Digital Subtraction Angiography Sequences Research Articles

DIAS: A dataset and benchmark for intracranial artery segmentation in DSA sequences

The automated segmentation of Intracranial Arteries (IA) in Digital Subtraction Angiography (DSA) plays a crucial role in the quantification of vascular morphology, significantly contributing to computer-assisted stroke research and clinical practice. Current research primarily focuses on the segmentation of single-frame DSA using proprietary datasets. However, these methods face challenges due to the inherent limitation of single-frame DSA, which only partially displays vascular contrast, thereby hindering accurate vascular structure representation. In this work, we introduce DIAS, a dataset specifically developed for IA segmentation in DSA sequences. We establish a comprehensive benchmark for evaluating DIAS, covering full, weak, and semi-supervised segmentation methods. Specifically, we propose the vessel sequence segmentation network, in which the sequence feature extraction module effectively captures spatiotemporal representations of intravascular contrast, achieving intracranial artery segmentation in 2D+Time DSA sequences. For weakly-supervised IA segmentation, we propose a novel scribble learning-based image segmentation framework, which, under the guidance of scribble labels, employs cross pseudo-supervision and consistency regularization to improve the performance of the segmentation network. Furthermore, we introduce the random patch-based self-training framework, aimed at alleviating the performance constraints encountered in IA segmentation due to the limited availability of annotated DSA data. Our extensive experiments on the DIAS dataset demonstrate the effectiveness of these methods as potential baselines for future research and clinical applications. The dataset and code are publicly available at https://doi.org/10.5281/zenodo.11401368 and https://github.com/lseventeen/DIAS.

Quantitative Assessment of Hemodynamics Associated With Embolization Degree in Brain Arteriovenous Malformations.

Grading systems, including the novel brain arteriovenous malformation endovascular grading scale (NBAVMES) and arteriovenous malformation embocure score (AVMES), predict embolization outcomes based on arteriovenous malformation (AVM) morphological features. The influence of hemodynamics on embolization outcomes remains unexplored. In this study, we investigated the relationship between hemodynamics and embolization outcomes. We conducted a retrospective study of 99 consecutive patients who underwent transarterial embolization at our institution between 2012 and 2018. Hemodynamic features of AVMs were derived from pre-embolization digital subtraction angiography sequences using quantitative digital subtraction angiography. Multivariate logistic regression analysis was performed to determine the significant factors associated with embolization outcomes. Complete embolization (CE) was achieved in 17 (17.2%) patients, and near-complete embolization was achieved in 18 (18.2%) patients. A slower transnidal relative velocity (TRV, odds ratio [OR] = 0.71, P = .002) was significantly associated with CE. Moreover, higher stasis index of the drainage vein (OR = 16.53, P = .023), shorter transnidal time (OR = 0.15, P = .013), and slower TRV (OR = 0.9, P = .049) were significantly associated with complete or near-complete embolization (C/nCE). The area under the receiver operating characteristic curve for predicting CE was 0.87 for TRV, 0.72 for NBAVMES scores (ρ = 0.287, P = .004), and 0.76 for AVMES scores. The area under the receiver operating characteristic curve for predicting C/nCE was 0.77 for TRV, 0.61 for NBAVMES scores, and 0.75 for AVMES scores. Significant Spearman correlation was observed between TRV and NBAVMES scores and AVMES scores (ρ = 0.512, P < .001). Preoperative hemodynamic factors have the potential to predict the outcomes of AVM embolization. A higher stasis index of the drainage vein, slower TRV, and shorter transnidal time may indicate a moderate blood flow status or favorable AVM characteristics that can potentially facilitate embolization.

Computational fluid dynamics-based virtual angiograms for the detection of flow stagnation in intracranial aneurysms.

