Time-of-flight Magnetic Resonance Angiography Images Research Articles

Synthesizing 4D Magnetic Resonance Angiography From 3D Time-of-Flight Using Deep Learning: A Feasibility Study.

Objective and background This study aimed to develop a deep convolutional neural network (DCNN) model capable of generating synthetic 4D magnetic resonance angiography (MRA) from 3D time-of-flight (TOF) images, allowing estimation of temporal changes in arterial flow. TOF MRA provides static information about arterial structures through maximum intensity projection (MIP) processing, but it does not capture the dynamic information of contrast agent circulation, which is lost during MIP processing. Considering the principles of TOF, it is hypothesized that dynamic information about arterial blood flow is latent within TOF signals. Although arterial spin labeling (ASL) can extract dynamic arterial information, ASL MRA has drawbacks, such as longer imaging times and lower spatial resolution than TOF MRA. This study's primary aim is to extend the utility of TOF MRA by training a machine-learning model on paired TOF and ASL data to extract latent dynamic information from TOF signals. Methods A DCNN combining a modified U-Net and a long-short-term memory (LSTM) network was trained on a dataset of 13 subjects (11 menand two women, aged 42-77 years) using paired 3D TOF MRA and 4D ASL MRA images. Subjects had no history of cerebral vessel occlusion or significant stenosis. The dataset was acquired using a 3T MRI system with a 32-channel head coil. Preprocessing involved resampling and intensity normalization of TOF and ASL images, followed by data augmentation and arterial mask generation. The model learned to extract flow information from TOF images and generate 8-phase 4D MRA images. The precision of flow estimation was evaluated using the coefficient of determination (R²) and Bland-Altman analysis. A board-certified neuroradiologist validated the quality of the images and the absence of significant stenosis in the major cerebral arteries. Results The generated 4D MRA images closely resembled the ground-truth ASL MRA data, with R² values of 0.92, 0.85, and 0.84 for the internal carotid artery (ICA), proximal middle cerebral artery (MCA), and distal MCA, respectively. Bland-Altman analysis revealed a systematic error of -0.06, with 95% agreement limits ranging from -0.18 to 0.12. Additionally, the model successfully identified flow abnormalities in a subject with left MCA stenosis, displaying a delayed peak and subsequent flattening distal to the stenosis, indicative of reduced blood flow. Visualization of the predicted arterial flow overlaid on the original TOF MRA images highlighted the spatial progression and dynamics of the flow. Conclusions The DCNN model effectively generated synthetic 4D MRA images from TOF images, demonstrating its potential to estimate temporal changes in arterial flow accurately. This non-invasive technique offers a promising alternative to conventional methods for visualizing and evaluating healthy and pathological flow dynamics. It has significant potential to improve the diagnosis and treatment of cerebrovascular diseases by providing detailed temporal flow information without the need for contrast agents or invasive procedures. The practical implementation of this model could enable the extraction of dynamic cerebral blood flow information from routine brain MRI examinations, contributing to the early diagnosis and management of cerebrovascular disorders.

Deep learning-based platform performs high detection sensitivity of intracranial aneurysms in 3D brain TOF-MRA: An external clinical validation study

