Lung lobe segmentation is clinically important for disease classification, treatment and follow-up of pulmonary diseases. Diseases such as tuberculosis and silicolis typically present in specific lobes i.e. almost exclusively the upper ones. However, the fissures separating different lobes are often difficult to detect because of their variable shape, appearance and low contrast in computed tomography images. In addition, a substantial fraction of patients have missing or incomplete fissures. To solve this problem, several methods have been employed to interpolate incomplete or missed fissures. For example, Pu et al. used an implicit surface fitting with different radial basis functions; Ukil et al. apply fast marching methods; and Ross et al. used an interactive thin plate spline (TPS) interpolation where the user selects the points that will be used to compute the fissure interpolation via TPS. In our study, results of an automated fissure detection method based on a plate-filter as well points derived from vessels were fed into an a robust TPS interpolation that ultimately defined the lobes. To improve the selection of detected points, we statistically determined the areas where fissures are localized from 19 data-sets. These areas were also used to constrain TPS fitting so it reflected the expected shape and orientation of the fissures, hence improving result accuracy. Regions where the detection step provided low response were replaced by points derived from a distance-to-vessels map. The error, defined as the Euclidian mean distance between ground truth points and the TPS fitted fissures, was computed for each dataset to validate our results. Ground truth points were defined for both exact fissure locations and approximate fissure locations (when the fissures were not clearly visible). The mean error was 5.64±4.83 mm for the exact ground truth points, and 10.01 ± 8.23 mm for the approximate ground truth points.
Recent studies have found correlation between the risk of rupture of saccular aneurysms and their morphological
characteristics, such as volume, surface area, neck length, among others. For reliably exploiting these parameters
in endovascular treatment planning, it is crucial that they are accurately quantified. In this paper, we present
a novel framework to assist physicians in accurately assessing saccular aneurysms and efficiently planning for
endovascular intervention. The approach consists of automatically segmenting the pathological vessel, followed
by the construction of its surface representation. The aneurysm is then separated from the vessel surface
through a graph-cut based algorithm that is driven by local geometry as well as strong prior information. The
corresponding healthy vessel is subsequently reconstructed and measurements representing the patient-specific
geometric parameters of pathological vessel are computed. To better support clinical decisions on stenting and
device type selection, the placement of virtual stent is eventually carried out in conformity with the shape of the
diseased vessel using the patient-specific measurements. We have implemented the proposed methodology as a
fully functional system, and extensively tested it with phantom and real datasets.
A simple and efficient method for guiding 2D-image reading for colon screening is proposed. It provides visual
feedback by highlighting the region of interest in the current 2D cross section and indicates the direction in which to
scroll based on the anatomical structure of the colon given by the centerline. Unobserved areas are calculated using a
region growing algorithm and displayed in a 3D view to guarantee a complete inspection. This technique is intended to
significantly reduce any chance of inadvertently skipping over portions of the colon in the inspection process and to
generate faster examination times. The visual feedback can also be used as a guided learning tool for inexperienced
radiologists.
The recent introduction of ultrathin bronchoscopy offers considerable promise for diagnosing even small peripheral lung nodules previously considered inaccessible for routine flexible bronchoscopy. However this requires obtaining an accurate roadmap prior to endoscopy. Although virtual bronchoscopy (VB) has proved to be a useful tool for planning transbronchial interventions involving the central airways, to date, VB has received little attention for providing roadmaps to peripheral lesions. This may be especially problematic, as ultrathin bronchoscopes can now access airways not visualized on routine high-resolution CT scanners. We propose to extend the reach of virtual bronchoscopy by using peripheral arteries as surrogates for peripheral bronchi that cannot be identified even with high-resolution CT technique. Since every bronchus is accompanied by an artery, it should hypothetically be possible to substitute one for another and derive useful navigational roadmaps. This paper presents a preliminary investigation of this concept, using a combination of virtual endoscopic techniques. Virtual angioscopic and bronchoscopic flythroughs are created and transition points are selected at points that can be easily identified on CT images as corresponding structures. The proximal bronchial path and the distal arterial path are then combined and presented as a single continuous flythrough. Our preliminary investigations show that as expected, the local geometry of the airway and corresponding artery are similar. In addition to visual inspection, we use the segmentation of the arterial and bronchial trees and their tree models. Selected paths from each tree model are compared by various similarity measures in order to demonstrate their correspondence. We anticipate that this technique for bronchoscopy planning will enable bronchoscopic evaluation of previously unreachable peripheral lung nodules.
