Generation of virtual monoenergetic images at 40 keV of the upper abdomen and image quality evaluation based on generative adversarial networks.

BACKGROUND: Abdominal CT scans are vital for diagnosing abdominal diseases but have limitations in tissue analysis and soft tissue detection. Dual-energy CT (DECT) can improve these issues by offering low keV virtual monoenergetic images (VMI), enhancing lesion detection and tissue characterization. However, its cost limits widespread use. PURPOSE: To develop a model that converts conventional images (CI) into generative virtual monoenergetic images at 40 keV (Gen-VMI40keV) of the upper abdomen CT scan. METHODS: Totally 444 patients who underwent upper abdominal spectral contrast-enhanced CT were enrolled and assigned to the training and validation datasets (7:3). Then, 40-keV portal-vein virtual monoenergetic (VMI40keV) and CI, generated from spectral CT scans, served as target and source images. These images were employed to build and train a CI-VMI40keV model. Indexes such as Mean Absolute Error (MAE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity (SSIM) were utilized to determine the best generator mode. An additional 198 cases were divided into three test groups, including Group 1 (58 cases with visible abnormalities), Group 2 (40 cases with hepatocellular carcinoma [HCC]) and Group 3 (100 cases from a publicly available HCC dataset). Both subjective and objective evaluations were performed. Comparisons, correlation analyses and Bland-Altman plot analyses were performed. RESULTS: The 192nd iteration produced the best generator mode (lower MAE and highest PSNR and SSIM). In the Test groups (1 and 2), both VMI40keV and Gen-VMI40keV significantly improved CT values, as well as SNR and CNR, for all organs compared to CI. Significant positive correlations for objective indexes were found between Gen-VMI40keV and VMI40keV in various organs and lesions. Bland-Altman analysis showed that the differences between both imaging types mostly fell within the 95% confidence interval. Pearson's and Spearman's correlation coefficients for objective scores between Gen-VMI40keV and VMI40keV in Groups 1 and 2 ranged from 0.645 to 0.980. In Group 3, Gen-VMI40keV yielded significantly higher CT values for HCC (220.5HU vs. 109.1HU) and liver (220.0HU vs. 112.8HU) compared to CI (p < 0.01). The CNR for HCC/liver was also significantly higher in Gen-VMI40keV (2.0 vs. 1.2) than in CI (p < 0.01). Additionally, Gen-VMI40keV was subjectively evaluated to have a higher image quality compared to CI. CONCLUSION: CI-VMI40keV model can generate Gen-VMI40keV from conventional CT scan, closely resembling VMI40keV.

Automated detection and classification of coronary atherosclerotic plaques on coronary CT angiography using deep learning algorithm.

Background: Coronary artery disease (CAD) is the leading cause of mortality worldwide. Recent advances in deep learning technology promise better diagnosis of CAD and improve assessment of CAD plaque buildup. The purpose of this study is to assess the performance of a deep learning algorithm in detecting and classifying coronary atherosclerotic plaques in coronary computed tomographic angiography (CCTA) images. Methods: Between January 2019 and September 2020, CCTA images of 669 consecutive patients with suspected CAD from Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine were included in this study. There were 106 patients included in the retrospective plaque detection analysis, which was evaluated by a deep learning algorithm and four independent physicians with varying clinical experience. Additionally, 563 patients were included in the analysis for plaque classification using the deep learning algorithm, and their results were compared with those of expert radiologists. Plaques were categorized as absent, calcified, non-calcified, or mixed. Results: The deep learning algorithm exhibited higher sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy {92% [95% confidence interval (CI): 89.5-94.1%], 87% (95% CI: 84.2-88.5%), 79% (95% CI: 76.1-82.4%), 95% (95% CI: 93.4-96.3%), and 89% (95% CI: 86.9-90.0%)} compared to physicians with ≤5 years of clinical experience in CAD diagnosis for the detection of coronary plaques. The algorithm's overall sensitivity, specificity, PPV, NPV, accuracy, and Cohen's kappa for plaque classification were 94% (95% CI: 92.3-94.7%), 90% (95% CI: 88.8-90.3%), 70% (95% CI: 68.3-72.1%), 98% (95% CI: 97.8-98.5%), 90% (95% CI: 89.8-91.1%) and 0.74 (95% CI: 0.70-0.78), indicating strong performance. Conclusions: The deep learning algorithm has demonstrated reliable and accurate detection and classification of coronary atherosclerotic plaques in CCTA images. It holds the potential to enhance the diagnostic capabilities of junior radiologists and junior intervention cardiologists in the CAD diagnosis, as well as to streamline the triage of patients with acute coronary symptoms.