PurposeTo evaluate the diagnostic efficacy of a developed artificial intelligence (AI) platform incorporating deep learning algorithms for the automated detection of intracranial aneurysms in time-of-flight (TOF) magnetic resonance angiography (MRA). MethodThis retrospective study encompassed 3D TOF MRA images acquired between January 2023 and June 2023, aiming to validate the presence of intracranial aneurysms via our developed AI platform. The manual segmentation results by experienced neuroradiologists served as the “gold standard”. Following annotation of MRA images by neuroradiologists using InferScholar software, the AI platform conducted automatic segmentation of intracranial aneurysms. Various metrics including accuracy (ACC), balanced ACC, area under the curve (AUC), sensitivity (SE), specificity (SP), F1 score, Brier Score, and Net Benefit were utilized to evaluate the generalization of AI platform. Comparison of intracranial aneurysm identification performance was conducted between the AI platform and six radiologists with experience ranging from 3 to 12 years in interpreting MR images. Additionally, a comparative analysis was carried out between radiologists’ detection performance based on independent visual diagnosis and AI-assisted diagnosis. Subgroup analyses were also performed based on the size and location of the aneurysms to explore factors impacting aneurysm detectability. Results510 patients were enrolled including 215 patients (42.16 %) with intracranial aneurysms and 295 patients (57.84 %) without aneurysms. Compared with six radiologists, the AI platform showed competitive discrimination power (AUC, 0.96), acceptable calibration (Brier Score loss, 0.08), and clinical utility (Net Benefit, 86.96 %). The AI platform demonstrated superior performance in detecting aneurysms with an overall SE, SP, ACC, balanced ACC, and F1 score of 91.63 %, 92.20 %, 91.96 %, 91.92 %, and 90.57 % respectively, outperforming the detectability of the two resident radiologists. For subgroup analysis based on aneurysm size and location, we observed that the SE of the AI platform for identifying tiny (diameter＜3mm), small (3 mm ≤ diameter＜5mm), medium (5 mm ≤ diameter＜7mm) and large aneurysms (diameter ≥ 7 mm) was 87.80 %, 93.14 %, 95.45 %, and 100 %, respectively. Furthermore, the SE for detecting aneurysms in the anterior circulation was higher than that in the posterior circulation. Utilizing the AI assistance, six radiologists (i.e., two residents, two attendings and two professors) achieved statistically significant improvements in mean SE (residents: 71.40 % vs. 88.37 %; attendings: 82.79 % vs. 93.26 %; professors: 90.07 % vs. 97.44 %; P < 0.05) and ACC (residents: 85.29 % vs. 94.12 %; attendings: 91.76 % vs. 97.06 %; professors: 95.29 % vs. 98.82 %; P < 0.05) while no statistically significant change was observed in SP. Overall, radiologists’ mean SE increased by 11.40 %, mean SP increased by 1.86 %, and mean ACC increased by 5.88 %, mean balanced ACC promoted by 6.63 %, mean F1 score grew by 7.89 %, and Net Benefit rose by 12.52 %, with a concurrent decrease in mean Brier score declined by 0.06. ConclusionsThe deep learning algorithms implemented in the AI platform effectively detected intracranial aneurysms on TOF-MRA and notably enhanced the diagnostic capabilities of radiologists. This indicates that the AI-based auxiliary diagnosis model can provide dependable and precise prediction to improve the diagnostic capacity of radiologists.

COSTA: A Multi-center TOF-MRA Dataset and A Style Self-Consistency Network for Cerebrovascular Segmentation.

Time-of-flight magnetic resonance angiography (TOF-MRA) is the least invasive and ionizing radiation-free approach for cerebrovascular imaging, but variations in imaging artifacts across different clinical centers and imaging vendors result in inter-site and inter-vendor heterogeneity, making its accurate and robust cerebrovascular segmentation challenging. Moreover, the limited availability and quality of annotated data pose further challenges for segmentation methods to generalize well to unseen datasets. In this paper, we construct the largest and most diverse TOF-MRA dataset (COSTA) from 8 individual imaging centers, with all the volumes manually annotated. Then we propose a novel network for cerebrovascular segmentation, namely CESAR, with the ability to tackle feature granularity and image style heterogeneity issues. Specifically, a coarse-to-fine architecture is implemented to refine cerebrovascular segmentation in an iterative manner. An automatic feature selection module is proposed to selectively fuse global long-range dependencies and local contextual information of cerebrovascular structures. A style self-consistency loss is then introduced to explicitly align diverse styles of TOF-MRA images to a standardized one. Extensive experimental results on the COSTA dataset demonstrate the effectiveness of our CESAR network against state-of-the-art methods. We have made 6 subsets of COSTA with the source code online available, in order to promote relevant research in the community.

Age‐ and Sex‐Related Morphological Changes in Cerebral Blood Vessels: a 7T TOF MRA Study

