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

Background Virtual non-calcium (VNCa) imaging based on dual-energy computed tomography (CT) plays an increasingly important role in diagnosing spinal diseases. However, the utility of VNCa technology in the measurement of vertebral bone mineral density (BMD) is limited, especially the VNCa CT value at multiple calcium suppression levels and the slope of VNCa curve. This retrospective cross-sectional study aimed to explore the correlation between vertebral BMD and new VNCa parameters from dual-layer spectral detector CT. Methods The dual-layer spectral detector CT and quantitative CT (QCT) data of 4 hydroxyapatite (HAP) inserts and 667 vertebrae of 234 patients (132 male and 102 female) who visited a university teaching hospital between April and May 2023 were retrospectively analyzed. The BMD values of 3 vertebrae (T12, L1, and L2) and inserts were measured using QCT, defined as QCT-BMD. The VNCa CT values and the slope λ of the VNCa attenuation curve of vertebrae and inserts were recorded. The correlations between VNCa parameters (VNCa CT value, slope λ) and QCT-BMD were analyzed. Results For the vertebrae, the correlation coefficient ranged from −0.904 to 0.712 (all P<0.05). As the calcium suppression index (CaSI) increased, the correlation degree exhibited a decrease first and then increased, with the best correlation (r=−0.904, P<0.001) observed at the index of 25%. In contrast, the correlation coefficient for the inserts remained relatively stable (r=−0.899 to −1, all P<0.05). For the vertebrae, the values of 3 slopes λ (λ1, λ2, and λ3) derived from the VNCa attenuation curve were 6.50±1.99, 3.75±1.15, and 2.04±0.62, respectively. Regarding the inserts, the λ1, λ2, and λ3 values were 11.56 [interquartile range (IQR): 2.40–22.62], 6.68 (IQR: 1.39–13.49), and 3.63 (IQR: 0.75–7.8), respectively. For the vertebrae, all 3 correlation coefficients between 3 slopes λ and QCT-BMD were 0.956 (all P<0.05). For the inserts, the 3 correlation coefficients were 0.996, 0.998, and 1 (all P<0.05), respectively. Conclusions A promising correlation was detected between VNCa CT parameters and QCT-BMD in vertebrae, warranting further investigation to explore the possibility of VNCa imaging to assess BMD.

Section: Discussionmentioning

confidence: 86%

Correlation between vertebral bone mineral density and multi-level virtual non-calcium imaging parameters from dual-layer spectral detector computed tomography

Yang,

Hou,

Niu

et al. 2024

“…Zhou et al ( 32 ) demonstrated in vivo that fast-switching DECT can accurately evaluate phantomless BMD, and Ca-fat and HAP-fat have similar and optimal predictive abilities, which is consistent with our findings. Wang et al ( 36 ) also reported a highly positive correlation between BMD assessment derived from new fast-switching DECT and QCT (R 2 =0.912) and found that the relative error of the new fast-switching DECT was smaller than that of QCT. In addition, the consensus for applying QCT recommends collecting two whole vertebral bodies in the T12–L3 range, typically the L1–L2 vertebral bodies ( 37 , 38 ).…”

Section: Discussionmentioning

confidence: 92%

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

Tong,

Fang,

Wang

et al. 2024

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, R 2 =0.8973; Ca-BMD: r=0.9470, R 2 =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.

“…Through continuous energy spectrum acquisition by dual-energy computed tomography (DECT), this principle can be utilized to evaluate material-specific attenuation behavior, which is characterized by disparate attenuation at different energy levels, and to obtain material-specific images ( 10 , 11 ). Consequently, the DECT enables differentiation, classification, and quantification of the mass density of various materials ( 12 - 14 ). Accordingly, iodine-specific images can represent iodine content in the tissues and provide quantitative enhancement information, which is beneficial for making the transition from qualitative to quantitative evaluation of contrast-enhanced CT images ( 12 ).…”

Section: Introductionmentioning

confidence: 99%

Harnessing dual-energy CT for glycogen quantification: a phantom analysis

Li¹,

Li²,

Wei³

et al. 2023

Background Non-invasive glycogen quantification in vivo could provide crucial information on biological processes for glycogen storage disorder. Using dual-energy computed tomography (DECT), this study aimed to assess the viability of quantifying glycogen content in vitro . Methods A fast kilovolt-peak switching DECT was used to scan a phantom containing 33 cylinders with different proportions of glycogen and iodine mixture at varying doses. The virtual glycogen concentration (VGC) was then measured using material composition images. Additionally, the correlations between VGC and nominal glycogen concentration (NGC) were evaluated using least-square linear regression, then the calibration curve was constructed. Quantitative estimation was performed by calculating the linearity, conversion factor (inverse of curve slope), stability, sensitivity (limit of detection/limit of quantification), repeatability (inter-class correlation coefficient), and variability (coefficient of variation). Results In all conditions, excellent linear relationship between VGC and NGC were observed (P<0.001, coefficient of determination: 0.989–0.997; residual root-mean-square error of glycogen: 1.862–3.267 mg/mL). The estimated conversion factor from VGC to NGC was 3.068–3.222. In addition, no significant differences in curve slope were observed among different dose levels and iodine densities. The limit of detection and limit of quantification had respective ranges of 6.421–15.315 and 10.95–16.46 mg/mL. The data demonstrated excellent scan-repeat scan agreement (inter-class correlation coefficient, 0.977–0.991) and small variation (coefficient of variation, 0.1–0.2%). Conclusions The pilot phantom analysis demonstrated the feasibility and efficacy of detecting and quantifying glycogen using DECT and provided good quantitative performance with significant stability and reproducibility/variability. Thus, in the future, DECT could be used as a convenient method for glycogen quantification to provide more reliable information for clinical decision-making.