Added value of arterial enhancement fraction derived from dual-energy computed tomography for preoperative diagnosis of cervical lymph node metastasis in papillary thyroid cancer: initial results.

OBJECTIVES: To explore the added value of arterial enhancement fraction (AEF) derived from dual-energy computed tomography CT (DECT) to conventional image features for diagnosing cervical lymph node (LN) metastasis in papillary thyroid cancer (PTC). METHODS: A total of 273 cervical LNs (153 non-metastatic and 120 metastatic) were recruited from 92 patients with PTC. Qualitative image features of LNs were assessed. Both single-energy CT (SECT)-derived AEF (AEFS) and DECT-derived AEF (AEFD) were calculated. Correlation between AEFD and AEFS was determined using Pearson's correlation coefficient. Multivariate logistic regression analysis with the forward variable selection method was used to build three models (conventional features, conventional features + AEFS, and conventional features + AEFD). Diagnostic performances were evaluated using receiver operating characteristic (ROC) curve analyses. RESULTS: Abnormal enhancement, calcification, and cystic change were chosen to build model 1 and the model provided moderate diagnostic performance with an area under the ROC curve (AUC) of 0.675. Metastatic LNs demonstrated both significantly higher AEFD (1.14 vs 0.48; p < 0.001) and AEFS (1.08 vs 0.38; p < 0.001) than non-metastatic LNs. AEFD correlated well with AEFS (r = 0.802; p < 0.001), and exhibited comparable performance with AEFS (AUC, 0.867 vs 0.852; p = 0.628). Combining CT image features with AEFS (model 2) and AEFD (model 3) could significantly improve diagnostic performances (AUC, 0.865 vs 0.675; AUC, 0.883 vs 0.675; both p < 0.001). CONCLUSIONS: AEFD correlated well with AEFS, and exhibited comparable performance with AEFS. Integrating qualitative CT image features with both AEFS and AEFD could further improve the ability in diagnosing cervical LN metastasis in PTC. CLINICAL RELEVANCE STATEMENT: Arterial enhancement fraction (AEF) values, especially AEF derived from dual-energy computed tomography, can help to diagnose cervical lymph node metastasis in patients with papillary thyroid cancer, and complement conventional CT image features for improved clinical decision making. KEY POINTS: • Metastatic cervical lymph nodes (LNs) demonstrated significantly higher arterial enhancement fraction (AEF) derived from dual-energy computed tomography (DECT) and single-energy CT (SECT)-derived AEF (AEFS) than non-metastatic LNs in patients with papillary thyroid cancer. • DECT-derived AEF (AEFD) correlated significantly with AEFS, and exhibited comparable performance with AEFS. • Integrating qualitative CT images features with both AEFS and AEFD could further improve the differential ability.

A nomogram for predicting postoperative complications based on tumor spectral CT parameters and visceral fat area in gastric cancer patients.

PURPOSE: To construct a nomogram combining tumor spectral CT parameters and visceral fat area (VFA) to predict postoperative complications (POCs) in patients with gastric cancer (GC). METHOD: This retrospective study included 101 GC patients who underwent preoperative abdominal spectral CT scan and were divided into two groups (37 with POCs and 64 without POCs) according to the Clavien-Dindo classification standard. Logistic regression was used to establish spectral, VFA, and combined models for predicting POCs. The combined prediction model was presented as a nomogram, and the diagnostic performance of each model was evaluated using receiver operating characteristic (ROC) curve analysis. RESULTS: The AUCs of the VFA and spectral model were 0.71 (95% CI: 0.62-0.80) and 0.81 (95% CI: 0.72-0.88), respectively. VFA, the slope of spectral curve (λ) in venous phase (λ-VP) and tumor Hounsfield units on monoenergetic images 40 keV in VP (MonoE40keV-VP) were independent predictors of POCs in GC. The nomogram yielded an AUC of 0.89 (95% CI: 0.81-0.94). The combined model was superior to the VFA or spectral models by comparing their AUCs (P = 0.000 and 0.022). CONCLUSIONS: The nomogram based on two tumor spectral parameters (λ-VP, MonoE40keV-VP) and VFA could serve as a convenient tool for predicting the POCs of GC patients.

Predictive modeling based on tumor spectral CT parameters and clinical features for postoperative complications in patients undergoing colon resection for cancer.

