result from a combination of osteoporosis and falls. Accurate assessment of hip fracture risk is essential for identifying high-risk individuals and implementing effective preventive strategies. Current clinical tools, such as the Fracture Risk Assessment Tool (FRAX), primarily rely on statistical models of clinical risk factors derived from large population studies. However, these tools often lack specificity in capturing the individual biomechanical factors that directly influence fracture susceptibility. Consequently, image-based biomechanical approaches, primarily leveraging dual-energy X-ray absorptiometry (DXA) and quantitative computed tomography (QCT), have garnered attention for their potential to provide a more precise evaluation of bone strength and the impact forces involved in falls, thereby enhancing risk prediction accuracy. Biomechanical approaches rely on two fundamental components: assessing bone strength and predicting fall-induced impact forces. While significant advancements have been made in image-based finite element (FE) modeling for bone strength analysis and dynamic simulations of fall-induced impact forces, substantial challenges remain. In this review, we examine recent progress in these areas and highlight the key challenges that must be addressed to advance the field and improve fracture risk prediction.

Utilizing artificial intelligence to determine bone mineral density using spectral CT

BACKGROUND

Dual-energy computed tomography (DECT) has demonstrated the feasibility of using HAP-water to respond to BMD changes without requiring dedicated software or calibration. Artificial intelligence (AI) has been utilized for diagnosising osteoporosis in routine CT scans but has rarely been used in DECT. This study investigated the diagnostic performance of an AI system for osteoporosis screening using DECT images with reference quantitative CT (QCT).

METHODS

This prospective study included 120 patients who underwent DECT and QCT scans from August to December 2023. Two convolutional neural networks, 3D RetinaNet and U-Net, were employed for automated vertebral body segmentation. The accuracy of the bone mineral density (BMD) measurement was assessed with relative measurement error (RME%). Linear regression and Bland-Altman analyses were performed to compare the BMD values between the AI and manual systems with those of the QCT. The diagnostic performance of the AI and manual systems for osteoporosis and low BMD was evaluated using receiver operating characteristic curve analysis.

RESULTS

The overall mean RME% for the AI and manual systems were - 15.93 ± 12.05 % and - 25.47 ± 14.83 %, respectively. BMD measurements using the AI system achieved greater agreement with the QCT results than those using the manual system (R2 = 0.973, 0.948, p < 0.001; mean errors, 23.27, 35.71 mg/cm3; 95 % LoA, -9.72 to 56.26, -11.45 to 82.87 mg/cm3). The areas under the curve for the AI and manual systems were 0.979 and 0.933 for detecting osteoporosis and 0.980 and 0.991 for low BMD.

CONCLUSION

This AI system could achieve relatively high accuracy for automated BMD measurement on DECT scans, providing great potential for the follow-up of BMD in osteoporosis screening.

Photon-counting CT Spectral Localizer Radiographs for Lumbar Areal Bone Mineral Density Quantification: A Clinical Study on Accuracy, Reliability, and Diagnostic Performance for Osteoporosis

RATIONALE AND OBJECTIVES

To explore the accuracy, reliability, and diagnostic performance of photon-counting CT (PCCT) spectral localizer radiographs (SLRs) for quantifying lumbar areal bone mineral density (BMD) and detecting osteoporosis (T-score ≤-2.5).

MATERIALS AND METHODS

This prospective study recruited consecutive participants from April to July 2024. Participants each underwent a dual-energy X-ray absorptiometry (DXA) examination serving as the gold-standard reference for aBMD (aBMDDXA) and a PCCT scan to obtain SLR. The SLRs were reconstructed into hydroxyapatite (HA) and water maps. Lumbar vertebrae (L1 to L4) and soft tissue were blindly and semiautomatically segmented on HA and water maps to calculate aBMDSLR. The agreement and relative absolute error (RAE) between aBMDSLR and aBMDDXA were calculated. Factors that might influence the RAE were evaluated. Using DXA results as the reference, the diagnostic performance of PCCT-SLRs for osteoporosis was assessed.

