Detection Of Aneurysms Research Articles

Risk stratification of delayed causative aneurysm detection and long-term outcome in angiographically negative spontaneous subarachnoid haemorrhage.

The risk factors of aetiology and poor outcome in angiographically negative subarachnoid haemorrhage (anSAH) were unclearly. The authors performed a retrospective review of a prospectively maintained database for anSAH patients between 2014 and 2018. AnSAH was defined as SAH presents in CT with no underlying vascular abnormality on initial digital subtraction angiography (DSA) within 72 hours of admission. Baseline and follow-up information, including medical history, bleeding pattern (perimesencephalic angiogram-negative SAH (PAN-SAH) and non-PAN-negative SAH (NPAN-SAH)), modified Fisher Scale (mFS), Glasgow Coma Score (GCS), Hunt-Hess grade, repeated imaging and causative vascular lesions and follow-up modified Rankin Scale (mRS) were reviewed. Poor outcome was defined as mRS scored 3-6 at last clinical follow-up. Among 303 enrolled patients, 272 patients underwent at least once repeated imaging examination (median follow-up time, 3.0 months). Twenty-one (7.7%) aneurysms were detected. Multivariate logistic analysis showed that NPAN-SAH and mFS 3-4 were associated with a high rate of aneurysm detection in anSAH patients. Based on risk stratification, the aneurysm detection rate in the high-risk group (both NPAN-SAH and mFS 3-4) was as high as 20.370 per 100 person-years. Furthermore, of 251 non-aneurysm anSAH patients, after a total follow-up time of 1265.83 patient-years, poor outcome occurred in 18 (7.2%) patients. Multivariate Cox analysis found that NPAN-SAH and GCS 3-12 were associated with a high rate of poor outcome of anSAH. The cumulative 5-year incidence rate for poor outcome in the non-aneurysm anSAH patients in the high-risk group (both NPAN-SAH and GCS 3-12) was as high as 75.302 per 100 person-years. Even in anSAH confirmed by initial DSA, patients with NPAN-SAH and mFS 3-4 should be monitored for delayed causative aneurysm detection, meanwhile in non-aneurysm anSAH patients, NPAN-SAH and initial functional impairment are associated with poor prognosis.

CACTUSS: Common Anatomical CT-US Space for US examinations

Purpose:The detection and treatment of abdominal aortic aneurysm (AAA), a vascular disorder with life-threatening consequences, is challenging due to its lack of symptoms until it reaches a critical size. Abdominal ultrasound (US) is utilized for diagnosis; however, its inherent low image quality and reliance on operator expertise make computed tomography (CT) the preferred choice for monitoring and treatment. Moreover, CT datasets have been effectively used for training deep neural networks for aorta segmentation. In this work, we demonstrate how leveraging CT labels can be used to improve segmentation in ultrasound and hence save manual annotations.Methods:We introduce CACTUSS: a common anatomical CT-US space that inherits properties from both CT and ultrasound modalities to produce an image in intermediate representation (IR) space. CACTUSS acts as a virtual third modality between CT and US to address the scarcity of annotated ultrasound training data. The generation of IR images is facilitated by re-parametrizing a physics-based US simulator. In CACTUSS we use IR images as training data for ultrasound segmentation, eliminating the need for manual labeling. In addition, an image-to-image translation network is employed for the model’s application on real B-modes.Results:The model’s performance is evaluated quantitatively for the task of aorta segmentation by comparison against a fully supervised method in terms of Dice Score and diagnostic metrics. CACTUSS outperforms the fully supervised network in segmentation and meets clinical requirements for AAA screening and diagnosis.Conclusion:CACTUSS provides a promising approach to improve US segmentation accuracy by leveraging CT labels, reducing the need for manual annotations. We generate IRs that inherit properties from both modalities while preserving the anatomical structure and are optimized for the task of aorta segmentation. Future work involves integrating CACTUSS into robotic ultrasound platforms for automated screening and conducting clinical feasibility studies.

Micro aneurysm detection using optimized residual-based temporal attention Convolutional Neural Network with Inception-V3 transfer learning.

