X-ray Digital Subtraction Angiography Research Articles

Multi-scale dual attention embedded U-shaped network for accurate segmentation of coronary vessels in digital subtraction angiography.

Most attention-based networks fall short in effectively integrating spatial and channel-wise information across different scales, which results in suboptimal performance for segmenting coronary vessels in x-ray digital subtraction angiography (DSA) images. This limitation becomes particularly evident when attempting to identify tinysub-branches. To address this limitation, a multi-scale dual attention embedded network (named MDA-Net) is proposed to consolidate contextual spatial and channel information across contiguous levels and scales. MDA-Net employs five cascaded double-convolution blocks within its encoder to adeptly extract multi-scale features. It incorporates skip connections that facilitate the retention of low-level feature details throughout the decoding phase, thereby enhancing the reconstruction of detailed image information. Furthermore, MDA modules, which take in features from neighboring scales and hierarchical levels, are tasked with discerning subtle distinctions between foreground elements, such as coronary vessels of diverse morphologies and dimensions, and the complex background, which includes structures like catheters or other tissues with analogous intensities. To sharpen the segmentation accuracy, the network utilizes a composite loss function that integrates intersection over union (IoU) loss with binary cross-entropy loss, ensuring the precision of the segmentation outcomes and maintaining an equilibrium between positive and negativeclassifications. Experimental results demonstrate that MDA-Net not only performs more robustly and effectively on DSA images under various image conditions, but also achieves significant advantages over state-of-the-art methods, achieving the optimal scores in terms of IoU, Dice, accuracy, and Hausdorff distance 95%. MDA-Net has high robustness for coronary vessels segmentation, providing an active strategy for early diagnosis of cardiovascular diseases. The code is publicly available at https://github.com/30410B/MDA-Net.git.

CAVE: Cerebral artery–vein segmentation in digital subtraction angiography

Cerebral X-ray digital subtraction angiography (DSA) is a widely used imaging technique in patients with neurovascular disease, allowing for vessel and flow visualization with high spatio-temporal resolution. Automatic artery–vein segmentation in DSA plays a fundamental role in vascular analysis with quantitative biomarker extraction, facilitating a wide range of clinical applications. The widely adopted U-Net applied on static DSA frames often struggles with disentangling vessels from subtraction artifacts. Further, it falls short in effectively separating arteries and veins as it disregards the temporal perspectives inherent in DSA. To address these limitations, we propose to simultaneously leverage spatial vasculature and temporal cerebral flow characteristics to segment arteries and veins in DSA. The proposed network, coined CAVE, encodes a 2D+time DSA series using spatial modules, aggregates all the features using temporal modules, and decodes it into 2D segmentation maps. On a large multi-center clinical dataset, CAVE achieves a vessel segmentation Dice of 0.84 (±0.04) and an artery–vein segmentation Dice of 0.79 (±0.06). CAVE surpasses traditional Frangi-based k-means clustering (P < 0.001) and U-Net (P < 0.001) by a significant margin, demonstrating the advantages of harvesting spatio-temporal features. This study represents the first investigation into automatic artery–vein segmentation in DSA using deep learning. The code is publicly available at https://github.com/RuishengSu/CAVE_DSA.

DFA-Net: Dual multi-scale feature aggregation network for vessel segmentation in X-ray digital subtraction angiography

Even though deep learning is fascinated in fields of coronary vessel segmentation in X-ray angiography and achieves prominent progresses, most of those models probably bring high false and missed detections due to indistinct contrast between coronary vessels and background, especially for tiny sub-branches. Image improvement technique is able to better such contrast, while boosting extraneous information, e.g., other tissues with similar intensities and noise. If incorporating features derived from original and enhanced images, the segmentation performance is improved because those images comprise complementary information from different contrasts. Accordingly, inspired from advantages of contrast improvement and encoding-decoding architecture, a dual multi-scale feature aggregation network (named DFA-Net) is introduced for coronary vessel segmentation in digital subtraction angiography (DSA). DFA-Net integrates the contrast improvement using exponent transformation into a semantic segmentation network that individually accepts original and enhanced images as inputs. Through parameter sharing, multi-scale complementary features are aggregated from different contrasts, which strengthens leaning capabilities of networks, and thus achieves an efficient segmentation. Meanwhile, a risk cross-entropy loss is enforced on the segmentation, for availably decreasing false negatives, which is incorporated with Dice loss for joint optimization of the proposed strategy during training. Experimental results demonstrate that DFA-Net can not only work more robustly and effectively for DSA images under diverse conditions, but also achieve better performance, in comparison with state-of-the-art methods. Consequently, DFA-Net has high fidelity and structure similarity to the reference, providing a way for early diagnosis of cardiovascular diseases.

