Fully automated coronary artery calcium quantification on electrocardiogram-gated non-contrast cardiac computed tomography using deep-learning with novel Heart-labelling method.

Aims: To develop an artificial intelligence (AI)-model which enables fully automated accurate quantification of coronary artery calcium (CAC), using deep learning (DL) on electrocardiogram (ECG)-gated non-contrast cardiac computed tomography (gated CCT) images. Methods and results: Retrospectively, 560 gated CCT images (including 60 synthetic images) performed at our institution were used to train AI-model, which can automatically divide heart region into five areas belonging to left main (LM), left anterior descending (LAD), circumflex (LCX), right coronary artery (RCA), and another. Total and vessel-specific CAC score (CACS) in each scan were manually evaluated. AI-model was trained with novel Heart-labelling method via DL according to the manual-derived results. Then, another 409 gated CCT images obtained in our institution were used for model validation. The performance of present AI-model was tested using another external cohort of 400 gated CCT images of Stanford Center for Artificial Intelligence of Medical Imaging by comparing with the ground truth. The overall accuracy of the AI-model for total CACS classification was excellent with Cohen's kappa of k = 0.89 and 0.95 (validation and test, respectively), which surpasses previous research of k = 0.89. Bland-Altman analysis showed little difference in individual total and vessel-specific CACS between AI-derived CACS and ground truth in test cohort (mean difference [95% confidence interval] were 1.5 [-42.6, 45.6], -1.5 [-100.5, 97.5], 6.6 [-60.2, 73.5], 0.96 [-59.2, 61.1], and 7.6 [-134.1, 149.2] for LM, LAD, LCX, RCA, and total CACS, respectively). Conclusion: Present Heart-labelling method provides a further improvement in fully automated, total, and vessel-specific CAC quantification on gated CCT.

Coronary Artery Vorticity to Predict Functional Plaque Progression in Participants with Type 2 Diabetes Mellitus.

Purpose: To investigate whether vorticity could predict functional plaque progression better than high-risk plaque (HRP) and lesion length (LL) in individuals with type 2 diabetes mellitus. Materials and Methods: This single-center prospective study included 61 participants (mean age, 61 years ± 9 [SD]; 43 male participants) who underwent serial coronary CT angiography at 2 years, with 20%-70% stenosis at initial CT between October 2015 and March 2020. The number of the following HRP characteristics was recorded: low attenuation, positive remodeling, spotty calcification, and napkin-ring sign. Vorticity was calculated using a mesh-free simulation. A decrease in CT fractional flow reserve larger than 0.05 indicated functional progression. Models using HRP and LL and vorticity were compared using receiver operating characteristic curve analysis. Results: Of the 94 vessels evaluated, 25 vessels (27%) showed functional progression. Vessels with functional progression showed higher vorticity at distal stenosis (984 sec-1; IQR: 730-1253 vs 443 sec-1; IQR: 295-602; P < .001) than vessels without progression. The area under the receiver operating characteristic curve of vorticity (0.91; 95% CI: 0.84, 0.97) was higher than that of HRP and LL (0.69; 95% CI: 0.56, 0.82; P < .01). Diagnostic accuracy of vorticity (85%; 80 of 94 vessels; 95% CI: 76, 92) was higher than that of HRP and LL (72%; 68 of 94 vessels; 95% CI: 62, 81; P = .004). Conclusion: In participants with type 2 diabetes mellitus, vorticity at distal stenosis was a better predictor of functional plaque progression than HRP and LL.Keywords: Coronary Artery, Vorticity, Functional Plaque Progression, Type 2 Diabetes, Vasculature, CT Angiography, Computational Fluid Dynamics, Fractional Flow Reserve Supplemental material is available for this article. © RSNA, 2023.

First validation of stress myocardial perfusion scintigraphy using a novel reconstruction process.