In this manuscript micro aneurysm detection using residual-based temporal attention Convolutional Neural Network (CNN) with Inception-V3 transfer learning optimized with equilibrium optimization algorithm (MA-RTCNN-Inception V3-EOA) is proposed. The proposed research work contains four phases: (1) pre-processing, (2) segmentation, (3) post-processing, and (4) classification. At first, guided box filtering for contrast enhancement and background exclusion of input image. The proposed MA-RTCNN-Inception V3-EOA based classification framework is implemented in MATLAB using several performances evaluating metrics like precision, sensitivity, f-measure, specificity, accuracy, classification error rate, and Matthews's correlation coefficient and RoC analysis. The experimental outcome demonstrates that the proposed method provides 23.56%, 14.99%, and 21.37% higher accuracy and 31.26%, 57.69%, and 21.14% minimum classification error rate compared to existing methods, such as diabetic retinopathy identification utilizing prognosis of micro aneurysm and early diagnosis for non-proliferative diabetic retinopathy depending on deep learning approaches (DRD-CNN-NPDR), a magnified adaptive feature pyramid network for automatic micro aneurysms identification (MAFPN-AMD-MAFP-Net) respectively. RESEARCH HIGHLIGHTS: Micro aneurysm detection using residual-based temporal attention Convolutional Neural Network (CNN) is proposed. To get rid of the retina background, guided box filtering is applied. COAT is used for segmenting the images into smaller parts RTCNN is used for accurate micro aneurysms disease classification. RT-CNN algorithm successfully identifies the micro aneurysms using EOA.

Building a Synthetic Vascular Model: Evaluation in an Intracranial Aneurysms Detection Scenario.

We hereby present a full synthetic model, able to mimic the various constituents of the cerebral vascular tree, including the cerebral arteries, bifurcations and intracranial aneurysms. This model intends to provide a substantial dataset of brain arteries which could be used by a 3D convolutional neural network to efficiently detect Intra-Cranial Aneurysms. The cerebral aneurysms most often occur on a particular structure of the vascular tree named the Circle of Willis. Various studies have been conducted to detect and monitor the aneurysms and those based on Deep Learning achieve the best performance. Specifically, in this work, we propose a full synthetic 3D model able to mimic the brain vasculature as acquired by Magnetic Resonance Angiography, Time Of Flight principle. Among the various MRI modalities, this latter allows for a good rendering of the blood vessels and is non-invasive. Our model has been designed to simultaneously mimic the arteries' geometry, the aneurysm shape, and the background noise. The vascular tree geometry is modeled thanks to an interpolation with 3D Spline functions, and the statistical properties of the background noise is collected from angiography acquisitions and reproduced within the model. In this work, we thoroughly describe the synthetic vasculature model, we build up a neural network designed for aneurysm segmentation and detection, finally, we carry out an in-depth evaluation of the performance gap gained thanks to the synthetic model data augmentation.

Implicit screening of abdominal aortic aneurysms among veterans using lumbar spine MRI

