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.
Diseased airways have been known for several years as a possible contributing factor to airflow limitation in Chronic
Obstructive Pulmonary Diseases (COPD). Quantification of disease severity through the evaluation of airway
dimensions - wall thickness and lumen diameter - has gained increased attention, thanks to the availability of multi-slice
computed tomography (CT). Novel approaches have focused on automated methods of measurement as a faster and
more objective means that the visual assessment routinely employed in the clinic. Since the Full-Width Half-Maximum
(FWHM) method of airway measurement was introduced two decades ago [1], several new techniques for quantifying
airways have been detailed in the literature, but no approach has truly become a standard for such analysis. Our own
research group has presented two alternative approaches for determining airway dimensions, one involving a minimum
path and the other active contours [2, 3]. With an increasing number of techniques dedicated to the same goal, we
decided to take a step back and analyze the differences of these methods. We consequently put to the test our two
methods of analysis and the FWHM approach. We first measured a set of 5 airways from a phantom of known
dimensions. Then we compared measurements from the three methods to those of two independent readers, performed
on 35 airways in 5 patients. We elaborate on the differences of each approach and suggest conclusions on which could
be defined as the best one.
Minimally invasive catheter ablation of electric foci, performed in electrophysiology labs, is an attractive treatment
option for atrial fibrillation (AF) - in particular if drug therapy is no longer effective or tolerated. There
are different strategies to eliminate the electric foci inducing the arrhythmia. Independent of the particular
strategy, it is essential to place transmural lesions. The impact of catheter contact force on the generated lesion
quality has been investigated recently, and first results are promising. There are different approaches to measure
catheter-tissue contact. Besides traditional haptic feedback, there are new technologies either relying on catheter
tip-to-tissue contact force or on local impedance measurements at the tip of the catheter.
In this paper, we present a novel tool for post-procedural ablation point evaluation and visualization of contact
force characteristics. Our method is based on localizing ablation points set during AF ablation procedures. The
3-D point positions are stored together with lesion specific catheter contact force (CF) values recorded during
the ablation. The force records are mapped to the spatial 3-D positions, where the energy has been applied.
The tracked positions of the ablation points can be further used to generate a 3-D mesh model of the left atrium
(LA). Since our approach facilitates visualization of different force characteristics for post-procedural evaluation
and verification, it has the potential to improve outcome by highlighting areas where lesion quality may be less
than desired.
Atrial fibrillation (AFib) has been identified as a major cause of stroke. Radiofrequency
catheter ablation has become an increasingly important treatment option, especially
when drug therapy fails. Navigation under X-ray can be enhanced by using augmented fluoroscopy.
It renders overlay images from pre-operative 3-D data sets which are then fused with
X-ray images to provide more details about the underlying soft-tissue anatomy. Unfortunately,
these fluoroscopic overlay images are compromised by respiratory and cardiac motion. Various
methods to deal with motion have been proposed. To meet clinical demands, they have to be
fast. Methods providing a processing frame rate of 3 frames-per-second (fps) are considered
suitable for interventional electrophysiology catheter procedures if an acquisition frame rate of
2 fps is used. Unfortunately, when working at a processing rate of 3 fps, the delay until the actual
motion compensated image can be displayed is about 300 ms. More recent algorithms can
achieve frame rates of up to 20 fps, which reduces the lag to 50 ms. By using a novel approach
involving a 3-D catheter model, catheter segmentation and a distance transform, we can speed
up motion compensation to 25 fps which results in a display delay of only 40 ms on a standard
workstation for medical applications. Our method uses a constrained 2-D/3-D registration to
perform catheter tracking, and it obtained a 2-D tracking error of 0.61 mm.
Electrophysiology (EP) procedures are conducted by cardiac specialists to help diagnose and treat abnormal heart
rhythms. Such procedures are conducted under mono-plane and bi-plane x-ray fluoroscopy guidance to allow the
specialist to target ablation points within the heart. Ablations lesions are usually set by applying radio-frequency energy
to endocardial tissue using catheters placed inside a patient's heart. Recently we have developed a system capable of
overlaying information involving the heart and targeted ablation locations from pre-operational image data for additional
assistance. Although useful, such information offers only approximate guidance due to heart beat and breathing motion.
As a solution to this problem, we propose to make use of a 2D lasso catheter tracking method. We apply it to bi-plane
fluoroscopy images to dynamically update fluoro overlays. The dynamic overlays are computed at 3.5 frames per second
to offer real-time updates matching the heart motion. During the course of our experiments, we found an average 3-D
error of 1.6 mm on average. We present the workflow and features of the motion-adjusted, augmented fluoroscopy
system and demonstrate the dramatic improvement in the overlay quality provided by this approach.