IMPI: portable human-sized magnetic particle imaging scanner for real-time endovascular interventions

Minimally invasive endovascular interventions have become an important tool for the treatment of cardiovascular diseases such as ischemic heart disease, peripheral artery disease, and stroke. X-ray fluoroscopy and digital subtraction angiography are used to precisely guide these procedures, but they are associated with radiation exposure for patients and clinical staff. Magnetic Particle Imaging (MPI) is an emerging imaging technology using time-varying magnetic fields combined with magnetic nanoparticle tracers for fast and highly sensitive imaging. In recent years, basic experiments have shown that MPI has great potential for cardiovascular applications. However, commercially available MPI scanners were too large and expensive and had a small field of view (FOV) designed for rodents, which limited further translational research. The first human-sized MPI scanner designed specifically for brain imaging showed promising results but had limitations in gradient strength, acquisition time and portability. Here, we present a portable interventional MPI (iMPI) system dedicated for real-time endovascular interventions free of ionizing radiation. It uses a novel field generator approach with a very large FOV and an application-oriented open design enabling hybrid approaches with conventional X-ray-based angiography. The feasibility of a real-time iMPI-guided percutaneous transluminal angioplasty (PTA) is shown in a realistic dynamic human-sized leg model.

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.

Semi-supervised segmentation of coronary DSA using mixed networks and multi-strategies

The coronary arteries supply blood to the myocardium, which originate from the root of the aorta and mainly branch into the left and right. X-ray digital subtraction angiography (DSA) is a technique for evaluating coronary artery plaques and narrowing, that is widely used because of its time efficiency and cost-effectiveness. However, automated coronary vessel classification and segmentation remains challenging using a little data. Therefore, the purpose of this study is twofold: one is to propose a more robust method for vessel segmentation, the other is to provide a solution that is feasible with a small amount of labeled data. Currently, there are three main types of vessel segmentation methods, i.e., graphical- and statistical-based; clustering theory based, and deep learning-based methods for pixel-by-pixel probabilistic prediction, among which the last method is the mainstream with high accuracy and automation. Under this trend, an Inception-SwinUnet (ISUnet) network combining the convolutional neural network and Transformer basic module was proposed in this paper. Considering that data-driven fully supervised learning (FSL) segmentation methods require a large set of paired data with high-quality pixel-level annotation, which is expertise-demanding and time-consuming, we proposed a Semi-supervised Learning (SSL) method to achieve better performance with a small amount of labeled and unlabeled data. Different from the classical SSL method, i.e., Mean-Teacher, our method used two different networks for cross-teaching as the backbone. Meanwhile, inspired by deep supervision and confidence learning (CL), two effective strategies for SSL were adopted, which were denominated Pyramid-consistency Learning (PL) and Confidence Learning (CL), respectively. Both were designed to filter the noise and improve the credibility of pseudo labels generated by unlabeled data. Compared with existing methods, ours achieved superior segmentation performance over other FSL and SSL ones by using data with a small equal number of labels. Code is available in https://github.com/Allenem/SSL4DSA.

Spatio-temporal deep learning for automatic detection of intracranial vessel perforation in digital subtraction angiography during endovascular thrombectomy.

Intracranial vessel perforation is a peri-procedural complication during endovascular therapy (EVT). Prompt recognition is important as its occurrence is strongly associated with unfavorable treatment outcomes. However, perforations can be hard to detect because they are rare, can be subtle, and the interventionalist is working under time pressure and focused on treatment of vessel occlusions. Automatic detection holds potential to improve rapid identification of intracranial vessel perforation. In this work, we present the first study on automated perforation detection and localization on X-ray digital subtraction angiography (DSA) image series. We adapt several state-of-the-art single-frame detectors and further propose temporal modules to learn the progressive dynamics of contrast extravasation. Application-tailored loss function and post-processing techniques are designed. We train and validate various automated methods using two national multi-center datasets (i.e., MR CLEAN Registry and MR CLEAN-NoIV Trial), and one international multi-trial dataset (i.e., the HERMES collaboration). With ten-fold cross-validation, the proposed methods achieve an area under the curve (AUC) of the receiver operating characteristic of 0.93 in terms of series level perforation classification. Perforation localization precision and recall reach 0.83 and 0.70 respectively. Furthermore, we demonstrate that the proposed automatic solutions perform at similar level as an expert radiologist.