BACKGROUND: A new image reconstruction process termed the MUS method (masking process on unsmoothed images) was developed to eliminate artifacts, especially those in the inferior wall. We compared diagnostic performance between the MUS and conventional method in stress myocardial perfusion SPECT (MPS). METHODS: Enrolled were 126 patients who underwent stress-rest MPS with 99 m Tc-MIBI. Patients were divided into two groups: 91 with < 50% stenosis in the RCA or LCX (non-ischemia group) and 35 patients with ≥ 90% stenosis or FFR-positive in the RCA (ischemia group), according to coronary CT or coronary angiography within 3 months of MPS. Ischemic heart disease (IHD) was considered positive when the summed difference score of five segments corresponding to the inferior wall region was ≥ 2. RESULTS: Sensitivity was comparable between the MUS method and the conventional method (ordered subset expectation maximization; OSEM) (51% vs 54%, respectively; (p = 0.366), specificity was significantly higher using the MUS method (87% vs 77%, respectively; p < 0.05), and diagnostic performance was higher using the MUS method (area under curve [AUC], conventional 0.61 vs. MUS 0.69, p = 0.138). In evaluation of 87 patients after excluding 39 who received additional prone imaging, sensitivity using the MUS method was 44%, which was comparable to 44% using the conventional method but specificity was 90%, which was significantly higher than 77% using the conventional method (p < 0.05). The diagnostic performance of the MUS method was higher (AUC, conventional 0.60 vs. MUS 0.67, p = 0.185). CONCLUSION: Use of the MUS method improved specificity in diagnosis of IHD while maintaining sensitivity, compared with the conventional method. The MUS method can achieve an improvement in diagnostic accuracy equivalent to the supine position, particularly in patients who have difficulty performing the prone position, without increasing the patient burden.

Validation and clinical impact of novel pericoronary adipose tissue measurement on ECG-gated non-contrast chest CT.

BACKGROUND AND AIMS: We aimed to develop a method for quantifying pericoronary adipose tissue (PCAT) on electrocardiogram (ECG)-gated non-contrast CT (NC-PCAT) and validate its efficacy and prognostic value. METHODS: We retrospectively studied two independent cohorts. PCAT was quantified conventionally. NC-PCAT was defined as the mean CT value of epicardial fat tissue adjacent to right coronary artery ostium on ECG-gated non-contrast CT. In cohort 1 (n = 300), we evaluated the correlation of two methods and the association between NC-PCAT and CT-verified high-risk plaque (HRP). We dichotomized cohort 2 (n = 333) by the median of NC-PCAT, and assessed the prognostic value of NC-PCAT for primary endpoint (all-cause death and non-fatal myocardial infarction) by Cox regression analysis. The median duration of follow-up was 2.9 years. RESULTS: NC-PCAT was correlated with PCAT (r = 0.68, p<0.0001). In multivariable logistic regression analysis, high NC-PCAT (OR:1.06; 95%CI:1.03-1.10; p = 0.0001), coronary artery calcium score (CACS) (OR:1.01 per 10 CACS increase, 95%CI:1.00-1.02; p = 0.013), and current smoking (OR:2.58; 95%CI:1.03-6.49; p = 0.044) were independent predictors of HRP. Among patients with CACS>0 (n = 193), NC-PCAT (OR:1.06; 95%CI:1.03-1.10; p = 0.0002), current smoking (OR:3.02; 95%CI:1.17-7.82; p = 0.027), and male sex (OR:2.81; 95%CI:1.06-7.48; p = 0.028) were independent predictors of HRP, whereas CACS was not (p = 0.15). Multivariable Cox regression analysis revealed high NC-PCAT as an independent predictor of the primary endpoint, even after adjustment for sex and age (HR:4.3; 95%CI:1.2-15.2; p = 0.012). CONCLUSIONS: There was a positive correlation between NC-PCAT and PCAT, with high NC-PCAT significantly associated with worse clinical outcome (independent of CACS) as well as presence of HRP.

Impact of a Deep Learning-based Super-resolution Image Reconstruction Technique on High-contrast Computed Tomography: A Phantom Study.

RATIONALE AND OBJECTIVES: Deep-learning-based super-resolution image reconstruction (DLSRR) is a novel image reconstruction technique that is expected to contribute to improvement in spatial resolution as well as noise reduction through learning from high-resolution computed tomography (CT). This study aims to evaluate image quality obtained with DLSRR and assess its clinical potential. MATERIALS AND METHODS: CT images of a Mercury CT 4.0 phantom were obtained using a 320-row multi-detector scanner at tube currents of 100, 200, and 300 mA. Image data were reconstructed by filtered back projection (FBP), hybrid iterative reconstruction (HIR), model-based iterative reconstruction (MBIR), deep-learning-based image reconstruction (DLR), and DLSRR at image reconstruction strength levels of mild, standard, and strong. Noise power spectrum (NPS), task transfer function (TTF), and detectability index were calculated. RESULTS: The magnitude of the noise-reducing effect in comparison with FBP was in the order MBIR

Dual-energy CT imaging of atherosclerotic plaque using novel ultrasmall superparamagnetic iron oxide in hyperlipidemic rabbits.

