Computed Tomography Angiography Images Research Articles

A Deep Learning Based on Automatic Cerebral Aneurysm Detection in Brain Computed Tomography Angiography Scan Images

Cerebral aneurysms are an important disease that threatens human life. Rupture of these aneurysms causes hemorrhages in the cerebral arteries. Computed Tomography Angiography (CTA) is widely used in the clinical diagnosis of cerebral aneurysms. Interpretation errors by radiologists in examining these Computed Tomography (CT) images are vital for patients. Based on this importance, deep learning-based studies aim to help keep these errors to a minimum. For this purpose, CTA images were used to detect cerebral aneurysms in this study. For CTA image analysis, deep learning methodology was preferred through Convolutional Neural Network (CNN). The validation accuracy of the training obtained as a result of deep learningg has a high rate of validation with 99.54% accuracy, 100% sensitivity, 98.89% specificity and 100% precision. As a training dataset, it yielded 127 true positives and 1 false positive for patient images with aneurysm, 89 true negatives and 0 false negative for images of patients with non-aneurysms. In this trained network, results were obtained with a high accuracy of 86.6% on 75 CTA images for external test. Regional dimension analysis was also performed for an image with an aneurysm detected in the test process.

A deep-learning model for intracranial aneurysm detection on CT angiography images in China: a stepwise, multicentre, early-stage clinical validation study

Artificial intelligence (AI) models in real-world implementation are scarce. Our study aimed to develop a CT angiography (CTA)-based AI model for intracranial aneurysm detection, assess how it helps clinicians improve diagnostic performance, and validate its application in real-world clinical implementation. We developed a deep-learning model using 16 546 head and neck CTA examination images from 14 517 patients at eight Chinese hospitals. Using an adapted, stepwise implementation and evaluation, 120 certified clinicians from 15 geographically different hospitals were recruited. Initially, the AI model was externally validated with images of 900 digital subtraction angiography-verified CTA cases (examinations) and compared with the performance of 24 clinicians who each viewed 300 of these cases (stage 1). Next, as a further external validation a multi-reader multi-case study enrolled 48 clinicians to individually review 298 digital subtraction angiography-verified CTA cases (stage 2). The clinicians reviewed each CTA examination twice (ie, with and without the AI model), separated by a 4-week washout period. Then, a randomised open-label comparison study enrolled 48 clinicians to assess the acceptance and performance of this AI model (stage 3). Finally, the model was prospectively deployed and validated in 1562 real-world clinical CTA cases. The AI model in the internal dataset achieved a patient-level diagnostic sensitivity of 0·957 (95% CI 0·939-0·971) and a higher patient-level diagnostic sensitivity than clinicians (0·943 [0·921-0·961] vs 0·658 [0·644-0·672]; p<0·0001) in the external dataset. In the multi-reader multi-case study, the AI-assisted strategy improved clinicians' diagnostic performance both on a per-patient basis (the area under the receiver operating characteristic curves [AUCs]; 0·795 [0·761-0·830] without AI vs 0·878 [0·850-0·906] with AI; p<0·0001) and a per-aneurysm basis (the area under the weighted alternative free-response receiver operating characteristic curves; 0·765 [0·732-0·799] vs 0·865 [0·839-0·891]; p<0·0001). Reading time decreased with the aid of the AI model (87·5 s vs 82·7 s, p<0·0001). In the randomised open-label comparison study, clinicians in the AI-assisted group had a high acceptance of the AI model (92·6% adoption rate), and a higher AUC when compared with the control group (0·858 [95% CI 0·850-0·866] vs 0·789 [0·780-0·799]; p<0·0001). In the prospective study, the AI model had a 0·51% (8/1570) error rate due to poor-quality CTA images and recognition failure. The model had a high negative predictive value of 0·998 (0·994-1·000) and significantly improved the diagnostic performance of clinicians; AUC improved from 0·787 (95% CI 0·766-0·808) to 0·909 (0·894-0·923; p<0·0001) and patient-level sensitivity improved from 0·590 (0·511-0·666) to 0·825 (0·759-0·880; p<0·0001). This AI model demonstrated strong clinical potential for intracranial aneurysm detection with improved clinician diagnostic performance, high acceptance, and practical implementation in real-world clinical cases. National Natural Science Foundation of China. For the Chinese translation of the abstract see Supplementary Materials section.

Follow-up infarct volume on fluid attenuated inversion recovery (FLAIR)imaging in distal medium vessel occlusions: the role of cerebral blood volume index.

