Automated flight path planning for virtual endoscopy

Coordinate-based fast lightweight path search algorithm for electromagnetic navigation bronchoscopy

Electromagnetic navigation bronchoscopy (ENB) uses electromagnetic positioning technology to guide the bronchoscope to accurately and quickly reach the lesion along the planned path. However, enormous data in high-resolution lung computed tomography (CT) and the complex structure of multilevel branching bronchial tree make fast path search challenging for path planning. We propose a coordinate-based fast lightweight path search (CPS) algorithm for ENB. First, the centerline is extracted from the bronchial tree by applying topological thinning. Then, Euclidean-distance-based coordinate search is applied. The centerline points are represented by their coordinates, and adjacent points along the navigation path are selected considering the shortest Euclidean distance to the target on the centerline nearest the lesion. From the top of the trachea centerline, search is repeated until reaching the target. In 50 high-resolution lung CT images acquired from five scanners, the CPS algorithm achieves accuracy, average search time, and average memory consumption of 100%, 88.5 ms, and 166.0 MB, respectively, reducing search time by 74.3% and 73.1% and memory consumption by 83.3% and 83.0% compared with Dijkstra and A* algorithms, respectively. CPS algorithm is suitable for path search in multilevel branching bronchial tree navigation based on high-resolution lung CT images.

Application of cardiovascular virtual endoscopy: a pilot study on roaming path planning for diagnosis of congenital heart diseases in children

To investigate roaming paths planning for diagnosis of congenital heart diseases (CHD) using a cardiovascular virtual endoscopy (VE) system. Forty children were enrolled. VE system was applied to support in establishing a diagnosis. Performance in diagnosing CHDs by CT, VE using automatically planned roaming paths (VE-auto, objects were treated as left heart system and right heart system), VE using manually planned paths (VE-manual), and VE using automatically planned path for left heart system and manually planned path for right heart system (VE-combined) were studied and compared. Comparable accuracy of 93%, 93%, 95% and 95% was found by CT, VE-auto, VE-manual and VE-combined. However, in diagnosing tetralogy of Fallot, significantly higher performance was found by VEs, compared with CT. For VE-auto, poor performance with an accuracy of 85% and sensitivity of 22% was revealed in diagnosing muscular ventricular septal defect, compared with VE-manual and VE-combined. Compared with VE-manual, VE-combined illustrated comparable diagnostic accuracy on all CHDs; however, significantly smaller diagnostic time was utilized (P < 0.05).Cardiovascular VE system demonstrated considerable clinical value in the diagnosis of CHDs. Left and right heart system should not be modeled as two cavity objects simultaneously. When one of two systems is treated as one object, the other system should be treated as three separate objects when using VE to diagnose CHDs.

Path Planning for Semi-automated Simulated Robotic Neurosurgery

This paper considers the semi-automated robotic surgical procedure for removing the brain tumor margins, where the manual operation is a tedious and time-consuming task for surgeons. We present robust path planning methods for robotic ablation of tumor residues in various shapes, which are represented in point-clouds instead of analytical geometry. Along with the path plans, corresponding metrics are also delivered to the surgeon for selecting the optimal candidate in the automated robotic ablation. The selected path plan is then executed and tested on RAVEN^™ II surgical robot platform as part of the semi-automated robotic brain tumor ablation surgery in a simulated tissue phantom.

CT virtual endoscopy for analyzing variations in the hepatic portal vein

PURPOSE

To evaluate the value of CT virtual endoscopy (CTVE) for accurately distinguishing varying types of portal veins (PVs).

MATERIALS AND METHODS

From 526 consecutive patients whose hepatic portal vein was well visualized, patients who were considered to have trifurcation of the main portal vein (MPV) or right portal vein (RPV) on volume rendering (VR) and maximum intensity projection (MIP) imaging were chosen for the analysis. Two radiologists evaluated the PV anatomy of these patients and re-classified the types of PV variation using the CTVE technique. The Wilcoxon rank sum test was used to calculate differences in age between males and females.

RESULTS

Thirty-two patients (20 males, 12 females; age range 21-68 years; mean age 43 years) were considered to have trifurcation of the MPV (15) or RPV (17) on VR and MIP imaging. Fifteen patients in total had a trifurcation confirmed by CTVE (6 patients were proven to have an MPV trifurcation by CTVE, and 9 were confirmed to have a RPV trifurcation).

CONCLUSIONS

CTVE can differentiate PV variations that cannot be identified accurately on MIP and VR.

Topology preserving thinning of cell complexes

A topology preserving skeleton is a synthetic representation of an object that retains its topology and many of its significant morphological properties. The process of obtaining the skeleton, referred to as skeletonization or thinning, is a very active research area. It plays a central role in reducing the amount of information to be processed during image analysis and visualization, computer-aided diagnosis, or by pattern recognition algorithms. This paper introduces a novel topology preserving thinning algorithm, which removes simple cells-a generalization of simple points-of a given cell complex. The test for simple cells is based on acyclicity tables automatically produced in advance with homology computations. Using acyclicity tables render the implementation of thinning algorithms straightforward. Moreover, the fact that tables are automatically filled for all possible configurations allows to rigorously prove the generality of the algorithm and to obtain fool-proof implementations. The novel approach enables, for the first time, according to our knowledge, to thin a general unstructured simplicial complex. Acyclicity tables for cubical and simplicial complexes and an open source implementation of the thinning algorithm are provided as an additional material to allow their immediate use in the vast number of applications arising in medical imaging and beyond.