RESULTS

A total of 159 participants (88 females) with a median age of 66 years (interquartile range [IQR], 55-72 years) were included. The median (IQR) aBMDDXA and aBMDSLR values were 1.095 (0.936-1.261) g/cm2 and 1.086 (0.932-1.255) g/cm2, respectively. There was excellent agreement between the two methods (mean bias=-0.57%). The median (IQR) RAE was 2.65% (1.23-4.07%). The RAE was unaffected by age, body mass index, aBMD, sex, tube voltage, or tube current. The sensitivity and specificity of PCCT-SLRs for osteoporosis diagnosis were 92.31% (12/13) and 98.63% (144/146), respectively.

CONCLUSION

The PCCT-SLR is an accurate and reliable approach for lumbar aBMD quantification in humans, with high diagnostic performance for osteoporosis.

The accuracy and clinical utility of spectral CT bone density measurement in the lumbar spine of unenhanced images: A narrative review

OBJECTIVES

To review and evaluate available literature on spectral computed tomography (SCT) bone mineral density (BMD) measurement in adult thoracolumbar vertebrae of unenhanced images compared to quantitative computed tomography (QCT), to understand its current clinical utility.

KEY FINDINGS

Keyword searches in four databases identified four cross-sectional studies which acquired an SCT BMD measurement in thoracolumbar vertebrae and compared this respectively to QCT, which were then critically appraised using the AXIS tool for cross-sectional studies. 862 vertebrae were measured between T10-S1 in 368 patients. Three studies demonstrated a statistically significant correlation between SCT and QCT for the measurement of Hydroxyapatite (HAP) and calcium (r = 0.86-0.96). One study demonstrated a diagnostic accuracy of 96% using a receiver operating curve.

CONCLUSIONS

SCT measurements of HAP and calcium in the lumbar vertebrae are comparable to QCT for patients with no additional pathology present. However, further research is required to evaluate diagnostic accuracy before clinical application.

IMPLICATIONS FOR PRACTICE

SCT BMD measurement has the potential to be developed as a screening tool for osteoporosis within the fracture liaison service (FLS). This could aid in the identification of patients with osteoporosis and address the current treatment gap. Nonetheless, many factors must be considered for this application including staff training, radiation protection and patient engagement with the screening programme.

Quantitative calcium-based assessment of osteoporosis in dual-layer spectral CT

OBJECTIVES

To evaluate a novel calcium-only imaging technique (VCa) with subtracted bone marrow in osteoporosis in dual-layer CT (DLCT) compared to conventional CT images (CI) and dual-energy X-ray absorptiometry (DXA).

MATERIAL AND METHODS

Images of a multi-energy CT phantom with calcium inserts, quantitative CT calibration phantom, and of 55 patients (mean age: 64.6 ± 11.5 years) were acquired on a DLCT to evaluate bone mineral density (BMD). CI, calcium-suppressed images, and VCa were calculated. For investigating the association of VCa and CI with DXA a subsample of 30 patients (<90 days between DXA and CT) was used. Multiple regression analysis was performed to identify further factors improving the prediction of DXA BMD.

RESULTS

The calcium concentrations of the CT phantom inserts were significantly associated with CT numbers from VCa (R2 = 0.94) and from CI (R2 = 0.89-0.92). VCa showed significantly higher CT numbers than CI in the phantom (p ≤ 0.001) and clinical setting (p < 0.001). CT numbers from VCa were significantly associated with CI (R2 = 0.95, p < 0.001) and with DXA (R2 = 0.31, p = 0.007), whereas no significant association between DXA and CI was found. Prediction of DXA BMD based on CT numbers derived from VCa yielded R2 = 0.76 in multiple regression analysis. ROC for the differentiation of normal from pathologic BMD in VCa yielded an AUC of 0.7, and a cut-off value of 126HU (sensitivity: 0.90; specificity: 0.47).

CONCLUSION

VCa images showed better agreement with DXA and known calcium concentrations than CI, and could be used to estimate BMD. A VCa cut-off of 126HU could be used to identify abnormal bone mineral density.