Distal medium vessel occlusions (DMVOs) contribute substantially to the incidence of acute ischemic strokes (AIS) and pose distinct challenges in clinical management and prognosis. Neuroimaging techniques, such as Fluid Attenuation Inversion Recovery (FLAIR) imaging and cerebral blood volume (CBV) index derived from perfusion imaging, have significantly improved our ability to assess the impact of strokes and predict their outcomes. The primary objective of this study was to investigate relationship between follow-up infarct volume (FIV) as assessed by FLAIR imaging in patients with DMVOs. This prospectively collected, retrospective reviewed cohort study included patients from two comprehensive stroke centers within the Johns Hopkins Medical Enterprise, spanning August 2018-October 2022. The cohort consisted of adults with AIS attributable to DMVO. Detailed imaging analyses were conducted, encompassing non-contrast CT, CT angiography (CTA), CT perfusion (CTP), and FLAIR imaging. Univariable and multivariable linear regression models were employed to assess the association between different factors and FIV. The study included 79 patients with DMVO stroke with a median age of 69years (IQR, 62-77years), and 57% (n = 45) were female. There was a negative correlation between the CBV index and FIV in a univariable linear regression analysis (Beta = - 16; 95% CI, - 23 to - 8.3; p < 0.001) and a multivariable linear regression model (Beta = - 9.1 per 0.1 change; 95% CI, - 15 to - 2.7; p = 0.006). Diabetes was independently associated with larger FIV (Beta = 46; 95% CI, 16 to 75; p = 0.003). Additionally, a higher baseline ASPECTS was associated with lower FIV (Beta = - 30; 95% CI, - 41 to - 20; p < 0.001). Our findings underscore the CBV index as an independent association with FIV in DMVOs, which highlights the critical role of collateral circulation in determining stroke outcomes in this patient population. In addition, our study confirms a negative association of ASPECTS with FLAIR FIV and identifies diabetes as independent factor associated with larger FIV. These insights pave the way for further large-scale, prospective studies to corroborate these findings, thereby refining the strategies for stroke prognostication and management.

Prediction of tissue outcome in acute ischemic stroke based on single-phase CT angiography at admission.

In acute ischemic stroke, prediction of the tissue outcome after reperfusion can be used to identify patients that might benefit from mechanical thrombectomy (MT). The aim of this work was to develop a deep learning model that can predict the follow-up infarct location and extent exclusively based on acute single-phase computed tomography angiography (CTA) datasets. In comparison to CT perfusion (CTP), CTA imaging is more widely available, less prone to artifacts, and the established standard of care in acute stroke imaging protocols. Furthermore, recent RCTs have shown that also patients with large established infarctions benefit from MT, which might not have been selected for MT based on CTP core/penumbra mismatch analysis. All patients with acute large vessel occlusion of the anterior circulation treated at our institution between 12/2015 and 12/2020 were screened (N = 404) and 238 patients undergoing MT with successful reperfusion were included for final analysis. Ground truth infarct lesions were segmented on 24 h follow-up CT scans. Pre-processed CTA images were used as input for a U-Net-based convolutional neural network trained for lesion prediction, enhanced with a spatial and channel-wise squeeze-and-excitation block. Post-processing was applied to remove small predicted lesion components. The model was evaluated using a 5-fold cross-validation and a separate test set with Dice similarity coefficient (DSC) as the primary metric and average volume error as the secondary metric. The mean ± standard deviation test set DSC over all folds after post-processing was 0.35 ± 0.2 and the mean test set average volume error was 11.5 mL. The performance was relatively uniform across models with the best model according to the DSC achieved a score of 0.37 ± 0.2 after post-processing and the best model in terms of average volume error yielded 3.9 mL. 24 h follow-up infarct prediction using acute CTA imaging exclusively is feasible with DSC measures comparable to results of CTP-based algorithms reported in other studies. The proposed method might pave the way to a wider acceptance, feasibility, and applicability of follow-up infarct prediction based on artificial intelligence.

Association between Left Atrial Appendage Morphology and Clot Histology in Patients with Embolic Ischemic Stroke: An Exploratory Study.