BackgroundEarly detection of abdominal aortic aneurysms (AAAs) is critical given the high morbidity and mortality of a ruptured aneurysm. Screening ultrasound is recommended for men 65 and 75 years of age with a smoking history. However, studies have shown that the rate of ultrasound screening is low and that implicit AAA screening by abdominal imaging studies that were not originally intended for AAA screening can play a major role in AAA detection. ObjectiveThe main objective was to evaluate the role of lumbar spine MRIs as an implicit AAA screening study by assessing the detection rate of AAAs in a broader cohort of veterans that included screening and non-screening populations. Methods4085 consecutive lumbar spine MRIs from our institution between 2/2020 and 9/2023 were retrospectively reviewed. Each study was labeled AAA present, AAA not present, or indeterminate by radiologists. The correlation between the presence of AAAs and cardiovascular risk factors was assessed using multinomial logistic regression. ResultsAAAs were present in 89 studies (2.2 %) from 80 patients (mean age 75.8 (56–93), M:F 10:0) and absent in 3935 cases (96.3 %) from 3310 patients (mean age 61.7 (19–100), M:F 9:1). Indeterminate cases (n = 61, 1.5 %) were mainly due to incomplete visualization (70.5 %). Mean AAA size was 3.6 cm with most AAAs (n = 43) smaller than 3.5 cm. Sixteen AAAs were 3.5–3.9 cm, 16 between 4 and 4.9cm, and 6 between 5 and 5.9 cm. Artifact precluded measurements in 8 cases. Among the AAA-positive cases, 20 had no prior documentation of AAA. Twenty-one patients with AAAs would not have met the criteria for the routine AAA screening ultrasound. Higher rates of hypertension, hyperlipidemia, and smoking were observed for the AAA cohort at 78.8 % (OR 2.037, CI 1.160–3.576, P = .013), 82.5 % (2.808, 1.543-5.110, P < .001), and 75 % (3.340, 1.979–5.638, P < .001), respectively, compared to the matched no-AAA cohort (58.2 %, 57.6 %, and 50.8 %; n = 2055). Conclusion: Lumbar spine MRI is a valid modality for implicit screening of AAAs. Clinical impactThose interpreting lumbar spine MRIs should be vigilant about assessing for AAAs, especially in men with a history of hypertension, hyperlipidemia, or tobacco smoking.

Prediction of aneurysmal subarachnoid hemorrhage in comparison with other stroke types using routine care data.

Aneurysmal subarachnoid hemorrhage (aSAH) can be prevented by early detection and treatment of intracranial aneurysms in high-risk individuals. We investigated whether individuals at high risk of aSAH in the general population can be identified by developing an aSAH prediction model with electronic health records (EHR) data. To assess the aSAH model's relative performance, we additionally developed prediction models for acute ischemic stroke (AIS) and intracerebral hemorrhage (ICH) and compared the discriminative performance of the models. We included individuals aged ≥35 years without history of stroke from a Dutch routine care database (years 2007-2020) and defined outcomes aSAH, AIS and ICH using International Classification of Diseases (ICD) codes. Potential predictors included sociodemographic data, diagnoses, medications, and blood measurements. We cross-validated a Cox proportional hazards model with an elastic net penalty on derivation cohorts and reported the c-statistic and 10-year calibration on validation cohorts. We examined 1,040,855 individuals (mean age 54.6 years, 50.9% women) for a total of 10,173,170 person-years (median 11 years). 17,465 stroke events occurred during follow-up: 723 aSAH, 14,659 AIS, and 2,083 ICH. The aSAH model's c-statistic was 0.61 (95%CI 0.57-0.65), which was lower than the c-statistic of the AIS (0.77, 95%CI 0.77-0.78) and ICH models (0.77, 95%CI 0.75-0.78). All models were well-calibrated. The aSAH model identified 19 predictors, of which the 10 strongest included age, female sex, population density, socioeconomic status, oral contraceptive use, gastroenterological complaints, obstructive airway medication, epilepsy, childbirth complications, and smoking. Discriminative performance of the aSAH prediction model was moderate, while it was good for the AIS and ICH models. We conclude that it is currently not feasible to accurately identify individuals at increased risk for aSAH using EHR data.

Detection of atrial septal aneurysm on ECG based on machine learning algorithm

Detection of atrial septal aneurysm on ECG based on machine learning algorithm

Automatic Approach for Brain Aneurysm Detection Using Convolutional Neural Networks

The paper introduces an approach for detecting brain aneurysms, a critical medical condition, by utilizing a combination of 3D convolutional neural networks (3DCNNs) and Convolutional Long Short-Term Memory (ConvLSTM). Brain aneurysms pose a significant health risk, and early detection is vital for effective treatment. Traditional methods for aneurysm detection often rely on complex and time-consuming procedures. A radiologist specialist annotates each aneurysm and supports our work with true-ground annotations. From the annotated data, we extract images to train proposed neural networks. The paper experiments with two different types of networks, specifically focusing on 2D convolutional neural networks (2DCNNs), 3D convolutional neural networks (3DCNNs), and Convolutional Long Short-Term Memory (ConvLSTM). Our goal is to create a virtual assistant to improve the search for aneurysm locations, with the aim of further realizing the virtual assistant. Subsequently, a radiologist specialist will confirm or reject the presence of an aneurysm, leading to a reduction in the time spent on the searching process and revealing hidden aneurysms. Our experimental results demonstrate the superior performance of the proposed approach compared to existing methods, showcasing its potential as a valuable tool in clinical settings for early and accurate brain aneurysm detection. This innovative fusion of 3DCNN and LSTM (3DCNN-ConvLSTM) techniques not only improves diagnostic precision but also holds promise for advancing the field of medical image analysis, particularly in the domain of neurovascular diseases. Overall, our research underscores the potential of neural networks for the machine detection of brain aneurysms.

