Purpose: The purpose of this study was to evaluate the diagnostic accuracy when using various base material pairs (BMPs) in dual-energy computed tomography (DECT), and to establish corresponding diagnostic standards for assessing bone status through comparison with quantitative computed tomography (QCT). Methods: This prospective study enrolled a total of 469 patients who underwent both non-enhanced chest CT scans under conventional kVp and abdominal DECT. The bone densities of hydroxyapatite (water), hydroxyapatite (fat), hydroxyapatite (blood), calcium (water), and calcium (fat) (D_{HAP (water)}, D_{HAP (fat)}, D_{HAP (blood)}, D_{Ca (water)}, and D_{Ca (fat)}) in the trabecular bone of vertebral bodies (T11–L1) were measured, along with bone mineral density (BMD) via QCT. Intraclass correlation coefficient (ICC) analysis was used to assess the agreement of the measurements. Spearman’s correlation test was performed to analyze the relationship between the DECT- and QCT-derived BMD. Receiver operator characteristic (ROC) curves were generated to determine the optimal diagnostic thresholds of various BMPs for diagnosing osteopenia and osteoporosis. Results: A total of 1371 vertebral bodies were measured, and QCT identified 393 with osteoporosis and 442 with osteopenia. Strong correlations were observed between D_{HAP (water)}, D_{HAP (fat)}, D_{HAP (blood)}, D_{Ca (water)}, and D_{Ca (fat)} and the QCT-derived BMD. D_{HAP (water)} showed the best predictive capability for osteopenia and osteoporosis. The area under the ROC curve, sensitivity, and specificity for identifying osteopenia were 0.956, 86.88%, and 88.91% with D_{HAP (water)} ≤ 107.4 mg/cm³, respectively. The corresponding values for identifying osteoporosis were 0.999, 99.24%, and 99.53% with D_{HAP (water)} ≤ 89.62 mg/cm³, respectively. Conclusions: Bone density measurement using various BMPs in DECT enables the quantification of vertebral BMD and the diagnosis of osteoporosis, with D_{HAP (water)} having the highest diagnostic accuracy.

Objective: Develop two fully automatic osteoporosis screening systems using deep learning (DL) and radiomics (Rad) techniques based on low-dose chest CT (LDCT) images and evaluate their diagnostic effectiveness. Methods: In total, 434 patients who underwent LDCT and bone mineral density (BMD) examination were retrospectively enrolled and divided into the development set (n = 333) and temporal validation set (n = 101). An automatic thoracic vertebra cancellous bone (TVCB) segmentation model was developed. The Dice similarity coefficient (DSC) was used to evaluate the segmentation performance. Furthermore, the three-class Rad and DL models were developed to distinguish osteoporosis, osteopenia, and normal bone mass. The diagnostic performance of these models was evaluated using the receiver operating characteristic (ROC) curve and decision curve analysis (DCA). Results: The automatic segmentation model achieved excellent segmentation performance, with a mean DSC of 0.96 ± 0.02 in the temporal validation set. The Rad model was used to identify osteoporosis, osteopenia, and normal BMD in the temporal validation set, with respective area under the receiver operating characteristic curve (AUC) values of 0.943, 0.801, and 0.932. The DL model achieved higher AUC values of 0.983, 0.906, and 0.969 for the same categories in the same validation set. The Delong test affirmed that both models performed similarly in BMD assessment. However, the accuracy of the DL model is 81.2%, which is better than the 73.3% accuracy of the Rad model in the temporal validation set. Additionally, DCA indicated that the DL model provided a greater net benefit compared to the Rad model across the majority of the reasonable threshold probabilities Conclusions: The automated segmentation framework we developed can accurately segment cancellous bone on low-dose chest CT images. These predictive models, which are based on deep learning and radiomics, provided comparable diagnostic performance in automatic BMD assessment. Nevertheless, it is important to highlight that the DL model demonstrates higher accuracy and precision than the Rad model.