BACKGROUND: Colon cancer is a particularly prevalent malignancy that produces postoperative complications (POCs). However, limited imaging modality exists on the accurate diagnosis of POCs. The purpose of this study was therefore to construct a model combining tumor spectral CT parameters and clinical features to predict POCs before surgery in colon cancer. METHODS: This retrospective study included 85 patients who had preoperative abdominal spectral CT scans and underwent radical colon cancer resection at our institution. The patients were divided into two groups based on the absence (no complication/grade I) or presence (grades II-V) of POCs according to the Clavien-Dindo grading system. The visceral fat areas (VFA) of patients were semi-automatically outlined and calculated on L3-level CT images using ImageJ software. Clinical features and tumor spectral CT parameters were statistically compared between the two groups. A combined model of spectral CT parameters and clinical features was established by stepwise regression to predict POCs in colon cancer. The diagnostic performance of the model was evaluated using the receiver operating characteristic (ROC) curve, including area under the curve (AUC), sensitivity, and specificity. RESULTS: Twenty-seven patients with POCs and 58 patients without POCs were included in this study. MonoE40keV-VP and VFA were independent predictors of POCs. The combined model based on predictors yielded an AUC of 0.84 (95% CI: 0.74-0.91), with a sensitivity of 77.8% and specificity of 87.9%. CONCLUSIONS: The model combining MonoE40keV-VP and VFA can predict POCs before surgery in colon cancer and provide a basis for individualized management plans. CRITICAL RELEVANCE STATEMENT: The model combining MonoE40keV-VP and visceral fat area can predict postoperative complications before surgery in colon cancer and provide a basis for individualized management plans. KEY POINTS: • Visceral fat area and MonoE40keV-VP were independent predictors of postoperative complications in colon cancer. • The combined model yielded a high AUC, sensitivity, and specificity in predicting postoperative complications. • The combined model was superior to the single visceral fat area or MonoE40keV-VP in predicting postoperative complications.

Comparative analysis of automatic segmentation of esophageal cancer using 3D Res-UNet on conventional and 40-keV virtual mono-energetic CT Images: a retrospective study.

Objectives: To assess the performance of 3D Res-UNet for fully automated segmentation of esophageal cancer (EC) and compare the segmentation accuracy between conventional images (CI) and 40-keV virtual mono-energetic images (VMI40 kev). Methods: Patients underwent spectral CT scanning and diagnosed of EC by operation or gastroscope biopsy in our hospital from 2019 to 2020 were analyzed retrospectively. All artery spectral base images were transferred to the dedicated workstation to generate VMI40 kev and CI. The segmentation model of EC was constructed by 3D Res-UNet neural network in VMI40 kev and CI, respectively. After optimization training, the Dice similarity coefficient (DSC), overlap (IOU), average symmetrical surface distance (ASSD) and 95% Hausdorff distance (HD_95) of EC at pixel level were tested and calculated in the test set. The paired rank sum test was used to compare the results of VMI40 kev and CI. Results: A total of 160 patients were included in the analysis and randomly divided into the training dataset (104 patients), validation dataset (26 patients) and test dataset (30 patients). VMI40 kevas input data in the training dataset resulted in higher model performance in the test dataset in comparison with using CI as input data (DSC:0.875 vs 0.859, IOU: 0.777 vs 0.755, ASSD:0.911 vs 0.981, HD_95: 4.41 vs 6.23, all p-value <0.05). Conclusion: Fully automated segmentation of EC with 3D Res-UNet has high accuracy and clinically feasibility for both CI and VMI40 kev. Compared with CI, VMI40 kev indicated slightly higher accuracy in this test dataset.

Added value of spectral parameters in diagnosing metastatic lymph nodes of pT1-2 rectal cancer.