Optimal procedure planning and guidance system for peripheral bronchoscopy

With the development of multidetector computed-tomography (MDCT) scanners and ultrathin bronchoscopes, the use of bronchoscopy for diagnosing peripheral lung-cancer nodules is becoming a viable option. The work flow for assessing lung cancer consists of two phases: 1) 3-D MDCT analysis and 2) live bronchoscopy. Unfortunately, the yield rates for peripheral bronchoscopy have been reported to be as low as 14%, and bronchoscopy performance varies considerably between physicians. Recently, proposed image-guided systems have shown promise for assisting with peripheral bronchoscopy. Yet, MDCT-based route planning to target sites has relied on tedious error-prone techniques. In addition, route planning tends not to incorporate known anatomical, device, and procedural constraints that impact a feasible route. Finally, existing systems do not effectively integrate MDCT-derived route information into the live guidance process. We propose a system that incorporates an automatic optimal route-planning method, which integrates known route constraints. Furthermore, our system offers a natural translation of the MDCT-based route plan into the live guidance strategy via MDCT/video data fusion. An image-based study demonstrates the route-planning method's functionality. Next, we present a prospective lung-cancer patient study in which our system achieved a successful navigation rate of 91% to target sites. Furthermore, when compared to a competing commercial system, our system enabled bronchoscopy over two airways deeper into the airway-tree periphery with a sample time that was nearly 2 min shorter on average. Finally, our system's ability to almost perfectly predict the depth of a bronchoscope's navigable route in advance represents a substantial benefit of optimal route planning.

Automated tracing of the adventitial contour of aortoiliac and peripheral arterial walls in CT angiography (CTA) to allow calculation of non-calcified plaque burden

Aortoiliac and lower extremity arterial atherosclerotic plaque burden is a risk factor for the development of visceral and peripheral ischemic and aneurismal vascular disease. While prior research allows automated quantification of calcified plaque in these body regions using CT angiograms, no automated method exists to quantify soft plaque. We developed an automatic algorithm that defines the outer wall contour and wall thickness of vessels to quantify non-calcified plaque in CT angiograms of the chest, abdomen, pelvis, and lower extremities. The algorithm encodes the search space as a constrained graph and calculates the outer wall contour by deriving a minimum cost path through the graph, following the visible outer wall contour while minimizing path tortuosity. Our algorithm was statistically equivalent to a reference standard made by two reviewers. Absolute error was 1.9 ± 2.3% compared to the inter-observer variability of 3.9 ± 3.6%. Wall thickness in vessels with atherosclerosis was 3.4 ± 1.6 mm compared to 1.2 ± 0.4 mm in normal vessels. The algorithm shows promise as a tool for quantification of non-calcified plaque in CT angiography. When combined with previous research, our method has the potential to quantify both non-calcified and calcified plaque in all clinically significant systemic arteries, from the thoracic aorta to the arteries of the calf, over a wide range of diameters. This algorithm has the potential to enable risk stratification of patients and facilitate investigations into the relationships between asymptomatic atherosclerosis and a variety of behavioral, physiologic, pathologic, and genotypic conditions.

Automated quantification of aortoaortic and aortoiliac angulation for computed tomographic angiography of abdominal aortic aneurysms before endovascular repair: preliminary study

The degree of angulation of abdominal aortic aneurysms (AAAs) has emerged as an important factor in assessing eligibility for endovascular aneurysm repair (EVAR). The authors developed an automatic algorithm that reduces variability of measurement of aortoiliac angulation. For highly structured manual methods, intraobserver variability was 8.2 degrees ± 5.0 (31% ± 20) and interobserver variability was 5.6 degrees ± 2.5 (20% ± 9.1) compared with 0.6 degrees ± 0.8 (2.2% ± 3.6) (intraobserver) and 0.4 degrees ± 0.4 (1.4% ± 1.9) (interobserver) for the automatic algorithm (P < .01). In phantoms, the automatically measured angles were equivalent to reference values (P < .05). This algorithm was also faster than manual methods and has the potential to enhance the clinical utility and reliability of computed tomographic angiography for preoperative assessment for EVAR.

Uncluttered single-image visualization of the abdominal aortic vessel tree: method and evaluation

PURPOSE

The authors develop a method to visualize the abdominal aorta and its branches, obtained by CT or MR angiography, in a single 2D stylistic image without overlap among branches.

METHODS

The abdominal aortic vasculature is modeled as an articulated object whose underlying topology is a rooted tree. The inputs to the algorithm are the 3D centerlines of the abdominal aorta, its branches, and their associated diameter information. The visualization problem is formulated as an optimization problem that finds a spatial configuration of the bounding boxes of the centerlines most similar to the projection of the input into a given viewing direction (e.g., anteroposterior), while not introducing intersections among the boxes. The optimization algorithm minimizes a score function regarding the overlap of the bounding boxes and the deviation from the input. The output of the algorithm is used to produce a stylistic visualization, made of the 2D centerlines modulated by the associated diameter information, on a plane. The authors performed a preliminary evaluation by asking three radiologists to label 366 arterial branches from the 30 visualizations of five cases produced by the method. Each of the five patients was presented in six different variant images, selected from ten variants with the three lowest and three highest scores. For each label, they assigned confidence and distortion ratings (low/medium/high). They studied the association between the quantitative metrics measured from the visualization and the subjective ratings by the radiologists.

RESULTS

All resulting visualizations were free from branch overlaps. Labeling accuracies of the three readers were 93.4%, 94.5%, and 95.4%, respectively. For the total of 1098 samples, the distortion ratings were low: 77.39%, medium: 10.48%, and high: 12.12%. The confidence ratings were low: 5.56%, medium: 16.50%, and high: 77.94%. The association study shows that the proposed quantitative metrics can predict a reader's subjective ratings and suggests that the visualization with the lowest score should be selected for readers.

CONCLUSIONS

The method for eliminating misleading false intersections in 2D projections of the abdominal aortic tree conserves the overall shape and does not diminish accurate identifiability of the branches.

