Additional prognostic impact of plaque characterization with on-site CT-derived fractional flow reserve in coronary CT angiography.

BACKGROUND: On-site computed tomography-derived fractional flow reserve (CT-FFR) is a feasible method for examining lesion-specific ischemia, and plaque analysis of coronary CT angiography (CCTA) is useful for predicting future cardiac events. However, their utility and association on a per-vessel level remain unclear. METHODS: We analyzed vessels showing 50-90 % stenosis on CCTA where planned revascularization was not performed after CCTA within 90 days. Relevant features, including CT-FFR and the plaque burden [necrotic core to the total plaque volume (% necrotic core), and non-calcified plaque (NCP) to vessel volume (% NCP)] using a novel algorithm for analyzing plaque to predict vessel-oriented composite outcomes (VOCO), including cardiac death, non-fatal myocardial infarction, and unplanned vessel-related revascularization, were assessed. RESULTS: In 256 patients (68.7 ±â€¯9.4 years; 73.8 % male) with 354 vessels (10.5 % CT-FFR ≤ 0.80), VOCO occurred in 24 vessels (6.8 %) during a median follow-up of 3.6 years. Multivariable Cox analysis revealed CT-FFR ≤ 0.80 had the pronounced impact on VOCO, and moreover, higher % necrotic core and % NCP were independently associated with VOCO [adjusted hazard ratio 3.43 (95 % confidence interval 1.42-8.29) and 4.05 (1.19-13.71), respectively], especially for vessels with CT-FFR > 0.80. CONCLUSIONS: In vessels without planned revascularization, per-vessel CT-FFR ≤ 0.80 was the notable predictor of future cardiac events. Additionally, necrotic core volume and NCP were identified as independent predictors along with CT-FFR.

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.

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.

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.

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.

Development and Validation of a Modified Three-Dimensional U-Net Deep-Learning Model for Automated Detection of Lung Nodules on Chest CT Images From the Lung Image Database Consortium and Japanese Datasets.

RATIONALE AND OBJECTIVES: A more accurate lung nodule detection algorithm is needed. We developed a modified three-dimensional (3D) U-net deep-learning model for the automated detection of lung nodules on chest CT images. The purpose of this study was to evaluate the accuracy of the developed modified 3D U-net deep-learning model. MATERIALS AND METHODS: In this Health Insurance Portability and Accountability Act-compliant, Institutional Review Board-approved retrospective study, the 3D U-net based deep-learning model was trained using the Lung Image Database Consortium and Image Database Resource Initiative dataset. For internal model validation, we used 89 chest CT scans that were not used for model training. For external model validation, we used 450 chest CT scans taken at an urban university hospital in Japan. Each case included at least one nodule of >5 mm identified by an experienced radiologist. We evaluated model accuracy using the competition performance metric (CPM) (average sensitivity at 1/8, 1/4, 1/2, 1, 2, 4, and 8 false-positives per scan). The 95% confidence interval (CI) was computed by bootstrapping 1000 times. RESULTS: In the internal validation, the CPM was 94.7% (95% CI: 89.1%-98.6%). In the external validation, the CPM was 83.3% (95% CI: 79.4%-86.1%). CONCLUSION: The modified 3D U-net deep-learning model showed high performance in both internal and external validation.

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.

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.

Plaque characteristics on coronary CT angiography associated with the positive findings of fractional flow reserve and instantaneous wave-free ratio.