AbstractBackgroundIn this study we evaluated age‐ and sex‐associated morphological changes of cerebral blood vessels using 7T Time‐of‐flight (TOF) magnetic resonance angiography (MRA).MethodOur study consisted of 25 cognitively normal older adults (20 female, mean age 68.2 years). Whole‐Brain TOF MRA images were collected on a 7T Siemens scanner at the University of Pittsburgh (slice number = 354, voxel size = 0.38*0.38*0.38 mm, 12 mins). An iterative vessel segmentation algorithm, VesselMapper, was used to segment blood vessels on TOF MRA images (Li et al., 2023). For each subject, vessels are further divided into large and small vessels by median split based on vessel diameters. Mean tortuosity of large vessels, small vessels and overall vessels was computed. Linear regression analyses were performed to evaluate the main effects of age and sex, and the age*sex interaction on vessel tortuosity and diameter.ResultAs shown in Figure 1, we found age‐related increase in mean vessel tortuosity (greater age is associated with greater tortuosity, p&lt;0.02). This age‐related difference is driven by small vessels (p = 0.002) rather than large vessels (p = 0.7). We also found that age is positively associated with mean voxel tortuosity (p&lt;0.001, black line in Figure 2). Furthermore, women showed greater age‐related increase in mean arterial tortuosity, compared to men (age*sex interaction p = 0.04, Figure 2).ConclusionOur findings validated that morphological markers of cerebral blood vessels are sensitive to aging. Small vessels in the brain are particularly vulnerable. Further, compared to men, older women are at a higher risk for cerebral small vessel disease, which may contribute to elevated SVD‐related AD risk in women.Limitation: The study has an unbalanced sample (more women than men) and a relatively modest sample size (N = 25). A cross‐sectional instead of longitudinal design was used in this analysis. Future study with a large balanced sample and a longitudinal design are required to confirm our findings.Reference: Li, Jiatai, et al. “VesselMapper ‐ A Robust Vessel Segmentation Algorithm for 3D Images.” Annual Meeting of the Organization for Human Brain Mapping. 2023

Age‐ and Sex‐Related Morphological Changes in Cerebral Blood Vessels: a 7T TOF MRA Study

AbstractBackgroundIn this study we evaluated age‐ and sex‐associated morphological changes of cerebral blood vessels using 7T Time‐of‐flight (TOF) magnetic resonance angiography (MRA).MethodOur study consisted of 25 cognitively normal older adults (20 female, mean age 68.2 years). Whole‐Brain TOF MRA images were collected on a 7T Siemens scanner at the University of Pittsburgh (slice number = 354, voxel size = 0.38*0.38*0.38 mm, 12 mins). An iterative vessel segmentation algorithm, VesselMapper, was used to segment blood vessels on TOF MRA images (Li et al., 2023). For each subject, vessels are further divided into large and small vessels by median split based on vessel diameters. Mean tortuosity of large vessels, small vessels and overall vessels was computed. Linear regression analyses were performed to evaluate the main effects of age and sex, and the age*sex interaction on vessel tortuosity and diameter.ResultAs shown in Figure 1, we found age‐related increase in mean vessel tortuosity (greater age is associated with greater tortuosity, p&lt;0.02). This age‐related difference is driven by small vessels (p = 0.002) rather than large vessels (p = 0.7). We also found that age is positively associated with mean voxel tortuosity (p&lt;0.001, black line in Figure 2). Furthermore, women showed greater age‐related increase in mean arterial tortuosity, compared to men (age*sex interaction p = 0.04, Figure 2).ConclusionOur findings validated that morphological markers of cerebral blood vessels are sensitive to aging. Small vessels in the brain are particularly vulnerable. Further, compared to men, older women are at a higher risk for cerebral small vessel disease, which may contribute to elevated SVD‐related AD risk in women. Limitation: The study has an unbalanced sample (more women than men) and a relatively modest sample size (N = 25). A cross‐sectional instead of longitudinal design was used in this analysis. Future study with a large balanced sample and a longitudinal design are required to confirm our findings. Reference: Li, Jiatai, et al. “VesselMapper ‐ A Robust Vessel Segmentation Algorithm for 3D Images.” Annual Meeting of the Organization for Human Brain Mapping. 2023

Integration- and separation-aware adversarial model for cerebrovascular segmentation from TOF-MRA

PurposeCerebrovascular segmentation from time-of-flight magnetic resonance angiography (TOF-MRA) is important but challenging for the simulation and measurement of cerebrovascular diseases. Recently, deep learning has promoted the rapid development of cerebrovascular segmentation. However, model optimization relies on voxel or regional punishment and lacks global awareness and interpretation from the texture and edge. To overcome the limitations of the existing methods, we propose a new cerebrovascular segmentation method to obtain more refined structures. MethodsIn this paper, we propose a new adversarial model that achieves segmentation using segmentation model and filters the results using discriminator. Considering the sample imbalance in cerebrovascular imaging, we separated the TOF-MRA images and utilized high- and low-frequency images to enhance the texture and edge representation. The encoder weight sharing from the segmentation model not only saves the model parameters, but also strengthens the integration and separation correlation. Diversified discrimination enhances the robustness and regularization of the model. ResultsThe adversarial model was tested using two cerebrovascular datasets. It scored 82.26% and 73.38%, respectively, ranking first on both datasets. The results show that our method not only outperforms the recent cerebrovascular segmentation model, but also surpasses the common adversarial models. ConclusionOur adversarial model focuses on improving the extraction ability of the model on texture and edge, thereby achieving awareness of the global cerebrovascular topology. Therefore, we obtained an accurate and robust cerebrovascular segmentation. This framework has potential applications in many imaging fields, particularly in the application of sample imbalance. Our code is available at the website https://github.com/MontaEllis/ISA-model.