Prevalence of tracheal collapse in an emphysema cohort as measured with end-expiration CT

RATIONALE AND OBJECTIVES

To retrospectively investigate the prevalence of tracheal collapse in an emphysema cohort. The occurrence of a large degree of tracheal collapse may have important implications for the clinical management of respiratory symptoms and air trapping in patients with emphysema.

MATERIALS AND METHODS

Paired full-inspiratory and end-expiratory thin-section volumetric computed tomographic scans were available for 1071 long-term smokers with clinically and physiologically confirmed emphysema. The percentage reduction in the cross-sectional tracheal luminal area from full-inspiration to end-expiration was automatically computed at 2.5-mm intervals along the centerline of the trachea using customized software.

RESULTS

Maximal tracheal collapse did not follow a normal distribution in the emphysema cohort (P < .0001, skewness/kurtosis tests for normality); the median collapse was 18% (intraquartile range, 11%-30%). Statistically significant differences were found in the distribution of maximal collapse by gender (P < .005, Wilcoxon rank sum test). Overall, 10.5% of men and 17.1% of women showed evidence of tracheomalacia on the basis of the criterion of a reduction of 50% or greater in cross-sectional tracheal luminal area at end-expiration.

CONCLUSION

This study offers insights into the prevalence of tracheal collapse in a cohort of patients with emphysema; future work is needed to determine the possible relationship between tracheal collapse and air trapping in subjects with emphysema.

An improved algorithm for femoropopliteal artery centerline restoration using prior knowledge of shapes and image space data

Accurate arterial centerline extraction is essential for comprehensive visualization in CT Angiography. Time consuming manual tracking is needed when automated methods fail to track centerlines through severely diseased and occluded vessels. A previously described algorithm, Partial Vector Space Projection (PVSP), which uses vessel shape information from a database to bridge occlusions of the femoropopliteal artery, has a limited accuracy in long (>100 mm) occlusions. In this article we introduce a new algorithm, Intermediate Point Detection (IPD), which uses calcifications in the occluded artery to provide additional information about the location of the centerline to facilitate improvement in PVSP performance. It identifies calcified plaque in image space to find the most useful point within the occlusion to improve the estimate from PVSP. In this algorithm candidates for calcified plaque are automatically identified on axial CT slices in a restricted region around the estimate obtained from PVSP. A modified Canny edge detector identifies the edge of the calcified plaque and a convex polygon fit is used to find the edge of the calcification bordering the wall of the vessel. The Hough transform for circles estimates the center of the vessel on the slice, which serves as a candidate intermediate point. Each candidate is characterized by two scores based on radius and relative position within the occluded segment, and a polynomial function is constructed to define a net score representing the potential benefit of using this candidate for improving the centerline. We tested our approach in 44 femoropopliteal artery occlusions of lengths up to 398 mm in 30 patients with peripheral arterial occlusive disease. Centerlines were tracked manually by four-experts, twice each, with their mean serving as the reference standard. All occlusions were first interpolated with PVSP using a database of femoropopliteal arterial shapes obtained from a total of 60 subjects. Occlusions longer than 80 mm (N = 20) were then processed with the IPD algorithm, provided calcifications were found (N = 14). We used the maximum point-wise distance of an interpolated curve from the reference standard as our error metric. The IPD algorithm significantly reduced the average error of the initial PVSP from 2.76 to 1.86 mm (p < 0.01). The error was less than the clinically desirable 3 mm (smallest radius of the femoropopliteal artery) in 13 of 14 occlusions. The IPD algorithm achieved results within the range of the human readers in 11 of 14 cases. We conclude that the additional use of sparse but specific image space information, such as calcified atherosclerotic plaque, can be used to substantially improve the performance of a previously described knowledge-based method to restore the centerlines of femoropopliteal arterial occlusions.

Fast path planning in virtual colonoscopy

We propose a fast path planning algorithm using multi-resolution path tree propagation and farthest visible point. Initial path points are robustly generated by propagating the path tree, and all internal voxels locally most distant from the colon boundary are connected. The multi-resolution scheme is adopted to increase computational efficiency. Control points representing the navigational path are successively selected from the initial path points by using the farthest visible point. The position of the initial path point in a down-sampled volume is accurately adjusted in the original volume. Using the farthest visible point, the number of control points is adaptively changed according to the curvature of the colon shape so that more control points are assigned to highly curved regions. Furthermore, a smoothing step is unnecessary since our method generates a set of control points to be interpolated with the cubic spline interpolation. We applied our method to 10 computed tomography datasets. Experimental results showed that the path was generated much faster than using conventional methods without sacrificing accuracy, and clinical efficiency. The average processing time was approximately 1s when down-sampling by a factor of 2, 3, or 4. We concluded that our method is useful in diagnosing colon cancer using virtual colonoscopy.

Semiautomated quantification of the mass and distribution of vascular calcification with multidetector CT: method and evaluation

Institutional review board approval was obtained for this HIPAA-compliant study. Informed consent was obtained for prospective evaluation in 21 asymptomatic volunteers (10 women, 11 men; mean age, 60 years) but waived for retrospective (10 patients with and five patients without disease) evaluation. Prospective validation was in phantoms. Quantification of mass and calcium distribution was performed with fast semiautomated method, without calibration. For actual versus measured mass in phantoms, R(2) was 0.98; absolute and percentage errors were 1.2 mg and 9.1%, respectively. In asymptomatic volunteers, mean interscan variability for calcium mass quantification in extracoronary arteries was 24.9 mg; mean was 991 units for Agatston scoring. In coronary arteries, mean variability was 5.5 mg; mean Agatston variability was 27.7 units. At retrospective computed tomography, mean total calcified mass was 321.3 mg. Accurate quantification of mass and distribution of calcification in simulated arteries with this method can be applied in vivo, with low interscan variability.

