Customizable tubular model for n-furcating blood vessels and its application to 3D reconstruction of the cerebrovascular system.

Understanding the 3D cerebral vascular network is one of the pressing issues impacting the diagnostics of various systemic disorders and is helpful in clinical therapeutic strategies. Unfortunately, the existing software in the radiological workstation does not meet the expectations of radiologists who require a computerized system for detailed, quantitative analysis of the human cerebrovascular system in 3D and a standardized geometric description of its components. In this study, we show a method that uses 3D image data from magnetic resonance imaging with contrast to create a geometrical reconstruction of the vessels and a parametric description of the reconstructed segments of the vessels. First, the method isolates the vascular system using controlled morphological growing and performs skeleton extraction and optimization. Then, around the optimized skeleton branches, it creates tubular objects optimized for quality and accuracy of matching with the originally isolated vascular data. Finally, it optimizes the joints on n-furcating vessel segments. As a result, the algorithm gives a complete description of shape, position in space, position relative to other segments, and other anatomical structures of each cerebrovascular system segment. Our method is highly customizable and in principle allows reconstructing vascular structures from any 2D or 3D data. The algorithm solves shortcomings of currently available methods including failures to reconstruct the vessel mesh in the proximity of junctions and is free of mesh collisions in high curvature vessels. It also introduces a number of optimizations in the vessel skeletonization leading to a more smooth and more accurate model of the vessel network. We have tested the method on 20 datasets from the public magnetic resonance angiography image database and show that the method allows for repeatable and robust segmentation of the vessel network and allows to compute vascular lateralization indices.

Quality improvement of OCT angiograms with elliptical directional filtering.

We present a method of OCT angiography (OCTA) data filtering for noise suppression and improved visualization of the retinal vascular networks in en face projection images. In our approach, we use a set of filters applied in three orthogonal axes in the three-dimensional (3-D) data sets. Minimization of artifacts generated in B-scan-wise data processing is accomplished by filtering the cross-sections along the slow scanning axis. A-scans are de-noised by axial filtering. The core of the method is the application of directional filtering to the C-scans, i.e. one-pixel thick sections of the 3-D data set, perpendicular to the direction of the scanning OCT beam. The method uses a concept of structuring, directional kernels of shapes matching the geometry of the image features. We use rotating ellipses to find the most likely local orientation of the vessels and use the best matching ellipses for median filtering of the C-scans. We demonstrate our approach in the imaging of a normal human eye with laboratory-grade spectral-domain OCT setup. The "field performance" is demonstrated in imaging of diabetic retinopathy cases with a commercial OCT device. The absolute complex differences method is used for the generation of OCTA images from the data collected in the most noise-wise unfavorable OCTA scanning regime-two frame scanning.

Detailed 3D-morphometry of the anterior communicating artery: potential clinical and neurosurgical implications.

PURPOSE: Although a site common for pathology and of great importance to the neurosurgeon, the three-dimensional (3D) morphometry of the anterior communicating artery (ACoA) has had incomplete descriptions in the literature. METHODS: Using a novel 3D digital-image computer data analysis system, 115 patients underwent evaluation of their ACoA based on DICOM files derived from CT angiography. Measurements included the length, internal diameter, volume, deviation index (DI) and tortuosity index (TI). RESULTS: Of 115 samples, 85 were visualized clearly enough for morphometric analysis. The mean internal diameter was 1.86 mm and this tended to be greater in males (P < 0.05). The mean length of the ACoA was 3.99 mm and the mean volume was 11.61 mm(3). The mean TI for the ACoA was 0.84 and the mean DI was 0.62 mm. A significant relationship between DI and length, DI and volume, and DI and TI were found. The significant correlation of diameter to volume, and length related to volume, DI and TI, as well as TI related to length, volume and DI were noticed. There were no relationship between any parameter and age. CONCLUSIONS: A detailed knowledge of the 3D-morphometry of the ACoA demonstrates that in almost 50% of individuals the ACoA is straight in their course. Detailed data regarding arterial topography and trajectory as found in our study may be also of use in detecting early changes in this vessel due to pathology and may assist in the treatment of vascular lesions and planning of neurosurgical or interventional radiological procedures in the region including ACoA aneurysms.