Felix Bourier, Alexander Brost, Andreas Kleinoeder, Tanja Kurzendorfer, Martin Koch, Attila Kiraly, Hans-Juergen Schneider, Joachim Hornegger, Norbert Strobel, Klaus Kurzidim
Atrial fibrillation (AFib), the most common arrhythmia, has been identified as a major
cause of stroke. The current standard in interventional treatment of AFib is the pulmonary
vein isolation (PVI). PVI is guided by fluoroscopy or non-fluoroscopic electro-anatomic mapping
systems (EAMS). Either classic point-to-point radio-frequency (RF)- catheter ablation or
so-called single-shot-devices like cryo-balloons are used to achieve electrically isolation of the
pulmonary veins and the left atrium (LA). Fluoroscopy-based systems render overlay images
from pre-operative 3-D data sets which are then merged with fluoroscopic imaging, thereby
adding detailed 3-D information to conventional fluoroscopy. EAMS provide tracking and
visualization of RF catheters by means of electro-magnetic tracking. Unfortunately, current
navigation systems, fluoroscopy-based or EAMS, do not provide tools to localize and visualize
single shot devices like cryo-balloon catheters in 3-D. We present a prototype software
for fluoroscopy-guided ablation procedures that is capable of superimposing 3-D datasets as
well as reconstructing cyro-balloon catheters in 3-D. The 3-D cyro-balloon reconstruction was
evaluated on 9 clinical data sets, yielded a reprojected 2-D error of 1.72 mm ± 1.02 mm.
We present a complete automatic system to extract 3D centerlines of ribs from thoracic CT scans. Our rib
centerline system determines the positional information for the rib cage consisting of extracted rib centerlines,
spinal canal centerline, pairing and labeling of ribs. We show an application of this output to produce an enhanced
visualization of the rib cage by the method of Kiraly et al., in which the ribs are digitally unfolded along their
centerlines. The centerline extraction consists of three stages: (a) pre-trace processing for rib localization, (b) rib
centerline tracing, and (c) post-trace processing to merge the rib traces. Then we classify ribs from non-ribs and
determine anatomical rib labeling. Our novel centerline tracing technique uses the Random Walker algorithm
to segment the structural boundary of the rib in successive 2D cross sections orthogonal to the longitudinal
direction of the ribs. Then the rib centerline is progressively traced along the rib using a 3D Kalman filter.
The rib centerline extraction framework was evaluated on 149 CT datasets with varying slice spacing, dose, and
under a variety of reconstruction kernels. The results of the evaluation are presented. The extraction takes
approximately 20 seconds on a modern radiology workstation and performs robustly even in the presence of
partial volume effects or rib pathologies such as bone metastases or fractures, making the system suitable for
assisting clinicians in expediting routine rib reading for oncology and trauma applications.
It has been known for several years that airflow limitations in the small airways may be an important contributor to
Chronic Obstructive Pulmonary Disease (COPD). Quantification of wall thickness has lately gained attention thanks to
the use of high resolution CT, with novel approaches focusing on automated methods that can substitute for visual
assessment [1, 2]. While increased thickening of the wall is considered evidence of inflammatory disease, we
hypothesize that there may be additional ways to detect and quantify inflammation, specifically the uptake of contrast
material. In this preliminary investigation, we selected patients with documented chronic airway inflammation, and for
whom pre and post contrast datasets were available. On targeted reconstruction of right upper and lower lobes, we
selected airways with no connections to surrounding structures, and used a modified Full-Width-Half-Max method for
quantification of lumen diameter, wall thickness, and wall density. Matching airway locations on the pre- and postcontrast
cases were compared. Airways from patients without airway disease served as a control. Results for the airway
disease cases showed an average enhancement of 72 HU within the airway walls, with a standard deviation of 59 HU. In
the control group the average enhancement was 16 HU with standard deviation of 22 HU. While this study is limited in
number of cases, we hypothesize that quantification of contrast uptake is an additional factor to consider in assessing
airway inflammation. At the same time we are currently investigating whether enhancement can be measured via a
"contrast" map created with dual energy scanning, where a 3-value decomposition algorithm differentiates iodine from
other materials. This technique would eliminate both the need for a pre-contrast scan, and the task of matching airway
locations on pre- and post- scans.
Bronchial wall thickening is commonly observed in airway diseases. One method often used to quantitatively evaluate
wall thickening in CT images is to estimate the ratio of the bronchial wall to the accompanying artery, or BWA ratio,
and then assign a severity score based on the ratio. Assessment by visual inspection is unfortunately limited to airways
perpendicular or parallel to the scanning plane. With high-resolution images from multi-detector CT scanners, it
becomes possible to assess airways in any orientation. We selected CT scans from 20 patients with mild to severe
COPD. A computer system automatically segmented each bronchial tree and measured the bronchial wall thicknesses.