Femoropopliteal artery centerline interpolation using contralateral shape

Curved planar reformation allows comprehensive visualization of arterial flow channels, providing information about calcified and noncalcified plaques and degrees of stenoses. Existing semiautomated centerline-extraction algorithms for curved planar reformation generation fail in severely diseased and occluded arteries. We explored whether contralateral shape information could be used to reconstruct centerlines through femoropopliteal occlusions. We obtained CT angiography data sets of 29 subjects (16m/13f, 19-86yo) without peripheral arterial occlusive disease and five consecutive subjects (1m/4f, 54-85yo) with unilateral femoropopliteal arterial occlusions. A gradient-based method was used to extract the femoropopliteal centerlines in nondiseased segments. Centerlines of the five occluded segments were manually determined by four experts, two times each. We interpolated missing centerlines in 2475 simulated occlusions of various occlusion lengths in nondiseased subjects. We used different curve registration methods (reflection, similarity, affine, and global polynomial) to align the nonoccluded segments, matched the end points of the occluded segments to the corresponding patent end points, and recorded maximum Euclidean distances to the known centerlines. We also compared our algorithm to an existing knowledge-based PCA interpolation algorithm using the nondiseased subjects. In the five subjects with real femoropopliteal occlusions, we measured the maximum Euclidean distance and the percentage of the interpolation that remained within a typical 3 mm radius vessel. In the nondiseased subjects, we found that the rigid registration methods were not significantly (p<0.750) different among themselves but were more accurate than the nonrigid methods (p<0.001). In simulations using nondiseased subjects, our method produced centerlines that stayed within 3 mm of a semiautomatically tracked centerline in occlusions up to 100 mm in length; however, the PCA method was significantly more accurate for all occlusions lengths. In the actual clinical cases, we found the following [occlusion length (mm):error (mm)]: 16.5:0.775, 42.0:1.54, 79.9:1.82, 145:3.23, and 292:6.13, which were almost always more accurate than the PCA algorithm. We conclude that the use of contralateral shape information, when available, is a promising method for the interpolation of centerlines through arterial occlusions.

Path planning for virtual bronchoscopy

We have developed an automated path planning method, which enables virtual bronchoscopic 3D multidetector computed tomography (MDCT) image analysis and follow on image-guided bronchoscopy. The method fundamentals are novel combination of distance transformation and snake-based models. The computation time of our algorithm is faster than similar works and there were no missing or false branches in the final path of airways. The planned path is suitable for quantitative airway analysis and smooth virtual navigation.

An automatic and fast centerline extraction algorithm for virtual colonoscopy

This paper introduces a new refined centerline extraction algorithm, which is based on and significantly improved from distance mapping algorithms. The new approach include three major parts: employing a colon segmentation method; designing and realizing a fast Euclidean Transform algorithm and inducting boundary voxels cutting (BVC) approach. The main contribution is the BVC processing, which greatly speeds up the Dijkstra algorithm and improves the whole performance of the new algorithm. Experimental results demonstrate that the new centerline algorithm was more efficient and accurate comparing with existing algorithms.

Knowledge-based interpolation of curves: application to femoropopliteal arterial centerline restoration

We present a novel algorithm, Partial Vector Space Projection (PVSP), for estimation of missing data given a database of similar datasets, and demonstrate its use in restoring the centerlines through simulated occlusions of femoropopliteal arteries, derived from CT angiography data. The algorithm performs Principal Component Analysis (PCA) on a database of centerlines to obtain a set of orthonormal basis functions defined in a scaled and oriented frame of reference, and assumes that any curve not in the database can be represented as a linear combination of these basis functions. Using a database of centerlines derived from 30 normal femoropopliteal arteries, we evaluated the algorithm, and compared it to a correlation-based linear Minimum Mean Squared Error (MMSE) method, by deleting portions of a centerline for several occlusion lengths (OL: 10 mm, 25 mm, 50 mm, 75 mm, 100 mm, 125 mm, 150 mm, 175 mm and 200 mm). For each simulated occlusion, we projected the partially known dataset on the set of basis functions derived from the remaining 29 curves to restore the missing segment. We calculated the maximum point-wise distance (Maximum Departure or MD) between the actual and estimated centerline as the error metric. Mean (standard deviation) of MD increased from 0.18 (0.14) to 4.35 (2.23) as OL increased. The results were fairly accurate even for large occlusion lengths and are clinically useful. The results were consistently better than those using the MMSE method. Multivariate regression analysis found that OL and the root-mean-square error in the 2 cm proximal and distal to the occlusion accounted for most of the error.

Fully automated system for three-dimensional bronchial morphology analysis using volumetric multidetector computed tomography of the chest

Recent advancements in computed tomography (CT) have enabled quantitative assessment of severity and progression of large airway damage in chronic pulmonary disease. The advent of fast multidetector computed tomography scanning has allowed the acquisition of rapid, low-dose 3D volumetric pulmonary scans that depict the bronchial tree in great detail. Volumetric CT allows quantitative indices of bronchial airway morphology to be calculated, including airway diameters, wall thicknesses, wall area, airway segment lengths, airway taper indices, and airway branching patterns. However, the complexity and size of the bronchial tree render manual measurement methods impractical and inaccurate. We have developed an integrated software package utilizing a new measurement algorithm termed mirror-image Gaussian fit that enables the user to perform automated bronchial segmentation, measurement, and database archiving of the bronchial morphology in high resolution and volumetric CT scans and also allows 3D localization, visualization, and registration.

An improved electronic colon cleansing method for detection of colonic polyps by virtual colonoscopy

Electronic colon cleansing (ECC) aims to segment the colon lumen from a patient abdominal image acquired using an oral contrast agent for colonic material tagging, so that a virtual colon model can be constructed. Virtual colonoscopy (VC) provides fly-through navigation within the colon model, looking for polyps on the inner surface in a manner analogous to that of fiber optic colonoscopy. We have built an ECC pipeline for a commercial VC navigation system. In this paper, we present an improved ECC method. It is based on a partial-volume (PV) image-segmentation framework, which is derived using the well-established statistical expectation-maximization algorithm. The presented ECC method was evaluated by both visual inspection and computer-aided detection of polyps (CADpolyp) within the cleansed colon lumens obtained using 20 patient datasets. Compared to our previous ECC pipeline, which does not sufficiently consider the PV effect, the method presented in this paper demonstrates improved polyp detection by both visual judgment and CADpolyp measure.