The goal of this study was to test if CFD-based virtual angiograms could be used to automatically discriminate between intracranial aneurysms (IAs) with and without flow stagnation. Time density curves (TDC) were extracted from patient digital subtraction angiography (DSA) image sequences by computing the average gray level intensity inside the aneurysm region and used to define injection profiles for each subject. Subject-specific 3D models were reconstructed from 3D rotational angiography (3DRA) and computational fluid dynamics (CFD) simulations were performed to simulate the blood flow inside IAs. Transport equations were solved numerically to simulate the dynamics of contrast injection into the parent arteries and IAs and then the contrast retention time (RET) was calculated. The importance of gravitational pooling of contrast agent within the aneurysm was evaluated by modeling contrast agent and blood as a mixture of two fluids with different densities and viscosities. Virtual angiograms can reproduce DSA sequences if the correct injection profile is used. RET can identify aneurysms with significant flow stagnation even when the injection profile is not known. Using a small sample of 14 IAs of which seven were previously classified as having flow stagnation, it was found that a threshold RET value of 0.46 s can successfully identify flow stagnation. CFD-based prediction of stagnation was in more than 90% agreement with independent visual DSA assessment of stagnation in a second sample of 34 IAs. While gravitational pooling prolonged contrast retention time it did not affect the predictive capabilities of RET. CFD-based virtual angiograms can detect flow stagnation in IAs and can be used to automatically identify aneurysms with flow stagnation even without including gravitational effects on contrast agents.

Towards quantitative digital subtraction perfusion angiography: An animal study.

X-ray digital subtraction angiography (DSA) is the imaging modality for peri-procedural guidance and treatment evaluation in (neuro-) vascular interventions. Perfusion image construction from DSA, as a means of quantitatively depicting cerebral hemodynamics, has been shown feasible. However, the quantitative property of perfusion DSA has not been well studied. To comparatively study the independence of deconvolution-based perfusion DSA with respect to varying injection protocols, as well as its sensitivity to alterations in brainconditions. We developed a deconvolution-based algorithm to compute perfusion parametric images from DSA, including cerebral blood volume (CBV ), cerebral blood flow (CBF ), time to maximum (Tmax), and mean transit time (MTT ) and applied it to DSA sequences obtained from two swine models. We also extracted the time intensity curve (TIC)-derived parameters, that is, area under the curve (AUC), peak concentration of the curve, and the time to peak (TTP) from these sequences. Deconvolution-based parameters were quantitatively compared to TIC-derived parameters in terms of consistency upon variations in injection profile and time resolution of DSA, as well as sensitivity to alterations of cerebralcondition. Comparing to TIC-derived parameters, the standard deviation (SD) of deconvolution-based parameters (normalized with respect to the mean) are two to five times smaller, indicating that they are more consistent across different injection protocols and time resolutions. Upon ischemic stroke induced in a swine model, the sensitivities of deconvolution-based parameters are equal to, if not higher than, those of TIC-derivedparameters. In comparison to TIC-derived parameters, deconvolution-based perfusion imaging in DSA shows significantly higher quantitative reliability against variations in injection protocols across different time resolutions, and is sensitive to alterations in cerebral hemodynamics. The quantitative nature of perfusion angiography may allow for objective treatment assessment in neurovascularinterventions.

Using temporal and structural data to reconstruct 3D cerebral vasculature from a pair of 2D digital subtraction angiography sequences.

The purpose of this work is to present a new method for reconstructing patient-specific three-dimensional (3D) vasculature of the brain from a pair of digital subtraction angiography (DSA) image sequences from different viewpoints, e.g., from bi-plane angiography. Our long-term goal is to provide high resolution visualization of 3D vasculature with dynamic flow of contrast agent from limited data that is readily available during surgical procedures. The proposed method is the second of a three-stage process composed of 1) augmenting vessel segmentation with vessel radii and timing of the arrival of a bolus of contrast agent, 2) reconstructing a volumetric representation of the augmented vessel data from the augmented 2D segmentations, and 3) generating a 3D model of vessels and flow of contrast agent from the volumetric reconstruction. Unlike previous methods, which are either limited to relatively simple vessel structures or rely on multiple views and/or prior models of the vasculature, our method requires only a single pair of 2D DSA sequences taken from different view directions. We developed a new mathematical algorithm that augments vessel centerlines with vessel radii and bolus arrival times derived directly from the 2D DSA sequences to constrain the 3D reconstruction. We validated this method on digital phantoms derived from clinical data and from fractal models of branching tree structures. In standard reconstruction methods, reconstruction by projection of two views into 3D space results in 'ghosting' artifacts, i.e., false 3D structure that occurs where vessels or vessel segments overlap in the 2D images. For the complex vascular of the brain, this ghosting is severe and is a major hurdle for methods that attempt to generate 3D structure from 2D images. We show that our approach reduces ghosting by up to 99% in digital phantoms derived from clinical data. Our dramatic reduction in ghosting artifacts in 3D reconstructions from a pair of 2D image sequences is an important step towards generating high resolution 3D vasculature with dynamic flow information from a single DSA sequence acquired using bi-plane angiography.