PURPOSE: To investigate the added value of spectral parameters derived from dual-layer spectral detector CT (SDCT) in diagnosing metastatic lymph nodes (LNs) of pT1-2 (stage 1-2 determined by pathology) rectal cancer. METHODS: A total of 80 LNs (57 non-metastatic LNs and 23 metastatic LNs) from 42 patients with pT1-T2 rectal cancer were retrospectively analyzed. The short-axis diameter of LNs was measured, then its border and enhancement homogeneity were evaluated. All spectral parameters, including iodine concentration (IC), effective atomic number (Zeff), normalized IC (nIC), normalized Zeff (nZeff), and slope of the attenuation curve (λ), were measured or calculated. The chi-square test, Fisher's exact test, independent-samples t-test, or Mann-Whitney U test was used to compare the differences of each parameter between the non-metastatic group and the metastatic group. Multivariable logistic regression analyses were used to determine the independent factors for predicting LN metastasis. Diagnostic performances were assessed by ROC curve analysis and compared with the DeLong test. RESULTS: The short-axis diameter, border, enhancement homogeneity, and each spectral parameter of LNs showed significant differences between the two groups (P < 0.05). The nZeff and short-axis diameter were independent predictors of metastatic LNs (P < 0.05), with areas under the curve (AUC) of 0.870 and 0.772, sensitivity of 82.5% and 73.9%, and specificity of 82.6% and 78.9%. After combining nZeff and the short-axis diameter, the AUC (0.966) was the highest with sensitivity of 100% and specificity of 87.7%. CONCLUSION: The spectral parameters derived from SDCT might help us to improve the diagnostic accuracy of metastatic LNs in patients with pT1-2 rectal cancer, the highest diagnostic performance can be achieved after combining nZeff with the short-axis diameter of LNs.

Feasibility and accuracy of coronary artery calcium score on virtual non-contrast images derived from a dual-layer spectral detector CT: A retrospective multicenter study.

Rationale and objective: This retrospective study was to evaluate the feasibility and accuracy of coronary artery calcium score (CACS) from virtual non-contrast (VNC) images in comparison with that from true non-contrast (TNC) images. Materials and methods: A total of 540 patients with suspected of coronary artery disease (CAD) who underwent a dual-layer spectral detector CT (SDCT) in three hospitals were eligible for this study and 233 patients were retrospectively enrolled for further analysis. The CACS was calculated from both TNC and VNC images and compared. Linear regression analysis of the CACS was performed between TNC and VNC images. Results: The correlation of overall CACS from VNC and TNC images was very strong (r = 0.923, p < 0.001). The CACS from VNC images were lower than that from TNC images (221 versus. 69, p < 0.001). When the regression equation of the overall coronary artery was applied, the mean calibrated CACS-VNC was 221 which had a significant difference from the CACS-TNC (p = 0.017). When the regression equation of each coronary branch artery was applied, the mean calibrated CACS-VNC was 221, which had a significant difference from the CACS-TNC (p = 0.003). But the mean difference between the CACS-TNC and the calibrated CACS-VNC in either way was less than 1. The agreement on risk stratification with CACS-TNC and CCACS-VNC was almost perfect. Conclusion: This multicenter study with dual-layer spectral detector CT showed that it was feasible to calculate CACS from the VNC images derived from the spectral coronary artery CT angiography scan, and the results were in good accordance with the TNC images after correction. Therefore, the TNC scan could be omitted, reducing the radiation dose to patients and saving examination time while using dual-layer spectral detector CT.

Role of iodine density value on dual-energy CT for detection of high tumor cell proportion region in lung cancer during CT-guided transthoracic biopsy.

OBJECTIVE: This study aimed to identify regions with at least 20% tumor cell content in lung cancer tumors by using spectral parameters from dual-layer spectral detector computed tomography (SDCT) to design the puncture path for transthoracic lung biopsy (TTLB). MATERIALS AND METHODS: This prospective study recruited patients with suspected lung cancer. Forty-one patients were enrolled to identify the high tumor cell proportion region (HTPR) and then another 15 patients to validate the accuracy of the HTPR. In each of the 41 patients, the suspected regions with high or low tumor cell proportions were punctured according to local iodine density (IoD) values for separate biopsies. The tumor cell proportions of 82 specimens were assessed and classified into high and low tumor cell proportions based on the threshold value of 20 %. The performance of spectral parameters was analyzed to distinguish the HTPR (tumor cell proportion ≥ 20 %) from the low tumor cell proportion region (LTPR). The cutoff value of optimal spectral parameter was used to prospectively guide the biopsy of the HTPR in 15 cases for further validation, and then the accuracy was calculated. RESULTS: The AUC values of spectral parameters were all higher than those of CTconventional in identifying the HTPR (all P < 0.05). The IoD with a cutoff value of 0.59 mg/mL in arterial phase (AP) yielded good performance (specificity: 97.10 %) in identifying the HTPR. It was applied to 15 cases for validation, and the accuracy rate was 100 %. CONCLUSION: Spectral CT parameters can be used to identify regions with at least 20% tumor cell content in lung cancer for biopsies.

Dual-layer dual-energy CT for improving differential diagnosis of squamous cell carcinoma from adenocarcinoma at gastroesophageal junction.