BACKGROUND: A study using magnetic resonance imaging (MRI) revealed that ultra-small superparamagnetic iron oxide is phagocytosed by macrophages. However, MRI has limitations in obtaining clear images due to its poor spatial and temporal resolutions. PURPOSE: To examine whether the use of dual-energy computed tomography (DECT) facilitated the visualization of carboxymethyl-diethylaminoethyl dextran magnetite ultra-small superparamagnetic iron oxide (CMEADM-U) accumulation in arteriosclerotic lesions using hyperlipidemic rabbits. MATERIAL AND METHODS: CMEADM-U at 0.5 mmol Fe/kg was administered to Watanabe hereditary atherosclerotic (WHHL) rabbits (n = 6, 24 sections) and New Zealand white (NZW) rabbits (n = 2, 6 sections). After 72 h, DECT was performed to prepare virtual monochromatic images (35 keV, 70 keV) and an iron-based map. Subsequently, the aorta was collected along with hematoxylin and eosin staining, Berlin blue (BB) staining, and RAM11 immunostaining. RESULTS: In the WHHL rabbits, CMEADM-U accumulation was not observed at 70 keV. However, CMEADM-U accumulation consistent with an arteriosclerotic lesion was observed at 35 keV and the iron-based map. On the other hand, in the NZW rabbits, there was no accumulation of CMEADM-U in any images. Further, there were significant differences in the iron-based map value at the site of accumulation among the grades of expression on BB staining and RAM11 immunostaining. In addition, there was a good correlation at 35 kev and iron-based map value (r = 0.42; P < 0.05). CONCLUSION: DECT imaging for CMEADM-U facilitated the assessment of macrophage accumulation in atherosclerotic lesions in an in vivo study using a rabbit model of induced aortic atherosclerosis.

Automation of a Rule-based Workflow to Estimate Age from Brain MR Imaging of Infants and Children Up to 2 Years Old Using Stacked Deep Learning.

PURPOSE: Myelination-related MR signal changes in white matter are helpful for assessing normal development in infants and children. A rule-based myelination evaluation workflow regarding signal changes on T1-weighted images (T1WIs) and T2-weighted images (T2WIs) has been widely used in radiology. This study aimed to simulate a rule-based workflow using a stacked deep learning model and evaluate age estimation accuracy. METHODS: The age estimation system involved two stacked neural networks: a target network-to extract five myelination-related images from the whole brain, and an age estimation network from extracted T1- and T2WIs separately. A dataset was constructed from 119 children aged below 2 years with two MRI systems. A four-fold cross-validation method was adopted. The correlation coefficient (CC), mean absolute error (MAE), and root mean squared error (RMSE) of the corrected chronological age of full-term birth, as well as the mean difference and the upper and lower limits of 95% agreement, were measured. Generalization performance was assessed using datasets acquired from different MR images. Age estimation was performed in Sturge-Weber syndrome (SWS) cases. RESULTS: There was a strong correlation between estimated age and corrected chronological age (MAE: 0.98 months; RMSE: 1.27 months; and CC: 0.99). The mean difference and standard deviation (SD) were -0.15 and 1.26, respectively, and the upper and lower limits of 95% agreement were 2.33 and -2.63 months. Regarding generalization performance, the performance values on the external dataset were MAE of 1.85 months, RMSE of 2.59 months, and CC of 0.93. Among 13 SWS cases, 7 exceeded the limits of 95% agreement, and a proportional bias of age estimation based on myelination acceleration was exhibited below 12 months of age (P = 0.03). CONCLUSION: Stacked deep learning models automated the rule-based workflow in radiology and achieved highly accurate age estimation in infants and children up to 2 years of age.

Energy loss is related to CT fractional flow reserve progression in type 2 diabetes mellitus patients.

