Deep Learning-based Prediction of Percutaneous Recanalization in Chronic Total Occlusion Using Coronary CT Angiography.

Background CT is helpful in guiding the revascularization of chronic total occlusion (CTO), but manual prediction scores of percutaneous coronary intervention (PCI) success have challenges. Deep learning (DL) is expected to predict success of PCI for CTO lesions more efficiently. Purpose To develop a DL model to predict guidewire crossing and PCI outcomes for CTO using coronary CT angiography (CCTA) and evaluate its performance compared with manual prediction scores. MATERIALS AND METHODS: Participants with CTO lesions were prospectively identified from one tertiary hospital between January 2018 and December 2021 as the training set to develop the DL prediction model for PCI of CTO, with fivefold cross validation. The algorithm was tested using an external test set prospectively enrolled from three tertiary hospitals between January 2021 and June 2022 with the same eligibility criteria. All participants underwent preprocedural CCTA within 1 month before PCI. The end points were guidewire crossing within 30 minutes and PCI success of CTO.Results A total of 534 participants (mean age, 57.7 years ± 10.8 [SD]; 417 [78.1%] men) with 565 CTO lesions were included. In the external test set (186 participants with 189 CTOs), the DL model saved 85.0% of the reconstruction and analysis time of manual scores (mean, 73.7 seconds vs 418.2-466.9 seconds) and had higher accuracy than manual scores in predicting guidewire crossing within 30 minutes (DL, 91.0%; CT Registry of Chronic Total Occlusion Revascularization, 61.9%; Korean Multicenter CTO CT Registry [KCCT], 68.3%; CCTA-derived Multicenter CTO Registry of Japan (J-CTO), 68.8%; P < .05) and PCI success (DL, 93.7%; KCCT, 74.6%; J-CTO, 75.1%; P < .05). For DL, the area under the receiver operating characteristic curve was 0.97 (95% CI: 0.89, 0.99) for the training test set and 0.96 (95% CI: 0.90, 0.98) for the external test set. Conclusion The DL prediction model accurately predicted the percutaneous recanalization outcomes of CTO lesions and increased the efficiency of noninvasively grading the difficulty of PCI. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Pundziute-do Prado in this issue.

Artificial intelligence-based full aortic CT angiography imaging with ultra-low-dose contrast medium: a preliminary study.

OBJECTIVES: To further reduce the contrast medium (CM) dose of full aortic CT angiography (ACTA) imaging using the augmented cycle-consistent adversarial framework (Au-CycleGAN) algorithm. METHODS: We prospectively enrolled 150 consecutive patients with suspected aortic disease. All received ACTA scans of ultra-low-dose CM (ULDCM) protocol and low-dose CM (LDCM) protocol. These data were randomly assigned to the training datasets (n = 100) and the validation datasets (n = 50). The ULDCM images were reconstructed by the Au-CycleGAN algorithm. Then, the AI-based ULDCM images were compared with LDCM images in terms of image quality and diagnostic accuracy. RESULTS: The mean image quality score of each location in the AI-based ULDCM group was higher than that in the ULDCM group but a little lower than that in the LDCM group (all p < 0.05). All AI-based ULDCM images met the diagnostic requirements (score ≥ 3). Except for the image noise, the AI-based ULDCM images had higher attenuation value than the ULDCM and LDCM images as well as higher SNR and CNR in all locations of the aorta analyzed (all p < 0.05). Similar results were also seen in obese patients (BMI > 25, all p < 0.05). Using the findings of LDCM images as the reference, the AI-based ULDCM images showed good diagnostic parameters and no significant differences in any of the analyzed aortic disease diagnoses (all K-values > 0.80, p < 0.05). CONCLUSIONS: The required dose of CM for full ACTA imaging can be reduced to one-third of the CM dose of the LDCM protocol while maintaining image quality and diagnostic accuracy using the Au-CycleGAN algorithm. KEY POINTS: • The required dose of contrast medium (CM) for full ACTA imaging can be reduced to one-third of the CM dose of the low-dose contrast medium (LDCM) protocol using the Au-CycleGAN algorithm. • Except for the image noise, the AI-based ultra-low-dose contrast medium (ULDCM) images had better quantitative image quality parameters than the ULDCM and LDCM images. • No significant diagnostic differences were noted between the AI-based ULDCM and LDCM images regarding all the analyzed aortic disease diagnoses.