BACKGROUND: Fractional flow reserve (FFR) and instantaneous wave-free ratio (iFR) are useful in determining indications for revascularization of coronary artery disease (CAD). Although the discordance of FFR and iFR was noted in approximately 20%, this cause has not been well established. We investigated patient background and features on coronary CT angiography (CCTA) showing not only FFR- and iFR-positive findings but also discordance between FFR ≤ 0.8 and iFR ≤ 0.89. METHODS: Subjects were consecutively treated in 83 cases with 105 vessels in which stenosis of 30-90% was detected at one vessel of at least 2 mm or more in the major epicardial vessels and FFR and iFR was performed within subsequent 90 days, among suspected CAD which underwent CCTA. The factors affecting not only FFR- and iFR-positive findings, respectively, but also discordance between FFR and iFR was evaluated using logistic regression analysis on per-patient and per-vessel basis. RESULTS: FFR- and iFR-positive findings were observed in 42 vessels (40.0%) and 34 vessels (32.3%), respectively. Discordance between FFR ≤ 0.8 and iFR ≤ 0.89 was observed in 22 vessels (21.0%) of 21 patients. In multivariate logistic analysis, LAD (OR 3.55; 95%CI 1.20-11.71; p = 0.0217) and lumen volume/myocardial weight (L/M) ratio (OR 0.93; 0.86-0.99, p = 0.0290) were significant predictors for FFR-positive findings. For iFR-positive findings, LAD (OR 3.86; 95%CI 1.12-13.31; p = 0.0236) was only significant predictor. In FFR ≤ 0.8 and iFR > 0.89 group (15 vessels, 14.3%), positive remodeling (PR) (OR 5.03, 95%CI 1.23-20.48, p = 0.0205) was significant predictor. In FFR > 0.8 and iFR ≤ 0.89 group (7 vessels, 6.7%), there were no significant predictors. CONCLUSION: On CCTA characteristics, a relevant predictor for FFR-positive findings included low L/M ratio. PR was significant predictor in FFR-positive, iFR-negative patients among those with discordance between the FFR and iFR.

A National Survey on Safety Management at MR Imaging Facilities in Japan.

PURPOSE: To investigate safety management at Japanese facilities performing human MRI studies. METHODS: All Japanese facilities performing human MRI studies were invited to participate in a comprehensive survey that evaluated their MRI safety management. The survey used a questionnaire prepared with the cooperation of the Safety Committee of the Japanese Society for Magnetic Resonance in Medicine. The survey addressed items pertaining to the overall MRI safety management, questions on the occurrence of incidents, and questions specific to facility and MRI scanner or examination. The survey covered the period from October 2017 to September 2018. Automated machine learning was used to identify factors associated with major incidents. RESULTS: Of 5914 facilities, 2015 (34%) responded to the questionnaire. There was a wide variation in the rate of compliance with MRI safety management items among the participating facilities. Among the facilities responding to this questionnaire, 5% reported major incidents and 27% reported minor incidents related to MRI studies. Most major incidents involved the administration of contrast agents. The most influential factor in major incidents was the total number of MRI studies performed at the facility; this number was significantly correlated with the risk of major incidents (P < 0.0001). CONCLUSION: There were large variations in the safety standards applied at Japanese facilities performing clinical MRI studies. The total number of MRI studies performed at a facility affected the number of major incidents.

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.

Impact of Abnormal Remote Stress Myocardial Blood Flow by Dynamic CT Perfusion on Clinical Outcomes.

The objective of this study was to investigate the incremental prognostic value for adverse events of myocardial blood flow (MBF) derived from stress computed tomography perfusion (CTP) at remote myocardium over cardiac risk factors and ischemia. We prospectively analyzed 242 patients who underwent dynamic CTP and CT angiography. Adverse events were defined as a composite of all-cause mortality, non-fatal myocardial infarction, unstable angina, heart failure requiring hospitalization, peripheral artery disease, and stroke. MBF value was calculated in each myocardial segment and ischemia was defined as mild decrease in MBF in two consecutive segments or moderate decrease in a single segment accompanied with a coronary stenosis ≥50%. The mean MBF of the non-ischemic segments was defined as remote MBF. We divided the patients into two groups by median MBF value of 1.15 ml/min/g. During a median follow-up of 18 months, 18 patients had adverse events. Annual event rate showed a significant difference between patients with low (≤1.15 ml/min/g) and high (>1.15 ml/min/g) MBF (6.1% vs 1.8%, p = 0.02). Univariate analysis showed that low MBF was a significant predictor of events (hazard ratio (HR): 3.4; 95% confidence interval (CI): 1.2 to 12.0; p = 0.02). This relationship maintained significant after adjusted for the presence of ischemia and cardiac risk factors (HR: 3.0; 95%CI: 1.1 to 11.1; p = 0.04). In conclusion, MBF value ≤1.15 ml/min/g derived from dynamic CTP in remote myocardium is significantly related with poor outcome and this relationship was independent of myocardial ischemia and cardiac risk factors.