Automated detection of incidental abdominal aortic aneurysms on computed tomography.

To detect and assess abdominal aortic aneurysms (AAAs) on CT in a large asymptomatic adult patient population using fully-automated deep learning software. The abdominal aorta was segmented using a fully-automated deep learning model trained on 66 manually-segmented abdominal CT scans from two datasets. The axial diameters of the segmented aorta were extracted to detect the presence of AAAs-maximum axial aortic diameter greater than 3cm were labeled as AAA positive. The trained system was then externally-validated on CT colonography scans of 9172 asymptomatic outpatients (mean age, 57 years) referred for colorectal cancer screening. Using a previously-validated automated calcified atherosclerotic plaque detector, we correlated abdominal aortic Agatston and volume scores with the presence of AAA. The deep learning software detected AAA on the external validation dataset with a sensitivity, specificity, and AUC of 96%, (95% CI 89%, 100%), 96% (96%, 97%), and 99% (98%, 99%) respectively. The Agatston and volume scores of reported AAA-positive cases were statistically significantly greater than those of reported AAA-negative cases (p<0.0001). Using plaque alone as a AAA detector, at a threshold Agatston score of 2871, the sensitivity and specificity were 84% (73%, 94%) and 87% (86%, 87%), respectively. Fully-automated detection and assessment of AAA on CT is feasible and accurate. There was a strong statistical association between the presence of AAA and the quantity of abdominal aortic calcified atherosclerotic plaque.

Efforts for detection of aortic aneurysms and human resources training for the optimization of their treatment.

To review admissions, interventions and in-hospital mortality associated to Abdominal Aortic Aneurysms (AAA), and to analyze the impact of the introduction of a training program and imaging screening at our institution. Retrospective study where hospitalizations, procedures and mortality secondary to AAA were recorded. The national databases (ND) from the Secretariat of Health were utilized from 2010 to 2020. In-hospital lethality was calculated and compared with the experience at the Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán (INCMNSZ). The statistical analysis was completed with the STATA version 17. According to the ND, 899 (91%) hospital admissions secondary to AAA occurred, while in the INCMNSZ 85 (9%). Most of them belonged to the male gender (68%); 811 (82%) patients underwent open surgical repair, and 173 (18%) to an endovascular exclusion (EVAR), the latter approach was significantly more frequently performed at our institution (p = 0.007). The 30-day hospital mortality was 22.5%; in the ND was 23.9 vs. a 16.4% in the INCMNSZ without significant difference (p = 0.1). AAA remain unrecognized in our country. The introduction of University programs and imaging screening might impact in the early detection, and to reduce the morbidity and mortality associated to emergency procedures.

Routine Abdominal Aortic Examination During Echocardiographic Studies Detects Significant Numbers of Abdominal Aortic Aneurysm and Should be a Part of Routine Echocardiographic Examinations.