Deep learning-based prognostic model using non-enhanced cardiac cine MRI for outcome prediction in patients with heart failure.

OBJECTIVES: To evaluate the performance of a deep learning-based multi-source model for survival prediction and risk stratification in patients with heart failure. METHODS: Patients with heart failure with reduced ejection fraction (HFrEF) who underwent cardiac magnetic resonance between January 2015 and April 2020 were retrospectively included in this study. Baseline electronic health record data, including clinical demographic information, laboratory data, and electrocardiographic information, were collected. Short-axis non-contrast cine images of the whole heart were acquired to estimate the cardiac function parameters and the motion features of the left ventricle. Model accuracy was evaluated using the Harrell's concordance index. All patients were followed up for major adverse cardiac events (MACEs), and survival prediction was assessed using Kaplan-Meier curves. RESULTS: A total of 329 patients were evaluated (age 54 ± 14 years; men, 254) in this study. During a median follow-up period of 1041 days, 62 patients experienced MACEs and their median survival time was 495 days. When compared with conventional Cox hazard prediction models, deep learning models showed better survival prediction performance. Multi-data denoising autoencoder (DAE) model reached the concordance index of 0.8546 (95% CI: 0.7902-0.8883). Furthermore, when divided into phenogroups, the multi-data DAE model could significantly discriminate between the survival outcomes of the high-risk and low-risk groups compared with other models (p < 0.001). CONCLUSIONS: The proposed deep learning (DL) model based on non-contrast cardiac cine magnetic resonance imaging could independently predict the outcome of patients with HFrEF and showed better prediction efficiency than conventional methods. CLINICAL RELEVANCE STATEMENT: The proposed multi-source deep learning model based on cardiac magnetic resonance enables survival prediction in patients with heart failure. KEY POINTS: • A multi-source deep learning model based on non-contrast cardiovascular magnetic resonance (CMR) cine images was built to make robust survival prediction in patients with heart failure. • The ground truth definition contains electronic health record data as well as DL-based motion data, and cardiac motion information is extracted by optical flow method from non-contrast CMR cine images. • The DL-based model exhibits better prognostic value and stratification performance when compared with conventional prediction models and could aid in the risk stratification in patients with HF.

Diagnostic performance of angiography-derived fractional flow reserve analysis based on bifurcation fractal law for assessing hemodynamic significance of coronary stenosis.

OBJECTIVES: Blood flow into the side branch affects the calculation of coronary angiography-derived fractional flow reserve (FFR), called Angio-FFR. Neglecting or improperly compensating for the side branch flow may decrease the diagnostic accuracy of Angio-FFR. This study aims to evaluate the diagnostic accuracy of a novel Angio-FFR analysis that considers the side branch flow based on the bifurcation fractal law. METHODS: A one-dimensional reduced-order model based on the vessel segment was used to perform Angio-FFR analysis. The main epicardial coronary artery was divided into several segments according to the bifurcation nodes. Side branch flow was quantified using the bifurcation fractal law to correct the blood flow in each vessel segment. In order to verify the diagnostic performance of our Angio-FFR analysis, two other computational methods were taken as control groups: (i) FFR_s: FFR calculated by delineating the coronary artery tree to consider side branch flow, (ii) FFR_n: FFR calculated by just delineating the main epicardial coronary artery and neglecting the side branch flow. RESULTS: The analysis of 159 vessels from 119 patients showed that our Anio-FFR calculation method had comparable diagnostic accuracy to FFR_s and provided significantly higher diagnostic accuracy than that of FFR_n. In addition, using invasive FFR as a reference, the Pearson correlation coefficients of Angio-FFR and FFRs were 0.92 and 0.91, respectively, while that of FFR_n was only 0.85. CONCLUSIONS: Our Angio-FFR analysis has demonstrated good diagnostic performance in assessing the hemodynamic significance of coronary stenosis by using the bifurcation fractal law to compensate for side branch flow. CLINICAL RELEVANCE STATEMENT: Bifurcation fractal law can be used to compensate for side branch flow during the Angio-FFR calculation of the main epicardial vessel. Compensating for side branch flow can improve the ability of Angio-FFR to diagnose stenosis functional severity. KEY POINTS: • The bifurcation fractal law could accurately estimate the blood flow from the proximal main vessel into the main branch, thus compensating for the side branch flow. • Angiography-derived FFR based on the bifurcation fractal law is feasible to evaluate the target diseased coronary artery without delineating the side branch.