Deep Learning-Based Automated Thrombolysis in Cerebral Infarction Scoring: A Timely Proof-of-Principle Study.

Mechanical thrombectomy is an established procedure for treatment of acute ischemic stroke. Mechanical thrombectomy success is commonly assessed by the Thrombolysis in Cerebral Infarction (TICI) score, assigned by visual inspection of X-ray digital subtraction angiography data. However, expert-based TICI scoring is highly observer-dependent. This represents a major obstacle for mechanical thrombectomy outcome comparison in, for instance, multicentric clinical studies. Focusing on occlusions of the M1 segment of the middle cerebral artery, the present study aimed to develop a deep learning (DL) solution to automated and, therefore, objective TICI scoring, to evaluate the agreement of DL- and expert-based scoring, and to compare corresponding numbers to published scoring variability of clinical experts. The study comprises 2 independent datasets. For DL system training and initial evaluation, an in-house dataset of 491 digital subtraction angiography series and modified TICI scores of 236 patients with M1 occlusions was collected. To test the model generalization capability, an independent external dataset with 95 digital subtraction angiography series was analyzed. Characteristics of the DL system were modeling TICI scoring as ordinal regression, explicit consideration of the temporal image information, integration of physiological knowledge, and modeling of inherent TICI scoring uncertainties. For the in-house dataset, the DL system yields Cohen’s kappa, overall accuracy, and specific agreement values of 0.61, 71%, and 63% to 84%, respectively, compared with the gold standard: the expert rating. Values slightly drop to 0.52/64%/43% to 87% when the model is, without changes, applied to the external dataset. After model updating, they increase to 0.65/74%/60% to 90%. Literature Cohen’s kappa values for expert-based TICI scoring agreement are in the order of 0.6. The agreement of DL- and expert-based modified TICI scores in the range of published interobserver variability of clinical experts highlights the potential of the proposed DL solution to automated TICI scoring.

Fabrication of Fe3O4@PVA microspheres by one-step electrospray for magnetic resonance imaging during transcatheter arterial embolization

Magnetic resonance imaging (MRI) has attracted increasing attention as a feasible alternative or adjunctive imaging modality for X-ray digital subtraction angiography because of the high tissue resolution and non-ionization radiation. In this study, a one-step electrospray method was developed to fabricate PVA microspheres encapsulated with in situ synthesized Fe3O4 nanoparticles. Fe3O4@PVA microspheres were mono-dispersed black spheres with a wide range of sizes (262–958 µm). The in situ-synthesized Fe3O4 nanoparticles were used as the contrast agent of MRI and the cross-linkers of PVA matrixes for the embolization purpose. In vivo evaluation of renal arteries of normal rabbits showed that Fe3O4@PVA microspheres had good embolic effect and enhanced capability of MRI. In vitro and in vivo biosafety assessment confirmed that Fe3O4@PVA microspheres had favorable biocompatibility. The DOX-loaded Fe3O4@PVA microspheres showed a typical drug-sustained release profile. These results suggest that the prepared DOX-loaded Fe3O4@PVA microspheres have the function of MRI, embolotherapy and chemotherapy. We expect our study could provide a simple and useful approach for the systematic design, fabrication, and application of a new type of magnetic microspheres as a triple-functional embolic agent for the development of MRI-guided TACE. Statement of significanceDue to the low tissue resolution and hazardous ionization radiation of X-ray digital subtraction angiography, it is beneficial to study MR imaging embolic microspheres for the development of MRI-guided TACE. In this study, a one-step electrospray method was firstly developed to fabricate PVA microspheres encapsulated with in situ synthesized Fe3O4 nanoparticles. Then, chemotherapeutic agent (DOX), contrast media of MRI (Fe3O4) and embolic agent (PVA matrix) were combined together in one body (DOX-loaded Fe3O4@PVA microspheres) to achieve the triple effects of chemotherapy, MR imaging and embolization. This triple-functional embolic agent offers potential for the future development of MRI-guided TACE.

Assessing Blood Flow in an Intracranial Stent: A Feasibility Study of MR Angiography Using a Silent Scan after Stent-Assisted Coil Embolization for Anterior Circulation Aneurysms.