Value of Dual-Energy CT-Derived Metrics for the Prediction of Bone Non-union in Distal Radius Fractures

RATIONALE AND OBJECTIVES

Bone non-union is a serious complication of distal radius fractures (DRF) that can result in functional limitations and persistent pain. However, no accepted method has been established to identify patients at risk of developing bone non-union yet. This study aimed to compare various CT-derived metrics for bone mineral density (BMD) assessment to identify predictive values for the development of bone non-union.

MATERIALS AND METHODS

CT images of 192 patients with DRFs who underwent unenhanced dual-energy CT (DECT) of the distal radius between 03/2016 and 12/2020 were retrospectively identified. Available follow-up imaging and medical health records were evaluated to determine the occurrence of bone non-union. DECT-based BMD, trabecular Hounsfield unit (HU), cortical HU and cortical thickness ratio were measured in normalized non-fractured segments of the distal radius.

RESULTS

Patients who developed bone non-union were significantly older (median age 72 years vs. 54 years) and had a significantly lower DECT-based BMD (median 68.1 mg/cm3 vs. 94.6 mg/cm3, p < 0.001). Other metrics (cortical thickness ratio, cortical HU, trabecular HU) showed no significant differences. ROC and PR curve analyses confirmed the highest diagnostic accuracy for DECT-based BMD with an area under the curve (AUC) of 0.83 for the ROC curve and an AUC of 0.46 for the PR curve. In logistic regression models, DECT-based BMD was the sole metric significantly associated with bone non-union.

CONCLUSION

DECT-derived metrics can accurately predict bone non-union in patients who sustained DRF. The diagnostic performance of DECT-based BMD is superior to that of HU-based metrics and cortical thickness ratio.

Photon-Counting Computed Tomography for Microstructural Imaging of Bone and Joints

PURPOSE OF REVIEW

Recently, photon-counting computed tomography (PCCT) has been introduced in clinical research and diagnostics. This review describes the technological advances and provides an overview of recent applications with a focus on imaging of bone.

RECENT FINDINGS

PCCT is a full-body scanner with short scanning times that provides better spatial and spectral resolution than conventional energy-integrating-detector CT (EID-CT), along with an up to 50% reduced radiation dose. It can be used to quantify bone mineral density, to perform bone microstructural analyses and to assess cartilage quality with adequate precision and accuracy. Using a virtual monoenergetic image reconstruction, metal artefacts can be greatly reduced when imaging bone-implant interfaces. Current PCCT systems do not allow spectral imaging in ultra-high-resolution (UHR) mode. Given its improved resolution, reduced noise and spectral imaging capabilities PCCT has diagnostic capacities in both qualitative and quantitative imaging that outperform those of conventional CT. Clinical use in monitoring bone health has already been demonstrated. The full potential of PCCT systems will be unlocked when UHR spectral imaging becomes available.

Performance of calcium quantifications on low-dose photon-counting detector CT with high-pitch: A phantom study

Purpose

To evaluate the performance of calcium quantification on photon-counting detector CT (PCD-CT) with high-pitch at low radiation doses compared to third-generation dual-source energy-integrating detector CT (EID-CT).

Materials and methods

The phantom with three calcium inserts (50, 100, and 300 mg of calcium per milliliter), with and without the elliptical outer layer, was evaluated using high-pitch (3.2) and standard pitch (0.8) on PCD-CT, and standard pitch on EID-CT. Scans were performed with different tube voltages (PCD-CT: 120 and 140 kilo-voltage peak [kVp]; EID-CT: 70/Sn150 and 100/Sn150 kVp) and four radiation doses (1, 3, 5, and, 10 milli-Gray [mGy]). Utilizing the true calcium concentrations (CCtrue) of the phantom as the gold standard references, regression equations for each kVp setting were formulated to convert CT attenuations (CaCT) into measured calcium concentrations (CCm). The correlation analysis between CaCT and CCtrue was performed. The percentage absolute bias (PAB) was calculated from the differences between CCm and CCtrue and used to analyze the effects of scanning parameters on calcium quantification accuracy.