Objective: To examine the clinical values of dual-energy CT parameters derived from dual-layer spectral detector CT (SDCT) in the differential diagnosis of squamous cell carcinoma (SCC) and adenocarcinoma (AC) of the gastroesophageal junction (GEJ). Methods: Totally 66 patients with SCC and AC of the GEJ confirmed by pathological analysis were retrospectively enrolled, and underwent dual-phase contrast-enhancement chest CT with SDCT. Plain CT value, CT attenuation enhancement (△CT), iodine concentration (IC), spectral slope (λHU), effective atomic number (Zeff) and 40keV CT value (CT40keV) of the lesion in the arterial phase (AP) and venous phase (VP) were assessed. Multivariate logistic regression analysis was performed to evaluate the diagnostic efficacies of different combinations of dual-energy CT parameters. Receiver operating characteristic (ROC) curves were used to analyze the accuracy of dual-energy CT parameters and Delong test was used to compare AUCs. Results: IC, λHU, Zeff and CT40keV in AP and VP and △CT in VP were significantly higher in the AC group than those in the SCC group (all P<0.05). ROC curve analysis showed that IC, λHU, Zeff and CT40keV in VP had high diagnostic performances, with AUCs of 0.74, 0.74, 0.79 and 0.78, respectively. Logistic regression showed the combination of ICVP, λHU VP, CT40keV VP and Zeff VP had the highest AUC (0.84), with a threshold of 0.40, sensitivity and specificity in distinguishing SCC and AC were 93.1% and 73.0%, respectively. Delong test showed that the AUC of △CTVP was lower than other AUCs of dual-energy CT parameters. Conclusion: Dual-energy CT parameters derived from SDCT provide added value in the differential diagnosis of SCC and AC of the GEJ, especially the combination of IC, λHU, CT40keV and Zeff in VP. Advances in knowledge: Dual-energy CT parameters derived from dual-layer spectral detector CT provide added value to differentiate AC from SCC at the GEJ, especially the combination of effective atomic number, spectral slope, iodine concentration and 40keV CT value in VP.

Iodine-based extracellular volume for evaluating myocardial status in patients undergoing percutaneous coronary intervention for acute myocardial infarction by using dual-layer spectral detector computed tomography: a comparison study with magnetic resonance.

Background: The myocardial status of patients who undergo percutaneous coronary intervention (PCI) must be evaluated accurately to enable treatment plans to be made for potential complications such as abrupt vessel closure, stent deformation, and myocardial chronic ischemia. This study examined the modality and clinical feasibility of iodine-based extracellular volume (ECV) assessment of the myocardium versus cardiovascular magnetic resonance (CMR) imaging in patients undergoing PCI. Methods: In all, 21 patients who underwent PCI were prospectively enrolled in the study. All patients underwent follow-up cardiac dual-layer spectral detector computed tomography (SDCT) and CMR imaging after PCI. Myocardial ECV was quantified by either computed tomography (ECVCT) or magnetic resonance (ECVMR) using iodine or T1-weighted mapping, respectively. The quality of SDCT and CMR images was independently assessed by two radiologists using a 4-point scale (1= poor and 4= excellent). Any patient with an image quality (IQ) score <2 was excluded. Consistency between radiologists was evaluated using intraclass correlation coefficients (ICC). Correlations between ECVCT and ECVMR values were analyzed using Pearson's test, and consistency was analyzed with Bland-Altman plots. Results: Nineteen of 21 patients completed both cardiac CT and CMR examinations, while three patients were excluded after IQ assessment (two with poor CMR IQ; one with a discontinuous coronary artery on CT images). The mean (±SD) IQ scores for CT and CMR images were 3.81±0.40 and 3.25±0.58, respectively, and interobserver agreement was good (ICC =0.93 and 0.92 for CT and CMR, respectively). The mean (±SD) ECVCT and ECVMR values were 35.93%±9.73% and 33.89%±7.51%, respectively, with good correlation (r=0.79, P<0.001). Bland-Altman analysis showed a difference of 2.04% (95% CI: -9.56%, 13.64%) between the ECVCT and ECVMR values. Conclusions: There is high correlation between iodine-based ECVCT and ECVMR values, which indicates that ECVCT is clinically feasible for evaluating the status of myocardial recovery in patients undergoing PCI.

Extracellular Volume Fraction Derived From Dual-Layer Spectral Detector Computed Tomography for Diagnosing Cervical Lymph Nodes Metastasis in Patients With Papillary Thyroid Cancer: A Preliminary Study.

