This study proposes a method for territorial segmentation and volumetric flow rate (VFR) distribution measurement of cerebral territories based on time-resolved contrast enhanced magnetic-resonance-angiography (MRA). The method uses an iterative region-growing algorithm based on bolus-arrival-time with increased temporal resolution. Eight territories were segmented: (1) right and (2) left internal carotid arteries, including the middle cerebral artery (ICA+MCA), excluding the anterior cerebral arteries (ACA); (3) right and left ACA (R+L-ACA); (4) right and (5) left external carotid arteries (ECA); (6) right and (7) left posterior cerebral arteries (PCA); and (8) vertebrobasilar territory. VFR percentage, relative to the entire brain (rVFR), was measured based on territorial volume as a function of time. Mean rVFR values of fifteen healthy subjects were: ICA+MCA = 23 ± 2%, R + L-ACA = 17 ± 3%, ECA = 4 ± 2%, PCA = 12 ± 2%, and vertebrobasilar territory = 31 ± 4%. Excluding the ECA-rVFR, which is underestimated, these values are comparable to previously reported values. Six subjects were scanned twice, demonstrating comparable and even higher reproducibility than previously reported using phase-contrast, yet with faster scan time (∼1 min). This method was implemented in one patient with MCA occlusion and one with Moyamoya syndrome scanned before and after bypass surgery, demonstrating its clinical potential for quantitative assessment of the degree of occlusion and the effect of surgery.

The precise three-dimensional (3-D) segmentation of cerebral vessels from magnetic resonance angiography (MRA) images is essential for the detection of cerebrovascular diseases (e.g., occlusion, aneurysm). The complex 3-D structure of cerebral vessels and the low contrast of thin vessels in MRA images make precise segmentation difficult. We present a fast, fully automatic segmentation algorithm based on statistical model analysis and improved curve evolution for extracting the 3-D cerebral vessels from a time-of-flight (TOF) MRA dataset. Cerebral vessels and other tissue (brain tissue, CSF, and bone) in TOF MRA dataset are modeled by Gaussian distribution and combination of Rayleigh with several Gaussian distributions separately. The region distribution combined with gradient information is used in edge-strength of curve evolution as one novel mode. This edge-strength function is able to determine the boundary of thin vessels with low contrast around brain tissue accurately and robustly. Moreover, a fast level set method is developed to implement the curve evolution to assure high efficiency of the cerebrovascular segmentation. Quantitative comparisons with 10 sets of manual segmentation results showed that the average volume sensitivity, the average branch sensitivity, and average mean absolute distance error are 93.6%, 95.98%, and 0.333 mm, respectively. By applying the algorithm to 200 clinical datasets from three hospitals, it is demonstrated that the proposed algorithm can provide good quality segmentation capable of extracting a vessel with a one-voxel diameter in less than 2 min. Its accuracy and speed make this novel algorithm more suitable for a clinical computer-aided diagnosis system.

Background: Reconstruction of patient-specific biomechanical model of intracranial aneurysm has been based on different imaging modalities. However, different imaging techniques may influence the model geometry and the computational fluid dynamics (CFD) simulation. The aim of this study is to evaluate the differences of the morphological and hemodynamic parameters in the computational models reconstructed from computed tomography angiography (CTA), magnetic resonance angiography (MRA) and 3D rotational angiography (3DRA). Methods: Ten patients with cerebral aneurysms were enrolled in the study. MRA, CTA and 3DRA were performed on all patients. For each patient, three patient-specific models were reconstructed respectively based on the three sets of imaging data of the patient. CFD simulations were performed on each model. Model geometry and hemodynamic parameters were compared between the three models. Results: In terms of morphological parameters, by comparing CTA based models (CM) and 3DRA based models (DM) which were treated as the "standard models", the aspect ratio had the minimum difference (Delta = 8.3 +/- 1.72 %, P = 0.953) and the surface distance was 0.25 +/- 0.07 mm. Meanwhile, by comparing MRA based models (MM) and DM, the size had the minimum difference (Delta = 6.6 +/- 1.85 %, P = 0.683) and the surface distance was 0.36 +/- 0.1 mm. In respect of hemodynamic parameters, all three models showed a similar distribution: low average WSS at the sack, high OSI at the body and high average WSSG at the neck. However, there was a large variation in the average WSS (Delta = 34 +/- 5.13 % for CM, Delta = 40.6 +/- 9.21 % for MM). Conclusion: CTA and MRA have no significant differences in reproducing intracranial aneurysm geometry. The CFD results suggests there might be some significant differences in hemodynamic parameters between the three imaging-based models and this needs to be considered when interpreting the CFD results of different imaging-based models. If we only need to study the main flow patterns, three types of image-based model might be all suitable for patient-specific computational modeling studies.