Results

A strong correlation was found between CaCT and CCtrue on PCD-CT (r > 0.99) and EID-CT (r > 0.98). For high- and standard-pitch scans on PCD-CT, the accuracy of calcium quantification is comparable (p = 0.615): the median (interquartile range [IQR]) of PAB was 5.59% (2.79%-8.31%) and 4.87 % (2.62%-8.01%), respectively. The PAB median (IQR) was 7.43% (3.77%-11.75%) for EID-CT. The calcium quantification accuracy of PCD-CT is superior to EID-CT at the large phantom (5.46% [2.68%-9.55%] versus 9.01% [6.22%-12.74%]), and at the radiation dose of 1 mGy (4.43% [2.08%-8.59%] versus 13.89% [8.93%-23.09%]) and 3 mGy (4.61% [2.75%-6.51%] versus 9.97% [5.17%-14.41%]), all p < 0.001.

Conclusions

Calcium quantification using low-dose PCD-CT with high-pitch scanning is feasible and accurate, and superior to EID-CT.

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.

Artificial Intelligence Applications for Osteoporosis Classification Using Computed Tomography

Osteoporosis, marked by low bone mineral density (BMD) and a high fracture risk, is a major health issue. Recent progress in medical imaging, especially CT scans, offers new ways of diagnosing and assessing osteoporosis. This review examines the use of AI analysis of CT scans to stratify BMD and diagnose osteoporosis. By summarizing the relevant studies, we aimed to assess the effectiveness, constraints, and potential impact of AI-based osteoporosis classification (severity) via CT. A systematic search of electronic databases (PubMed, MEDLINE, Web of Science, ClinicalTrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 39 articles were retrieved from the databases, and the key findings were compiled and summarized, including the regions analyzed, the type of CT imaging, and their efficacy in predicting BMD compared with conventional DXA studies. Important considerations and limitations are also discussed. The overall reported accuracy, sensitivity, and specificity of AI in classifying osteoporosis using CT images ranged from 61.8% to 99.4%, 41.0% to 100.0%, and 31.0% to 100.0% respectively, with areas under the curve (AUCs) ranging from 0.582 to 0.994. While additional research is necessary to validate the clinical efficacy and reproducibility of these AI tools before incorporating them into routine clinical practice, these studies demonstrate the promising potential of using CT to opportunistically predict and classify osteoporosis without the need for DEXA.

Opportunistic Screening With CT: Comparison of Phantomless BMD Calibration Methods

Opportunistic screening is a new promising technique to identify individuals at high risk for osteoporotic fracture using computed tomography (CT) scans originally acquired for an clinical purpose unrelated to osteoporosis. In these CT scans, a calibration phantom traditionally required to convert measured CT values to bone mineral density (BMD) is missing. As an alternative, phantomless calibration has been developed. This study aimed to review the principles of four existing phantomless calibration methods and to compare their performance against the gold standard of simultaneous calibration (ΔBMD). All methods were applied to a dataset of 350 females scanned with a highly standardized CT protocol (DS1) and to a second dataset of 114 patients (38 female) from clinical routine covering a large range of CT acquisition and reconstruction parameters (DS2). Three of the phantomless calibration methods must be precalibrated with a reference dataset containing a calibration phantom. Sixty scans from DS1 and 57 from DS2 were randomly selected for this precalibration. For each phantomless calibration method first the best combination of internal reference materials (IMs) was selected. These were either air and blood or subcutaneous adipose tissue, blood, and cortical bone. In addition, for phantomless calibration a fifth method based on average calibration parameters derived from the reference dataset was applied. For DS1, ΔBMD results (mean ± standard deviation) for the phantomless calibration methods requiring a precalibration ranged from 0.1 ± 2.7 mg/cm3 to 2.4 ± 3.5 mg/cm3 with similar means but significantly higher standard deviations for DS2. Performance of the phantomless calibration method, which does not require a precalibration was worse (ΔBMD DS1: 12.6 ± 13.2 mg/cm3 , DS2: 0.5 ± 8.8 mg/cm3 ). In conclusion, phantomless BMD calibration performs well if precalibrated with a reference dataset. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Association of Phantomless Dual-Energy CT-based Volumetric Bone Mineral Density with the Prevalence of Acute Insufficiency Fractures of the Spine

RATIONALE AND OBJECTIVES

To evaluate the bone mineral density (BMD) of the lumbar spine derived from dual-energy CT (DECT)-based volumetric material decomposition and its association with acute insufficiency fractures of the thoracolumbar spine.