A system for planning transbronchial needle aspiration (TBNA) based on high-resolution chest CT is presented, comprising 2D axial, coronal or sagittal views and a 3D perspective intra-luminal view of the airways. The biopsy site can be defined interactively on the 2D views, and is displayed as 3D object across the translucent bronchial wall. Reference points can be placed on anatomical landmarks like the carina, which allows measuring 3D distances to viewpoints or to other landmarks. Orientation of the targets can be estimated based on a consistent orientation of the virtual endoscopic view. The system can be used as a pre-interventional planning tool, or simultaneously during the biopsy, in order to select the optimal needle insertion points. The system does not provide registration between the virtual and the real images, and does not require special hardware for tracking or any modifications of the bronchoscope. A phantom study comprising three bronchoscopists with different levels of experience showed a significant increase in yield compared to the traditional procedure based on axial CT images alone.
To judge the potential benefit of a new x-ray detector technology and to be able to compare different technologies, some standard performance measurements must be defined. In addition to technology-related parameters which may influence weight, shape, image distortions and readout speed, there are fundamental performance parameters which directly influence the achievable image quality and dose efficiency of x-ray detectors. A standardization activity for detective quantum efficiency (DQE) for static detectors is already in progress. In this paper we present a methodology for noise power spectrum (NPS), low frequency drop (LFD) and signal to electronic noise ratio (SENR), and the influence of these parameters on DQE. The individual measurement methods are described in detail with their theoretical background and experimental procedure. Corresponding technical phantoms have been developed. The design of the measurement methods and technical phantoms is tuned so that only minimum requirements are placed on the detector properties. The measurement methods can therefore be applied to both static and dynamic x-ray systems. Measurement results from flat panel imagers and II/TV systems are presented.
We are developing a video see-through head-mounted display (HMD) augmented reality (AR) system for image-guided neurosurgical planning and navigation. The surgeon wears a HMD that presents him with the augmented stereo view. The HMD is custom fitted with two miniature color video cameras that capture a stereo view of the real-world scene. We are concentrating specifically at this point on cranial neurosurgery, so the images will be of the patient's head. A third video camera, operating in the near infrared, is also attached to the HMD and is used for head tracking. The pose (i.e., position and orientation) of the HMD is used to determine where to overlay anatomic structures segmented from preoperative tomographic images (e.g., CT, MR) on the intraoperative video images. Two SGI 540 Visual Workstation computers process the three video streams and render the augmented stereo views for display on the HMD. The AR system operates in real time at 30 frames/sec with a temporal latency of about three frames (100 ms) and zero relative lag between the virtual objects and the real-world scene. For an initial evaluation of the system, we created AR images using a head phantom with actual internal anatomic structures (segmented from CT and MR scans of a patient) realistically positioned inside the phantom. When using shaded renderings, many users had difficulty appreciating overlaid brain structures as being inside the head. When using wire frames, and texture-mapped dot patterns, most users correctly visualized brain anatomy as being internal and could generally appreciate spatial relationships among various objects. The 3D perception of these structures is based on both stereoscopic depth cues and kinetic depth cues, with the user looking at the head phantom from varying positions. The perception of the augmented visualization is natural and convincing. The brain structures appear rigidly anchored in the head, manifesting little or no apparent swimming or jitter. The initial evaluation of the system is encouraging, and we believe that AR visualization might become an important tool for image-guided neurosurgical planning and navigation.
KEYWORDS: 3D modeling, Image resolution, Reconstruction algorithms, Medical imaging, 3D image processing, 3D metrology, Tomography, Surgery, Radiotherapy, Scanners
We propose a solution to the problem of 3D reconstruction from cross-sections, based on the Delaunay triangulation of object contours. Its properties--especially the close relationship to the medial axis--enable us to do a compact tetrahedrization resulting in a nearest-neighbor connection. The reconstruction of complex shapes is improved by adding vertices on and inside contours.
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