Efficacy and safety of 755-nm picosecond alexandrite laser with topical tranexamic acid versus laser monotherapy for melasma and facial rejuvenation: a multicenter, randomized, double-blinded, split-face study in Chinese patients.

To compare the efficacy and safety of 755-nm picosecond alexandrite laser and topical tranexamic acid (TTA) combination therapy with laser monotherapy, for the treatment of melasma and facial rejuvenation. This multicenter, randomized, double-blinded, split-face study enrolled 37 patients who presented with melasma and photoaging. Facial halves were randomized to receive either laser and TTA combination therapy or laser monotherapy. Three treatments were delivered at 4-5 weeks intervals. Patients were followed up for 1, 3, and 6 months post-final treatment and evaluated by blinded investigators for hemi-Melasma Area and Severity Index (hemi-MASI), facial dyschromia, skin texture, laxity, and rhytids. Daily diaries rating healing progress for 7 days posttreatment and satisfaction grading were performed by all patients. Adverse events were recorded. Thirty-six patients completed the follow-up. Compared with the baseline, hemi-MASI, dyschromia, and skin texture on both halves improved significantly through the follow-up (p = 0.000). A significant difference in hemi-MASI and dyschromia between combination therapy halves and monotherapy halves was noticed at 1- and 3-month follow-ups (p < 0.05). The laser monotherapy halves displayed significantly less redness and sensitivity during the 7-day posttreatment recovery period (p < 0.05). Patients' satisfaction ratings for the combination therapy halves were higher than the monotherapy halves at 1-month follow-up (p < 0.05). No severe adverse events were observed. The picosecond alexandrite laser and TTA combination therapy demonstrated synergistic efficacy for hemi-MASI and dyschromia improvements over laser monotherapy. The optimization of the picosecond laser and TTA combination regimen needs further investigation.

Deep learning-based automated left ventricular ejection fraction assessment using 2-D echocardiography.

Deep learning (DL) has been applied for automatic left ventricle (LV) ejection fraction (EF) measurement, but the diagnostic performance was rarely evaluated for various phenotypes of heart disease. This study aims to evaluate a new DL algorithm for automated LVEF measurement using two-dimensional echocardiography (2DE) images collected from three centers. The impact of three ultrasound machines and three phenotypes of heart diseases on the automatic LVEF measurement was evaluated. Using 36890 frames of 2DE from 340 patients, we developed a DL algorithm based on U-Net (DPS-Net) and the biplane Simpson's method was applied for LVEF calculation. Results showed a high performance in LV segmentation and LVEF measurement across phenotypes and echo systems by using DPS-Net. Good performance was obtained for LV segmentation when DPS-Net was tested on the CAMUS data set (Dice coefficient of 0.932 and 0.928 for ED and ES). Better performance of LV segmentation in study-wise evaluation was observed by comparing the DPS-Net v2 to the EchoNet-dynamic algorithm (P = 0.008). DPS-Net was associated with high correlations and good agreements for the LVEF measurement. High diagnostic performance was obtained that the area under receiver operator characteristic curve was 0.974, 0.948, 0.968, and 0.972 for normal hearts and disease phenotypes including atrial fibrillation, hypertrophic cardiomyopathy, dilated cardiomyopathy, respectively. High performance was obtained by using DPS-Net in LV detection and LVEF measurement for heart failure with several phenotypes. High performance was observed in a large-scale dataset, suggesting that the DPS-Net was highly adaptive across different echocardiographic systems.NEW & NOTEWORTHY A new strategy of feature extraction and fusion could enhance the accuracy of automatic LVEF assessment based on multiview 2-D echocardiographic sequences. High diagnostic performance for the determination of heart failure was obtained by using DPS-Net in cases with different phenotypes of heart diseases. High performance for left ventricle segmentation was obtained by using DPS-Net, suggesting the potential for a wider range of application in the interpretation of 2DE images.