Early detection and follow-up of abdominal aortic aneurysm (AAA) are important and can be lifesaving. The goal of this study was to evaluate if routine abdominal aorta screenings during echocardiograms can be helpful in detecting asymptomatic abdominal aortic aneurysm. We retrospective studied consecutive patients who were sent for outpatient routine echocardiograms for various clinical reasons in 2015-2017 until we reached a total of 1000 patients. Starting from the subcostal and then proceeding to the abdomen, a long-axis screening of the abdominal aorta was attempted on all of the patients after the echocardiogram was completed. No patient preparation was given. Imaging began from the subcostal view and proceeded caudally with images obtained every 1-2 cm. Measurement of the aortic diameter was performed from the longitudinal plane using the leading-edge-to-leading-edge method. The age range was 33-96 years with a median age of 72.4. A total of 273 (27.3%) patients did not have an appropriate window to evaluate AAA. Among the remaining 727 screenings, 18 (2.4%) had dilatation of abdominal aorta or AAA. The dilatation and aneurysms ranged between 2.5 and 4.5 cm in size. Abnormal aortic diameters were as follows: 5 (27.7%) were between 2.5 and 2.9 cm, 6 (33.3%) between 3 and 3.4 cm, 1 (5.5%) between 3.5 and 3.9, 5 (27.7%) between 4.0 and 4.4 cm, and 1 (5.5%) between 4.5 and 4.9 cm. Performing routine abdominal aortic examinations during routine echocardiographic exams can detect a significant amount of abdominal aortic aneurysm which can be lifesaving. We suggest adding abdominal aortic assessment to routine echocardiographic examination.

Abstract 12986: Aorta Gene: Polygenic Prediction Improves Detection of Thoracic Aortic Aneurysm

Introduction: Thoracic aortic disease is an important cause of morbidity and mortality in the US, and aortic diameter is a heritable contributor to risk. Question: Could a polygenic prediction of ascending aortic diameter improve detection of aortic aneurysm? Methods: We used deep learning to measure ascending thoracic aortic diameter in 56,556 UK Biobank participants and conducted a genome-wide association study (GWAS) in 39,524. We built a 1.1-million SNP polygenic score using PRScs . In 4,896 non-GWAS participants (“training set”), we built linear models from clinical variables and the polygenic score (“AORTA Gene”) and simpler comparators. The models were assessed in the remaining 4,962 unrelated UK Biobank participants with imaging (“test set”). The models were externally validated in All Of Us participants over the age of 40. Results: In the test set, the AORTA Gene model explained 39.9% (95% CI 37.8-42.0%) of the variance in thoracic aortic diameter compared to 29.2% for the clinical model (95% CI 27.1-31.4%). For diameter ≥ 4cm, the area under the receiver operating characteristic curve (AUROC) was 0.834 for AORTA Gene, superior to the clinical model (0.765, P=7.3E-10). Thresholding at the top 10% of the score, AORTA Gene had a sensitivity of 52% and a specificity of 91.2% (F1: 0.197) for detecting the 113 participants with diameter ≥ 4. For the clinical score, the respective values were 36.3%, 90.9%, and 0.137. In 610 All Of Us participants with genetic data and aortic measurements, the AORTA Gene model had a correlation of 0.591 (95% CI 0.537-0.640) with aortic diameter vs 0.538 (95% CI 0.479-0.592) for the clinical model (P=2.2E-13 against a null hypothesis of the genetics being uninformative beyond clinical factors). It had an AUROC of 0.827 for identifying diameter ≥4cm vs 0.791 for the clinical model (P=7.8E-03). AORTA Gene also had a higher AUROC for prevalent thoracic aneurysm diagnoses (0.760 vs 0.739 for the clinical score, N=1,904 cases, P=2.4E-16) and incident diagnoses (0.748 vs 0.729, N=1,632 events, P=9.5E-10). Conclusions: Genetic information improved prediction of thoracic aortic diameter and aneurysm when added to clinical risk factors. Larger and more diverse samples will be needed to develop more powerful scores.