Acute embolic ischemic stroke poses a significant healthcare challenge. Histological clot features' variability among patients with acute ischemic stroke treated by mechanical thrombectomy has potential implications for determining treatment and etiology. This study investigated the clot histological feature differences among patients who experienced cardioembolic stroke and embolic stroke of undetermined source with different left atrial appendage (LAA) morphologies. We conducted a prospective observational study involving 79 patients with acute embolic ischemic stroke undergoing mechanical thrombectomy. Computed tomography angiography images were used to classify LAA morphologies. An artificial intelligence algorithm assessed the clot fibrin and red blood cell contents. Patients with chicken-wing LAA morphology exhibited lower mean clot fibrin proportions than did those with non-chicken-wing morphology (p < 0.001). Linear regression analysis showed that chicken-wing LAA was significantly associated with a lower clot fibrin proportion (estimate, -0.177; 95% CI [-0.259, -0.096]; p < 0.001). The successful recanalization rate and first-pass effect between the groups did not differ significantly. The chicken-wing LAA morphological type is associated with lower clot fibrin contents, suggesting potentially different embolism mechanisms or diverse embolic sources, compared with the non-chicken-wing LAA types. Further studies are required to investigate this association.

Cross-sectional angle prediction of lipid-rich and calcified tissue on computed tomography angiography images

PurposeThe assessment of vulnerable plaque characteristics and distribution is important to stratify cardiovascular risk in a patient. Computed tomography angiography (CTA) offers a promising alternative to invasive imaging but is limited by the fact that the range of Hounsfield units (HU) in lipid-rich areas overlaps with the HU range in fibrotic tissue and that the HU range of calcified plaques overlaps with the contrast within the contrast-filled lumen. This paper is to investigate whether lipid-rich and calcified plaques can be detected more accurately on cross-sectional CTA images using deep learning methodology.MethodsTwo deep learning (DL) approaches are proposed, a 2.5D Dense U-Net and 2.5D Mask-RCNN, which separately perform the cross-sectional plaque detection in the Cartesian and polar domain. The spread-out view is used to evaluate and show the prediction result of the plaque regions. The accuracy and F1-score are calculated on a lesion level for the DL and conventional plaque detection methods.ResultsFor the lipid-rich plaques, the median and mean values of the F1-score calculated by the two proposed DL methods on 91 lesions were approximately 6 and 3 times higher than those of the conventional method. For the calcified plaques, the F1-score of the proposed methods was comparable to those of the conventional method. The median F1-score of the Dense U-Net-based method was 3% higher than that of the conventional method.ConclusionThe two methods proposed in this paper contribute to finer cross-sectional predictions of lipid-rich and calcified plaques compared to studies focusing only on longitudinal prediction. The angular prediction performance of the proposed methods outperforms the convincing conventional method for lipid-rich plaque and is comparable for calcified plaque.

Qualitative and quantitative image quality of coronary CT angiography using photon-counting computed tomography: Standard and Ultra-high resolution protocols

PurposeWe aimed to identify the optimal reconstruction settings based on qualitative and quantitative image quality parameters on standard and ultra-high resolution (UHR) images using photon-counting CT (PCCT). MethodWe analysed 45 patients, 29 with standard and 16 with UHR acquisition, applying both smoother and sharper kernel settings. Coronary CT angiography images were performed on a dual-source PCCT system using standard (0.4/0.6 mm slice thickness, Bv40/Bv44 kernels, QIR levels 0–4) or UHR acquisition (0.2/0.4 mm slice thickness, Bv44/Bv56 kernels, QIR levels 0–4). Qualitative image quality was assessed using a 4-point Likert scale. Image noise (SD), signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated in both the proximal and distal segments. ResultsOn standard resolution, larger slice thickness resulted in an average increase of 12.5 % in CNR, whereas sharper kernel led to an average 8.7 % decrease in CNR. Highest CNR was measured on 0.6 mm, Bv40, QIR4 images and lowest on 0.4 mm, Bv44, QIR0 images: 25.8 ± 4.1vs.8.3 ± 1.6 (p < 0.001). On UHR images, highest CNR was observed on 0.4 mm, Bv40, QIR4 and lowest on 0.2 mm, Bv56 and QIR0 images: 21.5 ± 3.9vs.3.6 ± 0.8 (p < 0.001). Highest qualitative image quality was found on images with Bv44 kernel and QIR level 3/4 with both slice thicknesses on standard reconstruction. Additionally, Bv56 with QIR4 on 0.2 mm slice thickness images showed highest subjective image quality. Preserved distal vessel visualization was detected using QIR 2–4, Bv56 and 0.2 mm slice thickness. ConclusionsPhoton-counting CT demonstrated high qualitative and quantitative image quality for the assessment of coronaries and stents.