The human cerebrovasculature is extremely complicated and its three dimensional (3D) highly parcellated models, though necessary, are unavailable. We constructed a digital cerebrovascular model from a high resolution, 3T 3D time-of-flight magnetic resonance angiography scan. This model contains the arterial and venous systems and is 3D, geometric, highly parcellated, fully segmented, and completely labeled with name, diameter, and variants. Our approach replaces the tedious and time consuming process of checking and correcting automatic segmentation results done at 2D image level with an aggregate and faster process at 3D model level. The creation of the vascular model required vessel pre-segmentation, centerline extraction, vascular segments connection, centerline smoothing, vessel surface construction, vessel grouping, tracking, editing, labeling, setting diameter, and checking correctness and completeness. For comparison, the same scan was segmented automatically with 59.8% sensitivity and only 16.5% of vessels smaller than 1 pixel size were extracted. To check and correct this automatic segmentation requires 8 weeks. Conversely, the speedup of our approach (the number of 2D segmented areas/the number of 3D vascular segments) is 34. This cerebrovascular model can serve as a reference framework in clinical, research, and educational applications. The wealth of information aggregated with its quantification capabilities can augment or replace numerous textbook chapters. Five applications of the vascular model were described. The model is easily extendable in content, parcellation, and labeling, and the proposed approach is applicable for building a whole body vascular system.

Accurate and reliable vascular features extracted from 3D time-of-flight (TOF) magnetic resonance angiography (MRA) can help evaluate cerebral vascular diseases and conditions. The goal of this study was to evaluate the reproducibility of an intracranial artery feature extraction (iCafe) algorithm for quantitative analysis of intracranial arteries from TOF MRA.Twenty-four patients with known intracranial artery stenosis were recruited and underwent two separate MRA scans within 2 weeks of each other. Each dataset was blinded to associated imaging and clinical data and then processed independently using iCafe. Inter-scan reproducibility analysis was performed on the 24 pairs of scans while intra-/inter-operator reproducibility and stenosis detection were assessed on 8 individual MRA scans. After tracing the vessels visualized on TOF MRA, iCafe was used to automatically extract the locations with stenosis and eight other vascular features. The vascular features included the following six morphometry and two signal intensity features: artery length (total, distal, and proximal), volume, number of branches, average radius of the M1 segment of the middle cerebral artery, and average normalized intensity of all arteries and large vertical arteries. A neuroradiologist independently reviewed the images to identify locations of stenosis for the reference standard. Reproducibility of stenosis detection and vascular features was assessed using Cohen's kappa, the intra-class correlation coefficient (ICC), and within-subject coefficient of variation (CV).The segment-based sensitivity of iCafe for stenosis detection ranged from 83.3-91.7% while specificity was 97.4%. Kappa values for inter-scan and intra-operator reproducibility were 0.73 and 0.77, respectively. All vascular features demonstrated excellent inter-scan and intra-operator reproducibility (ICC = 0.91-1.00, and CV = 1.21-8.78% for all markers), and good to excellent inter-operator reproducibility (ICC = 0.76-0.99, and CV = 3.27-15.79% for all markers).Intracranial artery features can be reliably quantified from TOF MRA using iCafe to provide both clinical diagnostic assistance and facilitate future investigative quantitative analyses.Copyright © 2018 Elsevier Inc. All rights reserved.