Predicting changes in blood flow in patient-specific operative plans for treating aortoiliac occlusive disease

Traditionally, a surgeon will select a procedure for a particular patient on the basis of past experience with patients with a similar state of disease. The experience gained from this patient will be selectively used when treating the next patient with similar symptoms. This article describes a surgical planning system that was developed to enable a vascular surgeon to create and test alternative operative plans prior to surgery for a given patient. One-dimensional and three-dimensional hemodynamic (i.e., blood flow) simulations were performed for rest and exercise for operative plans for two aorto-femoral bypass patients and compared with actual postoperative data. The information obtained from one-dimensional (volume flow distribution and pressure losses) and three-dimensional (flow, pressure, and wall shear stress) hemodynamic simulations may be clinically relevant to vascular surgeons planning interventions.

Three-dimensional analysis of complex branching vessels in confocal microscopy images

The characteristic of confocal microscopy (CM) vascular data is that it contains many tiny vessels with branching and complex structure. In this work, an automated method for quantitative analysis and reconstruction of cerebral vessels from CM images is presented in which the extracted centerline of the vessels plays the key role. To assess the efficiency and accuracy of different centerline extraction methods, a comparison among three fully automated approaches is given. The centerline extraction methods studied in this work are a snake model, a path planning approach, and a distance transform-based method. To evaluate the accuracy of the quantitative parameters of vessels such as length and diameter, we apply the method to synthetic data. These results indicate that the snake model and the path planning method are more accurate in extracting the quantitative parameters. The efficiency of the approach in clinical applications is then confirmed by applying the method to real CM images. All three methods investigated in this work are accurate enough to correctly distinguish between normal and stroke brain data, while the snake model is the fastest for clinical applications. In addition, three-dimensional visualization, reconstruction, and characterization of CM vascular images of rat brains are presented.

Automatic navigation path generation based on two-phase adaptive region-growing algorithm for virtual angioscopy

In this paper, we propose a fast and automated navigation path generation algorithm to visualize inside of carotid artery using MR angiography images. The carotid artery is one of the body regions not accessible by real optical probe but can be visualized with virtual endoscopy. By applying two-phase adaptive region-growing algorithm, the carotid artery segmentation is started at the initial seed, which is located on the initially thresholded binary image. This segmentation algorithm automatically detects the branch position with stack feature. Combining with a priori knowledge of anatomic structure of carotid artery, the detected branch position is used to separate the carotid artery into internal carotid artery and external carotid artery. A fly-through path is determined to automatically move the virtual camera based on the intersecting coordinates of two bisectors on the circumscribed quadrangle of segmented carotid artery. In consideration of the interactive rendering speed and the usability of standard graphic hardware, endoscopic view of carotid artery is generated by using surface rendering algorithm with perspective projection method. In addition, the endoscopic view is provided with ray casting algorithm for off-line navigation of carotid artery. Experiments have been conducted on both mathematical phantom and clinical data sets. This algorithm is more effective than key-framing and topological thinning method in terms of automated features and computing time. This algorithm is also applicable to generate the centerline of renal artery, coronary artery, and airway tree which has tree-like cylinder shape of organ structures in the medical imagery.

A new path planning algorithm for maximizing visibility in computed tomography colonography

In virtual colonoscopy, minimizing the blind areas is important for accurate diagnosis of colonic polyps. Although useful for describing the shape of an object, the centerline is not always the optimal camera path for observing the object. Hence, conventional methods in which the centerline is directly used as a path produce considerable blind areas, especially in areas of high curvature. Our proposed algorithm first approximates the surface of the object by estimating the overall shape and cross-sectional thicknesses. View positions and their corresponding view directions are then jointly determined to enable us to maximally observe the approximated surface. Moreover, by adopting bidirectional navigations, we may reduce the blind area blocked by haustral folds. For comfortable navigation, we carefully smoothen the obtained path and minimize the amount of rotation between consecutive rendered images. For the evaluation, we quantified the overall observable area on the basis of the temporal visibility that reflects the minimum interpretation time of a human observer. The experimental results show that our algorithm improves visibility coverage and also significantly reduces the number of blind areas that have a clinically meaningful size. A sequence of rendered images shows that our algorithm can provide a sequence of centered and comfortable views of colonography.

Fast colon centreline calculation using optimised 3D topological thinning

Topological thinning can be used to accurately identify the central path through a computer model of the colon generated using computed tomography colonography. The central path can subsequently be used to simplify the task of navigation within the colon model. Unfortunately standard topological thinning is an extremely inefficient process. We present an optimised version of topological thinning that significantly improves the performance of centreline calculation without compromising the accuracy of the result. This is achieved by using lookup tables to reduce the computational burden associated with the thinning process.

Three-Dimensional Analysis of Renal Artery Bending Motion During Respiration

PURPOSE

To evaluate displacement and bending of the renal arteries during respiration.

METHODS

Seven men (mean age 59+/-7 years, range 54-71) were imaged with contrast-enhanced magnetic resonance angiography (MRA). Two phases of the MRA were acquired during separate normal inspiration and expiration breath-holds. Displacement of the kidneys and renal ostia and changes in renal branch angle were measured in both coronal and axial views. Arterial curvature and distances between inspiration and expiration renal artery centerlines were computed at 1-mm intervals for the first 2 cm of each branch.