Reproducibility of artificial intelligence-enabled plaque measurements between systolic and diastolic phases from coronary computed tomography angiography.

Current coronary CT angiography (CTA) guidelines suggest both end-systolic and mid-diastolic phases of the cardiac cycle can be used for CTA image acquisition. However, whether differences in the phase of the cardiac cycle influence coronary plaque measurements is not known. We aim to explore the potential impact of cardiac phases on quantitative plaque assessment. We enrolled 39 consecutive patients (23 male, age 66.2 ± 11.5 years) who underwent CTA with dual-source CT with visually evident coronary atherosclerosis and with good image quality. End-systolic and mid- to late-diastolic phase images were reconstructed from the same CTA scan. Quantitative plaque and stenosis were analyzed in both systolic and diastolic images using artificial intelligence (AI)-enabled plaque analysis software (Autoplaque). Overall, 186 lesions from 39 patients were analyzed. There were excellent agreement and correlation between systolic and diastolic images for all plaque volume measurements (Lin's concordance coefficient ranging from 0.97 to 0.99; R ranging from 0.96 to 0.98). There were no substantial intrascan differences per patient between systolic and diastolic phases (p > 0.05 for all) for total (1017.1 ± 712.9 mm3 vs. 1014.7 ± 696.2 mm3), non-calcified (861.5 ± 553.7 mm3 vs. 856.5 ± 528.7 mm3), calcified (155.7 ± 229.3 mm3 vs. 158.2 ± 232.4 mm3), and low-density non-calcified plaque volume (151.4 ± 106.1 mm3 vs. 151.5 ± 101.5 mm3) and diameter stenosis (42.5 ± 18.4% vs 41.3 ± 15.1%). Excellentagreement and no substantial differences were observed in AI-enabled quantitative plaque measurements on CTA in systolic and diastolic images. Following further validation, standardized plaque measurements can be performed from CTA in systolic or diastolic cardiac phase. Quantitative plaque assessment using artificial intelligence-enabled plaque analysis software can provide standardized plaque quantification, regardless of cardiac phase. • The impact of different cardiac phases on coronary plaque measurements is unknown. • Plaque analysis using artificial intelligence-enabled software on systolic and diastolic CT angiography images shows excellent agreement. • Quantitative coronary artery plaque assessment can be performed regardless of cardiac phase.

Diagnostic performance of deep learning to exclude coronary stenosis on CT angiography in TAVI patients.

We evaluated the diagnostic performance of a deep-learning model (DLM) (CorEx®, Spimed-AI, Paris, France) designed to automatically detect > 50% coronary stenosis on coronary computed tomography angiography (CCTA) images. We studied inter-observer variability as an additional aim.CCTA images obtained before transcatheter aortic valve implantation (TAVI) were assessed by two radiologists and the DLM, and the results were compared to those of invasive coronary angiography (ICA) used as the reference standard. 165 consecutive patients underwent both CCTA and ICA as part of their TAVI work-up. We excluded the 42 (25.5%) patients with a history of stenting or bypass grafting and the 23 (13.9%) patients with low-quality images. We retrospectively subjected the CCTA images from the remaining 100 patients to evaluation by the DLM and compared the DLM and ICA results. All 25 patients with > 50% stenosis by ICA also had > 50% stenosis by DLM evaluation of CCTA: thus, the DLM had 100% sensitivity and 100% negative predictive value. False-positive DLM results were common, yielding a positive predictive value of only 39% (95% CI, 27-51%). Two radiologists with 3 and 25 years' experience, respectively, performed similarly to the DLM in evaluating the CCTA images; thus, accuracy did not differ significantly between each reader and the DLM (p = 0.625 and p = 0.375, respectively).The DLM had 100% negative predictive value for > 50% stenosis and performed similarly to experienced radiologists. This tool may hold promise for identifying the up to one-third of patients who do not require ICA before TAVI.

The Relative Cerebral Blood Volume (rCBV) < 42% Is Independently Associated with Collateral Status in Anterior Circulation Large Vessel Occlusion.