Radiomics Nomogram Model Based on TOF-MRA Images: A New Effective Method for Predicting Microaneurysms.

To develop a radiomics nomogram model based on time-of-flight magnetic resonance angiography (TOF-MRA) images for preoperative prediction of true microaneurysms. 118 patients with Intracranial Aneurysm Sac (40 positive and 78 negative) were enrolled and allocated to training and validation groups (8:2 ratio). Findings of clinical characteristics and MRA features were analyzed. A radiomics signature was built on the basis of reproducible features by using the least absolute shrinkage and selection operator (LASSO) regression algorithm in the training group. The radiomics nomogram model was constructed by combining clinical risk factors and radiomics signature.In order to compare the classification performance of clinical models, radiomics model and radiomics nomogram model, AUC was used to evaluate them.The performance of the radiomics nomogram model was evaluated by calibration curve and decision curve analysis. Eleven features were selected to develop radiomics model with AUC of 0.875 (95% CI 0.78-0.97), sensitivity of 0.84, and specificity of 0.68. The radiomics model achieved a better diagnostic performance than the clinic model (AUC = 0.75, 95% CI: 0.53-0.97) and even radiologists. The radiomics nomogram model, which combines radiomics signature and clinical risk factors, is effective too (AUC = 0.913, 95% CI: 0.87-0.96). Furthermore, the decision curve analysis demonstrated significantly better net benefit in the radiomics nomogram model. Radiomics features derived from TOF-MRA can reliably be used to build a radiomics nomogram model for effectively differentiating between pseudo microaneurysms and true microaneurysms, and it can provide an objective basis for the selection of clinical treatment plans.

Anatomical labeling of intracranial arteries with deep learning in patients with cerebrovascular disease.

Brain arteries are routinely imaged in the clinical setting by various modalities, e.g., time-of-flight magnetic resonance angiography (TOF-MRA). These imaging techniques have great potential for the diagnosis of cerebrovascular disease, disease progression, and response to treatment. Currently, however, only qualitative assessment is implemented in clinical applications, relying on visual inspection. While manual or semi-automated approaches for quantification exist, such solutions are impractical in the clinical setting as they are time-consuming, involve too many processing steps, and/or neglect image intensity information. In this study, we present a deep learning-based solution for the anatomical labeling of intracranial arteries that utilizes complete information from 3D TOF-MRA images. We adapted and trained a state-of-the-art multi-scale Unet architecture using imaging data of 242 patients with cerebrovascular disease to distinguish 24 arterial segments. The proposed model utilizes vessel-specific information as well as raw image intensity information, and can thus take tissue characteristics into account. Our method yielded a performance of 0.89 macro F1 and 0.90 balanced class accuracy (bAcc) in labeling aggregated segments and 0.80 macro F1 and 0.83 bAcc in labeling detailed arterial segments on average. In particular, a higher F1 score than 0.75 for most arteries of clinical interest for cerebrovascular disease was achieved, with higher than 0.90 F1 scores in the larger, main arteries. Due to minimal pre-processing, simple usability, and fast predictions, our method could be highly applicable in the clinical setting.

Dual U-Net-Based Conditional Generative Adversarial Network for Blood Vessel Segmentation with Reduced Cerebral MR Training Volumes.

Segmenting vessels in brain images is a critical step for many medical interventions and diagnoses of illnesses. Recent advances in artificial intelligence provide better models, achieving a human-like level of expertise in many tasks. In this paper, we present a new approach to segment Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) images, relying on fewer training samples than state-of-the-art methods. We propose a conditional generative adversarial network with an adapted generator based on a concatenated U-Net with a residual U-Net architecture (UUr-cGAN) to carry out blood vessel segmentation in TOF-MRA images, relying on data augmentation to diminish the drawback of having few volumes at disposal for training the model, while preventing overfitting by using regularization techniques. The proposed model achieves 89.52% precision and 87.23% in Dice score on average from the cross-validated experiment for brain blood vessel segmentation tasks, which is similar to other state-of-the-art methods while using considerably fewer training samples. UUr-cGAN extracts important features from small datasets while preventing overfitting compared to other CNN-based methods and still achieve a relatively good performance in image segmentation tasks such as brain blood vessels from TOF-MRA.