Background: We aimed to investigate the diagnostic value of energy loss (EL) and baseline CT fractional flow reserve (CT-FFR) computed using computational fluid dynamics to predict functional progression of coronary stenosis in patients with type 2 diabetes mellitus. Methods: This single-center prospective study included 61 patients with type 2 diabetes mellitus (mean age, 61 years ±9 [SD]; 43 men) showing 20-70 % stenosis who underwent serial coronary CT performed at 2-year interval between October 2015 and March 2020. A mesh-free simulation was performed to calculate the CT-FFR and EL. Functional progression was defined as ≥ 0.05 decrease in CT-FFR on the second coronary CT. Models using baseline CT-FFR and EL were compared by analyzing the receiver operating characteristic (ROC) curve. Results: Of the 94 vessels evaluated, 25 vessels (27 %) showed functional progression. EL at distal stenosis (ELdis) of vessels with functional progression was higher than that of vessels without functional progression (27.6 W/m3 [interquartile range (IQR): 15.0, 53.0] vs. 5.7 W/m3 [IQR: 2.3, 10.1], p < 0.001). Multivariable analysis showed that ELdis (per unit Ln(EL); odds ratio, 11.8; 95 % CI: 4.0-34.9; p < 0.001) remained as a predictor of functional progression after adjustment for diameter stenosis and baseline CT-FFR. The area under the ROC curve using ELdis (0.89; 95 % CI: 0.82-0.96) was higher than that using baseline CT-FFR (0.71; 95 % CI: 0.59-0.83; p < 0.001). Conclusion: When ELdis and baseline CT-FFR were considered, ELdis was a better predictor of functional progression of coronary stenosis.

Coronary flow disturbance assessed by vorticity as a cause of functionally significant stenosis.

OBJECTIVES: Vorticity calculated using computational fluid dynamics (CFD) could assess the flow disturbance generated by coronary stenosis. The purpose of this study was to investigate whether vorticity would be an underlying cause of functionally significant stenosis assessed by invasive fractional flow reserve (FFR). METHODS: This retrospective study included 113 patients who underwent coronary CT angiography showing intermediate stenosis and subsequent invasive FFR between December 2015 and March 2020. Vorticity at the stenosis site was calculated using a mesh-free CFD method. We also evaluated the minimum lumen area (MLA) and diameter stenosis (DS) of the lesion. Invasive FFR of ≤ 0.80 was considered functionally significant. Data were compared using Student's t-test and logistic regression analysis was performed. RESULTS: Of the evaluated 144 vessels, 53 vessels (37%) showed FFR ≤ 0.80. Vorticity of significant stenosis was significantly higher than non-significant stenosis (569 ± 78 vs. 328 ± 34 s-1, p < 0.001). A significant negative relationship was present between vorticity and invasive FFR (R2 = 0.31, p < 0.001). Multivariate logistic regression analysis including MLA and DS showed that vorticity (per 100 s-1, odds ratio: 1.36, 95% confidence interval: 1.21-1.57, p < 0.001) was a statistically significant factor to detect functional significance. The area under the receiver operating characteristic curve statistically significantly increased when vorticity was combined with DS and MLA (0.76 vs. 0.87, p = 0.001). CONCLUSIONS: Vorticity had a statistically significant negative relationship with invasive FFR independent of geometric stenosis. KEY POINTS: • Flow disturbance caused by coronary stenosis could be evaluated by calculating vorticity which is defined as the norm of the rotation of the velocity vector. • Vorticity was statistically significantly higher in stenosis with functional significance than stenosis without. • Vorticity has an additive value to detect functionally significant stenosis over geometrical stenosis.

Use of a deep-learning-based lumen extraction method to detect significant stenosis on coronary computed tomography angiography in patients with severe coronary calcification.