RESULTS

Significant kidney displacement was observed in both the coronal and axial views, with maximum displacement on the right side; the right kidney at expiration was 13.2+/-7 mm superior and 6.3+/-3.4 mm posterior of its position during inspiration. By comparison, the renal ostia were relatively fixed, displacing 10-fold less than the kidneys. This displacement differential resulted in significant renal branch angle changes between inspiration and expiration, with the branches being more perpendicular at expiration. Right and left branch angles were significantly different from each other in the axial view, with the right artery taking off more anteriorly. The renal artery centerlines were displaced approximately 2.5 mm at a distance of 1 cm from the ostia, with little displacement change in the second centimeter. The right renal artery was more curved than the left, with more respiratory-induced curvature change near the ostia.

CONCLUSIONS

Positional change of the kidneys during respiration induces both bending and change in angulation of the renal arteries. This bending can have a complex 3-dimensional shape near the ostia. In the setting of renal artery stenting, this motion may adversely affect the artery and/or the stent, possibly contributing to restenosis.

3D reconstruction techniques made easy: know-how and pictures

Three-dimensional reconstructions represent a visual-based tool for illustrating the basis of three-dimensional post-processing such as interpolation, ray-casting, segmentation, percentage classification, gradient calculation, shading and illumination. The knowledge of the optimal scanning and reconstruction parameters facilitates the use of three-dimensional reconstruction techniques in clinical practise. The aim of this article is to explain the principles of multidimensional image processing in a pictorial way and the advantages and limitations of the different possibilities of 3D visualisation.

Three-dimensional path planning for virtual bronchoscopy

Multidetector computed-tomography (MDCT) scanners provide large high-resolution three-dimensional (3-D) images of the chest. MDCT scanning, when used in tandem with bronchoscopy, provides a state-of-the-art approach for lung-cancer assessment. We have been building and validating a lung-cancer assessment system, which enables virtual-bronchoscopic 3-D MDCT image analysis and follow-on image-guided bronchoscopy. A suitable path planning method is needed, however, for using this system. We describe a rapid, robust method for computing a set of 3-D airway-tree paths from MDCT images. The method first defines the skeleton of a given segmented 3-D chest image and then performs a multistage refinement of the skeleton to arrive at a final tree structure. The tree consists of a series of paths and branch structural data, suitable for quantitative airway analysis and smooth virtual navigation. A comparison of the method to a previously devised path-planning approach, using a set of human MDCT images, illustrates the efficacy of the method. Results are also presented for human lung-cancer assessment and the guidance of bronchoscopy.

Fast and robust computation of colon centerline in CT colonography

Although several methods for generating the centerline of a colon from CT colonographic scans have been proposed, in general they are time-consuming and do not take into account that the images of the colon may be of nonoptimal quality, with collapsed regions, and stool within the colon. Furthermore, the colonic lumen or wall, which is often used as a basis for computation of a centerline, is not always precisely segmented. In this study, we have developed an algorithm for computation of a colon centerline that is fast compared to the centerline algorithms presented in the reviewed literature, and that relies little on a complete colon segments identification. The proposed algorithm first extracts local maxima in a distance map of a segmented colonic lumen. The maxima are considered to be nodes in a set of graphs, and are iteratively linked together, based on a set of connection criteria, giving a minimum distance spanning tree. The connection criteria are computed from the distance from object boundary, the Euclidean distance between nodes and the voxel values on the pathway between pairs of nodes. After the last iteration, redundant branches are removed and end segments are recovered for each remaining graph. A subset of the initial maxima is used for distinguishing between the colon and noncolonic centerline segments among the set of graphs, giving the final centerline representation. A phantom study showed that, with respect to phantom variations, the algorithm achieved nearly constant computation time (2.3-2.9 s) except for the most extreme setting (20.2 s). The algorithm successfully found all, or most of, the centerline (93% - 100%). Displacement from optimum varied with colon diameter (1.2-6.6 mm). By use of 40 CT colonographic scans, the computer-generated centerlines were compared with the centerlines generated by three radiologists. The similarity was measured based on percent coverage and average displacement. The computer-generated centerlines, when compared with human-generated centerlines, had approximately the same displacement as when the human-generated centerlines were compared among each other (3.8 mm versus 4.0 mm). The coverage of the computer-generated centerlines was slightly less than that of the human-generated centerlines (92% versus 94%). The 40 centerlines were, on average, computed in 10.5 seconds, including computation time for the distance transform, with an Intel Pentium-based 800 MHz computer, as compared with 12-17 seconds or more (excluding computation time for the distance transform needed) per centerline as reported in other studies.

Registration of central paths and colonic polyps between supine and prone scans in computed tomography colonography: Pilot study

Computed tomography colonography (CTC) is a minimally invasive method that allows the evaluation of the colon wall from CT sections of the abdomen/pelvis. The primary goal of CTC is to detect colonic polyps, precursors to colorectal cancer. Because imperfect cleansing and distension can cause portions of the colon wall to be collapsed, covered with water, and/or covered with retained stool, patients are scanned in both prone and supine positions. We believe that both reading efficiency and computer aided detection (CAD) of CTC images can be improved by accurate registration of data from the supine and prone positions. We developed a two-stage approach that first registers the colonic central paths using a heuristic and automated algorithm and then matches polyps or polyp candidates (CAD hits) by a statistical approach. We evaluated the registration algorithm on 24 patient cases. After path registration, the mean misalignment distance between prone and supine identical anatomic landmarks was reduced from 47.08 to 12.66 mm, a 73% improvement. The polyp registration algorithm was specifically evaluated using eight patient cases for which radiologists identified polyps separately for both supine and prone data sets, and then manually registered corresponding pairs. The algorithm correctly matched 78% of these pairs without user input. The algorithm was also applied to the 30 highest-scoring CAD hits in the prone and supine scans and showed a success rate of 50% in automatically registering corresponding polyp pairs. Finally, we computed the average number of CAD hits that need to be manually compared in order to find the correct matches among the top 30 CAD hits. With polyp registration, the average number of comparisons was 1.78 per polyp, as opposed to 4.28 comparisons without polyp registration.