Next, neighboring arteries were detected and measured to determine BWA ratios. A score characterizing the extent and
severity of wall thickening within each lobe was computed according to recommendations by Sheehan et al [1]. Two
experienced radiologists independently scored wall thickening using visual assessment. Spearman's rank correlation
showed a non-significant negative correlation (r=-0.1) between the computer and the reader average (p=0.4), while the
correlation between readers was significant at r=0.65 (p=0.001). We subsequently identified 24 lobes with high
discrepancies between visual and automated scoring. The readers re-examined those lobes and measured wall thickness
using electronic calipers on perpendicular cross sections, rather than visual assessment. Using this more objective
standard of wall thickness, the reader estimates of wall thickening increased to reach a significant positive correlation
with automated scoring of r=0.65 (p=0.001). These results indicate that subjectivity is an important problem with visual
evaluation, and that visual inspection may frequently underestimate disease extent and severity. Given that a manual
evaluation of all airways is infeasible in routine clinical practice, we argue that automated methods should be developed
and utilized.
Chronic Obstructive Pulmonary Disease (COPD) is often characterized by partial or complete obstruction of airflow in
the lungs. This can be due to airway wall thickening and retained secretions, resulting in foci of mucoid impactions.
Although radiologists have proposed scoring systems to assess extent and severity of airway diseases from CT images,
these scores are seldom used clinically due to impracticality. The high level of subjectivity from visual inspection and
the sheer number of airways in the lungs mean that automation is critical in order to realize accurate scoring. In this
work we assess the feasibility of including an automated mucus detection method in a clinical scoring system. Twenty
high-resolution datasets of patients with mild to severe bronchiectasis were randomly selected, and used to test the
ability of the computer to detect the presence or absence of mucus in each lobe (100 lobes in all). Two experienced
radiologists independently scored the presence or absence of mucus in each lobe based on the visual assessment method
recommended by Sheehan et al [1]. These results were compared with an automated method developed for mucus plug
detection [2]. Results showed agreement between the two readers on 44% of the lobes for presence of mucus, 39% of
lobes for absence of mucus, and discordant opinions on 17 lobes. For 61 lobes where 1 or both readers detected mucus,
the computer sensitivity was 75.4%, the specificity was 69.2%, and the positive predictive value (PPV) was 79.3%. Six
computer false positives were a-posteriori reviewed by the experts and reassessed as true positives, yielding results of
77.6% sensitivity, 81.8% for specificity, and 89.6% PPV.
Pulmonary vascular tree segmentation has numerous applications in medical imaging and computer-aided diagnosis (CAD), including detection and visualization of pulmonary emboli (PE), improved lung nodule detection, and quantitative vessel analysis. We present a novel approach to pulmonary vessel segmentation based on a fuzzy segmentation concept, combining the strengths of both threshold and seed point based methods. The lungs of the original image are first segmented and a threshold-based approach identifies core vessel components with a high specificity. These components are then used to automatically identify reliable seed points for a fuzzy seed point based segmentation method, namely fuzzy connectedness. The output of the method consists of the probability of each voxel belonging to the vascular tree. Hence, our method provides the possibility to adjust the sensitivity/specificity of the segmentation result a posteriori according to application-specific requirements, through definition of a minimum vessel-probability required to classify a voxel as belonging to the vascular tree. The method has been evaluated on contrast-enhanced thoracic CT scans from clinical PE cases and demonstrates overall promising results. For quantitative validation we compare the segmentation results to randomly selected, semi-automatically segmented sub-volumes and present the resulting receiver operating characteristic (ROC) curves. Although we focus on contrast enhanced chest CT data, the method can be generalized to other regions of the body as well as to different imaging modalities.
Bronchiectasis, the permanent dilatation of the airways, is frequently evaluated by computed tomography (CT) in order
to determine disease progression and response to treatment. Normal airways have diameters of approximately the same
size as their accompanying artery, and most scoring systems for quantifying bronchiectasis severity ask physicians to
estimate the broncho-arterial ratio. However, the lack of standardization coupled with inter-observer variability limits
diagnostic sensitivity and the ability to make reliable comparisons with follow-up CT studies. We have developed a
Computer Aided Diagnosis method to detect airway disease by locating abnormal broncho-arterial ratios. Our approach
is based on computing a tree model of the airways followed by automated measurements of broncho-arterial ratios at
peripheral airway locations. The artery accompanying a given bronchus is automatically determined by correlation of its
orientation and proximity to the airway, while the diameter measurements are based on the full-width half maximum
method. This method was previously evaluated subjectively; in this work we quantitatively evaluate the airway and
vessel measurements on 9 CT studies and compare the results with three independent readers. The automatically selected
artery location was in agreement with the readers in 75.3% of the cases compared with 65.6% agreement of the readers
with each other. The reader-computer variability in lumen diameters (7%) was slightly lower than that of the readers
with respect to each other (9%), whereas the reader-computer variability in artery diameter (18%) was twice that of the
readers (8%), but still acceptable for detecting disease. We conclude that the automatic system has comparable accuracy
to that of readers, while providing greater speed and consistency.