BACKGROUND: Coronary computed tomography angiography examinations are increasingly becoming established as a minimally invasive method for diagnosing coronary diseases. However, although various imaging and processing methods have been developed, coronary artery calcification remains a major limitation in the evaluation of the vascular lumen. Subtraction coronary computed tomography angiography (Sub-CCTA) is a method known to be able to reduce the influence of coronary artery calcification and is therefore feasible for improving the diagnosis of significant stenosis in patients with severe calcification. However, Sub-CCTA still involves some problems, such as the increased radiation dose due to plain (mask) imaging, extended breath-holding time, and misregistration due to differences in the imaging phase. Therefore, we considered using artificial intelligence instead of Sub-CCTA to visualize the coronary lumen with high calcification. Given this background, the present study aimed to evaluate the diagnostic performance of a deep learning-based lumen extraction method (DL-LEM) to detect significant stenosis on CCTA in 99 consecutive patients (891 segments) with severe coronary calcification from November 2015 to March 2018. We also estimated the impact of DL-LEM on the medical economics in Japan. RESULTS: The DL-LEM slightly improved the per-segment diagnostic accuracy from 74.5 to 76.4%, and the area under the curve (AUC) slightly improved from 0.752 to 0.767 (p = 0.030). When analyzing the 228 segments that could not be evaluated because of severe calcification on the original CCTA images, the DL-LEM improved the accuracy from 35.5 to 42.5%, and the AUC improved from 0.500 to 0.587 (p = 0.00018). As a result, DL-LEM analysis could have avoided invasive coronary angiography in 4/99 cases (per patient). From the calculated results, it was estimated that the number of exams that can be avoided in Japan in one year is approximately 747 for invasive coronary angiography, 219 for fractional flow reserve, and 248 for nuclear exam. The total amount of medical fee that could be reduced was 225,629,368 JPY. CONCLUSIONS: These findings suggest that the DL-LEM may improve the diagnostic performance in detecting significant stenosis in patients with severe coronary calcification. In addition, the results suggest that not a small medical economic effect can be expected.

Prognostic value of the optimal measurement location of on-site CT-derived fractional flow reserve.

BACKGROUND: On-site computed tomography-derived fractional flow reserve (CT-FFR), using fluid structure interaction during multiple optimal diastolic phases, is of incremental diagnostic value. However, few studies have investigated prognosis, with the appropriate measurement location of CT-FFR, as a stand-alone modality. The aim of the present study was to assess the clinical impact on CT-FFR with an appropriate measurement. METHODS: A total of 370 consecutive patients (68 ±â€¯10 years, 75% male) who underwent coronary CT angiography (CCTA), showing 50-90% stenosis in at least one major epicardial vessel, were retrospectively analyzed and followed up for a median 2.9 years. CT-FFR values were measured at three points: 1 to 2 cm distal to the target lesion (CT-FFR1cm, 2cm) and the vessel terminus (CT-FFRlowest), and a CT-FFR value ≤0.80 was considered to be abnormal. The endpoint was major adverse cardiovascular events (MACE), a composite of cardiac death, non-fatal myocardial infarction, and unplanned revascularization. RESULTS: The incidence of MACE was 6.8% (25/370 patients). The Kaplan-Meier survival analysis in negative CT-FFR1/2cm revealed no significant difference in MACE between negative and positive CT-FFRlowest [p = 0.11/0.23 (1/2 cm vs lowest)]. Among 221 patients who did not undergo planned revascularization within 90 days of CCTA, no significant differences were noted in the incidence of MACE between negative and positive CT-FFRlowest (p = 0.11). In contrast, the risk of MACE was significantly higher with positive CT-FFR1/2cm [p = 0.0198/0.0002 (1/2 cm)]. CONCLUSIONS: In terms of the prognosis of patients with moderate to severe stenosis on CCTA, CT-FFR measured 1 to 2 cm distal to the target lesion may be feasible for the safe deferral of unnecessary invasive coronary angiography. Moreover, CT-FFR1/2cm showed better risk stratification than CT-FFRlowest based on future adverse cardiac events.

Identification of Risk Factors for Coronary Artery Disease in Asymptomatic Patients with Type 2 Diabetes Mellitus.

BACKGROUND: Patients with diabetes mellitus (DM) are a high-risk group for coronary artery disease (CAD). In the present study, we investigated predictive factors to identify patients at high risk of CAD among asymptomatic patients with type 2 DM based on coronary computed tomographic angiography (CCTA) findings. METHODS: A single-center prospective study was performed on 452 consecutive patients with type 2 DM who were provided with a weekly hospital-based diabetes education program between 3 October 2015, and 31 March 2020. A total of 161 consecutive asymptomatic patients (male/female: 111/50, age: 57.3 ± 9.3 years) with type 2 DM without any known CAD underwent CCTA. Based on conventional coronary risk factors and non-invasive examination, i.e., measurement of intima-media thickness, subcutaneous and visceral fat area, a stress electrocardiogram test, and the Agatston score, patients with obstructive CAD, CT-verified high-risk plaques (CT-HRP), and optimal revascularization within 90 days were evaluated. RESULTS: Current smoking (OR, 4.069; 95% C.I., 1.578-10.493, p = 0.0037) and the Agatston score ≥100 (OR, 18.034; 95% C.I., 6.337-51.324, p = 0.0001) were independent predictive factors for obstructive CAD, while current smoking (OR, 5.013; 95% C.I., 1.683-14.931, p = 0.0038) was an independent predictive factor for CT-HRP. Furthermore, insulin treatment (OR, 5.677; 95% C.I., 1.223-26.349, p = 0.0266) was the only predictive factor that correlated with optimal revascularization within 90 days. CONCLUSIONS: In asymptomatic patients with type 2 DM, current smoking, an Agatston score ≥100, and insulin treatment were independent predictive factors of patients being at high-risk for CAD. However, non-invasive examinations except for Agatston score were not independent predictors of patients being at high risk of CAD.