Background: The pretreatment CT perfusion (CTP) marker the relative cerebral blood volume (rCBV) < 42% lesion volume has recently been shown to predict 90-day functional outcomes; however, studies assessing correlations of the rCBV < 42% lesion volume with other outcomes remain sparse. Here, we aim to assess the relationship between the rCBV < 42% lesion volume and the reference standard digital subtraction angiography (DSA)-derived American Society of Interventional and Therapeutic Neuroradiology/Society of Interventional Radiology (ASITN) collateral score, hereby referred as the DSA CS. Methods: In this retrospective evaluation of our prospectively collected database, we included acute stroke patients triaged by multimodal CT imaging, including CT angiography and perfusion imaging, with confirmed anterior circulation large vessel occlusion between 1 September 2017 and 1 October 2023. Group differences were assessed using the Student's t test, Mann-Whitney U test and Chi-Square test. Spearman's rank correlation and logistic regression analyses were used to assess associations between rCBV < 42% and DSA CS. Results: In total, 222 patients (median age: 69 years, 56.3% female) met our inclusion criteria. In the multivariable logistic regression analysis, taking into account age, sex, race, hypertension, hyperlipidemia, diabetes, atrial fibrillation, prior stroke or transient ischemic attack, the admission National Institute of Health stroke scale, the premorbid modified Rankin score, the Alberta stroke program early CT score (ASPECTS), and segment occlusion, the rCBV < 42% lesion volume (adjusted OR: 0.98, p < 0.05) was independently associated with the DSA CS. Conclusion: The rCBV < 42% lesion volume is independently associated with the DSA CS.

Circulating miR-6821-5p levels and coronary calcification in asymptomatic familial hypercholesterolemia patients

Background and aimsPremature atherosclerotic cardiovascular disease (CVD) is a clinic characteristic of familial hypercholesterolemia (FH). Coronary calcium score (CCS) is a highly used imaging modality to evidence atherosclerotic plaque burden. microRNAs (miRNAs) are non-coding RNAs that epigenetically regulate gene expression. Here, we investigated whether CCS associates with a specific miRNA-signature in FH-patients. MethodsPatients with genetic diagnosis of FH (N = 86) from the nationwide SAFEHEART-cohort were investigated by computed tomography angiography imaging and classified depending on the presence of coronary calcification in FH-CCS (+) and FH-CCS (−) groups by the Agatston score. Differential miRNA profiling was performed in two stages: first by Affymetrix microarray technology (high-throughput differential profiling-studies) and second by RT-PCR using TaqMan-technology (analytical RT-qPCR study) in plasma of the two patient groups. ResultsmiR-193a-5p, miR-30e-5p and miR-6821-5p levels were significantly higher in FH-CCS (+) compared to FH-CCS (−). miR-6821-5p was the best miRNA to discriminate FH-patients CCS(+), according to receiver operating characteristic (ROC) analysis (AUC: 0.70 ± 0.06, p = 0.006). High miR-6821-5p levels were associated with older age (p = 0.03) and high LDL-burden (p = 0.014) using a ROC-derived cut-off value. However, miR-6821-5p did not correlate with age in either the CCS- or CCS + group. Genes involved in calcification processes were identified by in silico analysis. The relation of cell-calcification and expression levels of miR-6821-5p, BMP2 and SPP1 was validated experimentally in human vascular smooth muscle cell cultures. ConclusionsUp-regulated levels of miR-6821-5p are found in the plasma of asymptomatic FH-patients with coronary calcified atherosclerotic plaques, as well as in isolated human vascular smooth muscle cells expressing the pro-calcification genes BMP2 and SPP1. These findings highlight the impact of epigenetic regulation on the development of subclinical atherosclerosis.

Hemodynamics in left coronary artery with ramus intermedius: A patient-specific computational study

Clinical studies based on coronary computed tomography angiography (CCTA) images suggest that the presence of ramus intermedius (RI) in the left coronary artery (LCA) may aggravate the atherosclerotic depositions in the furcation region. In this study, computational fluid dynamics simulations are performed in patient-specific coronary models consisting of bifurcating LCA and two models having trifurcating LCA. Three-dimensional patient-specific coronary models are constructed from CCTA images obtained from North Eastern Indira Gandhi Regional Institute of Health and Medical Sciences, Shillong, India. Results indicate that the flow redistribution happens in the left coronary branch due to the presence of RI and the flow to left anterior descending artery (LAD) is reduced significantly. Recirculation and flow separation regions are observed in the proximal parts of the LAD and left circumflex artery for trifurcation geometries. The qualitative and quantitative analysis of wall shear stress-based descriptors suggests that the presence of the intermediate branch may affect the atherosusceptibility of the proximal region of LAD. The study indicates that the presence of RI may act as a contributing factor for plaque development and progression in the furcation region and proximal parts of LAD.