MATERIALS AND METHODS

L1 of 160 patients (77 men, 83 women; mean age 64.3 years, range, 22-94 years) who underwent third-generation dual-source DECT between January 2016 and December 2021 due to suspected insufficiency fractures was retrospectively analyzed. All depicted vertebrae were examined for signs of recent fractures. A dedicated DECT postprocessing software using material decomposition was applied for phantomless BMD assessment. Receiver-operating characteristic (ROC) analysis identified optimal BMD thresholds. Associations of BMD, sex, and age with the occurrence of insufficiency fractures were examined with logistic regression models.

RESULTS

A DECT-derived BMD threshold of 120.40 mg/cm³ yielded 90.1% specificity and 59.32% sensitivity to differentiate patients with at least one insufficiency fracture from patients without fracture. No patient without fracture had a DECT-derived BMD below 85 mg/cm3. Lower DECT-derived bone mineral density was associated with an increased risk of insufficiency fractures (Odds ratio of 0.93, 95% CI, 0.91-0.96, p < 0.001). Overall ROC-derived AUC was 0.82 (p < 0.0001) for the differentiation of patients that sustained an insufficiency fracture from the control group.

CONCLUSION

Dual-Energy CT-based BMD assessment can accurately differentiate patients with acute insufficiency fractures of the thoracolumbar spine from patients without fracture. This algorithm can be used for phantomless risk stratification of patients undergoing routine CT to sustain insufficiency fractures of the thoracolumbar spine The identified cut-off value of 120.4 mg/cm³ is in line with current American College of Radiology (ACR) recommendations to differentiate healthy individuals from those with reduced bone mineral density.

Quantification of bone quality and distribution of the proximal humerus with dual-energy computed tomography

Background

The proximal humerus is a common site of osteoporotic fractures, and bone quality is a predictor of surgical reduction quality. Dual-energy computed tomography (DECT) is assuming an increasingly important role in the quantification of bone mineral density (BMD) due it is ability to perform three-material decomposition. We aimed to analyze the bone quality and distribution of the proximal humerus with DECT quantitatively.

Methods

Sixty-five consecutive patients (average age 49.5±15.2 years; male: female ratio 32:33) without proximal humerus fractures who had undergone DECT were retrospectively selected. The humeral head was divided into 4 regions on a cross section in the medial plane between the greater tuberosity and the surgical neck. The quantitative parameters, including virtual noncalcium (VNCa) value, computed tomography value of calcium (CaCT), computed tomography value of mixed-energy images (regular CT value) (rCT), and relative calcium density (rCaD), were measured. The correlations between the quantitative parameters and age and body mass index (BMI) were analyzed, and the correlations of age, sex, BMI, region of the humeral head, and VNCa value on CaCT were evaluated.

Results

The differences in CaCT, rCT, and rCaD between the 4 regions of proximal humerus were statistically significant (P<0.001), while the difference in VNCa values was not (P=0.688). The calcium concentration (CaCT and rCaD) was the densest in the posteromedial zone. The differences of CaCT, rCT, and rCaD between males and females in the 4 regions of proximal humerus were statistically significant (P<0.05), while those of the posterolateral zone were not (rCT; P>0.05). The differences in VNCa values between males and females were also not significant (P>0.05). Multivariable linear regression analysis indicated that sex, age, BMI, regions, and VNCa were significant (P<0.05) predictors of the CaCT value.

Conclusions

The concentration of calcium was the densest in the posteromedial region of proximal humerus, and the VNCa value of DECT may be used for quantifying the BMD of the proximal humerus.

Comparison of volumetric and areal bone mineral density in CT and scout scans using spectral detector technology

BACKGROUND

To determine whether denoised areal bone mineral density (BMD) measurements from scout scans in spectral detector computed tomography (CT) correlate with volumetric trabecular BMD for opportunistic osteoporosis screening.