Clinical significance of 123I-BMIPP washout rate in patients with uncertain chronic heart failure.

BACKGROUND: Recently, triglyceride deposit cardiomyovasculopathy (TGCV) with defective intracellular lipolysis was found to be a disease that causes heart failure. As a diagnostic criterion for TGCV, an Iodaine-123-ß-methyl iodophenyl-pentadecanoic acid washout rate (BMIPP WOR) of < 10% is used, but its clinical significance in patients with heart failure remains to be clarified. METHODS: In 62 hospitalized patients with chronic heart failure, 123I-BMIPP myocardial single-photon emission computed tomography (SPECT) was performed predischarge state. The prevalence of TGCV was investigated. Subsequently, follow-up was conducted for ≥ 90 days (mean: 724.6 ± 392.7 days), and the association between the BMIPP WOR and cardiac events was examined, establishing all-cause mortality and admission due to heart failure as endpoints. RESULTS: Of the 62 patients, the WOR was < 10% in 41 (66.1%). Of these, 26 (41.9%) were diagnosed with definite TGCV. Furthermore, cardiac events were noted in 12 patients (19.4%). Analysis with Cox proportional hazards models showed that the BMIPP WOR < 4.5% was a significant event-predicting factor [HR 4.29, 95% CI: 1.20-16.87; p = 0.0245]. On a Kaplan-Meier curve, the WOR was 4.5%; there was a significant difference in the incidence of events (p = 0.0298). CONCLUSION: In the predischarge state of heart failure, 123I-BMIPP myocardial SPECT was performed. In approximately 40% of the patients, a diagnosis of TGCV was made. The results suggested that the BMIPP WOR is useful for predicting the prognosis of chronic heart failure patients regardless of TGCV.

Feasibility of CT Angiography-derived Kinetic Energy of Coronary Flow to Improve the Detection of Hemodynamically Significant Coronary Stenosis.

Purpose: To investigate whether coronary flow kinetic energy has incremental value over simulated fractional flow reserve (sFFR) in diagnosing hemodynamically significant stenosis assessed with coronary CT angiography and invasive fractional flow reserve (FFR). Materials and Methods: This single-center retrospective study included 113 patients (mean age, 68 years ± 9 [SD]; 80 men) who underwent coronary CT angiography showing intermediate stenosis (30%-70% stenosis) and subsequent invasive FFR between December 2015 and March 2020. Kinetic energy was calculated using proximal coronary diameter and myocardial mass of the stenotic region. A mesh-free simulation was performed to calculate the sFFR. Invasive FFR of 0.80 or less indicated hemodynamically significant stenosis. Models using diameter stenosis, kinetic energy, and sFFR were compared by analyzing the receiver operating characteristic curve. Results: Of the 144 vessels evaluated, 53 vessels (37%) had hemodynamically significant stenosis. Kinetic energy of vessels with significant stenosis was higher than that of vessels with nonsignificant stenosis (79 mJ/kg [IQR, 58-104 mJ/kg] vs 36 mJ/kg [IQR, 23-59 mJ/kg]; P < .001). Multivariable analysis including diameter stenosis and sFFR showed that kinetic energy (per 20 mJ/kg; odds ratio, 1.92; 95% CI: 1.37, 2.95; P < .001) was a predictor of hemodynamically significant stenosis. Adding kinetic energy to diameter stenosis and sFFR improved the area under the receiver operating characteristic curve from 0.89 (95% CI: 0.84, 0.95) to 0.93 (95% CI: 0.89, 0.97) (P = .04). Conclusion: Kinetic energy had incremental value over sFFR in detecting hemodynamically significant stenosis assessed with invasive FFR.Keywords: Coronary CT Angiography, Coronary Arteries, Fractional Flow Reserve, Kinetic Energy, Cardiac Supplemental material is available for this article © RSNA, 2022.