Comprehensive quantification of intracranial artery features may help us assess and understand variations of blood supply during brain development and aging. We analyzed vasculature features of 163 participants (age 56-85 years, mean of 71) from a community study to investigate if any of the features varied with age. Three-dimensional time-of-flight magnetic resonance angiography images of these participants were processed in IntraCranial artery feature extraction technique (a recently developed technique to obtain quantitative features of arteries) to divide intracranial vasculatures into anatomical segments and generate 8 morphometry and intensity features for each segment. Overall, increase in age was found negatively associated with number of branches and average order of intracranial arteries while positively associated with tortuosity, which remained after adjusting for cardiovascular risk factors. The associations with number of branches and average order were consistently found between 3 main intracranial artery regions, whereas the association with tortuosity appeared to be present only in middle cerebral artery/distal arteries. The combination of time-of-flight magnetic resonance angiography and IntraCranial artery feature extraction technique may provide an effective way to study vascular conditions and changes in the aging brain.Copyright © 2019 Elsevier Inc. All rights reserved.

Active contour segmentation and its robust implementation using level set methods are well-established theoretical approaches that have been studied thoroughly in the image analysis literature. Despite the existence of these powerful segmentation methods, the needs of clinical research continue to be fulfilled, to a large extent, using slice-by-slice manual tracing. To bridge the gap between methodological advances and clinical routine, we developed an open source application called ITK-SNAP, which is intended to make level set segmentation easily accessible to a wide range of users, including those with little or no mathematical expertise. This paper describes the methods and software engineering philosophy behind this new tool and provides the results of validation experiments performed in the context of an ongoing child autism neuroimaging study. The validation establishes SNAP intrarater and interrater reliability and overlap error statistics for the caudate nucleus and finds that SNAP is a highly reliable and efficient alternative to manual tracing. Analogous results for lateral ventricle segmentation are provided.

Distal anterior cerebral artery aneurysms are rare and compose about 4.5% of all intracranial aneurysms. They generally arise at the bifurcation of the pericallosal and callosomarginal arteries. Their surgical approach is different from those of other anterior circulation aneurysms. These aneurysms present some special difficulties for neurosurgeons, including narrow exposure in the interhemispheric fissure, dense adhesions between the cingulate gyri, difficulty in controlling the parent artery, and the association of multiple aneurysms and vascular anomalies.Between January 1975 and May 1996, 14 cases of saccular aneurysms of the distal anterior cerebral artery were operated at the University of Hacettepe. The clinical presentations, neuroradiological findings, and operative approaches of these aneurysms were analyzed. In addition, the clinical series and isolated case reports in the English literature were also extensively reviewed.The incidence of the aneurysms in this location was 2.8% of a total of 494 surgically treated cases in our center. Of 14 patients, eight were women and six were men. Multiple aneurysms were found in five patients (35%). All patients were operated via the interhemispheric route. Thirteen patients had good outcome and one patient died.We believe that all difficulties related to distal anterior cerebral artery aneurysms can be minimized with sufficient knowledge of microsurgery and surgical anatomy, using microtechniques and experience.

Biomedical imaging is a driver of scientific discovery and a core component of medical care and is being stimulated by the field of deep learning. While semantic segmentation algorithms enable image analysis and quantification in many applications, the design of respective specialized solutions is non-trivial and highly dependent on dataset properties and hardware conditions. We developed nnU-Net, a deep learning-based segmentation method that automatically configures itself, including preprocessing, network architecture, training and post-processing for any new task. The key design choices in this process are modeled as a set of fixed parameters, interdependent rules and empirical decisions. Without manual intervention, nnU-Net surpasses most existing approaches, including highly specialized solutions on 23 public datasets used in international biomedical segmentation competitions. We make nnU-Net publicly available as an out-of-the-box tool, rendering state-of-the-art segmentation accessible to a broad audience by requiring neither expert knowledge nor computing resources beyond standard network training.