Initial evaluation of a convolutional neural network used for noninvasive assessment of coronary artery disease severity from coronary computed tomography angiography data.

PURPOSE: Coronary computed tomography angiography (CTA) has one of the highest diagnostic sensitivities for detection of the significance of coronary artery disease (CAD); however, sensitivity is moderate and may result in increased catheterization rates. We performed an efficacy study to determine whether a trained machine learning algorithm that uses coronary CTA data may improve CAD diagnosis accuracy. METHODS: Sixty-four-patient image datasets based on coronary CTA were retrospectively collected to generate eight views considering 45° increments around the coronary artery centerline. The dataset was randomly split into training and testing cohorts. Invasive FFR measurements were used as ground truth labels. A convolutional neural network (CNN) was trained and the model's capacity to predict severity of CAD was assessed on the testing cohort. Classification accuracy and area under the receiver operating characteristic curve (AUROC) analysis were performed. Similar CAD severity classification accuracy and AUROC analyses were performed using only percent diameter stenosis (%DS) and CT-derived FFR performed by 13 operators with various levels of expertise. RESULTS: Classification accuracy over the test cohort was 80.9% using the trained network and 72.4% using the user-operated CT-derived FFR software. AUROC over the test cohort was 0.862 using the trained network, 0.807 using %DS, and 0.758 using the human-operated CT-derived FFR software. CONCLUSIONS: A trained neural network compared noninferiorly in-terms of classification accuracy and AUROC with human operators of a CT-derived FFR software, and in-terms of AUROC with clinical decision-making using %DS.

Work-style reform and use of information and communication technology among diagnostic radiologists in Japan: results of the 2018 JRS/JCR joint survey.

PURPOSE: The purpose of this study was to investigate recent trends in work-style reform and the use of information and communication technology (ICT) among board-certified diagnostic radiologists in Japan. MATERIALS AND METHODS: We conducted online questionnaire surveys of board-certified radiologists of the Japan Radiological Society (JRS) and registered training institutions. Completed surveys were obtained from 1192 radiologists and 275 training institutions (response rates of 25.5% and 38.1%, respectively). Respondents were assured of confidentiality. RESULTS: 13.5% (134/991) of full-time radiologists and 56.7% (89/157) of part-time radiologists had shifted some of their work to teleradiology at home. In addition, 52.9% (83/157) of part-time radiologists and 27.3% (12/44) of board-certified individuals who had stopped working as radiologists responded that they would consider starting full-time work in hospitals, if teleradiology at home was permitted as part of full-time work. Furthermore, 16.7% of training institutions (46/275) had introduced teleradiology systems for radiologists, and 47.2% (108/229) of the remaining training institutions wanted to introduce teleradiology systems in the future. CONCLUSION: Teleradiology using ICT is already a part of Japanese radiologists' workload. Work-style reform may progress with the use of ICT, such as part-time radiologists, and board-certified individuals who stop working as radiologists, becoming full-time radiologists.

Neurocognitive and psychiatric disorders-related axonal degeneration in Parkinson's disease.

Neurocognitive and psychiatric disorders have significant consequences for quality of life in patients with Parkinson's disease (PD). In the current study, we evaluated microstructural white matter (WM) alterations associated with neurocognitive and psychiatric disorders in PD using neurite orientation dispersion and density imaging (NODDI) and linked independent component analysis (LICA). The indices of NODDI were compared between 20 and 19 patients with PD with and without neurocognitive and psychiatric disorders, respectively, and 25 healthy controls using tract-based spatial statistics and tract-of-interest analyses. LICA was applied to model inter-subject variability across measures. A widespread reduction in axonal density (indexed by intracellular volume fraction [ICVF]) was demonstrated in PD patients with and without neurocognitive and psychiatric disorders, as compared with healthy controls. Compared with patients without neurocognitive and psychiatric disorders, patients with neurocognitive and psychiatric disorders exhibited more extensive (posterior predominant) decreases in axonal density. Using LICA, ICVF demonstrated the highest contribution (59% weight) to the main effects of diagnosis that reflected widespread decreases in axonal density. These findings suggest that axonal loss is a major factor underlying WM pathology related to neurocognitive and psychiatric disorders in PD, whereas patients with neurocognitive and psychiatric disorders had broader axonal pathology, as compared with those without. LICA suggested that the ICVF can be used as a useful biomarker of microstructural changes in the WM related to neurocognitive and psychiatric disorders in PD.