A 2-year investigation of the impact of the computed tomography-derived fractional flow reserve calculated using a deep learning algorithm on routine decision-making for coronary artery disease management.

OBJECTIVE: This study aims to investigate the safety and feasibility of using a deep learning algorithm to calculate computed tomography angiography-based fractional flow reserve (DL-FFRCT) as an alternative to invasive coronary angiography (ICA) in the selection of patients for coronary intervention. MATERIALS AND METHODS: Patients (N = 296) with symptomatic coronary artery disease identified by coronary computed tomography angiography (CTA) with stenosis over 50% were retrospectively enrolled from a single centre in this study. ICA-guided interventions were performed in patients at admission, and DL-FFRCT was conducted retrospectively. The influences on decision-making by using DL-FFRCT and the clinical outcome were compared to those of ICA-guided care for symptomatic CAD at the 2-year follow-up evaluation. RESULT: Two hundred forty-three patients were evaluated. Up to 72% of diagnostic ICA studies could have been avoided by using a DL-FFRCT value > 0.8 as a cut-off for intervention. A similar major adverse cardiovascular event (MACE) rate was observed in patients who underwent revascularisation with a DL-FFRCT value ≤ 0.8 (2.9%) compared to that of ICA-guided interventions (3.3%) (stented lesions with ICA stenosis > 75%) (p = 0.838). CONCLUSION: DL-FFRCT can reduce the need for diagnostic coronary angiography when identifying patients suitable for coronary intervention. A low MACE rate was found in a 2-year follow-up investigation. KEY POINTS: • Seventy-two percent of diagnostic ICA studies could have been avoided by using a DL-FFRCT value > 0.8 as a cut-off for intervention. • Coronary artery stenting based on the diagnosis by using a 320-detector row CT scanner and a positive DL-FFRCT value could potentially be associated with a lower occurrence rate of major adverse cardiovascular events (2.9%) within the first 2 years. • A low event rate was found when intervention was performed in tandem lesions with haemodynamic significance based on DL-FFRCT < 0.8 as a cut-off value.

Simultaneous left atrium anatomy and scar segmentations via deep learning in multiview information with attention.

Three-dimensional late gadolinium enhanced (LGE) cardiac MR (CMR) of left atrial scar in patients with atrial fibrillation (AF) has recently emerged as a promising technique to stratify patients, to guide ablation therapy and to predict treatment success. This requires a segmentation of the high intensity scar tissue and also a segmentation of the left atrium (LA) anatomy, the latter usually being derived from a separate bright-blood acquisition. Performing both segmentations automatically from a single 3D LGE CMR acquisition would eliminate the need for an additional acquisition and avoid subsequent registration issues. In this paper, we propose a joint segmentation method based on multiview two-task (MVTT) recursive attention model working directly on 3D LGE CMR images to segment the LA (and proximal pulmonary veins) and to delineate the scar on the same dataset. Using our MVTT recursive attention model, both the LA anatomy and scar can be segmented accurately (mean Dice score of 93% for the LA anatomy and 87% for the scar segmentations) and efficiently ( ∼ 0.27 s to simultaneously segment the LA anatomy and scars directly from the 3D LGE CMR dataset with 60-68 2D slices). Compared to conventional unsupervised learning and other state-of-the-art deep learning based methods, the proposed MVTT model achieved excellent results, leading to an automatic generation of a patient-specific anatomical model combined with scar segmentation for patients in AF.

Deep Learning for Diagnosis of Chronic Myocardial Infarction on Nonenhanced Cardiac Cine MRI.