Toward Sharing Brain Images: Differentially Private TOF-MRA Images With Segmentation Labels Using Generative Adversarial Networks.

Sharing labeled data is crucial to acquire large datasets for various Deep Learning applications. In medical imaging, this is often not feasible due to privacy regulations. Whereas anonymization would be a solution, standard techniques have been shown to be partially reversible. Here, synthetic data using a Generative Adversarial Network (GAN) with differential privacy guarantees could be a solution to ensure the patient's privacy while maintaining the predictive properties of the data. In this study, we implemented a Wasserstein GAN (WGAN) with and without differential privacy guarantees to generate privacy-preserving labeled Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) image patches for brain vessel segmentation. The synthesized image-label pairs were used to train a U-net which was evaluated in terms of the segmentation performance on real patient images from two different datasets. Additionally, the Fréchet Inception Distance (FID) was calculated between the generated images and the real images to assess their similarity. During the evaluation using the U-Net and the FID, we explored the effect of different levels of privacy which was represented by the parameter ϵ. With stricter privacy guarantees, the segmentation performance and the similarity to the real patient images in terms of FID decreased. Our best segmentation model, trained on synthetic and private data, achieved a Dice Similarity Coefficient (DSC) of 0.75 for ϵ = 7.4 compared to 0.84 for ϵ = ∞ in a brain vessel segmentation paradigm (DSC of 0.69 and 0.88 on the second test set, respectively). We identified a threshold of ϵ <5 for which the performance (DSC <0.61) became unstable and not usable. Our synthesized labeled TOF-MRA images with strict privacy guarantees retained predictive properties necessary for segmenting the brain vessels. Although further research is warranted regarding generalizability to other imaging modalities and performance improvement, our results mark an encouraging first step for privacy-preserving data sharing in medical imaging.

Machine Learning-Enabled Determination of Diffuseness of Brain Arteriovenous Malformations from Magnetic Resonance Angiography.

The diffuseness of brain arteriovenous malformations (bAVMs) is a significant factor in surgical outcome evaluation and hemorrhagic risk prediction. However, there are still predicaments in identifying diffuseness, such as the judging variety resulting from different experience and difficulties in quantification. The purpose of this study was to develop a machine learning (ML) model to automatically identify the diffuseness of bAVM niduses using three-dimensional (3D) time-of-flight magnetic resonance angiography (TOF-MRA) images. A total of 635 patients with bAVMs who underwent TOF-MRA imaging were enrolled. Three experienced neuroradiologists delineated the bAVM lesions and identified the diffuseness on TOF-MRA images, which were considered the ground-truth reference. The U-Net-based segmentation model was trained to segment lesion areas. Eight mainstream ML models were trained through the radiomic features of segmented lesions to identify diffuseness, based on which an integrated model was built and yielded the best performance. In the test set, the Dice score, F2 score, precision, and recall for the segmentation model were 0.80 [0.72-0.84], 0.80 [0.71-0.86], 0.84 [0.77-0.93], and 0.82 [0.69-0.89], respectively. For the diffuseness identification model, the ensemble-based model was applied with an area under the Receiver-operating characteristic curves (AUC) of 0.93 (95% CI 0.87-0.99) in the training set. The AUC, accuracy, precision, recall, and F1 score for the diffuseness identification model were 0.95, 0.90, 0.81, 0.84, and 0.83, respectively, in the test set. The ML models showed good performance in automatically detecting bAVM lesions and identifying diffuseness. The method may help to judge the diffuseness of bAVMs objectively, quantificationally, and efficiently.

Non-Pathological Opacification of the Cavernous Sinus on Brain CT Angiography: Comparison with Flow-Related Signal Intensity on Time-of-Flight MR Angiography.

