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 &lt; 0.001) and U-Net (P &lt; 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.</p

Spatio-Temporal U-Net for Cerebral Artery and Vein Segmentation in Digital Subtraction Angiography

X-ray digital subtraction angiography (DSA) is widely used for vessel and/or flow visualization and interventional guidance during endovascular treatment of patients with a stroke or aneurysm. To assist in peri-operative decision making as well as post-operative prognosis, automatic DSA analysis algorithms are being developed to obtain relevant image-based information. Such analyses include detection of vascular disease, evaluation of perfusion based on time intensity curves (TIC), and quantitative biomarker extraction for automated treatment evaluation in endovascular thrombectomy. Methodologically, such vessel-based analysis tasks may be facilitated by automatic and accurate artery-vein segmentation algorithms. The present work describes to the best of our knowledge the first study that addresses automatic artery-vein segmentation in DSA using deep learning. We propose a novel spatio-temporal U-Net (ST U-Net) architecture which integrates convolutional gated recurrent units (ConvGRU) in the contracting branch of U-Net. The network encodes a 2D+t DSA series of variable length and decodes it into a 2D segmentation image. On a multi-center routinely acquired dataset, the proposed method significantly outperformed U-Net (P<0.001) and traditional Frangi-based K-means clustering (P$<$0.001). Particularly in artery-vein segmentation, ST U-Net achieved a Dice coefficient of 0.794, surpassing the existing state-of-the-art methods by a margin of 12\%-20\%. Code will be made publicly available upon acceptance

Cost-efficient anthropomorphic head phantom for quantitative image quality assessment in cone beam CT

In this study, a novel anthropomorphic head phantom for quantitative image quality assessment in cone beam computed tomography (CBCT) is proposed. The phantom is composed of tissue equivalent materials (TEMs) which are suitable for cost-efficient fabrication methods such as silicone casting and 3D printing. A monocalcium phosphate/gypsum mixture (MCPHG), nylon and a silyl modified polymer gel (SMP) are proposed as bone, muscle and brain equivalent materials respectively. The TEMs were evaluated for their radiodensity in terms of Hounsfield Units (HU) and their x-ray scatter characteristics. The median radiodensity and inter quartile range (IQR) of the MCPHG and SMP were found to be within the range of the theoretical radiodensity for bone and brain tissue: 922 (IQR = 156) and 47 (IQR = 7) HU respectively. The median radiodensity of nylon was slightly outside of the HU range of muscle tissue, but within the HU range of a combination of muscle and adipose tissue: −18 (IQR = 40) HU. The median ratios between the measured scatter characteristics and simulated tissues were between 0.84 and 1.13 (IQR between 0.05 and 0.14). The preliminary results of this study show that the proposed design and TEMs are potentially suitable for the fabrication of a cost-efficient anthropomorphic head phantom for quantitative image quality assessment in CT or CBCT.</p

Fused magnetic resonance angiography and 2D fluoroscopic visualization for endovascular intracranial neuronavigation Technical note

Advanced transluminal neurovascular navigation is an indispensable image-guided method that allows for real-time navigation of endovascular material in critical neurovascular settings. Thus far, it has been primarily based on 2D and 3D angiography, burdening the patient with a relatively high level of iodinated contrast. However, in the patients with renal insufficiency, this method is no longer tolerable due to the contrast load. The authors present a novel image guidance technique based on periprocedural fluoroscopic images fused with a preinterventionally acquired MRI data set. The technique is illustrated in a case in which the fused image combination was used for endovascular treatment of a giant cerebral aneurysm. (http://thejns.org/doi/abs/10.3171/2012.11.JNS111355

NeRF for 3D Reconstruction from X-ray Angiography:Possibilities and Limitations

Neural Radiance Field (NeRF) is a promising deep learning technique based on neural rendering for three-dimensional (3D) reconstruction. This technique has overcome several limitations of 3D reconstruction techniques, such as removing the need for 3D ground truth or two-dimensional (2D) segmentations. In the medical context, the 3D reconstruction of vessels from 2D X-ray angiography is a relevant problem. For example, the treatment of coronary arteries could still benefit from 3D reconstruction solutions, as common solutions do not suffice. Challenging areas in the 3D reconstruction from X-ray angiography are the vessel morphology characteristics, such as sparsity, overlap, and the distinction between foreground and background. Moreover, sparse view and limited angle X-ray projections restrict the information available for the 3D reconstructions. Many traditional and machine learning methods have been proposed, but they rely on demanding user interactions or require large amounts of training data. NeRF could solve these limitations, given that promising results have been shown for medical (X-ray) applications. However, to the best of our knowledge, no results have been shown with X-ray angiography projections or consider the vessel morphology characteristics. This paper explores the possibilities and limitations of using NeRF for 3D reconstruction from X-ray angiography. An extensive experimental analysis is conducted to quantitatively and qualitatively evaluate the effects of the X-ray angiographic challenges on the reconstruction quality. We demonstrate that NeRF has the potential for 3D X-ray angiography reconstruction (e.g., reconstruction with sparse and limited angle X-ray projections) but also identify explicit limitations (e.g., the overlap of background structures) that must be addressed in future works.</p

Iterative reconstruction anti-correlated ROF model for noise reduction in dual-energy CBCT imaging

A Dual-Energy CT (DECT) with a spectral detector greatly extends the capabilities of CT by incorporating energy-dependent information of the X-ray attenuation. In order to fully exploit DECT capabilities, it is required to perform a process known as spectral decomposition. However, this process is sensitive to noise, suffers from reduced photon count per layer in DECT scans and generates anti-correlated noise in the estimated material specific images. In order to overcome these problems, the Anti-Correlated Rudin, Osher and Fatemi (AC-ROF) model is applied for noise reduction, exploiting the relationship between the material-specific images. However, this model deteriorates the structural information with intense noise. In this paper we propose to extend this method by integrating it into an iterative reconstruction procedure to improve the noise reduction performance. The resulting algorithm is called Iterative Reconstruction AC-ROF, or IR-AC-ROF. We have tested AC-ROF and IR-AC-ROF algorithms with realistic brain simulation phantoms and show encouraging results indicating that the resulting material-specific images of IR-AC-ROF can generate better mono-energetic images with improved brain structure visibility. This demonstrates the benefit of including the noise reduction constraints within the reconstruction procedure, rather than using them in a post-processing step

Towards quantitative digital subtraction perfusion angiography: An animal study

Background: 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. Purpose: To comparatively study the independence of deconvolution-based perfusion DSA with respect to varying injection protocols, as well as its sensitivity to alterations in brain conditions. Methods: We developed a deconvolution-based algorithm to compute perfusion parametric images from DSA, including cerebral blood volume (CBV (Formula presented.)), cerebral blood flow (CBF (Formula presented.)), time to maximum (Tmax), and mean transit time (MTT (Formula presented.)) 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 cerebral condition. Results: 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-derived parameters. Conclusions: 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 neurovascular interventions

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