Background Renal impairment is common in patients with coronary artery disease and, if severe, late gadolinium enhancement (LGE) imaging for myocardial infarction (MI) evaluation cannot be performed. Purpose To develop a fully automatic framework for chronic MI delineation via deep learning on non-contrast material-enhanced cardiac cine MRI. Materials and Methods In this retrospective single-center study, a deep learning model was developed to extract motion features from the left ventricle and delineate MI regions on nonenhanced cardiac cine MRI collected between October 2015 and March 2017. Patients with chronic MI, as well as healthy control patients, had both nonenhanced cardiac cine (25 phases per cardiac cycle) and LGE MRI examinations. Eighty percent of MRI examinations were used for the training data set and 20% for the independent testing data set. Chronic MI regions on LGE MRI were defined as ground truth. Diagnostic performance was assessed by analysis of the area under the receiver operating characteristic curve (AUC). MI area and MI area percentage from nonenhanced cardiac cine and LGE MRI were compared by using the Pearson correlation, paired t test, and Bland-Altman analysis. Results Study participants included 212 patients with chronic MI (men, 171; age, 57.2 years ± 12.5) and 87 healthy control patients (men, 42; age, 43.3 years ± 15.5). Using the full cardiac cine MRI, the per-segment sensitivity and specificity for detecting chronic MI in the independent test set was 89.8% and 99.1%, respectively, with an AUC of 0.94. There were no differences between nonenhanced cardiac cine and LGE MRI analyses in number of MI segments (114 vs 127, respectively; P = .38), per-patient MI area (6.2 cm2 ± 2.8 vs 5.5 cm2 ± 2.3, respectively; P = .27; correlation coefficient, r = 0.88), and MI area percentage (21.5% ± 17.3 vs 18.5% ± 15.4; P = .17; correlation coefficient, r = 0.89). Conclusion The proposed deep learning framework on nonenhanced cardiac cine MRI enables the confirmation (presence), detection (position), and delineation (transmurality and size) of chronic myocardial infarction. However, future larger-scale multicenter studies are required for a full validation. Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Leiner in this issue.

Radiomics Nomogram Building From Multiparametric MRI to Predict Grade in Patients With Glioma: A Cohort Study.

BACKGROUND: Accurate classification of gliomas is crucial for prescribing therapy and assessing the prognosis of patients. PURPOSE: To develop a radiomics nomogram using multiparametric MRI for predicting glioma grading. STUDY TYPE: Retrospective. POPULATION: This study involved 85 patients (training cohort: n = 56; validation cohort: n = 29) with pathologically confirmed gliomas. FIELD STRENGTH/SEQUENCE: 1.5T MR, containing contrast-enhanced T1 -weighted (CET1 WI), axial T2 -weighted (T2 WI), and apparent diffusion coefficient (ADC) sequences. ASSESSMENT: A region of interest of the tumor was delineated. A total of 652 radiomics features were extracted and were reduced using least absolute shrinkage and selection operator regression. STATISTICAL TESTING: Radiomic signature, participant's age, and gender were analyzed as potential predictors to perform logistic regression analysis and develop a prediction model of glioma grading, and a radiomics nomogram was used to represent this model. The performance of the nomogram was assessed in terms of discrimination, calibration, and clinical value in glioma grading. RESULTS: The radiomic signature was significantly associated with glioma grade (P < 0.001) in both the training and validation cohorts. The performance of the radiomics nomogram derived from three MRI sequences (with C-index of 0.971 and 0.961 in the training and validation cohorts, respectively) was improved compared to those based on either CET1 WI, T2 WI, or ADC alone in glioma grading (with C-index of 0.914, 0.714, 0.842 in the training cohort, and 0.941, 0.500, 0.730 in the validation cohort). The nomogram derived from three sequences showed good calibration: the calibration curve showed good agreement between the estimated and the actual probability. The decision curve demonstrated that combining three sequences had more favorable clinical predictive value than single sequence imaging. DATA CONCLUSION: We created and assessed a multiparametric MRI-based radiomics nomogram that may help clinicians classify gliomas more accurately. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:825-833.

Evaluation of fractional flow reserve in patients with stable angina: can CT compete with angiography?