Deep learning-based classification of DSA image sequences of patients with acute ischemic stroke

PurposeRecently, a large number of patients with acute ischemic stroke benefited from the use of thrombectomy, a minimally invasive intervention technique for mechanically removing thrombi from the cerebrovasculature. During thrombectomy, 2D digital subtraction angiography (DSA) image sequences are acquired simultaneously from the posterior-anterior and the lateral view to control whether thrombus removal was successful, and to possibly detect newly occluded areas caused by thrombus fragments split from the main thrombus. However, such new occlusions, which would be treatable by thrombectomy, may be overlooked during the intervention. To prevent this, we developed a deep learning-based approach to automatic classification of DSA sequences into thrombus-free and non-thrombus-free sequences.MethodsWe performed a retrospective study based on the single-center DSA data of thrombectomy patients. For classifying the DSA sequences, we applied Long Short-Term Memory or Gated Recurrent Unit networks and combined them with different Convolutional Neural Networks used as feature extractor. These network variants were trained on the DSA data by using five-fold cross-validation. The classification performance was determined on a test data set with respect to the Matthews correlation coefficient (MCC) and the area under the curve (AUC). Finally, we evaluated our models on patient cases, in which overlooking thrombi during thrombectomy had happened.ResultsDepending on the specific model configuration used, we obtained a performance of up to 0.77mid 0.94 for the MCCmid AUC, respectively. Additionally, overlooking thrombi could have been prevented in the reported patient cases, as our models would have classified the corresponding DSA sequences correctly.ConclusionOur deep learning-based approach to thrombus identification in DSA sequences yielded high accuracy on our single-center test data set. External validation is now required to investigate the generalizability of our method. As demonstrated, using this new approach may help reduce the incident risk of overlooking thrombi during thrombectomy in the future.

Quantitative angiography prognosis of intracranial aneurysm treatment failure using parametric imaging and distal vessel analysis.

Data-driven methods based on x-ray angiographic parametric imaging (API) have been successfully used to provide prognosis for intracranial aneurysm (IA) treatment outcome. Previous studies have mainly focused on embolization devices where the flow pattern visualization is in the aneurysm dome; however, this is not possible in IAs treated with endovascular coils due to high x-ray attenuation of the devices. To circumvent this challenge, we propose to investigate whether flow changes in the parent artery distal to the coil-embolized IAs could be used to achieve the same accuracy of surgical outcome prognosis. Eighty digital subtraction angiography sequences were acquired from patients with IA embolized with coils. Five API parameters were recorded from a region of interest (ROI) placed distal to the IA neck in the main artery. Average API values were recorded and pre-treatment values. A supervised machine learning algorithm was trained to provide a six-month post procedure binary outcome (occluded/not occluded). Receiver operating characteristic (ROC) analysis was used to assess the accuracy of the method. Use of API parameters with data driven methods yielded an area under the ROC curve of 0.77 ±0.11 and accuracy of 78.6%. Single parameter-based analysis yielded accuracies which were suboptimal for clinical acceptance. We determined that data-driven method based on API analysis of flow in the parent artery of IA treated with coils provide clinically acceptable accuracy for the prognosis of six months occlusion outcome.