Abstract 18814: Glycoprotein VI is Critical for the Detection and Progression of Abdominal Aortic Aneurysms

Objective: Abdominal aortic aneurysms (AAA) frequently feature the formation of a nonocclusive intraluminal thrombus (ILT) along the site of aortic dilation. Platelets are known to maintain hemostasis and propagate thrombosis through several redundant activation mechanisms, yet the role of platelet activation in the pathogenesis of AAA associated ILT is still poorly understand. Thus, we sought to investigate how platelet activation via the glycoprotein VI (GPVI) platelet receptor impacts the pathogenesis of AAA. Methods and Results: RNA sequencing of age-matched control human aortic tissue, AAA aortic tissue, and AAA ILT demonstrates that platelet transcripts comprised 25% of significantly enriched genes AAA thrombus compared to AAA tissue and control aortic wall, with GPVI increased by 9.1 Log2-FC (P = 3.21E -08 ). Examination of soluble GPVI (sGPVI), a specific marker of platelet activity, in 2 independent AAA patient cohorts is predictive of AAA case status, associates strongly with aneurysm growth rate, and better predicts the presence of a fast-growing AAA when compared to D-dimer. To determine if GPVI blockade ameliorates AAA progression, mice were subjected to either the angiotensin II (AngII) infusion or the topical elastase models of aneurysm formation. Following model initiation, aortic diameter was monitored by weekly ultrasounds. After two weeks, mice which developed an aneurysm (defined as an aortic diameter of &gt;1.2mm) were randomized into control (IgG-50μg) or treatment (JAQ1-50μg, monoclonal GPVI antibody) groups. Treatment with JAQ1 blunted GPVI mediated platelet activation, attenuated aneurysm progression, and increased survival in both murine AAA models versus control. Conclusions: The highly specific platelet activation biomarker soluble glycoprotein VI strongly predicts both AAA case status and growth rate with potential use in identifying high risk patients. Specific blockade of GPVI-mediated platelet activation attenuates progression of established aneurysms in two independent murine models. Thus, our work demonstrates that specific targeting of GPVI to blunt platelet activation in AAA could be a potential therapeutic for a pathology with no current pharmacological treatments.

Geometric Deep Learning Using Vascular Surface Meshes for Modality-Independent Unruptured Intracranial Aneurysm Detection.

Early detection of unruptured intracranial aneurysms (UIAs) enables better rupture risk and preventative treatment assessment. UIAs are usually diagnosed on Time-of-Flight Magnetic Resonance Angiographs (TOF-MRA) or contrast-enhanced Computed Tomography Angiographs (CTA). Various automatic voxel-based deep learning UIA detection methods have been developed, but these are limited to a single modality. We propose a modality-independent UIA detection method using a geometric deep learning model with high resolution surface meshes of brain vessels. A mesh convolutional neural network with ResU-Net style architecture was used. UIA detection performance was investigated with different input and pooling mesh resolutions, and including additional edge input features (shape index and curvedness). Both a higher resolution mesh (15,000 edges) and additional curvature edge features improved performance (average sensitivity: 65.6%, false positive count/image (FPC/image): 1.61). UIAs were detected in an independent TOF-MRA test set and a CTA test set with average sensitivity of 52.0% and 48.3% and average FPC/image of 1.04 and 1.05 respectively. We provide modality-independent UIA detection using a deep-learning vascular surface mesh model with comparable performance to state-of-the-art UIA detection methods.

Advancements in Machine Learning for Cerebral Aneurysm Detection: Progress at a Steady Pace

Advancements in Machine Learning for Cerebral Aneurysm Detection: Progress at a Steady Pace

A Systematic Review of Deep-Learning Methods for Intracranial Aneurysm Detection in CT Angiography.