Three-dimensional morphometrical analysis of the M1 segment of the middle cerebral artery: potential clinical and neurosurgical implications.

With an increase in the understanding of the formation and treatment of cerebral aneurysms and an improvement in imaging technology, actual standardized measurement values for the cerebral arteries are necessary. Therefore, the aim of this study was to provide a detailed assessment of the three-dimension (3D) morphology (vessel's curvature and trajectory) and 3D-morphometry of the M1 segment of the middle cerebral artery using computer tomography angiography (CTA) images. The DICOM files from CTA of 40 male and 75 female individuals with a mean age of 50.1 years were analyzed using an interactive postprocessing 3D volume-rendering algorithm. Specifically, the M1 segment was evaluated. Calculations included the length, internal diameter, volume, deviation (DI) and tortuosity indices (TI). The M1 segment had a mean internal diameter of 2.23 mm and was greater in men. M1 asymmetry was identified in 23.4% of the individuals and was more common in women. The mean length was 15.62 mm and the left M1 segments were a little longer. The mean volume of the M1 segments was 63.92 mm(3) , and this was typically greater in men and on the left sides. The mean TI and DI for the M1 segment were 0.91 and 2.17 mm, respectively. Therefore, the M1 segments are only slightly curved or straight in their course. In addition, the longest vascular M1 segments are more deviated (curved) and more tortuous. Such standardized data as presented herein may be useful in the preprocedural evaluation of patients with intracranial vascular pathology of the M1 segment.

Three-dimensional morphometry of the A2 segment of the anterior cerebral artery with neurosurgical relevance.

Most prior morphometry data regarding the A2 segment of the anterior cerebral artery (ACA) have been based on cadaveric measurements. With newer imaging modalities, surgical techniques, and minimally invasive procedures, new standards for the anatomy of this vessel are necessary. A novel computer-based data system was used to analyze the three-dimensional (3D) morphometry of 230 A2 segments. In addition, tortuosity (TI) and deviation indices (DI) for this segment were calculated. The mean internal diameter of the A2 segment was 1.86 mm, and segments tended to be larger in men and on left sides. A2 segments were asymmetrical in 43%, and this was more common in women. Lengths tended to be greater on right sides and in men. Volumes were greater in men and increased with age, which was statistically significant. These gender differences were found to be statistically significant (P < 0.05), for both volume and diameter. TI was equal among sides, but DI was more often greater on right sides. The correlation coefficient ratio for length and DI was statistically significant. It is important to understand various 3D morphometrical differences particularly between genders. By constructing blood flow simulation models and during revascularization procedures, surgeons are able to gain a better understanding of each patient's vascular anatomy. These additional 3D data regarding the anatomy of the postcommunicating parts of the ACA may be useful to the neurosurgeon and interventional neuroradiologist. These data may assist with an earlier diagnosis of pathologies affecting the 3D morphology of the ACA.

Segmentation of human brain MR images using rule-based fuzzy logic inference.

The analysis of medical images for the purpose of computer-aided diagnosis and therapy planning includes the segmentation as a preliminary stage for the visualization or the quantification of such data. In this paper, we present a fuzzy segmentation system that is capable of segmenting magnetic resonance (MR) images of a human brain. The presented method consists of two main stages. The histogram analysis based on the S-function membership and Shannon's entropy function is the first step. In the final stage, pixel classification is performed using the rule-based fuzzy logic inference. After the segmentation is complete, attributes of different tissue classes may be determined (e.g., volumes), or the classes may be visualized as spatial objects. The implemented system provides many advanced 3D imaging tools, which enable visual exploration of segmented anatomical structures.