CT-based radiomics models predict spontaneous intracerebral hemorrhage expansion and are comparable with CT angiography spot sign.

This study aimed to investigate the efficacy of radiomics, based on non-contrast computed tomography (NCCT) and computed tomography angiography (CTA) images, in predicting early hematoma expansion (HE) in patients with spontaneous intracerebral hemorrhage (SICH). Additionally, the predictive performance of these models was compared with that of the established CTA spot sign. A retrospective analysis was conducted using CT images from 182 patients with SICH. Data from the patients were divided into a training set (145 cases) and a testing set (37 cases) using random stratified sampling. Two radiomics models were constructed by combining quantitative features extracted from NCCT images (the NCCT model) and CTA images (the CTA model) using a logistic regression (LR) classifier. Additionally, a univariate LR model based on the CTA spot sign (the spot sign model) was established. The predictive performance of the two radiomics models and the spot sign model was compared according to the area under the receiver operating characteristic (ROC) curve (AUC). For the training set, the AUCs of the NCCT, CTA, and spot sign models were 0.938, 0.904, and 0.726, respectively. Both the NCCT and CTA models demonstrated superior predictive performance compared to the spot sign model (all P < 0.001), with the performance of the two radiomics models being comparable (P = 0.068). For the testing set, the AUCs of the NCCT, CTA, and spot sign models were 0.925, 0.873, and 0.720, respectively, with only the NCCT model exhibiting significantly greater predictive value than the spot sign model (P = 0.041). Radiomics models based on NCCT and CTA images effectively predicted HE in patients with SICH. The predictive performances of the NCCT and CTA models were similar, with the NCCT model outperforming the spot sign model. These findings suggest that this approach has the potential to reduce the need for CTA examinations, thereby reducing radiation exposure and the use of contrast agents in future practice for the purpose of predicting hematoma expansion.

Clinical comparative analysis of 3D printing-assisted extracorporeal pre-fenestration and Castor integrated branch stent techniques in treating type B aortic dissections with inadequate proximal landing zones

BackgroundThis study aims to compare the clinical effects of two distinct surgical approaches, namely 3D printing-assisted extracorporeal pre-fenestration and Castor integrated branch stent techniques, in treating patients with Stanford type B aortic dissections (TBAD) characterized by inadequate proximal landing zones.MethodsA retrospective analysis was conducted on 84 patients with type B aortic dissection (TBAD) who underwent thoracic endovascular aortic repair (TEVAR) with left subclavian artery (LSA) reconstruction at our center from January 2022 to July 2023. Based on the different surgical approaches, the patients were divided into two groups: the group assisted by 3D printing for extracorporeal pre-fenestration (n = 44) and the group using the castor integrated branch stent (n = 40). Clinical indicators: including general patient information, operative time, surgical success rate, intraoperative and postoperative complication rates, re-intervention rate, and mortality, as well as postoperative aortic remodeling, were compared between the two groups. The endpoint of this study is the post-TEVAR mortality rate in patients.ResultsThe surgical success rate and device deployment success rate were 100% in both groups, with no statistically significant difference (P > 0.05). However, the group assisted by 3D printing for extracorporeal pre-fenestration had a significantly longer operative time (184.20 ± 54.857 min) compared to the group using the castor integrated branch stent (152.75 ± 33.068 min), with a statistically significant difference (t = 3.215, p = 0.002, P < 0.05). Moreover, the incidence of postoperative cerebral infarction and beak sign was significantly lower in the group assisted by 3D printing for extracorporeal pre-fenestration compared to the castor-integrated branch stent group, demonstrating statistical significance. There were no significant differences between the two groups in terms of other postoperative complication rates and aortic remodeling (P > 0.05). Notably, computed tomography angiography images revealed the expansion of the vascular true lumen and the reduction of the false lumen at three specified levels of the thoracic aorta. The follow-up duration did not show any statistically significant difference between the two groups (10.59 ± 4.52 vs. 9.08 ± 4.35 months, t = 1.561, p = 0.122 > 0.05). Throughout the follow-up period, neither group experienced new endoleaks, spinal cord injuries, nor limb ischemia. In the castor-integrated branch stent group, one patient developed a new distal dissection, prompting further follow-up. Additionally, there was one case of mortality due to COVID-19 in each group. There were no statistically significant differences between the two groups in terms of re-intervention rate and survival rate (P > 0.05).ConclusionBoth 3D printing-assisted extracorporeal pre-fenestration TEVAR and castor-integrated branch stent techniques demonstrate good safety and efficacy in treating Stanford type B aortic dissection with inadequate proximal anchoring. The 3D printing-assisted extracorporeal pre-fenestration TEVAR technique has a lower incidence of postoperative cerebral infarction and beak sign, while the castor-integrated branch stent technique has advantages in operative time.