Background: Subarachnoid hemorrhage resulting from cerebral aneurysm rupture is a significant cause of morbidity and mortality. Early identification of aneurysms on Computed Tomography Angiography (CTA), a frequently used modality for this purpose, is crucial, and artificial intelligence (AI)-based algorithms can improve the detection rate and minimize the intra- and inter-rater variability. Thus, a systematic review and meta-analysis were conducted to assess the diagnostic accuracy of deep-learning-based AI algorithms in detecting cerebral aneurysms using CTA. Methods: PubMed (MEDLINE), Embase, and the Cochrane Library were searched from January 2015 to July 2023. Eligibility criteria involved studies using fully automated and semi-automatic deep-learning algorithms for detecting cerebral aneurysms on the CTA modality. Eligible studies were assessed using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines and the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. A diagnostic accuracy meta-analysis was conducted to estimate pooled lesion-level sensitivity, size-dependent lesion-level sensitivity, patient-level specificity, and the number of false positives per image. An enhanced FROC curve was utilized to facilitate comparisons between the studies. Results: Fifteen eligible studies were assessed. The findings indicated that the methods exhibited high pooled sensitivity (0.87, 95% confidence interval: 0.835 to 0.91) in detecting intracranial aneurysms at the lesion level. Patient-level sensitivity was not reported due to the lack of a unified patient-level sensitivity definition. Only five studies involved a control group (healthy subjects), whereas two provided information on detection specificity. Moreover, the analysis of size-dependent sensitivity reported in eight studies revealed that the average sensitivity for small aneurysms (<3 mm) was rather low (0.56). Conclusions: The studies included in the analysis exhibited a high level of accuracy in detecting intracranial aneurysms larger than 3 mm in size. Nonetheless, there is a notable gap that necessitates increased attention and research focus on the detection of smaller aneurysms, the use of a common test dataset, and an evaluation of a consistent set of performance metrics.

Self-navigated coronary MR angiography for coronary aneurysm detection in Kawasaki disease at 3T: comparison with conventional diaphragm-navigated coronary MR angiography.

To assess the scan time, image quality, and diagnostic performance of self-navigated coronary MR angiography (SN-CMRA) for coronary aneurysm (CAA) detection in Kawasaki disease (KD) patients and compare it with diaphragm-navigated CMRA (DN-CMRA). SN-CMRA and DN-CMRA were performed on 76 pediatric patients with KD (48 males, 6.75 ± 3.59 years). Thirty-three of whom underwent coronary CT angiography (CCTA)/invasive coronary angiography (ICA). The scan time and qualitative and quantitative image quality assessment were compared between the two sequences. The diagnostic performance for CAA detection by the two approaches using CCTA/ICA as the reference standard was compared on per-patient, per-vessel, and per-segment basis. The scan time of SN-CMRA was significantly shorter than that of DN-CMRA (7.49 ± 2.31 min vs. 10.03 ± 4.47 min, p< 0.001). There was no difference in overall and segmental image quality to reach the clinical diagnostic criteria between the two sequences (all p> 0.05). No significant difference in vessel length of the three main coronary arteries was found between the two approaches (all p> 0.05). Moreover, SN-CMRA showed no difference from DN-CMRA in contrast ratio of blood-myocardium (1.25 (interquartile range [IQR], 1.06 to 1.51) vs. 1.18 (IQR, 0.95 to 1.64), p= 0.706). There was no difference in the diagnostic accuracy of SN-CMRA and DN-CMRA for CAA detection on per-patient, per-vessel, or per-segment basis (all p> 0.05). SN-CMRA at 3T showed reliable diagnostic performance and application value for CAA detection in children with KD. Compared with DN-CMRA, SN-CMRA can simplify the scanning procedure and shorten the scan time, achieving comparable image quality and diagnostic accuracy. Coronary aneurysm in children with Kawasaki disease (KD) can be detected by self-navigated coronary MR angiography (CMRA) non-invasively and without radiation, achieving comparable image quality and diagnostic performance as diaphragm-navigated CMRA while shortening scanning time. It can provide reference for risk stratification and treatment management of KD. • Evaluating the size of coronary aneurysm is important for risk stratification and treatment of Kawasaki disease. • Self-navigated coronary MR angiography (SN-CMRA) shortens scan time and achieves comparable image quality and diagnostic performance compared with diaphragm-navigated coronary MR angiography. • SN-CMRA can evaluate coronary aneurysm non-invasively and without radiation, providing information for risk stratification and treatment.

A deep learning framework for intracranial aneurysms automatic segmentation and detection on magnetic resonance T1 images.