On the role of medial geometry in human vision

A key challenge underlying theories of vision is how the spatially restricted, retinotopically represented feature analysis can be integrated to form abstract, coordinate-free object models. A resolution likely depends on the use of intermediate-level representations which can on the one hand be populated by local features and on the other hand be used as atomic units underlying the formation of, and interaction with, object hypotheses. The precise structure of this intermediate representation derives from the varied requirements of a range of visual tasks which motivate a significant role for incorporating a geometry of visual form. The need to integrate input from features capturing surface properties such as texture, shading, motion, color, etc., as well as from features capturing surface discontinuities such as silhouettes, T-junctions, etc., implies a geometry which captures both regional and boundary aspects. Curves, as a geometric model of boundaries, have been extensively used as an intermediate representation in computational, perceptual, and physiological studies, while the use of the medial axis (MA) has been popular mainly in computer vision as a geometric region-based model of the interior of closed boundaries. We extend the traditional model of the MA to represent images, where each MA segment represents a region of the image which we call a visual fragment. We present a unified theory of perceptual grouping and object recognition where through various sequences of transformations of the MA representation, visual fragments are grouped in various configurations to form object hypotheses, and are related to stored models. The mechanisms underlying both the computation and the transformation of the MA is a lateral wave propagation model. Recent psychophysical experiments depicting contrast sensitivity map peaks at the medial axes of stimuli, and experiments on perceptual filling-in, and brightness induction and modulation, are consistent with both the use of an MA representation and a propagation-based scheme. Also, recent neurophysiological recordings in V1 correlate with the MA hypothesis and a horizontal propagation scheme. This evidence supports a geometric computational paradigm for processing sensory data where both dynamic in-plane propagation and feedforward-feedback connections play an integral role.

A method for detecting undisplayed regions in virtual colonoscopy and its application to quantitative evaluation of fly-through methods1

RATIONALE AND OBJECTIVES

When virtual endoscopy is used as a diagnostic tool, especially as a tool for detecting colon polyps, the user often performs automated fly-through based on automatically generated paths. In the case of automated fly-through in the colon, there are some blind areas at the backs of folds. The aim of this study is to detect undisplayed regions during fly-through and to perform quantitative evaluation.

MATERIALS AND METHODS

Undisplayed regions are detected by marking displayed triangles for surface rendering or displayed voxels for volume rendering. The voxels or triangles not having displayed marks are considered to be undisplayed triangles or voxels. Various kinds of automated fly-through paths generated from medial axes of the colon and flattened views of the colon from the viewpoint of the rate of undisplayed regions are evaluated.

RESULTS

The experiment results show that about 30% of colon regions are classified as undisplayed regions by the conventional automated fly-through along the medial axis and that the flattened view results in very few undisplayed regions.

CONCLUSION

There is a possibility that the automated fly-through methods may cause many undisplayed regions.

Snake modeling and distance transform approach to vascular centerline extraction and quantification

A new method for fully automated centerline extraction and quantification of microvascular structures in confocal microscopy (CM) images is presented. Our method uses the idea of active contour models as well as path planning and distance transforms for the three-dimensional centerline extraction of elongated objects such as vessels. The proposed approach is especially efficient for centerline extraction of complex branching structures. The method performance is validated in several CM images of both normal and stroked rat brains as well as simulated objects. The results confirm the efficiency of the proposed method in extracting the medial curve of vessels, which is essential for the computation of quantitative parameters.

Automated centerline for computed tomography colonography1

RATIONALE AND OBJECTIVES

A novel method to compute the centerline of the human colon obtained from computed tomography colonography is proposed. Two applications of this method are demonstrated: to compute local colonic distension (caliber), and to match polyps on supine and prone images.

MATERIALS AND METHODS

The centerline algorithm involves multiple steps including simplification of the colonic surface by decimation; thinning of the decimated colon to create a preliminary centerline; selection of equally spaced points on the preliminary centerline; grouping neighboring points; and mapping them back to rings in the original colon. This method was tested on 20 human computed tomography colonography datasets (supine and prone examinations of 10 patients) and on a computer-generated colon phantom.

RESULTS

Visual inspection of the colons and their centerlines showed the centerline to be accurate. For the colon phantom, the average error was only 1 mm. For 11 polyps visualized in both the supine and prone positions and found by computer-aided detection, the normalized distance along the centerline to each polyp was not significantly different on the supine and prone views (r = 0.999; P < .001).

CONCLUSION

This method produces an accurate colon centerline that may be useful for flight path planning, matching detections on the supine and prone views, and computing local colonic distension.

Curved-slab maximum intensity projection: method and evaluation

The authors developed and evaluated a method to produce curved-slab maximum intensity projections (MIPs) through blood vessels that semiautomatically excludes soft tissue and bone. Results obtained with the algorithm were compared with those obtained with rectangular-slab MIPs by using computed tomographic (CT) data from four patients with abdominal aortic aneurysms. Curved-slab MIPs exhibited increased mean vessel-to-perivascular tissue contrast of 55.1 HU (36%), allowed a 10% increase in contrast-to-noise ratio, and decreased apparent vessel narrowing by 0.12-1.09 mm, without increasing processing time. Curved-slab MIPs may also include multiple vessels in a single image, thereby improving interpretation efficiency by reducing the number of MIPs required in these patients from eight to three.