BACKGROUND: We aimed to compare the performance of FFRCT and FFRQCA in assessing the functional significance of coronary artery stenosis in patients suffering from coronary artery disease with stable angina. METHOD: A total of 101 stable coronary heart disease (CAD) patients with 181 lesions were recruited. FFRCT and FFRQCA were compared using invasive fractional flow reserve (FFR) as a reference standard. Comparisons between FFRCT and FFRQCA were conducted based on strategies of the geometric reconstruction, boundary conditions, and geometric characteristics. The performance of FFRCT and FFRQCA in detecting hemodynamic significance was also investigated. RESULTS: The performance of FFRCT and FFRQCA in discriminating hemodynamically significant lesions was compared. Good correlation and agreement with invasive FFR was found using FFRCT and FFRQCA (r = 0.809, p < 0.001 and r = 0.755, p < 0.001). A significant difference was observed in the complex coronary artery tree, in which relatively better prediction was observed using FFRCT than FFRQCA when analyzing the stenosis distributed in the middle segment of a stenotic branch (p = 0.036). Moreover, FFRCT was found to be better at predicting hemodynamically insignificant stenosis than FFRQCA (p = 0.007), while the performance of the two parameters was similar in discriminating functional significant lesions using an FFR threshold of ≤ 0.8 as a reference standard. CONCLUSION: FFRCT and FFRQCA could both accurately rule out functional insignificant lesions in stable CAD patients. FFRCT was found to be better for the noninvasive screening of CAD patients with stable angina than FFRQCA. KEY POINTS: • FFR CT and FFR QCA were both in good correlation and agreement with invasive FFR measurements. • FFR CT is superior in accuracy and consistency compared to FFR QCA in patients with stenoses distributed in left coronary artery. • The noninvasive nature of FFR CT could provide potential benefit for stable CAD patients on disease management.

Precise diagnosis of intracranial hemorrhage and subtypes using a three-dimensional joint convolutional and recurrent neural network.

OBJECTIVES: To evaluate the performance of a novel three-dimensional (3D) joint convolutional and recurrent neural network (CNN-RNN) for the detection of intracranial hemorrhage (ICH) and its five subtypes (cerebral parenchymal, intraventricular, subdural, epidural, and subarachnoid) in non-contrast head CT. METHODS: A total of 2836 subjects (ICH/normal, 1836/1000) from three institutions were included in this ethically approved retrospective study, with a total of 76,621 slices from non-contrast head CT scans. ICH and its five subtypes were annotated by three independent experienced radiologists, with majority voting as reference standard for both the subject level and the slice level. Ninety percent of data was used for training and validation, and the rest 10% for final evaluation. A joint CNN-RNN classification framework was proposed, with the flexibility to train when subject-level or slice-level labels are available. The predictions were compared with the interpretations from three junior radiology trainees and an additional senior radiologist. RESULTS: It took our algorithm less than 30 s on average to process a 3D CT scan. For the two-type classification task (predicting bleeding or not), our algorithm achieved excellent values (≥ 0.98) across all reporting metrics on the subject level. For the five-type classification task (predicting five subtypes), our algorithm achieved > 0.8 AUC across all subtypes. The performance of our algorithm was generally superior to the average performance of the junior radiology trainees for both two-type and five-type classification tasks. CONCLUSIONS: The proposed method was able to accurately detect ICH and its subtypes with fast speed, suggesting its potential for assisting radiologists and physicians in their clinical diagnosis workflow. KEY POINTS: • A 3D joint CNN-RNN deep learning framework was developed for ICH detection and subtype classification, which has the flexibility to train with either subject-level labels or slice-level labels. • This deep learning framework is fast and accurate at detecting ICH and its subtypes. • The performance of the automated algorithm was superior to the average performance of three junior radiology trainees in this work, suggesting its potential to reduce initial misinterpretations.

Carotid DSA based CFD simulation in assessing the patient with asymptomatic carotid stenosis: a preliminary study.