Objectives: The current study evaluates the performance of dual-energy computed tomography (DECT) derived extracellular volume (ECV) fraction based on dual-layer spectral detector CT for diagnosing cervical lymph nodes (LNs) metastasis from papillary thyroid cancer (PTC) and compares it with the value of ECV derived from conventional single-energy CT (SECT). Methods: One hundred and fifty-seven cervical LNs (81 non-metastatic and 76 metastatic) were recruited. Among them, 59 cervical LNs (27 non-metastatic and 32 metastatic) were affected by cervical root artifact on the contrast-enhanced CT images in the arterial phase. Both the SECT-derived ECV fraction (ECVS) and the DECT-derived ECV fraction (ECVD) were calculated. A Pearson correlation coefficient and a Bland-Altman analysis were performed to evaluate the correlations between ECVD and ECVS. Receiver operator characteristic curves analysis and the Delong method were performed to assess and compare the diagnostic performance. Results: ECVD correlated significantly with ECVS (r = 0.925; p <0.001) with a small bias (-0.6). Metastatic LNs showed significantly higher ECVD (42.41% vs 22.53%, p <0.001) and ECVS (39.18% vs 25.45%, p <0.001) than non-metastatic LNs. By setting an ECVD of 36.45% as the cut-off value, optimal diagnostic performance could be achieved (AUC = 0.813), which was comparable with that of ECVS (cut-off value = 34.99%; AUC = 0.793) (p = 0.265). For LNs affected by cervical root artifact, ECVD also showed favorable efficiency (AUC = 0.756), which was also comparable with that of ECVS (AUC = 0.716) (p = 0.244). Conclusions: ECVD showed a significant correlation with ECVS. Compared with ECVS, ECVD showed comparable performance in diagnosing metastatic cervical LNs in PTC patients, even though the LNs were affected by cervical root artifacts on arterial phase CT.

Clinical feasibility of using effective atomic number maps derived from non-contrast spectral computed tomography to identify non-calcified atherosclerotic plaques: a preliminary study.

Background: To assess the clinical feasibility of using effective atomic number (Zeff) maps derived from non-contrast-enhanced computed tomography (NCECT) scans obtained by dual-layer spectral computed tomography (DLCT) to identify non-calcified atherosclerotic plaques. Methods: A total of 37 patients with 86 non-calcified atherosclerotic plaques confirmed by contrast-enhanced CT (CECT) were enrolled in this retrospective study. Both spectral-based-images (SBI) and conventional images (CI) were reconstructed from NCECT and CECT scans. The presence of plaques on NCECT Zeff maps and CIs were independently assessed by 2 radiologists. In CECT scans, plaques and regions of interest (ROIs) in vessel lumens were assessed with CT attenuation and Zeff values, and the proportion of plaques was determined as Area (plaque)/Area (vessel). The CT and Zeff values for plaques and blood were recorded from both CECT and NCECT scans. Contrast-to-noise ratios (CNRs) of the plaques were calculated and compared using CT attenuation and Zeff values. Finally, interobserver agreement was evaluated. Results: A total of 47 of the 86 (54.7%) plaques were identified on Zeff map images derived from the NCECT scans while only 7 (8.1%) plaques were identified on the CI. There was no significant difference between the mean vessel ROI area measured on CIs and that measured on Zeff map images (502.19 vs. 498.14 mm2; P=0.28), while the mean plaque ROI area was larger (81.45 vs. 75.46 mm2). The observer consensus of vessel and plaque ROI area measurements using both methods was excellent, with interclass correlation coefficients (ICCs) of 0.99 and 0.94, respectively. For the 7 plaques detected both by NCECT CI and Zeff mapping, the CT attenuation and Zeff blood values were both larger than the plaque values [42.00 vs. 25.67 Hounsfield unit (HU); 7.33 vs. 7.19 HU; both P<0.05]; the plaque ROI area measurement on the NCE Zeff map was smaller than that on the CE CI (48.73 vs. 77.76 mm2), but was much larger than that on the NCE CI (18.39 mm2). For all 47 plaques detected by NCE Zeff mapping, the CT attenuation and Zeff values of blood and plaques on the NCECT images showed no significant differences (42.53 vs. 35.14 HU; P=0.18; 7.32 vs. 7.31, P=0.71); however, the CNR of Zeff was significantly higher than the CT attenuation value (1.69 vs. 1.12; P<0.05) derived from the NCECT scans. Inter-reviewer agreement was good (ICC =0.78). Conclusions: Zeff map images derived from NCECT SBI with DLCT provide a potentially feasible approach for identifying non-calcified atherosclerotic plaques, which might be clinically useful for the screening of asymptomatic at-risk patients.