METHODS

A 64-slice single-source dual-layer spectral CT scanner was used to acquire scout scan data of 228 lumbar vertebral bodies within 57 patients. Scout scans in anterior-posterior (AP) view were performed with a dose of < 0.06 mSv and spectrally decomposed into areal BMD (aBMD) values. A spectral dictionary denoising algorithm was applied to increase the signal-to-noise ratio (SNR). Volumetric trabecular bone mineral density (vBMD) was determined via material decomposition. A 3D convolutional network for image segmentation and labeling was applied for automated vBMD quantification. Projected maps were used to compare the classification accuracy of AP and lateral scout scans.

RESULTS

The denoising algorithm led to the minimization of anticorrelated noise in spectral maps and an SNR increase from 5.23 to 13.4 (p < 0.002). Correlation analysis between vBMD and measured AP aBMD, projected AP, and lateral aBMD showed a Pearson correlation coefficient of 0.68, 0.81, and 0.90, respectively. The sensitivity and specificity for the osteoporosis classification task were higher in lateral projection images than in AP crystallizing in an increased area under the curve value of 0.99 versus 0.90.

CONCLUSION

Denoised material-specific aBMD maps show a positive correlation to vBMD, enabling spectral scout scans as an opportunistic predictor for osteoporotic patients. This could be applied routinely as a screening tool in patients undergoing a CT examination.

RELEVANCE STATEMENT

Scout-based DEXA could be applied routinely as a screening tool in patients undergoing a CT examination.

KEY POINTS

• Spectral scout scans can be used as a dual-energy x-ray absorptiometry-like screening tool. • Spectral dictionary denoising on projection images increases the signal-to-noise ratio. • Positive correlation between volumetric and areal bone mineral density is observed. • Lateral projections increase osteoporosis classification accuracy compared to anterior-posterior projections.

Dual-Energy CT-based Opportunistic Volumetric Bone Mineral Density Assessment of the Distal Radius

Background In patients with distal radius fractures (DRFs), low bone mineral density (BMD) is associated with bone substitute use during surgery and bone nonunion, but BMD information is not regularly available. Purpose To evaluate the feasibility of dual-energy CT (DECT)-based BMD assessment from routine examinations in the distal radius and the relationship between the obtained BMD values, the occurrence of DRFs, bone nonunion, and use of surgical bone substitute. Materials and Methods Scans in patients who underwent routine dual-source DECT in the distal radius between January 2016 and December 2021 were retrospectively acquired. Phantomless BMD assessment was performed using the delineated trabecular bone of a nonfractured segment of the distal radius and both DECT image series. CT images and health records were examined to determine fracture severity, surgical management, and the occurrence of bone nonunion. Associations of BMD with the occurrence of DRFs, bone nonunion, and bone substitute use at surgical treatment were examined with generalized additive models and receiver operating characteristic analysis. Results This study included 263 patients (median age, 52 years; IQR, 36-64 years; 132 female patients), of whom 192 were diagnosed with fractures. Mean volumetric BMD was lower in patients who sustained a DRF (93.9 mg/cm3 vs 135.4 mg/cm3; P < .001), required bone substitutes (79.6 mg/cm3 vs 95.5 mg/cm3; P < .001), and developed bone nonunion (71.1 mg/cm3 vs 96.5 mg/cm3; P < .001). Receiver operating characteristic curve analysis identified these patients with an area under the curve of 0.71-0.91 (P < .001). Lower BMD increased the risk to sustain DRFs, develop bone nonunion, and receive bone substitutes at surgery (P < .001). Conclusion DECT-based BMD assessment at routine examinations is feasible and could help predict surgical bone substitute use and the occurrence of bone nonunion in patients with DRFs. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Carrino in this issue.

Automatic segmentation and radiomic texture analysis for osteoporosis screening using chest low-dose computed tomography

OBJECTIVE

This study developed a diagnostic tool combining machine learning (ML) segmentation and radiomic texture analysis (RTA) for bone density screening using chest low-dose computed tomography (LDCT).

METHODS

A total of 197 patients who underwent LDCT followed by dual-energy X-ray absorptiometry were analyzed. First, an autosegmentation model was trained using LDCT to delineate the thoracic vertebral body (VB). Second, a two-level classifier was developed using radiomic features extracted from VBs for the hierarchical pairwise classification of each patient's bone status. All the patients were initially classified as either normal or abnormal, and all patients with abnormal bone density were then subdivided into an osteopenia group and an osteoporosis group. The performance of the classifier was evaluated through fivefold cross-validation.

