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Objective: This study aimed to investigate the diagnostic performance of radiomics features derived from coronary computed tomography angiography (CCTA) in the identification of ischemic coronary stenosis plaque using invasive fractional flow reserve (FFR) as the reference standard.Materials and Methods: 174 plaques of 149 patients (age: 62.21 ± 8.47 years, 96 males) with at least one lesion stenosis degree between 30 % and 90 % were retrospectively included. Stenosis degree and plaque characteristics were recorded, and a conventional multivariate logistic model was established. Over 1000 radiomics features of the plaque were derived from CCTA images. The plaques were randomly divided into training set (n = 139) and validation set (n = 35). A random forest model was built. The area under the curve (AUC) of the models was compared.Results: Fifty-eight radiomics features were correlated with functionally significant stenosis (p <  0.05), wherein 56 features had an AUC of >0.6. NCP volume, NRS, remodeling index, and spotty calcification were included in the conventional model. Ultimately, 14 features were integrated to build the radiomics model. The AUC showed an improvement: 0.71 vs 0.82 for the training set and 0.70 vs 0.77 for the validation set (conventional model and radiomics model, respectively); however, it was not statistically significant (p =  0.58).Conclusion: The radiomics analysis of plaques showed improvement compared with conventional plaques assessment in identifying hemodynamically significant coronary stenosis. The statistical advancement of machine learning for plaques to predict hemodynamic stenosis with a noninvasive approach still needs further studies on a large-scale dataset. [ABSTRACT FROM AUTHOR]

• Assessing the degree of stenosis of the carotid artery is essential in clinical practice. • A modified dual-energy decomposition algorithm was introduced to remove calcification. • Improvement in the accuracy of stenosis assessment was particularly noted in the C3–6 segments. To evaluate the performance of a dual-energy (DE) calcium removal software based on a modified three-material decomposition algorithm in assessing the stenosis of the internal carotid artery (ICA) in comparison with mixed images using digital subtraction angiography (DSA) as the reference standard. Forty-six patients (38 men; 67 ± 8 years old), including 154 calcified ICA segments C1–C2 (59), C3–C5 (63), C6 (24), and C7 (8), were recruited in this retrospective study. Mixed images and virtual non-calcium (VNCa) images using the modified dual-energy computed tomography (DECT) algorithm were reconstructed. The differences between VNCa and DSA images vs. mixed and DSA images of degree of stenosis were compared. The intraclass correlation coefficient (ICC) was used for assessing the agreement between VNCa, mixed images, and DSA. The degree of stenosis differed significantly between mixed and DSA images in the C3–C5 (30% ± 17.9% vs. 23.0% ± 16.9%, p = 0.026) and C6 (38.3% ± 15.4% vs. 28.5% ± 13.3%, p = 0.023) segments. The stenosis of VNCa images showed no significant difference with DSA images in all segments (all p > 0.05). The ICCs between VNCa and DSA images (0.86–0.97) were higher than those between the mixed and DSA images (0.68–0.96) in all segments. The performance of a modified three-material decomposition DECT algorithm for calcium removal in ICA stenosis evaluation, particularly for the C3–C5 and C6 ICA segments, was promising. [ABSTRACT FROM AUTHOR]

Purpose: To explore whether CT texture analysis can identify thin-cap fibroatheroma (TCFA) determined by optical coherence tomography (OCT).Methods: Thirty-three patients with 43 lesions who underwent both CCTA and OCT within 3 months were retrospectively included. 12 conventional CT-derived plaque features, fat attenuation index (FAI) and 1691 plaque radiomics features were extracted to discriminate TCFA lesions and non-TCFA lesions determined by OCT. Minimum redundancy and maximum relevance (mRMR) method was employed to select radiomics features. The top ranked features were used to construct a forward stepwise logistic radiomics model. The performance of radiomics model was compared with the conventional high-risk plaque (HRP) features model and FAI model for the detection of TCFA.Results: Out of 1691 features, 35 features were significantly different between TCFA and non-TCFA lesions (all p＜0.05) while only low attenuation plaque (LAP) was more frequent in TCFA group (p = 0.004). There was no significant difference in FAI between TCFA and non-TCFA lesions. Five features were ultimately integrated into the radiomics model after mRMR analysis, which demonstrated significantly higher AUC for the detection of TCFA (0.952; 95 % CI: 0.897-1.000) compared with the conventional HRP features model (0.621; 95 % CI: 0.469-0.773, p < 0.001) and FAI model (0.52; 95 % CI: 0.33-0.70, p < 0.001).Conclusion: CT texture analysis performs better at identifying TCFA determined by OCT compared with conventional CT-derived plaque parameters and FAI. Texture analysis may serve as a potential non-invasive method of evaluating vulnerable plaque. [ABSTRACT FROM AUTHOR]

Purpose: To compare the objective and subjective image quality between composed images from split-filter twin beam dual energy (TBDE) and single-energy computed tomography (SECT) in abdominal CT.Methods: In this prospective study, 103 patients were imaged using TBDE (n = 51) or SECT (n = 52). The CT number and noise were measured for the following six abdominal structures: liver, spleen, fat, muscle, aorta and portal vein. The normalised noise level for the liver was separately measured and compared. The consistency of the SNR and CT number was compared between the two groups. The subjective image quality was evaluated using six aspects in a blinded manner. Cohen's Kappa statistic was used to determine the level of agreement between the two radiologists.Results: For the objective image quality comparison, the SNR of all structures was higher using TBDE compared to SECT (p < 0.05). The CT value for different structures were comparable between the two groups (p > 0.05). Among all patient sizes, the noise level for TBDE images was significantly lower (7-17% reduction) compared to the SECT images (p < 0.01). Furthermore, noise reduction's magnitude increases with body size. For image quality's subjective evaluation, TBDE images are superior for certain aspects. Cohen's Kappa values (0.7634-0.8460) suggest an adequate level of agreement between the two observers.Conclusions: TBDE scan mode can yield similar or even better objective and subjective image quality at the same level of radiation than conventional SECT. Quantitatively, TBDE images have a 7-17% reduction in noise, depending on the size of the scanned body regions. [ABSTRACT FROM AUTHOR]