Blood flow in an intracranial stent cannot be visualized with 3D time-of-flight MR angiography owing to magnetic susceptibility and radiofrequency shielding. As a novel follow-up tool after stent-assisted coil embolization, we applied MRA by using a Silent Scan algorithm that contains an ultrashort TE combined with an arterial spin-labeling technique (Silent MRA). The purpose of this study was to determine whether Silent MRA could visualize flow in an intracranial stent placed in the anterior circulation. Nine patients treated with stent-assisted coil embolization for anterior circulation aneurysms underwent MRAs (Silent MRA and TOF MRA) and x-ray digital subtraction angiography. MRAs were performed in the same session on a 3T unit. Two neuroradiologists independently reviewed the MRA images and subjectively scored flow in a stent as 1 (not visible) to 4 (excellent) by referring to the latest x-ray digital subtraction angiography image as a criterion standard. Both observers gave MRA higher scores than TOF MRA for flow in a stent in all cases. The mean score for Silent MRA was 3.44 ± 0.53, and for TOF MRA, it was 1.44 ± 0.46 (P < .001). Silent MRA was able to visualize flow in an intracranial stent more effectively than TOF MRA. Silent MRA might be useful for follow-up imaging after stent-assisted coil embolization, though these study results may be only preliminary due to some limitations.

Biplane Angiography for Experimental Validation of Computational Fluid Dynamic Models of Blood Flow in Artificial Lungs

This article presents an investigation into the validation of velocity fields obtained from computational fluid dynamic (CFD) models of flow through the membrane oxygenators using x-ray digital subtraction angiography (DSA). Computational fluid dynamic is a useful tool in characterizing artificial lung devices, but numerical results must be experimentally validated. We used DSA to visualize flow through a membrane oxygenator at 2 L/min using 37% glycerin at 22°C. A Siemens Artis Zee system acquired biplane x-ray images at 7.5 frames per second, after infusion of an iodinated contrast agent at a rate of 33 ml/s. A maximum cross-correlation (MCC) method was used to track the contrast perfusion through the fiber bundle. For the CFD simulations, the fiber bundle was treated as a single momentum sink according to the Ergun equation. Blood was modeled as a Newtonian fluid, with constant viscosity (3.3 cP) and density (1050 kg/m3). Although CFD results and experimental pressure measurements were in general agreement, the simulated 2 L/min perfusion did not reproduce the flow behavior seen in vitro. Simulated velocities in the fiber bundle were on average 42% lower than experimental values. These results indicate that it is insufficient to use only pressure measurements for validation of the flow field because pressure-validated CFD results can still significantly miscalculate the physical velocity field. We have shown that a clinical x-ray modality, together with a MCC tracking algorithm, can provide a nondestructive technique for acquiring experimental data useful for validation of the velocity field inside membrane oxygenators.

Improved Computational Fluid Dynamic Simulations of Blood Flow in Membrane Oxygenators from X-ray Imaging

Computational fluid dynamics (CFD) is a useful tool in characterizing artificial lung designs by providing predictions of device performance through analyses of pressure distribution, perfusion dynamics, and gas transport properties. Validation of numerical results in membrane oxygenators has been predominantly based on experimental pressure measurements with little emphasis placed on confirmation of the velocity fields due to opacity of the fiber membrane and limitations of optical velocimetric methods. Biplane X-ray digital subtraction angiography was used to visualize flow of a blood analogue through a commercial membrane oxygenator at 1-4.5 L/min. Permeability and inertial coefficients of the Ergun equation were experimentally determined to be 180 and 2.4, respectively. Numerical simulations treating the fiber bundle as a single momentum sink according to the Ergun equation accurately predicted pressure losses across the fiber membrane, but significantly underestimated velocity magnitudes in the fiber bundle. A scaling constant was incorporated into the numerical porosity and reduced the average difference between experimental and numerical values in the porous media regions from 44±4% to 6±5%.