RESULTS

The model for automated VB segmentation achieved a Sorenson-Dice coefficient of 0.87 ± 0.01. Furthermore, the area under the receiver operating characteristic curve scores for the two-level classifier were 0.96 ± 0.01 for detecting abnormal bone density (accuracy = 0.91 ± 0.02; sensitivity = 0.93 ± 0.03; specificity = 0.89 ± 0.03) and 0.98 ± 0.01 for distinguishing osteoporosis (accuracy = 0.94 ± 0.02; sensitivity = 0.95 ± 0.03; specificity = 0.93 ± 0.03). The testing prediction accuracy levels for the first- and second-level classifiers were 0.92 ± 0.04 and 0.94 ± 0.05, respectively. The overall testing prediction accuracy of our method was 0.90 ± 0.05.

CONCLUSION

The combination of ML segmentation and RTA for automated bone density prediction based on LDCT scans is a feasible approach that could be valuable for osteoporosis screening during lung cancer screening.

KEY POINTS

• This study developed an automatic diagnostic tool combining machine learning-based segmentation and radiomic texture analysis for bone density screening using chest low-dose computed tomography. • The developed method enables opportunistic screening without quantitative computed tomography or a dedicated phantom. • The developed method could be integrated into the current clinical workflow and used as an adjunct for opportunistic screening or for patients who are ineligible for screening with dual-energy X-ray absorptiometry.

Dual-Energy CT in Cardiothoracic Imaging: Current Developments

This article describes the technical principles and clinical applications of dual-energy computed tomography (DECT) in the context of cardiothoracic imaging with a focus on current developments and techniques. Since the introduction of DECT, different vendors developed distinct hard and software approaches for generating multi-energy datasets and multiple DECT applications that were developed and clinically investigated for different fields of interest. Benefits for various clinical settings, such as oncology, trauma and emergency radiology, as well as musculoskeletal and cardiovascular imaging, were recently reported in the literature. State-of-the-art applications, such as virtual monoenergetic imaging (VMI), material decomposition, perfused blood volume imaging, virtual non-contrast imaging (VNC), plaque removal, and virtual non-calcium (VNCa) imaging, can significantly improve cardiothoracic CT image workflows and have a high potential for improvement of diagnostic accuracy and patient safety.

Opportunistic Screening Techniques for Analysis of CT Scans

PURPOSE OF REVIEW

Opportunistic screening is a combination of techniques to identify subjects of high risk for osteoporotic fracture using routine clinical CT scans prescribed for diagnoses unrelated to osteoporosis. The two main components are automated detection of vertebral fractures and measurement of bone mineral density (BMD) in CT scans, in which a phantom for calibration of CT to BMD values is not used. This review describes the particular challenges of opportunistic screening and provides an overview and comparison of current techniques used for opportunistic screening. The review further outlines the performance of opportunistic screening.

RECENT FINDINGS

A wide range of technologies for the automatic detection of vertebral fractures have been developed and successfully validated. Most of them are based on artificial intelligence algorithms. The automated differentiation of osteoporotic from traumatic fractures and vertebral deformities unrelated to osteoporosis, the grading of vertebral fracture severity, and the detection of mild vertebral fractures is still problematic. The accuracy of automated fracture detection compared to classical radiological semi-quantitative Genant scoring is about 80%. Accuracy errors of alternative BMD calibration methods compared to simultaneous phantom-based calibration used in standard quantitative CT (QCT) range from below 5% to about 10%. The impact of contrast agents, frequently administered in clinical CT on the determination of BMD and on fracture risk determination is still controversial. Opportunistic screening, the identification of vertebral fracture and the measurement of BMD using clinical routine CT scans, is feasible but corresponding techniques still need to be integrated into the clinical workflow and further validated with respect to the prediction of fracture risk.