Using a Convolutional Neural Network and Convolutional Long Short-term Memory to Automatically Detect Aneurysms on 2D Digital Subtraction Angiography Images: Framework Development and Validation.

BackgroundIt is hard to distinguish cerebral aneurysms from overlapping vessels in 2D digital subtraction angiography (DSA) images due to these images’ lack of spatial information.ObjectiveThe aims of this study were to (1) construct a deep learning diagnostic system to improve the ability to detect posterior communicating artery aneurysms on 2D DSA images and (2) validate the efficiency of the deep learning diagnostic system in 2D DSA aneurysm detection.MethodsWe proposed a 2-stage detection system. First, we established the region localization stage to automatically locate specific detection regions of raw 2D DSA sequences. Second, in the intracranial aneurysm detection stage, we constructed a bi-input+RetinaNet+convolutional long short-term memory (C-LSTM) framework to compare its performance for aneurysm detection with that of 3 existing frameworks. Each of the frameworks had a 5-fold cross-validation scheme. The receiver operating characteristic curve, the area under the curve (AUC) value, mean average precision, sensitivity, specificity, and accuracy were used to assess the abilities of different frameworks.ResultsA total of 255 patients with posterior communicating artery aneurysms and 20 patients without aneurysms were included in this study. The best AUC values of the RetinaNet, RetinaNet+C-LSTM, bi-input+RetinaNet, and bi-input+RetinaNet+C-LSTM frameworks were 0.95, 0.96, 0.92, and 0.97, respectively. The mean sensitivities of the RetinaNet, RetinaNet+C-LSTM, bi-input+RetinaNet, and bi-input+RetinaNet+C-LSTM frameworks and human experts were 89% (range 67.02%-98.43%), 88% (range 65.76%-98.06%), 87% (range 64.53%-97.66%), 89% (range 67.02%-98.43%), and 90% (range 68.30%-98.77%), respectively. The mean specificities of the RetinaNet, RetinaNet+C-LSTM, bi-input+RetinaNet, and bi-input+RetinaNet+C-LSTM frameworks and human experts were 80% (range 56.34%-94.27%), 89% (range 67.02%-98.43%), 86% (range 63.31%-97.24%), 93% (range 72.30%-99.56%), and 90% (range 68.30%-98.77%), respectively. The mean accuracies of the RetinaNet, RetinaNet+C-LSTM, bi-input+RetinaNet, and bi-input+RetinaNet+C-LSTM frameworks and human experts were 84.50% (range 69.57%-93.97%), 88.50% (range 74.44%-96.39%), 86.50% (range 71.97%-95.22%), 91% (range 77.63%-97.72%), and 90% (range 76.34%-97.21%), respectively.ConclusionsAccording to our results, more spatial and temporal information can help improve the performance of the frameworks. Therefore, the bi-input+RetinaNet+C-LSTM framework had the best performance when compared to that of the other frameworks. Our study demonstrates that our system can assist physicians in detecting intracranial aneurysms on 2D DSA images.

Learning spatiotemporal features of DSA using 3D CNN and BiConvGRU for ischemic moyamoya disease detection

Background Moyamoya disease (MMD) is a serious intracranial cerebrovascular disease. Cerebral hemorrhage caused by MMD will bring life risk to patients. Therefore, MMD detection is of great significance in the prevention of cerebral hemorrhage. In order to improve the accuracy of digital subtraction angiography (DSA) in the diagnosis of ischemic MMD, in this paper, a deep network architecture combined with 3D convolutional neural network (3D CNN) and bidirectional convolutional gated recurrent unit (BiConvGRU) is proposed to learn the spatiotemporal features for ischemic MMD detection. Methods Firstly, 2D convolutional neural network (2D CNN) is utilized to extract spatial features for each frame of DSA. Secondly, the long-term spatiotemporal features of DSA sequence are extracted by BiConvGRU. Thirdly, the short-term spatiotemporal features of DSA are further extracted by 3D convolutional neural network (3D CNN). In addition, different features are extracted when gray images and optical flow images pass through the network, and multiple features are extracted by features fusion. Finally, the fused features are utilized to classify. Results The proposed method was quantitatively evaluated on a data sets of 630 cases. The experimental results showed a detection accuracy of 0.9788, sensitivity and specificity were 0.9780 and 0.9796, respectively, and area under curve (AUC) was 0.9856. Compared with other methods, we can get the highest accuracy and AUC. Conclusions The experimental results show that the proposed method is stable and reliable for ischemic MMD detection, which provides an option for doctors to accurately diagnose ischemic MMD.