Purpose: To investigate the non-pathological opacification of the cavernous sinus (CS) on brain computed tomography angiography (CTA) and compare it with flow-related signal intensity (FRSI) on time-of-flight magnetic resonance angiography (TOF-MRA). Methods: Opacification of the CS was observed in 355 participants who underwent CTA and an additional 77 participants who underwent examination with three diagnostic modalities: CTA, TOF-MRA, and digital subtraction angiography (DSA). Opacification of the CS, superior petrosal sinus (SPS), inferior petrosal sinus (IPS), and pterygoid plexus (PP) were also analyzed using a five-point scale. The Wilcoxon test was used to determine the frequencies of the findings on each side. Additionally, the findings on CTA images were compared with those on TOF-MRA images in an additional 77 participants without dural arteriovenous fistula (DAVF) using weighted kappa (κ) statistics. Results: Neuroradiologists identified non-pathological opacification of the CS (n = 100, 28.2%) on brain CTA in 355 participants. Asymmetry of opacification in the CS was significantly correlated with the grade difference between the right and left CS, SPS, IPS, and PP (p < 0.0001 for CS, p < 0.0001 for SPS, p < 0.0001 for IPS, and p < 0.05 for PP). Asymmetry of the opacification and FRSI in the CS was observed in 77 participants (CTA: n = 21, 27.3%; TOF-MRA: n = 22, 28.6%). However, there was almost no agreement between CTA and TOF-MRA (κ = 0.10, 95% confidence interval: −0.12–0.32). Conclusion: Asymmetry of non-pathological opacification and FRSI in the CS may be seen to some extent on CTA and TOF-MRA due to anatomical variance. However, it shows minimal reliable association with the FRSI on TOF-MRA.

Added diagnostic values of three-dimensional high-resolution proton density-weighted magnetic resonance imaging for unruptured intracranial aneurysms in the circle-of-Willis: Comparison with time-of-flight magnetic resonance angiography.

Advanced imaging methods can enhance the identification of aneurysms of the infundibula, which can reduce unnecessary follow-ups or further work-up, fear, and anxiety in patients. This study aimed to evaluate the added diagnostic value of three-dimensional proton density-weighted vessel wall magnetic resonance imaging (3D-PD MRI) in identifying aneurysms from index lesions refer to vascular bulging lesions without vessels arising from the apex, observed using volume-rendered TOF-MRA in the circle-of-Willis compared with time-of-flight magnetic resonance angiography (TOF-MRA). Retrospective. A total of 299 patients who underwent 3D-PD MRI, digital subtraction angiography (DSA), and TOF-MRA between January 2012 and December 2016 were retrospectively enrolled in this study. 3 Tesla, 3D-PD MRI. Three neuroradiologists independently evaluated TOF-MRA and 3D-PD MRI combined with TOF-MRA for the determination of intracranial aneurysms in patients with index lesions within the circle of Willis. Final diagnoses were made by another neuroradiologist and neurointerventionist in consensus using DSA as the reference standard. The diagnostic performance and proportions of undetermined lesions on TOF-MRA and 3D-PD MRI with TOF-MRA were assessed based on the final diagnoses. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy for the diagnosis of unruptured intracranial aneurysms were calculated for each imaging modality. Of 452 lesions identified on volume-rendered TOF-MRA images, 173 (38%) aneurysms and 276 (61%) infundibula were finally diagnosed on DSA. 3D-PD MRI with TOF-MRA showed superior diagnostic performance (p = .001; accuracy, 85.5% versus 95.4%), superior area under the receiver operating characteristic curve over TOF-MRA (p = .001; 0.837 versus 0.947), and a lower proportion of undetermined lesions than TOF-MRA (p = .001; 25.1% versus 2.3%). For unruptured intracranial aneurysms in the circle of Willis, 3D-PD MRI can complement TOF-MRA to improve diagnostic performance and lower the proportion of undetermined lesions.

Characteristics of Wall Shear Stress and Pressure of Intracranial Atherosclerosis Analyzed by a Computational Fluid Dynamics Model: A Pilot Study.

Background: Although wall shear stress (WSS) and pressure play important roles in plaque vulnerability, characteristics of the two indices in intracranial atherosclerosis (ICAS) have not been fully investigated yet. This study aimed to elucidate this issue by means of establishing a non-invasive computational fluid dynamics method with time-of-flight magnetic resonance angiography (TOF-MRA) of the whole cerebral artery.Materials and Methods: Subjects with symptomatic ICAS in the middle cerebral artery domain were enrolled, excluding those with concomitant internal carotid artery stenosis. Based on patient-specific TOF-MRA images for three-dimensional (3D) meshes and arterial blood pressure with patient-specific carotid artery ultrasonography for inlet boundary conditions, patients' three-dimensional hemodynamics were modeled by a finite element method governed by Navier-Stokes equations.Results: Among the 55 atherosclerotic lesions analyzed by this TOF-MRA based computational fluid dynamics model, the maximum WSS (WSSmax) was most frequently detected at the apex points and the upper half of the upstream sections of the lesions, whereas the maximum pressure was most often located at the lower half of the upstream sections. As the percent stenosis increases, the relative value of WSSmax and pressure drop increased with significantly increasing steep beyond 50% stenosis. Moreover, WSSmax was found to linearly correlate with pressure drop in ICAS.Conclusions: This study on ICAS revealed certain trends of longitudinal distribution of WSS and pressure and the influences of percent stenosis on cerebral hemodynamics, as well as the correlations between WSS and pressure drop. It represents a step forward in applying computational flow simulation techniques in studying ICAS and stroke, in a patient-specific manner.