Computational geometry for patient-specific reconstruction and meshing of blood vessels from MR and CT angiography

Investigation of three-dimensional (3-D) geometry and fluid-dynamics in human arteries is an important issue in vascular disease characterization and assessment. Thanks to recent advances in magnetic resonance (MR) and computed tomography (CT), it is now possible to address the problem of patient-specific modeling of blood vessels, in order to take into account interindividual anatomic variability of vasculature. Generation of models suitable for computational fluid dynamics is still commonly performed by semiautomatic procedures, in general based on operator-dependent tasks, which cannot be easily extended to a significant number of clinical cases. In this paper, we overcome these limitations making use of computational geometry techniques. In particular, 3-D modeling was carried out by means of 3-D level sets approach. Model editing was also implemented ensuring harmonic mean curvature vectors distribution on the surface, and model geometric analysis was performed with a novel approach, based on solving Eikonal equation on Voronoi diagram. This approach provides calculation of central paths, maximum inscribed sphere estimation and geometric characterization of the surface. Generation of adaptive-thickness boundary layer finite elements is finally presented. The use of the techniques presented here makes it possible to introduce patient-specific modeling of blood vessels at clinical level.

Efficient center-line extraction for quantification of vessels in confocal microscopy images

In this paper we present a novel method for the three-dimensional (3-D) centerline extraction of elongated objects such as vessels. This method combines the basic ideas in distance transform-based, thinning, and path planning methods to extract thin and connected centerlines. This efficient approach needs no user interaction or any prior knowledge of the object shape. We used the path planning approach, which has exclusively been used in the virtual endoscopy or robotics, to obtain the medial curve of the objects. To make our approach fully automated, a distance transform mapping is used to identify the end points of the object branches. The initial paths are also constructed on the surface of the object, traversing the same distance map. Then a thinning algorithm centralizes the paths. The proposed approach is especially efficient for centerline extraction of the complex branching structures. The method has been applied on the confocal microscopy images of rat brains and the results confirm its efficiency in extracting the medial curve of vessels, essential for the computation of quantitative parameters.

Automatic centerline extraction for virtual colonoscopy

In this paper, we introduce a concise and concrete definition of an accurate colon centerline and provide an efficient automatic means to extract the centerline and its associated branches (caused by a forceful touching of colon and small bowel or a deep fold in twisted colon lumen). We further discuss its applications on fly-through path planning and endoscopic simulation, as well as its potential to solve the challenging touching and colon collapse problems in virtual colonoscopy. Experimental results demonstrated its centeredness, robustness, and efficiency.

Edge displacement field-based classification for improved detection of polyps in CT colonography

Colorectal cancer can easily be prevented provided that the precursors to tumors, small colonic polyps, are detected and removed. Currently, the only definitive examination of the colon is fiber-optic colonoscopy, which is invasive and expensive. Computed tomographic colonography (CTC) is potentially a less costly and less invasive alternative to FOC. It would be desirable to have computer-aided detection (CAD) algorithms to examine the large amount of data CTC provides. Most current CAD algorithms have high false positive rates at the required sensitivity levels. We developed and evaluated a postprocessing algorithm to decrease the false positive rate of such a CAD method without sacrificing sensitivity. Our method attempts to model the way a radiologist recognizes a polyp while scrolling a cross-sectional plane through three-dimensional computed tomography data by classification of the changes in the location of the edges in the two-dimensional plane. We performed a tenfold cross-validation study to assess its performance using sensitivity/specificity analysis on data from 48 patients. The mean specificity over all experiments increased from 0.19 (0.35) to 0.47 (0.56) for a sensitivity of 1.00 (0.95).

Automated generation of curved planar reformations from volume data: method and evaluation

The authors developed and evaluated a method to automatically create interactive vascular curved planar reformations with computed tomographic (CT) angiographic data. The method decreased user interaction time by 86%, from 15 to 2 minutes. Expert reviewers were asked to indicate their confidence in differentiating automatically created images from clinical-quality manually produced images. The area under the receiver operating characteristic curve was 0.45 (95% CI: 0.39, 0.51), and a test of equivalency indicated that reviewers could not distinguish between images. They also graded image quality as equivalent to that with manual methods and found fewer artifacts on automatically created images. Automatic methods rapidly produce curved planar reformations of equivalent quality with reduced time and effort.

Automatic axis generation for virtual bronchoscopic assessment of major airway obstructions

Virtual bronchoscopy (VB) has emerged as a paradigm for more effective 3D CT image evaluation. Systematic evaluation of a 3D CT chest image using VB techniques, however, requires precomputed guidance data. This guidance data takes the form of central axes, or centerlines, through the major airways. We propose an axes-generation algorithm for VB assessment of 3D CT chest images. For a typical high-resolution 3D CT chest image, the algorithm produces a series of airway-tree axes, corresponding airway cross-sectional area measurements, and a segmented airway tree in a few minutes on a standard PC. Results for phantom and human airway-obstruction cases demonstrate the efficacy of the algorithm. Also, the algorithm is demonstrated in the context of VB-based 3D CT assessment.

Quantification of distention in CT colonography: development and validation of three computer algorithms

Three bowel distention-measuring algorithms for use at computed tomographic (CT) colonography were developed, validated in phantoms, and applied to a human CT colonographic data set. The three algorithms are the cross-sectional area method, the moving spheres method, and the segmental volume method. Each algorithm effectively quantified distention, but accuracy varied between methods. Clinical feasibility was demonstrated. Depending on the desired spatial resolution and accuracy, each algorithm can quantitatively depict colonic diameter in CT colonography.

Carotid disease: automated analysis with cardiac-gated three-dimensional US technique and preliminary results

Automatic analysis was performed of four-dimensional ultrasonographic (US) data in the carotid artery. The data, which were acquired in 31 subjects (eight healthy volunteers and 23 patients) by using a US scanner fitted with a special probe, were successfully processed. Acquisition time averaged 12 minutes. Data for all healthy volunteers (n = 8) and patients with complete occlusions (n = 3) were correctly classified. Data for two of the 12 patients with mild to severe (but not occlusive) disease were misclassified by one category.