Estimation of High Framerate Digital Subtraction Angiography Sequences at Low Radiation Dose.

Digital Subtraction Angiography (DSA) provides high resolution image sequences of blood flow through arteries and veins and is considered the gold standard for visualizing cerebrovascular anatomy for neurovascular interventions. However, acquisition frame rates are typically limited to 1-3 fps to reduce radiation exposure, and thus DSA sequences often suffer from stroboscopic effects. We present the first approach that permits generating high frame rate DSA sequences from low frame rate acquisitions eliminating these artifacts without increasing the patient's exposure to radiation. Our approach synthesizes new intermediate frames using a phase-aware Convolutional Neural Network. This network accounts for the non-linear blood flow progression due to vessel geometry and initial velocity of the contrast agent. Our approach out-performs existing methods and was tested on several low frame rate DSA sequences of the human brain resulting in sequences of up to 17 fps with smooth and continuous contrast flow, free of flickering artifacts.

Fully automated intracranial aneurysm detection and segmentation from digital subtraction angiography series using an end-to-end spatiotemporal deep neural network

BackgroundIntracranial aneurysms (IAs) are common in the population and may cause death.ObjectiveTo develop a new fully automated detection and segmentation deep neural network based framework to assist neurologists in evaluating...

A Motion Artifact Reduction Method in Cerebrovascular DSA Sequence Images

Digital Subtraction Angiography (DSA) can be used for diagnosing the pathologies of vascular system including systemic vascular disease, coronary heart disease, arrhythmia, valvular disease and congenital heart disease. Previous studies have provided some image enhancement algorithms for DSA images. However, these studies are not suitable for automated processes in huge amounts of data. Furthermore, few algorithms solved the problems of image contrast corruption after artifact removal. In this paper, we propose a fully automatic method for cerebrovascular DSA sequence images artifact removal based on rigid registration and guided filter. The guided filtering method is applied to fuse the original DSA image and registered DSA image, the results of which preserve clear vessel boundary from the original DSA image and remove the artifacts by the registered procedure. The experimental evaluation with 40 DSA sequence images shows that the proposed method increases the contrast index by 24.1% for improving the quality of DSA images compared with other image enhancement methods, and can be implemented as a fully automatic procedure.

TU‐H‐CAMPUS‐IeP3‐02: Neurovascular 4D Parametric Imaging Using Co‐Registration of Biplane DSA Sequences with 3D Vascular Geometry Obtained From Cone Beam CT

Purpose:To create 4D parametric images using biplane Digital Subtraction Angiography (DSA) sequences co‐registered with the 3D vascular geometry obtained from Cone Beam‐CT (CBCT).Methods:We investigated a method to derive multiple 4D Parametric Imaging (PI) maps using only one CBCT acquisition. During this procedure a 3D‐DSA geometry is stored and used subsequently for all 4D images. Each time a biplane DSA is acquired, we calculate 2D parametric maps of Bolus Arrival Time (BAT), Mean Transit Time (MTT) and Time to Peak (TTP). Arterial segments which are nearly parallel with one of the biplane imaging planes in the 2D parametric maps are co‐registered with the 3D geometry. The values in the remaining vascular network are found using spline interpolation since the points chosen for co‐registration on the vasculature are discrete and remaining regions need to be interpolated. To evaluate the method we used a patient CT volume data set for 3D printing a neurovascular phantom containing a complete Circle of Willis. We connected the phantom to a flow loop with a peristaltic pump, simulating physiological flow conditions. Contrast media was injected with an automatic injector at 10 ml/sec. Images were acquired with a Toshiba Infinix C‐arm and 4D parametric image maps of the vasculature were calculated.Results:4D BAT, MTT, and TTP parametric image maps of the Circle of Willis were derived. We generated color‐coded 3D geometries which avoided artifacts due to vessel overlap or foreshortening in the projection direction.Conclusion:The software was tested successfully and multiple 4D parametric images were obtained from biplane DSA sequences without the need to acquire additional 3D‐DSA runs. This can benefit the patient by reducing the contrast media and the radiation dose normally associated with these procedures.Partial support from NIH Grant R01‐EB002873 and Toshiba Medical Systems Corp.