Effect of different reconstruction algorithms on coronary artery calcium scores using the reduced radiation dose protocol: a clinical and phantom study.

BACKGROUND: This study aimed to evaluate the effects of different iterative reconstruction (IR) algorithms on coronary artery calcium (CAC) score quantification using the reduced radiation dose (RRD) protocol in an anthropomorphic phantom and in patients. METHODS: A thorax phantom, containing 9 calcification inserts with varying hydroxyapatite (HA) densities, was scanned with the reference protocol [120 kv, 80 mAs, filtered back projection (FBP)] and RRD protocol (120 kV, 20-80 mAs, 5 mAs interval) using a 256-slice computed tomography (CT) scanner. Raw data were reconstructed with different reconstruction algorithms [iDose4 levels 1-7 and iterative model reconstruction (IMR) levels 1-3]. Signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and Agatston score (AS) were calculated for each image series. The correction factor was derived from linear regression analysis between the reference image series and other image series with different parameters. Additionally, 40 patients were scanned with the RRD protocol (50 mAs) and reconstructed with FBP, iDose4 level 4, and IMR level 2. AS was calculated for the 3-group image series, and was corrected by applying a correction factor for the IMR group. The agreement of risk stratification with different reconstruction algorithms was also analyzed. RESULTS: For the phantom study, the iDose4 and IMR groups had significantly higher SNR and CNR than the FBP group (all P<0.05). There were no significant differences in the total AS after comparing image series reconstructed with iDose4 (level 1-7) and FBP (all P>0.05), while AS from the IMR (level 1-3) image series were lower than the FBP group (all P<0.05). The tube current of 50 mAs was determined for the clinical study, and the correction factor was 1.14. For the clinical study, the median AS from the iDose4 and IMR groups were both significantly lower compared to the FBP image series [(112.89 (63.01, 314.09), 113.22 (64.78, 364.95) vs. 118.59 (65.05, 374.48), both P<0.05]. After applying the correction factor, the adjusted AS from the IMR group was not significantly different from that of the FBP group [126.48 (69.62, 355.85) vs. 118.59 (65.05, 374.48), P=0.145]. Moreover, the agreement in risk stratification between FBP and IMR improved from 0.81 to 0.85. CONCLUSIONS: The RRD CAC scoring scan using the IMR reconstruction algorithm is clinically feasible, and a correction factor can help reduce the AS underestimation effect.

Effects of radiation dose levels and spectral iterative reconstruction levels on the accuracy of iodine quantification and virtual monochromatic CT numbers in dual-layer spectral detector CT: an iodine phantom study.

BACKGROUND: The purpose of this study is to investigate the accuracy of iodine quantification and virtual monochromatic CT numbers obtained with the dual-layer spectral CT (DLCT) using a phantom at different radiation dose levels and spectral iterative reconstruction (IR) levels. METHODS: An abdomen phantom with seven iodine inserts (2.0, 2.5, 5.0, 7.5, 10.0, 15.0, 20.0 mg/mL) was imaged using a DLCT scanner. Five repeated scans were performed at computed tomography dose index volume (CTDIvol) levels of 5, 10, 15, 20, 25 mGy at tube voltages of 120 and 140 kVp, respectively. Spectral-based images were reconstructed using four spectral IR levels (spectral level of 0, 2, 4, 6). Iodine density images and virtual monochromatic images (VMI) at energy levels of 50, 70 and 120 keV were created. The absolute percentage bias (APB) of the measured iodine concentration and the true iodine concentration, and the measured VMI CT numbers and the theoretical VMI CT numbers were compared to determine the difference of radiation dose levels and different spectral IR levels. RESULTS: At CTDIvol levels of 25, 20, 15, 10 mGy, radiation dose levels had no effect on the accuracy of iodine quantitation; at CTDIvol level of 5 mGy, the accuracy of iodine quantification was the poorest, with the mean APBiodine of 4.33% (P<0.05). There was no significant difference in the accuracy of iodine quantitation between 120 and 140 kVp (P=0.648). At energy levels of 50, 70 and 120 keV, there was no significant difference in the accuracy of the VMI CT numbers among the CTDIvol levels of 25, 20 and 15 mGy. However, the accuracy of VMI CT numbers was significantly degraded at the CTDIvol levels of 10 and 5 mGy (P<0.05). At energy level of 50 keV, the accuracy of VMI CT numbers was not affected by tube voltages (kVps) used (P=0.125). At the energy levels of 70 and 120 keV, 140 kVp produced a smaller bias than 120 kVp, with the mean APBHU at 120 and 140 kVp being of 3.62% vs. 2.99% for 70 keV (P<0.01), and 11.65% vs. 9.28% for 120 keV (P<0.01), respectively. Spectral IR levels did not affect the accuracy of iodine quantification and VMI CT numbers (P=0.998, P=0.963). CONCLUSIONS: The accuracy of iodine quantification and VMI CT numbers was only affected by very low radiation dose levels. At the clinically applied radiation dose levels of >10 mGy, the accuracy of both iodine quantification and VMI CT numbers is relatively stable and high.