To design a deep learning-based framework for automatic segmentation and detection of intracranial aneurysms (IAs) on magnetic resonance T1 images and test the robustness and performance of framework. A retrospective diagnostic study was conducted based on 159 IAs from 136 patients who underwent the T1 images. Among them, 127 cases were randomly selected for training and validation, and 32 cases were used to assess the accuracy and consistency of our algorithm. We developed and assembled three convolutional neural networks for the segmentation and detection of IAs. The segmentation and detection performance of the model were compared with the ground truth, and various metrics were calculated at the voxel level, IAs level, and patient level to show the performance of our framework. Our assembled model achieved overall Dice, voxel-level sensitivity, specificity, balanced accuracy, and F1 score of 0.802, 0.874, 0.9998, 0.937, and 0.802, respectively. A coincidence greater than 0.7 between the aneurysms predicted by the model and the ground truth was considered as a true positive. For IAs detection, the sensitivity reached 90.63% with 0.58 false positives per case. The volume of IAs segmented by our model showed a high agreement and consistency with the volume of IAs labeled by experts. The deep learning framework is achievable and robust for IAs segmentation and detection. Our model offers more clinical application opportunities compared to digital subtraction angiography (DSA)-based, CTA-based, and MRA-based methods. Our deep learning framework effectively detects and segments intracranial aneurysms using clinical routine T1 sequences, showing remarkable effectiveness and offering great potential for improving the detection of latent intracranial aneurysms and enabling early identification. •There is no segmentation method based on clinical routine T1 images. Our study shows that the proper deep learning framework can effectively localize the intracranial aneurysms. •The T1-based segmentation and detection method is more universal than other angiography-based detection methods, which can potentially reduce missed diagnoses caused by the absence of angiography images. •The deep learning framework is robust and has the potential to be applied in a clinical setting.

Deep geometric learning for intracranial aneurysm detection: towards expert rater performance

BackgroundEarly detection of intracranial aneurysms (IAs) is crucial for patient outcomes. Typically identified on angiographic scans such as CT angiography (CTA) or MR angiography (MRA), the sensitivity of experts in...

Comparison of Ultra-High-Resolution and Normal-Resolution CT-Angiography for Intracranial Aneurysm Detection in Patients with Subarachnoid Hemorrhage

Ruptured intracranial aneurysms (IAs) are the leading cause for atraumatic subarachnoid hemorrhage. In case of aneurysm rupture, patients may face life-threatening complications and require aneurysm occlusion. Detection of the aneurysm in computed tomography (CT) imaging is therefore essential for patient outcome. This study provides an evaluation of the diagnostic accuracy of Ultra-High-Resolution Computed Tomography Angiography (UHR-CTA) and Normal-Resolution Computed Tomography Angiography (NR-CTA) concerning IA detection and characterization. Consecutive patients with atraumatic subarachnoid hemorrhage who received Digital Subtraction Angiography (DSA) and either UHR-CTA or NR-CTA were retrospectively included. Three readers evaluated CT-Angiography regarding image quality, diagnostic confidence and presence of IAs. Sensitivity and specificity were calculated on patient-level and segment-level with reference standard DSA-imaging. CTA patient radiation exposure (effective dose) was compared. One hundred and eight patients were identified (mean age = 57.8 ± 14.1 years, 65 women). UHR-CTA revealed significantly higher image quality and diagnostic confidence (P < 0.001) for all readers and significantly lower effective dose (P < 0.001). Readers correctly classified ≥55/56 patients on UHR-CTA and ≥44/52 patients on NR-CTA. We noted significantly higher patient-level sensitivity for UHR-CTA compared to NR-CTA for all three readers (reader 1: 41/41 [100%] vs. 28/34 [82%], reader 2: 41/41 [100%] vs. 30/34 [88%], reader 3: 41/41 [100%] vs. 30/34 [88%], P ≤ 0.04). Segment-level analysis also revealed significantly higher sensitivity for UHR-CTA compared to NR-CTA for all three readers (reader 1: 47/49 [96%] vs. 34/45 [76%], reader 2: 47/49 [96%] vs. 37/45 [82%], reader 3: 48/49 [98%] vs. 37/45 [82%], P ≤ 0.04). Specificity was comparable for both techniques. We found Ultra-High-Resolution CT-Angiography to provide higher sensitivity than Normal-Resolution CT-Angiography for the detection of intracranial aneurysms in patients with aneurysmal subarachnoid hemorrhage while improving image quality and reducing patient radiation exposure.