Perfusion Angiography in Acute Ischemic Stroke.

Visualization and quantification of blood flow are essential for the diagnosis and treatment evaluation of cerebrovascular diseases. For rapid imaging of the cerebrovasculature, digital subtraction angiography (DSA) remains the gold standard as it offers high spatial resolution. This paper lays out a methodological framework, named perfusion angiography, for the quantitative analysis and visualization of blood flow parameters from DSA images. The parameters, including cerebral blood flow (CBF) and cerebral blood volume (CBV), mean transit time (MTT), time-to-peak (TTP), and T max, are computed using a bolus tracking method based on the deconvolution of the time-density curve on a pixel-by-pixel basis. The method is tested on 66 acute ischemic stroke patients treated with thrombectomy and/or tissue plasminogen activator (tPA) and also evaluated on an estimation task with known ground truth. This novel imaging tool provides unique insights into flow mechanisms that cannot be observed directly in DSA sequences and might be used to evaluate the impact of endovascular interventions more precisely.

MO‐F‐CAMPUS‐I‐02: Stability Investigation of a Gamma Fitting Algorithm for Angiographic Parametric Imaging at Low X‐Ray Exposures Using a Patient Specific Neurovascular Phantom

Purpose:To analyze the stability of a Gamma fitting algorithm based on digital subtraction angiography sequences, at different exposure, hence noise levels.Methods:Starting with a patient CT volume, we built a 3D printed neurovascular phantom which contained a complete Circle of Willis with major arteries and five aneurysms. We placed the phantom in a 15 cm water bath and connected it to a flow loop containing a peristaltic pump which simulated physiological relevant flow conditions. We injected 10 ml contrast boluses using an automatic contrast injector at a rate of 10 ml/sec. Digital Subtraction Angiography images were acquired at 30 frames/sec and processed with a gamma fitting based algorithm, to yield parametric maps of: Mean transit time (MTT), Time‐to‐Peak (TTP), Bolus Arrival Time (BAT). Starting with the optimal exposure parameters selected by the x‐ray system automatic exposure control, while keeping the same kV, we lowered the mA and exposure per frame in four steps until we reached the minimum value allowed by the system. We analyzed the variation of the MTT, TTP and BAT for various artery signal to noise ratios using four ROIs.Results:The peak (maximum opacification) SNRs for full dose and minimum dose were: 35 and 13 for 4 mm arteries and 18 and 7 for 2 mm arteries. The parameters standard deviation expressed as a percent fraction of the value measured at the full dose value, were: MTT=2.4%, BAT=0.5% and TTP=1.9% for 4 mm vessels and MTT=3.7%, BAT=5.4% and TTP=6.3% for 2 mm vessels.Conclusion:Despite a significant decrease of the peak SNR the algorithm performed very well displaying variations less than 6.3% of the ideal conditions. Partial Support: NIH grant R01EB002873 and Toshiba Medical Systems Corp.Partial Support: NIH grant R01EB002873 and Toshiba Medical Systems Corp.