Spatially regularized region-based perfusion estimation in peripherals using angiographic C-arm systems

The outcome assessment of endovascular revascularization procedures in the lower limbs is currently carried out by x-ray digital subtraction angiography (DSA). Due to the two-dimensional nature of this technique, only visual assessment of arterial blood flow is possible and no tissue blood flow information (i.e. perfusion) is available to assess the effective restoration of blood supply to the tissue. In this work, we propose a method for interventional perfusion estimation in peripherals using C-arms which is based on DSA and two additional 3D images reconstructed from rotational scans. The method assumes spatial homogeneity of contrast within multiple regions identified by segmentation of the reconstructed 3D images. A dedicated segmentation method which relies on local contrast homogeneity and connectivity of anatomical structures is introduced. Region-based perfusion is obtained by mapping the 2D blood flow information from DSA to the 3D segments by solving an inverse problem. Instability of the solution due to the spatial overlap of the regions is addressed by applying spatial and temporal regularizations. The method was evaluated on data simulated from CT perfusion scans of the lower limb. Blood flow values estimated with the optimal number of segmented regions exhibited errors of 1 ± 4 and 2 ± 11 ml/100 ml min−1 for the two analyzed cases, respectively, which showed to be sufficient to differentiate hypoperfused and normally perfused areas. The use of spatial and temporal regularization proved to be an effective way to limit inaccuracies due to instability in the solution of the inverse problem. Results in general proved the feasibility of C-arm interventional perfusion imaging by a combination of temporal information derived from DSA and spatial information derived from 3D reconstructions.

In Vivo X-Ray Digital Subtraction and CT Angiography of the Murine Cerebrovasculature Using an Intra-Arterial Route of Contrast Injection

Investigation of the anatomy, patency, and blood flow of arterial and venous vessels in small animal models of cerebral ischemia, venous thrombosis, or vasospasm is of major interest. However, due to their small caliber, in vivo examination of these vessels is technically challenging. Using micro-CT, we compared the feasibility of in vivo DSA and CTA of the murine cerebrovasculature using an intra-arterial route of contrast administration. The ECA was catheterized in 5 C57BL/6J mice. During intra-arterial injection of an iodized contrast agent (30 μL/1 sec), DSA of the intra- and extracranial vessels was performed in mice breathing room air and repeated in hypoxic/hypercapnic mice. Micro-CTA was performed within 20 seconds of intra-arterial contrast injection (220 μL/20 sec). Image quality of both methods was compared. Radiation dose measurements were performed with thermoluminescence dosimeters. Both methods provided high-resolution images of the murine cerebrovasculature, with the smallest identifiable vessel calibers of ≤ 50 μm. Due to its high temporal resolution of 30 fps, DSA allowed identification of anastomoses between the ICA and ECA by detection of retrograde flow within the superficial temporal artery. Micro-CTA during intra-arterial contrast injection resulted in a reduced injection volume and a higher contrast-to-noise ratio (19.0 ± 1.0) compared with DSA (10.0 ± 1.8) or micro-CTA when using an intravenous injection route (1.3 ± 0.4). DSA of the murine cerebrovasculature is feasible using micro-CT and allows precise and repeated measurements of the vessel caliber, and changes of the vessel caliber, while providing relevant information on blood flow in vivo.

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.

A theoretical comparison of x-ray angiographic image quality using energy-dependent and conventional subtraction methods

X-ray digital subtraction angiography (DSA) is widely used for vascular imaging. However, the need to subtract a mask image can result in motion artifacts and compromised image quality. The current interest in energy-resolving photon-counting (EPC) detectors offers the promise of eliminating motion artifacts and other advanced applications using a single exposure. The authors describe a method of assessing the iodine signal-to-noise ratio (SNR) that may be achieved with energy-resolved angiography (ERA) to enable a direct comparison with other approaches including DSA and dual-energy angiography for the same patient exposure. A linearized noise-propagation approach, combined with linear expressions of dual-energy and energy-resolved imaging, is used to describe the iodine SNR. The results were validated by a Monte Carlo calculation for all three approaches and compared visually for dual-energy and DSA imaging using a simple angiographic phantom with a CsI-based flat-panel detector. The linearized SNR calculations show excellent agreement with Monte Carlo results. While dual-energy methods require an increased tube heat load of 2× to 4× compared to DSA, and photon-counting detectors are not yet ready for angiographic imaging, the available iodine SNR for both methods as tested is within 10% of that of conventional DSA for the same patient exposure over a wide range of patient thicknesses and iodine concentrations. While the energy-based methods are not necessarily optimized and further improvements are likely, the linearized noise-propagation analysis provides the theoretical framework of a level playing field for optimization studies and comparison with conventional DSA. It is concluded that both dual-energy and photon-counting approaches have the potential to provide similar angiographic image quality to DSA.