A phantom and in vivo simulation of coronary flow to calculate fractional flow reserve using a mesh-free model.

Moving particle semi-implicit (MPS) method is a mesh-free method to perform computational fluid dynamics (CFD). The purpose of this study was to calculate the simulated fractional flow reserve (sFFR) using a coronary stenosis model, and to validate the MPS-derived sFFR against invasive FFR using clinical coronary CT data. Coronary flow simulation included 21 stenosis models with stenosis ranging 30-70%. Patient coronary analysis was performed in 76 consecutive patients (100 vessels) who underwent coronary CT angiography and subsequent invasive FFR between November 2016 and March 2020. Accuracy of sFFR and CT angiography for diagnosis of invasive FFR ≤ 0.80 was compared. Quantitative morphological stenosis data of CT angiography were also obtained. Area stenosis showed a good correlation to sFFR (R2 = 0.996, p < 0.001) in coronary stenosis models. In the patient study, the mean FFR value was 0.82 ± 0.10, and 37 out of 100 vessels showed FFR ≤ 0.80. FFR and sFFR values showed a good correlation (R2 = 0.59, p < 0.001) with a slight underestimation of sFFR as compared with FFR (mean difference - 0.015 ± 0.096, p = 0.12). The sensitivity, specificity, positive predictive value, and negative predictive value of sFFR to predict FFR ≤ 0.80 was 86%, 89%, 82%, 92%, respectively. The accuracy to predict FFR ≤ 0.80 using sFFR was greater than using diameter stenosis and minimum lumen area (88% vs. 74%, p = 0.008). CFD using the MPS method showed feasible results validated against invasive FFR. The accuracy to predict significant stenosis was higher than morphological stenosis.

Incremental Diagnostic Value of CT Fractional Flow Reserve Using Subtraction Method in Patients with Severe Calcification: A Pilot Study.

Although on-site workstation-based CT fractional flow reserve (CT-FFR) is an emerging method for assessing vessel-specific ischemia in coronary artery disease, severe calcification is a significant factor affecting CT-FFR's diagnostic performance. The subtraction method significantly improves the diagnostic value with respect to anatomic stenosis for patients with severe calcification in coronary CT angiography (CCTA). We evaluated the diagnostic capability of CT-FFR using the subtraction method (subtraction CT-FFR) in patients with severe calcification. This study included 32 patients with 45 lesions with severe calcification (Agatston score >400) who underwent both CCTA and subtraction CCTA using 320-row area detector CT and also received invasive FFR within 90 days. The diagnostic capabilities of CT-FFR and subtraction CT-FFR were compared. The sensitivities, specificities, positive predictive values (PPVs), and negative predictive values (NPVs) of CT-FFR vs. subtraction CT-FFR for detecting hemodynamically significant stenosis, defined as FFR ≤ 0.8, were 84.6% vs. 92.3%, 59.4% vs. 75.0%, 45.8% vs. 60.0%, and 90.5% vs. 96.0%, respectively. The area under the curve for subtraction CT-FFR was significantly higher than for CT-FFR (0.84 vs. 0.70) (p = 0.04). The inter-observer and intra-observer variabilities of subtraction CT-FFR were 0.76 and 0.75, respectively. In patients with severe calcification, subtraction CT-FFR had an incremental diagnostic value over CT-FFR, increasing the specificity and PPV while maintaining the sensitivity and NPV with high reproducibility.

Multi-agent system collision model to predict the transmission of seasonal influenza in Tokyo from 2014-2015 to 2018-2019 seasons.

The objective of this study was to apply the multi-agent system (MAS) collision model to predict seasonal influenza epidemic in Tokyo for 5 seasons (2014-2015 to 2018-2019 seasons). The MAS collision model assumes each individual as a particle inside a square domain. The particles move within the domain and disease transmission occurs in a certain probability when an infected particle collides a susceptible particle. The probability was determined based on the basic reproduction number calculated using the actual data. The simulation started with 1 infected particle and 999 susceptible particles to correspond to the onset of an influenza epidemic. We performed the simulation for 150 days and the calculation was repeated 500 times for each season. To improve the accuracy of the prediction, we selected simulations which have similar incidence number to the actual data in specific weeks. Analysis including all simulations corresponded good to the actual data in 2014-2015 and 2015-2016 seasons. However, the model failed to predict the sharp peak incidence after the New Year Holidays in 2016-2017, 2017-2018, and 2018-2019 seasons. A model which included simulations selected by the week of peak incidence predicted the week and number of peak incidence better than a model including all simulations in all seasons. The reproduction number was also similar to the actual data in this model. In conclusion, the MAS collision model predicted the epidemic curve with good accuracy by selecting the simulations using the actual data without changing the initial parameters such as the basic reproduction number and infection time.