BACKGROUND: Cerebrovascular events are frequently associated with hemodynamic disturbance caused by internal carotid artery (ICA) stenosis. It is challenging to determine the ischemia-related carotid stenosis during the intervention only using digital subtracted angiography (DSA). Inspired by the performance of well-established FFRct technique in hemodynamic assessment of significant coronary stenosis, we introduced a pressure-based carotid arterial functional assessment (CAFA) index generated from computational fluid dynamic (CFD) simulation in DSA data, and investigated its feasibility in the assessment of hemodynamic disturbance preliminarily using pressure-wired measurement and arterial spin labeling (ASL) MRI as references. METHODS: The cerebral multi-delay multi-parametric ASL-MRI and carotid DSA including trans-stenotic pressure-wired measurement were implemented on a 65-year-old man with asymptomatic unilateral (left) ICA stenosis. A CFD simulation using simplified boundary condition was performed in DSA data to calculate the CAFA index. The cerebral blood flow (CBF) and arterial transit time (ATT) of ICA territories were acquired. RESULTS: CFD simulation showed good correlation (r = 0.839, P = 0.001) with slight systematic overestimation (mean difference - 0.007, standard deviation 0.017) compared with pressure-wired measurement. No significant difference was observed between them (P = 0.09). Though the narrowing degree of in the involved ICA was about 70%, the simulated and measured CAFA (0.942/0.937) revealed a functionally nonsignificant stenosis which was also verified by a compensatory final CBF (fronto-temporal/fronto-parietal region: 51.58/45.62 ml/100 g/min) and slightly prolonged ATT (1.23/1.4 s) in the involved territories, together with a normal left-right percentage difference (2.1-8.85%). CONCLUSIONS: The DSA based CFD simulation showed good consistence with invasive approach and could be used as a cost-saving and efficient way to study the relationship between hemodynamic disorder caused by ICA stenosis and subsequent perfusion variations in brain. Further research should focus on the role of noninvasive pressure-based CAFA in screening asymptomatic ischemia-causing carotid stenosis.

A study of noninvasive fractional flow reserve derived from a simplified method based on coronary computed tomography angiography in suspected coronary artery disease.

BACKGROUND: The invasive fractional flow reserve has been considered the gold standard for identifying ischaemia-related stenosis in patients with suspected coronary artery disease. Determining non-invasive FFR based on coronary computed tomographic angiography datasets using computational fluid dynamics tends to be a demanding process. Therefore, the diagnostic performance of a simplified method for the calculation of FFRCTA requires further evaluation. OBJECTIVES: The aim of this study was to investigate the diagnostic performance of FFRCTA calculated based on a simplified method by referring to the invasive FFR in patient-specific coronary arteries and clinical decision-making. METHODS: Twenty-nine subjects included in this study underwent CCTA before undergoing clinically indicated invasive coronary angiography for suspected coronary artery disease. Pulsatile flow simulation and a novel boundary condition were used to obtain FFRCTA based on the CCTA datasets. The Pearson correlation, Bland-Altman plots and the diagnostic performance of FFRCTA and CCTA stenosis were analyzed by comparison to the invasive FFR reference standard. Ischaemia was defined as an FFR or FFRCTA ≤0.80, and anatomically obstructive CAD was defined as a CCTA stenosis >50%. RESULTS: FFRCTA and invasive FFR were well correlated (r = 0.742, P = 0.001). Slight systematic underestimation was found in FFRCTA (mean difference 0.03, standard deviation 0.05, P = 0.001). The area under the receiver-operating characteristic curve was 0.93 for FFRCTA and 0.75 for CCTA on a per-vessel basis. Per-patient accuracy, sensitivity and specificity were 79.3, 93.7 and 61.5%, respectively, for FFRCTA and 62.1, 87.5 and 30.7%, respectively, for CCTA. Per-vessel accuracy, sensitivity and specificity were 80.6, 94.1 and 68.4%, respectively, for FFRCTA and 61.6, 88.2 and 36.8%, respectively, for CCTA. CONCLUSIONS: FFRCTA derived from pulsatile simulation with a simplified novel boundary condition was in good agreement with invasive FFR and showed better diagnostic performance compared to CCTA, suggesting that the simplified method has the potential to be an alternative and accurate way to assess the haemodynamic characteristics for coronary stenosis.

Hemodynamics analysis of the serial stenotic coronary arteries.