Abstract 102: Coregistration of Serial Angiograms Using Point Cloud Matching

Introduction: Digital subtraction angiography (DSA) is the gold standard to assess reperfusion during endovascular procedures. Visualization and quantification of changes between successive runs is challenged by patient motion and variations in acquisition parameters such as zoom and pose of the x-ray receptor. Automated coregistration of successive DSA sequences would allow for the visualization of serial changes in perfusion during the procedure. The objective of this study is to develop a fully automated framework for the coregistration of patient-specific DSA acquired at different time points during an endovascular reperfusion procedure. Methods: The dataset was established retrospectively from patients admitted at a stroke care center and diagnosed with acute ischemic stroke. Included patients underwent a clot retrieval procedure. Biplane DSA was performed before and after endovascular reperfusion intervention. A neurologist manually coregistered the anterior-posterior (AP) view of successive DSA sequences from each patient using anatomical reference landmarks. The developed computer vision framework processed each DSA to extract the vasculature using a vessel detector, followed by resampling of the responses. The resulting set of points in the pre- and post-intervention DSA were then matched using a point cloud matching algorithm. In this study, we provide an experimental analysis with the conventional RANSAC algorithm. Evaluation was performed by measuring the error between the estimated affine transform, that relates the pre- to the post-intervention DSA, and the groundtruth established manually. Results: A total of 20 patients were included in the analysis. Mean age was 66.8 (range 34-91). Distribution of the TICI scores was as follows: TICI 0(3), TICI 1(0), TICI 2a(5), TICI 2b(10), TICI 3(2). Overall coregistration error was as follows: angle (8.6+- 3.1 degrees), shift (24.8 +- 19 mm), respectively. Conclusions: RANSAC point cloud matching algorithm can be used to accurately coregister serial angiograms during endovascular procedures. This could lead to near real-time visualization and quantification of revascularization.

TU‐A‐116‐06: Pre and Post‐Treatment Temporal Parametric Analysis of Neurovascular Disease Using Gamma Variate Fitting of Time Density Curves From DSA Sequences

Purpose: To calculate temporal parameters of contrast flow, using digital subtraction angiography (DSA), and provide parametric imaging (PI) of pre‐and post‐treated neurovascular lesions to aid neuro‐interventionalists decision. Methods: We developed a program to evaluate changes in contrast flow before and after treatment of neurovascular lesions. The program records each pixel value in a DSA sequence and fits the data with a gamma‐variate function. Using the function we calculate parameters such as mean‐transit‐time (MTT), bolus‐arrival‐time (BAT) and time‐to‐peak (TTP) and generate PIs for each quantity. The program was used for pre and post‐treatment parametric calculations for four representative neurovascular lesions: ischemic stroke, intracranial stenosis, arteriovenous malformation (AVM) and intracranial aneurysm treated with a flow diverter. Finally we compared the pre and post‐treatment PIs for changes in temporal parameters of the contrast flow. Results: Processing times for a typical DSA acquisition at 10 frames per second were between 15 and 20 minutes. In the ischemic stroke and intracranial stenosis cases we observed increased flow after clot removal and balloon angioplasty respectively, BAT decreased by 2x while MTT and TTP decreased 1.5x as compared with the pre‐treatment PI. For the AVM the venous arrival time increased three times after glue embolization, indicating decreased flow through the AVM. For the aneurysm treatment the MTT in the aneurysm dome was 5x larger compared with the pretreatment run indicating a reduced flow in the aneurysm. Conclusion: Pre and post‐treatment PIs showed significant differences, indicating reestablished flow for ischemic stroke and intracranial stenosis and decreased flow for the AVM and intracranial aneurysm. Execution time can be decreased significantly if software and hardware is optimized. This kind of information could be a very useful tool for treatment assistance during intracranial minimally invasive procedures in the angiographic suites where CT‐perfusion is not immediately available. Support NIH‐2R01EB002873; Support from NIH Grant 2R01EB002873