Comparison of diagnostic performance in on-site based CT-derived fractional flow reserve measurements.

BACKGROUND: Computed tomography fractional flow reserve (CT-FFR), which can be acquired on-site workstation using fluid structure interaction during the multiple optimal diastolic phase, has an incremental diagnostic value over conventional coronary computed tomography angiography (CCTA). However, the appropriate location for CT-FFR measurement remains to be clarified. METHOD: A total of 115 consecutive patients with 149 vessels who underwent CCTA showing 30-90% stenosis with invasive FFR within 90 days were retrospectively analyzed. CT-FFR values were measured at three points: 1 and 2 cm distal to the target lesion (CT-FFR1cm, 2cm) and the vessel terminus (CT-FFRlowest). The diagnostic accuracies of CT-FFR ≤ 0.80 for detecting hemodynamically significant stenosis, defined as invasive FFR ≤ 0.80, were compered. RESULT: Fifty-five vessels (36.9%) had invasive FFR ≤ 0.80. The accuracy and AUC for CT-FFR1cm and 2cm were comparable, while the AUC for CT-FFRlowest was significantly lower than CT-FFR1cm and 2cm. (lowest/1cm, 2 cm = 0.68 (95 %CI 0.63-0.73) vs 0.79 (0.72-0.86, p = 0.006), 0.80 (0.73-0.87, p = 0.002)) The sensitivity and negative predictive value of CT-FFRlowest were 100%. The reclassification rates from positive CT-FFRlowest to negative CT-FFR1cm and 2cm were 55.7% and 54.2%, respectively. CONCLUSION: The diagnostic performance of CT-FFR was comparable when measured at 1-to-2 cm distal to the target lesion, but significantly higher than CT-FFRlowest. The lesion-specific CT-FFR could reclassify false positive cases in patients with positive CT-FFRlowest, while all patients with negative CT-FFRlowest were diagnosed as negative by invasive FFR.

A study on the prevalence, distribution and related factors of heart valve calcification using coronary CT angiography.

BACKGROUND: The concept of active atherosclerotic disease has been accepted for heart valve calcification (HVC). We investigated prevalence, distribution and related factors of HVC in patients who had undergone coronary CT angiography (CCTA). METHODS: Subjects were consecutive 200 patients who underwent CCTA. The prevalence and the distribution of HVC using ECG gated non-contrast CT were investigated. Logistic regression analysis and simple regression analysis for factors associated with presence of the calcification and quantitative calcification in the aortic and mitral valve were conducted. RESULTS: HVC was detected in 48.0%. Aortic valve calcification (AVC) was found in 92 cases, the most, followed by mitral valve calcification (MVC) in 25 cases, pulmonary valve in 3 cases, and tricuspid valve in 1 case. Although the left coronary cusp showed the most in 65.2%, no statistic significant difference for Agatston score was detected among each cusp in AVC. Multiple logistic regression analysis showed that age (OR:1.211, 95%C.I.:1.0716-1.1728, p < 0.0001) and coronary artery calcium score (CACS) grade (grade2 OR:7.3393, 95%C.I.:1.7699-30.4349, p = 0.0060, grade3 OR:7.2214, 95%C.I.:1.4376-36.2762, p = 0.0164) were significant factors associated with presence of AVC. The significant factors associated with quantitative AVC were age (p = 0.0043), dyslipidemia (p = 0.0117), and statin use (p = 0.0221). Only age (OR:1.1589, 95%C.I.:1.0726-1.2520, p = 0.0002) was significant factor related to presence of MVC. No significant related factor was found in quantitative MVC. CONCLUSIONS: There was an association between presence of AVC and CACS, but not a significant association with presence of MVC. Neither quantitative AVC nor MVC had a significant association with CACS or coronary artery disease.