Adjustment of CT-fractional flow reserve based on fluid-structure interaction underestimation to minimize 1-year cardiac events.

The purpose of the study was to evaluate the optimal cut-off value of CT-Fractional Flow Reserve (CT-FFR) using fluid-structure interaction and how to adjust the CT-FFR's underestimation from a standpoint of minimize 1-year cardiac events. Subjects were 38 cases with 44 vessels in which stenosis of 30-90% was detected using one-rotation scanning by 320-row coronary CT angiography (CCTA) and invasive FFR (i-FFR) was performed within subsequent 90 days. CT-FFR was calculated using on-site from the multiple cardiac phases. A hypothetical 1-year cardiac event incidence was estimated using previous evidences when revascularization was decided based on CT-FFR. We assessed the optimal cut-off value of CT-FFR and how to correct the CT-FFR to minimize hypothetical cardiac events under four different disease prevalence (20%, 25%, 30%, 35%, and 40%). A total of 16 vessels had i-FFR ≤ 0.8. On per-patient basis, the sensitivity, specificity, positive predict value, negative predict value, and diagnostic accuracy of CT-FFR â‰¦ 0.8 vs CCTA > 50% to detect functional stenosis defined as invasive FFR â‰¦ 0.80 were 93.3% vs 73.3%, 73.9% vs 26.1%, 70.0% vs 39.3%, 94.4% vs 60.0%, and 81.6% vs 44.7%, respectively. For minimize 1-year cardiac events, the optimal cut-off value for more than 30% of disease prevalence was 0.80. However, the optimal cut-off value for 20, 25, and 30% was 0.54 in any cases. After the adjustment of CT-FFR using a formula of 0.3X + 0.634 for CT-FFR < 0.7 to counteract its underestimation, the % reduction of the events for 20, 25, 30, 35, and 40% at a 0.80 cut-off were 19.0%, 15.6%, 12.6%, 10.0%, and 7.7% respectively. It was reasonable to support that the optimal cut-off value was 0.80 in disease prevalence of more than 30% for minimize 1-year cardiac events. However, underestimation should be adjusted to reduce cardiac events, especially when disease prevalence is low.

Diagnostic accuracy of 3D deep-learning-based fully automated estimation of patient-level minimum fractional flow reserve from coronary computed tomography angiography.

AIMS: Although deep-learning algorithms have been used to compute fractional flow reserve (FFR) from coronary computed tomography angiography (CCTA), no study has achieved 'fully automated' (i.e. free from human input) FFR calculation using deep-learning algorithms. The purpose of the study was to evaluate the accuracy of a fully automated 3D deep-learning model for estimating minimum FFR from CCTA data, with invasive FFR as the reference standard. METHODS AND RESULTS: This retrospective study of 1052 patients included 131 patients whose CCTA studies showed 30-90% stenosis and underwent invasive FFR (abnormal FFR observed in 72/131, 55%), and 921 patients who underwent clinically indicated CCTA without invasive FFR. We designed a fully automated 3D deep-learning model that inputs CCTA data and outputs minimum FFR without requiring human input. The model comprised a series of deep-learning algorithms: a conditional generative adversarial network, a 3D convolutional ladder network, and two independent neural networks with integrated virtual adversarial training. We used Monte Carlo cross-validation to evaluate the accuracy of the model for estimating FFR, with invasive FFR as the reference standard. The deep-learning FFR achieved area under the receiver-operating characteristic curve of 0.78 for detection of abnormal FFR; and was significantly higher than for visually determined CCTA >50% stenosis (area under the curve = 0.56). The deep-learning FFR model achieved 76% accuracy for detecting abnormal FFR, with sensitivity of 85% (79-89%) and specificity of 63% (54-70%). CONCLUSION: The 3D deep-learning model, which performs fully automatic estimation of minimum FFR from cardiac CT data, achieved 76% accuracy in detecting abnormal FFR.