A novel hybrid machine learning model for auxiliary diagnosing myocardial ischemia.

Accurate identification of the myocardial texture features of fat around the coronary artery on coronary computed tomography angiography (CCTA) images are crucial to improve clinical diagnostic efficiency of myocardial ischemia (MI). However, current coronary CT examination is difficult to recognize and segment the MI characteristics accurately during earlier period of inflammation. We proposed a random forest model to automatically segment myocardium and extract peripheral fat features. This hybrid machine learning (HML) model is integrated by CCTA images and clinical data. A total of 1,316 radiomics features were extracted from CCTA images. To further obtain the features that contribute the most to the diagnostic model, dimensionality reduction was applied to filter features to three: LNS, GFE, and WLGM. Moreover, statistical hypothesis tests were applied to improve the ability of discriminating and screening clinical features between the ischemic and non-ischemic groups. By comparing the accuracy, recall, specificity and AUC of the three models, it can be found that HML had the best performance, with the value of 0.848, 0.762, 0.704 and 0.729. In sum, this study demonstrates that ML-based radiomics model showed good predictive value in MI, and offer an enhanced tool for predicting prognosis with greater accuracy.

64-Slice ECG-gated computed tomographic angiography for assessment of coronary arteries in brachycephalic dogs with pulmonary stenosis.

Brachycephalic dogs with pulmonary stenosis are known to have a higher incidence of concurrent coronary artery abnormalities than non-brachycephalic breeds, which increases risk when performing balloon valvuloplasty. The use of ECG-gated CT angiography has been reported for the evaluation of coronary arteries in normal dogs and dogs with pulmonary stenosis. The purpose of this study was to report findings of coronary artery origination and morphology of main branches using ECG-gated CT angiography in brachycephalic dogs with pulmonary stenosis. An ECG-gated CT angiographic protocol was used to image coronary artery anatomy in nine brachycephalic dogs with pulmonary stenosis. Images were assessed for quality as well as coronary artery morphology by one veterinary radiologist, one veterinary cardiologist and one veterinary radiology resident. All nine dogs had good to excellent image quality. Coronary artery anomalies were identified in three of nine dogs: one R2A anomaly, one L2A anomaly and one L2C anomaly. Two dogs were assessed to be poor balloon valvuloplasty candidates based on CT angiographic images. Coronary artery morphology was not confirmed via postmortem examination in all patients. ECG-gated CT angiography is a minimally invasive imaging modality capable of diagnosing various coronary artery anomalies in brachycephalic dogs with pulmonary stenosis and aiding in the determination of patient candidacy for balloon valvuloplasty.

Transient ischemic attack in the vertebrobasilar vascular territory as a cause of isolated vertigo

Transient ischemic attack (TIA) in the vertebrobasilar vascular territory (VB) causes difficulties in diagnosis when it manifests only with vestibular symptoms. Issues relating to the differential diagnosis of TIA are discussed, awareness of which is necessary for the selection of informative methods of examination and the prescription of effective stroke prevention in patients with an episode of isolated dizziness. The likelihood of TIA as the cause of dizziness is increased by the patients' high cardiovascular risk, the presence of atrial fibrillation, severe instability during an attack, and head and/or neck pain. If a TIA in VB is suspected, it is advisable to perform a minimal instrumental examination, including computed tomography (CT) of the brain and CT angiography or diffusion-weighted magnetic resonance imaging (MRI) and MRI angiography. In case of doubt, additional information can be obtained by a perfusion CT or MRI as well as a post-contrast MRI. When interpreting the results of these methods of examination, their limitations in terms of application time and resolution should be taken into account.

Deep Circumflex Iliac Artery-vascularized Iliac Bone Graft for Femoral Head Osteonecrosis: Computed Tomography Anatomical Study.