Quantification of arterial flow using digital subtraction angiography

In this paper, a method for the estimation of arterial hemodynamic flow from x-ray video densitometry data is proposed and validated using an in vitro setup. The method is based on the acquisition of three-dimensional rotational angiography and digital subtraction angiography sequences. A modest contrast injection rate (between 1 and 4 ml/s) leads to a contrast density that is modulated by the cardiac cycle, which can be measured in the x-ray signal. An optical flow based approach is used to estimate the blood flow velocities from the cyclic phases in the x-ray signal. The authors have validated this method in vitro, and present three clinical cases. The in vitro experiments compared the x-ray video densitometry results with the gold standard delivered by a flow meter. Linear correlation analysis and regression fitting showed that the ideal slope of 1 and intercept of 0 were contained within the 95 percentile confidence interval. The results show that a frame rate higher than 50 Hz allows measuring flows in the range of 2 ml/s to 6 ml/s within an accuracy of 5%. The in vitro and clinical results indicate that it is feasible to estimate blood flow in routine interventional procedures. The availability of an x-ray based method for quantitative flow estimation is particularly clinically useful for intra-cranial applications, where other methods, such as ultrasound Doppler, are not available.

Magnetization spoiling in radial FLASH contrast‐enhanced MR digital subtraction angiography

To increase the in-plane spatial resolution and image update rates of 2D magnetic resonance (MR) digital subtraction angiography (DSA) pulse sequences to 0.57 × 0.57 mm and 6 frames/sec, respectively, for intracranial vascular disease applications by developing a radial FLASH protocol and to characterize a new artifact, not previously described in the literature, which arises in the presence of such pulse sequences. The pulse sequence was optimized and artifacts were characterized using simulation and phantom studies. With Institutional Review Board (IRB) approval, the pulse sequence was used to acquire time-resolved images from healthy human volunteers and patients with x-ray DSA-confirmed intracranial vascular disease. Artifacts were shown to derive from inhomogeneous spoiling due to the nature of radial waveforms. Gradient spoiling strategies were proposed to eliminate the observed artifact by balancing gradient moments across TR intervals. The resulting radial 2D MR DSA sequence (2.6 sec temporal footprint, 6 frames/sec with sliding window factor 16, 0.57 × 0.57 mm in-plane) demonstrated small vessel detail and corroborated x-ray DSA findings in intracranial vascular imaging studies. Appropriate gradient spoiling in radial 2D MR DSA pulse sequences improves intracranial vascular depiction by eliminating circular banding artifacts. The proposed pulse sequence may provide a useful addition to clinically applied 2D MR DSA scans.

In vivo imaging of rat coronary arteries using bi-plane digital subtraction angiography

Introduction X-ray based digital subtraction angiography (DSA) is a common clinical imaging method for vascular morphology and function. Coronary artery characterization is one of its most important applications. We show that bi-plane DSA of rat coronary arteries can provide a powerful imaging tool for translational safety assessment in drug discovery. Methods A novel, dual tube/detector system, constructed explicitly for preclinical imaging, supports image acquisition at 10 frames/s with 88-micron spatial resolution. Ventilation, x-ray exposure, and contrast injection are all precisely synchronized using a biological sequence controller implemented as a LabVIEW application. A set of experiments were performed to test and optimize the sampling and image quality. We applied the DSA imaging protocol to record changes in the visualization of coronaries and myocardial perfusion induced by a vasodilator drug, nitroprusside. The drug was infused into a tail vein catheter using a peristaltic infusion pump at a rate of 0.07 mL/h for 3 min (dose: 0.0875 mg). Multiple DSA sequences were acquired before, during, and up to 25 min after drug infusion. Perfusion maps of the heart were generated in MATLAB to compare the drug effects over time. Results The best trade-off between the injection time, pressure, and image quality was achieved at 60 PSI, with the injection of 150 ms occurring early in diastole (60 ms delay) and resulting in the delivery of 113 μL of contrast agent. DSA images clearly show the main branches of the coronary arteries in an intact, beating heart. The drug test demonstrated that DSA can detect relative changes in coronary circulation via perfusion maps. Conclusions The methodology for DSA imaging of rat coronary arteries can serve as a template for future translational studies to assist in safety evaluation of new pharmaceuticals. Although x-ray imaging involves radiation, the associated dose (0.4 Gy) is not a major limitation.