Unsupervised Cerebrovascular Segmentation of TOF-MRA Images Based on Deep Neural Network and Hidden Markov Random Field Model.

Automated cerebrovascular segmentation of time-of-flight magnetic resonance angiography (TOF-MRA) images is an important technique, which can be used to diagnose abnormalities in the cerebrovascular system, such as vascular stenosis and malformation. Automated cerebrovascular segmentation can direct show the shape, direction and distribution of blood vessels. Although deep neural network (DNN)-based cerebrovascular segmentation methods have shown to yield outstanding performance, they are limited by their dependence on huge training dataset. In this paper, we propose an unsupervised cerebrovascular segmentation method of TOF-MRA images based on DNN and hidden Markov random field (HMRF) model. Our DNN-based cerebrovascular segmentation model is trained by the labeling of HMRF rather than manual annotations. The proposed method was trained and tested using 100 TOF-MRA images. The results were evaluated using the dice similarity coefficient (DSC), which reached a value of 0.79. The trained model achieved better performance than that of the traditional HMRF-based cerebrovascular segmentation method in binary pixel-classification. This paper combines the advantages of both DNN and HMRF to train the model with a not so large amount of the annotations in deep learning, which leads to a more effective cerebrovascular segmentation method.

Use of three-dimensional time-of-flight magnetic resonance angiography at 1.5 Tesla to evaluate the intracranial arteries of 39 dogs with idiopathic epilepsy.

To assess visualization of the intracranial arteries and internal carotid artery (ICA) on 3-D time-of-flight (TOF) magnetic resonance angiography (MRA) images obtained at 1.5 T and to investigate factors that affect the image quality of those arteries in dogs. 39 dogs with idiopathic epilepsy. Each dog underwent 3-D TOF MRA, and 5 pairs of intracranial arteries, the basilar artery, and both ICAs were evaluated. Each artery was assigned an image-quality score on a scale of 0 to 3, where 0 = poor and 3 = excellent. Multivariable regression analysis was used to assess whether age, body weight (BW), serum total cholesterol concentration, intracranial volume (ICV), and mean arterial pressure were significantly associated with the image quality of each vessel. In all dogs, the image-quality score was 2 or 3 for the proximal middle cerebral arteries, basilar artery, and caudal aspect of the caudal communicating arteries. In some dogs, the rostral cerebellar arteries, rostral aspect of the caudal communicating arteries, and middle and rostral aspects of the ICA were poorly visualized. For various arteries, image quality was negatively associated with age and positively associated with BW and ICV. Results indicated that 3-D TOF MRA images obtained at 1.5 T did not consistently and clearly delineate the ICA and narrow or peripheral intracranial arteries of dogs; therefore, careful attention is required when such images are assessed. Patient age, BW, and ICV can also affect the image quality of some intracranial arteries on 3-D TOF MRA images. (Am J Vet Res 2019;80:480-489).

Usefulness of contrast-enhanced and TOF MR angiography for follow-up after low-profile stent-assisted coil embolization of intracranial aneurysms.

Low-profile, self-expandable stents are used to treat wide-neck aneurysms located on the smaller distal intracranial arteries. This study aimed to assess the usefulness of time-of-flight (TOF) and contrast-enhanced (CE) magnetic resonance angiography (MRA) for follow-up after LEO Baby stent (LBS)-assisted coil embolization. Twenty-four aneurysms treated with LBS-assisted coil embolization were evaluated. Researchers reviewed TOF MRA and CE MRA images in terms of occlusion and stent patency. Aneurysm occlusion was graded according to Raymond-Roy classification as follows: total occlusion (grade 1), residual neck (grade 2), and residual aneurysm (grade 3). Stent patency was scored as follows: occlusion (1), stenosis (2), and normal (3). Interobserver and intermodality agreement values were determined by weighted kappa (κ) statistics. Intermodality and interobserver values of TOF MRA and CE MRA with digital subtraction angiography (DSA) were perfect (κ = 1.00, p < 0.001) in terms of aneurysm occlusion. Rate of stent occlusion and stenosis in DSA, TOF, and MRA, respectively, were as follows: 0 and 12.5%, 16.6 and 70.8%, and 0 and 62.5%. Intermodality agreement values of TOF MRA and CE MRA with DSA were insignificant in terms of stent patency (κ = 0.065, p = 0.27; κ = 0.158, p = 0.15, respectively). Interobserver agreement was substantial in both TOF MRA (κ = 0.71, p < 0.001) and CE MRA (κ = 0.64, p = 0.001). Both TOF and CE MRA techniques have strong concordance with DSA for the detection of aneurysm occlusion status. CE MRA can be used as a first-line noninvasive imaging modality due to its superiority to TOF MRA with respect to the visualization of in-stent signals.