Initial exploration of coronary stent image subtraction using dual-layer spectral CT.

OBJECTIVES: This study aimed to investigate the feasibility of coronary stent image subtraction using spectral tools derived from dual-layer spectral computed tomography (CT). METHODS: Forty-three patients (65 stents) who underwent coronary CT angiography using dual-layer spectral CT were included. Conventional, 50-keV (kilo electron-volt), 100-keV, and virtual non-contrast (VNC) images were reconstructed from the same cardiac phase. Stents were subtracted on VNC images from conventional (convsub), 100-keV (100-keVsub), and 50-keV (50-keVsub) images. The in-stent lumen diameters were measured on subtraction, conventional, and 100-keV images. Subjective evaluation of reader confidence and subtractive quality was evaluated. Friedman tests were performed to compare in-stent lumen diameters and subjective evaluation among different images. Correlation between stent diameter and subjective evaluation was expressed as Spearman's rank correlation coefficient (rs). The diagnostic accuracy was assessed according to invasive coronary angiography (ICA) performed in 11 patients (20 stents). RESULTS: In-stent lumen diameters were significantly larger on subtraction images than those on conventional and 100-keV images (p < 0.05). Higher reader confidence was found on 100-keV, convsub, 100-keVsub, and 50-keVsub images compared with conventional images (p < 0.05). Subtractive quality of 100-keVsub images was better than that of convsub images (p = 0.037). A moderate-to-strong correlation between stent diameter and subjective evaluation was found (rs = 0.527~0.790, p < 0.05). Higher specificity, positive predictive value, and negative predictive value of subtraction images were shown by ICA results. CONCLUSIONS: Subtraction images derived from dual-layer spectral CT enhanced in-stent lumen visibility and could potentially improve diagnostic performance for evaluating coronary stents. KEY POINTS: • Dual-layer spectral CT enabled good subtractive quality of coronary stents without misregistration artifacts. • Subtraction images could improve in-stent lumen visibility. • Reader confidence and diagnostic performance were enhanced with subtraction images.

Quantification and differentiation of composition of mixed pancreatic duct stones using single-source dual-energy CT: an ex vivo study.

PURPOSE: To evaluate the feasibility of using single-source dual-energy CT (SS DECT) to quantify and differentiate calcium carbonate (CA) and non-calcium carbonate (NCA) components of pancreatic duct stones (PDS) with mixed composition. MATERIALS AND METHODS: A total of 12 PDS harvested from general surgery department in our hospital were analyzed with micro-CT as a reference standard for CA and NCA composition. These stones were placed in a TOS water phantom of 35 cm diameter to simulate standard adult body size. High- and low-energy image sets were acquired from SS DECT scans with high/low tube potential pairs of 80 kVp/140 kVp. All the image sets were imported into an in-house software for further post-processing. CT number ratio (CTR), defined as the ratio of the CT number at 80 kVp to 140 kVp was calculated for each pixel of the images. Threshold was preset between 1.00 and 1.25 to classify CA and NCA components. Pixels in PDS with CTR higher than the threshold were classified as CA, and those with CTR lower than the threshold were classified as NCA. The percentages of CA and NCA for each stone were determined by calculating the number of CA and NCA pixels. Finally, the minimal, maximal and root-mean-square errors (RMSE) of composition measured by SS DECT under each threshold were calculated by referring to the composition data from micro-CT. The optimal threshold was determined with the minimal RMSE. A paired t test was used to compare the stone composition determined by DECT with micro-CT. RESULTS: The optimal CTR threshold was 1.16, with RMSE of 6.0%. The minimum and maximum absolute errors were 0.22% and 11.35%, respectively. Paired t test showed no significant difference between DECT and micro-CT for characterizing CA and NCA composition (p = 0.414). CONCLUSION: SS DECT is a potential approach for quantifying and differentiating CA and NCA components in PDS with mixed composition.