Objective: The purpose of this study was to evaluate the accuracy of quantifying hepatic fat fraction (HFF) in nonalcoholic fatty liver disease patients with multimaterial decomposition (MMD) and fat (water)-based material decomposition by single-source dual-energy computed tomography. Methods: Hepatic fat fractions were quantified by noncontrast (HFF_non-CE) and contrast-enhanced single-source dual-energy computed tomography in arterial phase (HFF_AP), portal venous phase (HFF_PVP) and equilibrium phase (HFF_EP) using MMD in 19 nonalcoholic fatty liver disease patients. The fat concentration was measured on fat (water)-based images. As the standard of reference, magnetic resonance iterative decomposition of water and fat with echo asymmetry and least-squares estimation-iron quantification images were reconstructed to obtain HFF (HFF_IDEAL-IQ). Results: There was a strong correlation between HFF_non-CE, HFF_AP, HFF_PVP, HFF_EP, fat concentration and HFF_IDEAL-IQ (r = 0.943, 0.923, 0.942, 0.952, and 0.726) with HFFs having better correlation with HFF_IDEAL-IQ. Hepatic fat fractions did not significantly differ across scanning phases. The HFFs of 3-phase contrast-enhanced computed tomography had a good consistency with HFF_non-CE. Conclusions: Hepatic fat fraction using MMD has excellent correlation with that of magnetic resonance imaging, is independent of the computed tomography scanning phases, and may be used as a routine technique for quantitative assessment of HFF.

Background: To evaluate the predictive value of dual-energy computed tomography (DECT)-based quantitative parameters and radiomics models for the preoperative differentiation between benign and malignant thyroid nodules, and to compare their performance with radiologists’ interpretations. Methods: A retrospective analysis was conducted on 215 patients who underwent contrast-enhanced DECT of the thyroid, with pathological outcomes for thyroid nodules obtained. Patients were randomly assigned to training and testing groups in a 7:3 ratio. The images were evaluated by radiologists. Quantitative parameters derived from DECT were identified through univariate and multivariate logistic regression analyses to construct a DECT model. Radiomics features were extracted from the 40, 70, and 100 keV virtual monochromatic images, as well as iodine-based material-decomposition (IMD) images in the arterial phase (AP) and venous phase (VP) to develop radiomics models from both individual and combined images using a support vector machine (SVM), with the optimal performing model selected as the final radiomics model. Subsequently, a fusion model combining DECT parameters and the radiomics model was established. The diagnostic performances of the radiologist, DECT, radiomics and fusion models were evaluated using receiver operating characteristic (ROC) curves, and clinical usefulness was assessed through decision curve analysis (DCA). Results: The normalized iodine concentration (NIC) in DECT emerged as an independent factor for the preoperative differentiation between benign and malignant thyroid nodules. The multi-image radiomics model demonstrated excellent predictive performance in the test cohort, achieving an area under the curve (AUC) of 0.966 and was selected as the final model among the radiomics models. The ROC curves indicate that the radiomics model outperformed the radiologist model in predicting the thyroid nature in both the training (0.990 vs. 0.746) and test cohorts (0.966 vs. 0.697) (all p < 0.05). The diagnostic efficacy of the fusion model showed slight improvement over the radiomics model, although this difference was not statistically significant. Furthermore, the fusion model performed exceptionally well in DCA. Conclusions: The fusion model, which combined the NIC and the optimal multi-image radiomics model, demonstrates strong diagnostic capability in predicting benign and malignant thyroid nodules.

Background: We evaluated an individualized dual-energy computed tomography (DECT) scan protocol by combining optimal monochromatic images with an appropriate ASIR-V reconstruction strength in computed tomography pulmonary angiography (CTPA) to reduce radiation and iodine doses and superior vena cava (SVC) artifacts.Material/Methods: A total of 127 patients who underwent CTPA were prospectively enrolled and randomly divided into a standard (n=63) and individualized group (n=64). The standard group used 120 kVp, 150 mAs, and 60 mL contrast media at an injection rate of 5 mL/s; the individualized group used DECT imaging mode with tube current selected according to patients' BMI (BMI <= 20 kg/m2, 200 mA; 20< BMI <= 23 kg/m2, 240 mA; 23< BMI <= 25 kg/m2, 280 mA; BMI >25 kg/m2, 320 mA). Contrast media intake was 130 mgI/kg with an injection time of 7 s. The data in the individualized group was reconstructed to 55-70 keV (5 keV interval) monochromatic images combined with 40-80% ASIR-V (10% interval). Radiation dose, contrast dose, and image quality were compared between the groups.Results: There were no significant differences in patient habitus. Compared with the standard group, the individualized group significantly decreased radiation dose by 33.93% (3.31 +/- 0.57 mSv vs 5.01 +/- 0.34 mSv) and contrast dose by 56.95% (9.04 +/- 1.40 gI vs 21.00 +/- 0.00 gI). The 60 keV image with 80%ASIR-V in the individualized group provided the best image quality and further reduced SVC beam-hardening artifacts.Conclusions: The use of BMI-dependent DECT protocol in CTPA further reduces radiation dose, contrast agent dose, and SVC artifacts, with the 60 keV images reconstructed using 80%ASiR-V having the best image quality.