A Parallel Markov Cerebrovascular Segmentation Algorithm Based on Statistical Model

For segmenting cerebral blood vessels from the time-of-flight magnetic resonance angiography (TOF-MRA) images accurately, we propose a parallel segmentation algorithm based on statistical model with Markov random field (MRF). Firstly, we improve traditional non-local means filter with patch-based Fourier transformation to preprocess the TOF-MRA images. In this step, we mainly utilize the sparseness and self-similarity of the MRA brain images sequence. Secondly, we add the MRF information to the finite mixture mode (FMM) to fit the intensity distribution of medical images. We make use of the MRF in image sequence to estimate the proportion of cerebral tissues. Finally, we choose the particle swarm optimization (PSO) algorithm to parallelize the parameter estimation of FMM. A large number of experiments verify the high accuracy and robustness of our approach especially for narrow vessels. The work will offer significant assistance for physicians on the prevention and diagnosis of cerebrovascular diseases.

Automated segmentation of cerebral vasculature with aneurysms in 3DRA and TOF‐MRA using geodesic active regions: An evaluation study

To evaluate the suitability of an improved version of an automatic segmentation method based on geodesic active regions (GAR) for segmenting cerebral vasculature with aneurysms from 3D x-ray reconstruction angiography (3DRA) and time of flight magnetic resonance angiography (TOF-MRA) images available in the clinical routine. Three aspects of the GAR method have been improved: execution time, robustness to variability in imaging protocols, and robustness to variability in image spatial resolutions. The improved GAR was retrospectively evaluated on images from patients containing intracranial aneurysms in the area of the Circle of Willis and imaged with two modalities: 3DRA and TOF-MRA. Images were obtained from two clinical centers, each using different imaging equipment. Evaluation included qualitative and quantitative analyses of the segmentation results on 20 images from 10 patients. The gold standard was built from 660 cross-sections (33 per image) of vessels and aneurysms, manually measured by interventional neuroradiologists. GAR has also been compared to an interactive segmentation method: isointensity surface extraction (ISE). In addition, since patients had been imaged with the two modalities, we performed an intermodality agreement analysis with respect to both the manual measurements and each of the two segmentation methods. Both GAR and ISE differed from the gold standard within acceptable limits compared to the imaging resolution. GAR (ISE) had an average accuracy of 0.20 (0.24) mm for 3DRA and 0.27 (0.30) mm for TOF-MRA, and had a repeatability of 0.05 (0.20) mm. Compared to ISE, GAR had a lower qualitative error in the vessel region and a lower quantitative error in the aneurysm region. The repeatability of GAR was superior to manual measurements and ISE. The intermodality agreement was similar between GAR and the manual measurements. The improved GAR method outperformed ISE qualitatively as well as quantitatively and is suitable for segmenting 3DRA and TOF-MRA images from clinical routine.

Hybrid of Opposite-Contrast Magnetic Resonance Angiography of the Brain by Combining Time-of-Flight and Black Blood Sequences

We assessed the value of a new magnetic resonance angiography (MRA) technique named hybrid of opposite-contrast (HOP) MRA in the diagnosis of moyamoya disease. Using a dual-echo sequence, we obtained the first echo for time-of-flight (TOF) MRA followed by the second echo for black blood MRA. We then subtracted the black blood MRA data set from that of the TOF MRA followed by maximum-intensity projection. In 14 patients, we performed HOP MRA and compared the findings with those on 3-dimensional TOF MRA and MR images. The HOP MRA was also compared with a radionuclide perfusion study (7 patients). The HOP MRA technique improved the demonstration of distal arteries in 13 patients. The findings in HOP MRA correlated with MR images in 9 patients as well as with the perfusion study in 6 patients. The HOP MRA technique demonstrated the distal arterial branches in moyamoya disease well and facilitated the perfusion assessment with MR imaging.