Coronary arterial stenoses, particularly serial stenoses in a single branch, are responsible for complex hemodynamic properties of the coronary arterial trees, and the uncertain prognosis of invasive intervention. Critical information of the blood flow redistribution in the stenotic arterial segments is required for the adequate treatment planning. Therefore, in this study, an image based non-invasive functional assessment is performed to investigate the hemodynamic significances of serial stenoses. Twenty patient-specific coronary arterial trees with different combinations of stenoses were reconstructed from the computer tomography angiography for the evaluation of the hemodynamics. Our results showed that the computed FFR based on CTA images (FFRCT) pullback curves with wall shear stress (WSS) distribution could provide more effectively examine the physiological significance of the locations of the segmental narrowing and the curvature of the coronary arterial segments. The paper thus provides the diagnostic efficacy of FFRCT pullback curve for noninvasive quantification of the hemodynamics of stenotic coronary arteries with serial lesions, compared to the gold standard invasive FFR, to provide a reliable physiological assessment of significant amount of coronary artery stenosis. Further, we were also able to demonstrate the potential of carrying out virtual revascularization, to enable more precise PCI procedures and improve their outcomes.

Erratum: Sandeep P., et al. An Efficient Biometric-Based Algorithm Using Heart Rate Variability for Securing Body Sensor Networks. Sensors 2015, 15, 15067-15089.

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Erratum: Sandeep P., et al. A Novel Secure IoT-Based Smart Home Automation System Using a Wireless Sensor Network. Sensors 2017, 17, 69.

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Functional assessment of the stenotic carotid artery by CFD-based pressure gradient evaluation.

The functional assessment of a hemodynamic significant stenosis base on blood pressure variation has been applied for evaluation of the myocardial ischemic event. This functional assessment shows great potential for improving the accuracy of the classification of the severity of carotid stenosis. To explore the value of grading the stenosis using a pressure gradient (PG)-we had reconstructed patient-specific carotid geometries based on MRI images-computational fluid dynamics were performed to analyze the PG in their stenotic arteries. Doppler ultrasound image data and the corresponding MRI image data of 19 patients with carotid stenosis were collected. Based on these, 31 stenotic carotid arterial geometries were reconstructed. A combinatorial boundary condition method was implemented for steady-state computer fluid dynamics simulations. Anatomic parameters, including tortuosity (T), the angle of bifurcation, and the cross-sectional area of the remaining lumen, were collected to investigate the effect on the pressure distribution. The PG is highly correlated with the severe stenosis (r = 0.902), whereas generally, the T and the angle of the bifurcation negatively correlate to the pressure drop of the internal carotid artery stenosis. The calculation required <10 min/case, which made it prepared for the fast diagnosis of the severe stenosis. According to the results, we had proposed a potential threshold value for distinguishing severe stenosis from mild-moderate stenosis (PG = 0.88). In conclusion, the PG could serve as the additional factor for improving the accuracy of grading the severity of the stenosis.

A Novel Secure IoT-Based Smart Home Automation System Using a Wireless Sensor Network.

Wireless sensor networks (WSNs) provide noteworthy benefits over traditional approaches for several applications, including smart homes, healthcare, environmental monitoring, and homeland security. WSNs are integrated with the Internet Protocol (IP) to develop the Internet of Things (IoT) for connecting everyday life objects to the internet. Hence, major challenges of WSNs include: (i) how to efficiently utilize small size and low-power nodes to implement security during data transmission among several sensor nodes; (ii) how to resolve security issues associated with the harsh and complex environmental conditions during data transmission over a long coverage range. In this study, a secure IoT-based smart home automation system was developed. To facilitate energy-efficient data encryption, a method namely Triangle Based Security Algorithm (TBSA) based on efficient key generation mechanism was proposed. The proposed TBSA in integration of the low power Wi-Fi were included in WSNs with the Internet to develop a novel IoT-based smart home which could provide secure data transmission among several associated sensor nodes in the network over a long converge range. The developed IoT based system has outstanding performance by fulfilling all the necessary security requirements. The experimental results showed that the proposed TBSA algorithm consumed less energy in comparison with some existing methods.