The new fast kilovoltage-switching dual-energy computed tomography for measuring bone mineral density

Background

The update in technology may impact the accuracy in measuring bone mineral density (BMD). However, the application of the new fast kilovoltage (kV)-switching dual-energy computed tomography (DECT) for BMD measurement has not yet been reported. This study aimed to examine the accuracy and precision of the new fast kV-switching DECT in measuring BMD and to evaluate its applicability in clinical BMD measurement.

Methods

Forty sets of the new fast kV-switching DECT scans and one quantitative computed tomography (QCT) scan were performed on the European Spine Phantom. Their relative errors and relative standard deviations were compared. A retrospective analysis was performed on patients who underwent chest plain DECT and abdominal monoenergetic plain CT at the same time. The relationship between hydroxyapatite-water and hydroxyapatite-fat measured using DECT and BMD measured using QCT was analyzed by multivariate regression analysis.

Results

The relative errors of the new fast kV-switching DECT with low tube speeds (0.8 and 1.0 s/r) were all less than 6% and were less than those of QCT, except for those at 515 mA. The relative standard deviation values with high tube rotation speeds (0.5 and 0.6 s/r) were higher than those with low tube speeds (0.8 and 1.0 s/r) under most tube current conditions. The new fast kV-switching DECT-derived BMD values corrected by multiple linear regression (predicted hydroxyapatite) were significantly positively correlated with the QCT-based BMD values (R²=0.912; P<0.001). The results of the Bland-Altman analysis demonstrated high consistency between the 2 measurement methods.

Conclusions

Results of the phantom measurements indicated that the new fast kV-switching DECT could measure BMD with relatively high accuracy and precision. The results of a subsequent clinical in vivo experiment demonstrated that vertebral BMD measurements derived from DECT and QCT were mostly consistent and highly accurate. Therefore, patients who undergo DECT for other clinical indications can simultaneously have their BMD determined.

Emerging Role of 18F-NaF PET/Computed Tomographic Imaging in Osteoporosis: A Potential Upgrade to the Osteoporosis Toolbox

Osteoporosis is a metabolic bone disorder that leads to a decline in bone microarchitecture, predisposing individuals to catastrophic fractures. The current standard of care relies on detecting bone structural change; however, these methods largely miss the complex biologic forces that drive these structural changes and response to treatment. This review introduces sodium fluoride (18F-NaF) positron emission tomography/computed tomography (PET/CT) as a powerful tool to quantify bone metabolism. Here, we discuss the methods of 18F-NaF PET/CT, with a special focus on dynamic scans to quantify parameters relevant to bone health, and how these markers are relevant to osteoporosis.

Dual-Energy CT-Based Bone Mineral Density Has Practical Value for Osteoporosis Screening around the Knee

Introduction: Adequate bone quality is essential for long term biologic fixation of cementless total knee arthroplasty (TKA). Recently, vertebral bone quality evaluation using dual-energy computed tomography (DECT) has been introduced. However, the DECT bone mineral density (BMD) in peripheral skeleton has not been correlated with Hounsfield units (HU) or central dual-energy X-ray absorptiometry (DXA), and the accuracy remains unclear. Materials and methods: Medical records of 117 patients who underwent TKA were reviewed. DXA was completed within three months before surgery. DECT was performed with third-generation dual source CT in dual-energy mode. Correlations between DXA, DECT BMD and HU for central and periarticular regions were analyzed. Receiver operating characteristic (ROC) curves were plotted and area under the curve (AUC), optimal threshold, and sensitivity and specificity of each region of interest (ROI) were calculated. Results: Central DXA BMD was correlated with DECT BMD and HU in ROIs both centrally and around the knee (all p < 0.01). The diagnostic accuracy of DECT BMD was higher than that of DECT HU and was also higher when the T-score for second lumbar vertebra (L2), rather than for the femur neck, was used as the reference standard (all AUC values: L2 > femur neck; DECT BMD > DECT HU, respectively). Using the DXA T-score at L2 as the reference standard, the optimal DECT BMD cut-off values for osteoporosis were 89.2 mg/cm3 in the distal femur and 78.3 mg/cm3 in the proximal tibia. Conclusion: Opportunistic volumetric BMD assessment using DECT is accurate and relatively simple, and does not require extra equipment. DECT BMD and HU are useful for osteoporosis screening before cementless TKA.