Application of ICA to X-ray coronary digital subtraction angiography

In this paper, an effective method is developed to extract coronary vessels from X-ray digital subtraction angiography (DSA). Unlike the traditional image subtraction technique, we used the independent component analysis (ICA) to separate the coronary vessels and backgrounds. Experimental results indicate that the proposed method can achieve better performance than the traditional subtraction technique and the segmentation method.

Phenylephrine-modulated cardiopulmonary blood flow measured with use of X-ray digital subtraction angiography

Introduction Cardiopulmonary blood flow is an important indicator of organ function. Limitations in measuring blood flow in live rodents suggest that rapid physiological changes may be overlooked. For instance, relative measurements limit imaging to whole organs or large sections without adequately visualizing vasculature. Additionally, current methods use small samples and invasive techniques that often require killing animals, limiting sampling speed, or both. A recently developed high spatial- and temporal-resolution X-ray digital subtraction angiography (DSA) system visualizes vasculature and measures blood flow in rodents. This study was the first to use this system to measure changes in cardiopulmonary blood flow in rats after administering the vasoconstrictor phenylephrine. Methods Cardiopulmonary blood flow and vascular anatomy were assessed in 11 rats before, during, and after recovery from phenylephrine. After acquiring DSA images at 12 time points, a calibrated non-parametric deconvolution technique using singular value decomposition (SVD) was applied to calculate quantitative aortic blood flow in absolute metrics (mL/min). Trans-pulmonary transit time was calculated as the time interval between maximum signal enhancement in the pulmonary trunk and aorta. Pulmonary blood volume was calculated based on the central volume principle. Statistical analysis compared differences in trans-pulmonary blood volume and pressure, and aortic diameter using paired t-tests on baseline, peak, and late-recovery time points. Results Phenylephrine had dramatic qualitative and quantitative effects on vascular anatomy and blood flow. Major vessels distended significantly (aorta, ~ 1.2-times baseline) and mean arterial blood pressure increased ~ 2 times. Pulmonary blood volume, flow, pressure, and aortic diameter were not significantly different between baseline and late recovery, but differences were significant between baseline and peak, as well as peak and recovery time points. Discussion The DSA system with calibrated SVD technique acquired blood flow measurements every 30 s with a high level of regional specificity, thus providing a new option for in vivo functional assessment in small animals.

P-003 Intraarterial MR perfusion imaging of meningiomas: comparison to digital subtraction angiography

PurposeMeningiomas often benefit from preoperative embolization in order to reduce operative blood loss and associated morbidity. Intraarterial (IA) administration of contrast directly into the external carotid artery (ECA) and internal...

Comparative imaging study in ultrasound, MRI, CT, and DSA using a multimodality renal artery phantom

A range of anatomically realistic multimodality renal artery phantoms consisting of vessels with varying degrees of stenosis was developed and evaluated using four imaging techniques currently used to detect renal artery stenosis (RAS). The spatial resolution required to visualize vascular geometry and the velocity detection performance required to adequately characterize blood flow in patients suffering from RAS are currently ill-defined, with the result that no one imaging modality has emerged as a gold standard technique for screening for this disease. The phantoms, which contained a range of stenosis values (0%, 30%, 50%, 70%, and 85%), were designed for use with ultrasound, magnetic resonance imaging, x-ray computed tomography, and x-ray digital subtraction angiography. The construction materials used were optimized with respect to their ultrasonic speed of sound and attenuation coefficient, MR relaxometry (T1, T2) properties, and Hounsfield number/x-ray attenuation coefficient, with a design capable of tolerating high-pressure pulsatile flow. Fiducial targets, incorporated into the phantoms to allow for registration of images among modalities, were chosen to minimize geometric distortions. High quality distortion-free images of the phantoms with good contrast between vessel lumen, fiducial markers, and background tissue to visualize all stenoses were obtained with each modality. Quantitative assessments of the grade of stenosis revealed significant discrepancies between modalities, with each underestimating the stenosis severity for the higher-stenosed phantoms (70% and 85%) by up to 14%, with the greatest discrepancy attributable to DSA. The design and construction of a range of anatomically realistic renal artery phantoms containing varying degrees of stenosis is described. Images obtained using the main four diagnostic techniques used to detect RAS were free from artifacts and exhibited adequate contrast to allow for quantitative measurements of the degree of stenosis in each phantom. Such multimodality phantoms may prove useful in evaluating current and emerging US, MRI, CT, and DSA technology.