Deep circumflex iliac artery (DCIA)-vascularized iliac graft transposition is a method for treating femoral head osteonecrosis but with inconsistent efficacy. We aim to improve the method of this surgery by recommending the optimal location of the iliac pedicle to satisfy the vascular length for transposition and the blood supply of the vascularized iliac graft. The DCIA and its surrounding tissues were assessed on computed tomography angiography images for 100 sides (left and right) of 50 patients. The length of the vascular pedicle required for transposition and the length of the pedicle at different iliac spine positions were compared. The diameter and cross-sectional area of the DCIA and the distance between the DCIA and iliac spine were measured at different points to assess blood supply. We also compared differences in sex and left-right position. The diameter and cross-sectional area of the DCIA gradually decreased after crossing the anterior superior iliac spine (ASIS), and it approached the iliac bone. However, when the DCIA was 4 cm behind the ASIS (54 sides, 54%), it coursed posteriorly and superiorly away from the iliac spine. The vascular length of the pedicle was insufficient to transpose the vascularized iliac graft to the desired position when it was within 1 cm of the ASIS. The vascular length requirement was satisfied, and the blood supply was sufficient when the pedicle was positioned at 2 or 3 cm. To obtain a satisfactory pedicle length and sufficient blood supply, the DCIA pedicle of the vascularized iliac graft should be placed 2 to 3 cm behind the ASIS. The dissection of DCIA has slight differences in sex and left-right position due to anatomical differences.

Unsuspected Limitations of 3D Printed Model in Planning of Complex Aortic Aneurysm Endovascular Treatment.

Static 3-dimensional (3D) printing became attractive for operative planning in cases that involve difficult anatomy. An interactive (low cost, fast) 3D print allowing deliberate surgical practice can be used to improve interventional simulation and planning. Endovascular treatment of complex aortic aneurysms is technically challenging, especially in case of narrow aortic lumen or significant aortic angulation (hostile anatomy). The risk of complications such as graft kinking and target vessel occlusion is difficult to assess based solely on traditional software measuring methods and remain highly dependent on surgeon skills and expertise. A patient with juxtarenal AAA with hostile anatomy had a 3-dimensional printed model constructed preoperatively according to computed tomography images. Endovascular graft implantation in the 3D printed aorta with a standard T-Branch Cook (Cook® Medical, Bloomington, IN, USA) was performed preoperatively in the simulation laboratory enabling optimized feasibility, surgical planning and intraoperative decision making. The 3D printed aortic model proved to be radio-opaque and allowed simulation of branched endovascular aortic repair (BREVAR). The assessment of intervention feasibility, as well as optimal branch position and orientation was found to be useful for surgeon confidence and the actual intervention in the patient. There was a remarkable agreement between the 3D printed model and both CT and X-ray angiographic images. Although the technical success was achieved as planned, a previously deployed renal stent caused unexpected difficulty in advancing the renal stent, which was not observed in the 3D model simulation. The 3D printed aortic models can be useful for determining feasibility, optimizing planning and intraoperative decision making in hostile anatomy improving the outcome. Despite already offering satisfying accuracy at present, further advancements could enhance the 3D model capability to replicate minor anatomical deformities and variations in tissue density.

Hemodynamic Study of Cerebral Arteriovenous Malformation: Newtonian and Non-Newtonian Blood Flow

ObjectiveArteriovenous malformation is a disease of the vascular system that occurs mainly in the cerebral arteries and spine. Numerical simulation as a powerful method is used to investigate the Cerebral Arteriovenous Malformation hemodynamic after occlusion of the abnormality step by step by embolization. MethodsThe CT Angiographic imaging data of two patients are used and a geometric model is extracted by the Mimics software. Numerical simulation of blood flow is performed in both Newtonian and non-Newtonian models. The Navier–Stokes, and continuity governing equations are solved by finite element method using the COMSOL Multiphysics software (the commercial computational fluid dynamics (CFD) simulation software). To validate the numerical results, the real data on blood flow rate in the feeding artery and draining veins are used, as well as angiographic images at different times. ResultsRegarding the comparison of pressure contours for different occlusions of 0, 30, 50, and 90%, by increasing the amount of occlusion in the nidus, there is an increase in the blood pressure. Regarding the comparison of the blood flow velocity in the feeding artery, draining veins, and inside the AVM nidus for Newtonian and non-Newtonian models, there is a significant difference between these two simulations in vessels with smaller dimensions (such as vessels inside the nidus). ConclusionBy increasing the amount of nidus occlusion, the blood pressure is increased, so the blood supply process is better. According to a significant difference between the Newtonian and non-Newtonian simulations in vessels with smaller dimensions (such as vessels inside the nidus), therefore, non-Newtonian simulation should be done for different occlusions of 30, 50, and 90%.