Chronic airway disease causes structural changes in the lungs including peribronchial thickening and airway dilatation.
Multi-detector computed tomography (CT) yields detailed near-isotropic images of the lungs, and thus the potential to
obtain quantitative measurements of lumen diameter and airway wall thickness. Such measurements would allow
standardized assessment, and physicians to diagnose and locate airway abnormalities, adapt treatment, and monitor
progress over time. However, due to the sheer number of airways per patient, systematic analysis is infeasible in routine
clinical practice without automation. We have developed an automated and real-time method based on active contours to
estimate both airway lumen and wall dimensions; the method does not require manual contour initialization but only a
starting point on the targeted airway. While the lumen contour segmentation is purely region-based, the estimation of the
outer diameter considers the inner wall segmentation as well as local intensity variation, in order anticipate the presence
of nearby arteries and exclude them. These properties make the method more robust than the Full-Width Half Maximum
(FWHM) approach. Results are demonstrated on a phantom dataset with known dimensions and on a human dataset
where the automated measurements are compared against two human operators. The average error on the phantom
measurements was 0.10mm and 0.14mm for inner and outer diameters, showing sub-voxel accuracy. Similarly, the mean
variation from the average manual measurement was 0.14mm and 0.18mm for inner and outer diameters respectively.
The lymphatic system comprises a series of interconnected lymph nodes that are commonly distributed along branching
or linearly oriented anatomic structures. Physicians must evaluate lymph nodes when staging cancer and planning
optimal paths for nodal biopsy. This process requires accurately determining the lymph node's position with respect to
major anatomical landmarks. In an effort to standardize lung cancer staging, The American Joint Committee on Cancer
(AJCC) has classified lymph nodes within the chest into 4 groups and 14 sub groups. We present a method for
automatically labeling lymph nodes according to this classification scheme, in order to improve the speed and accuracy
of staging and biopsy planning. Lymph nodes within the chest are clustered around the major blood vessels and the
airways. Our fully automatic labeling method determines the nodal group and sub-group in chest CT data by use of
computed airway and aorta centerlines to produce features relative to a given node location. A classifier then determines
the label based upon these features. We evaluate the efficacy of the method on 10 chest CT datasets containing 86
labeled lymph nodes. The results are promising with 100% of the nodes assigned to the correct group and 76% to the
correct sub-group. We anticipate that additional features and training data will further improve the results. In addition to
labeling, other applications include automated lymph node localization and visualization. Although we focus on chest
CT data, the method can be generalized to other regions of the body as well as to different imaging modalities.
Pulmonary diseases characterized by chronic airway inflammation, such as Chronic Obstructive Pulmonary (COPD),
result in abnormal bronchial wall thickening, lumen dilatation and mucus plugs. Multi-Slice Computed Tomography
(MSCT) allows for assessment of these abnormalities, even in airways that are obliquely oriented to the scan plane.
Chronic airway inflammation typically results in limitations of airflow, allowing for the accumulation of mucus,
especially in the distal airways. In addition to obstructing airways, retained secretions make the airways prone to
infection. Patients with chronic airway disease are clinically followed over time to assess disease progression and
response to treatment. In this regard, the ability to obtain an automatic standardized method to rapidly and objectively
assess the entire airway tree morphologically, including the extent of mucus plugging, would be of particular clinical
value. We have developed a method to automatically detect the presence and location of mucus plugs within the
peripheral airways. We first start with segmentation of the bronchial tree using a previously developed method. The
skeleton-based tree structure is then computed and each terminal branch is individually extended using an adaptive
threshold algorithm. We compute a local 2-dimensional model, based on airway luminal diameter and wall thickness.
We then select a few points along the principal axis beyond the terminal branches, to extract 2D cross sections for
correlation with a model of mucus plugging. Airway shape is validated with a correlation value, and the lumen
distribution is analyzed and compared to the model. A high correlation indicates the presence of a mucus plug. We tested
our method on 5 datasets containing a total of 40 foci of mucoid impaction. Preliminary results show sensitivity of
77.5% with a specificity of 98.2% and positive predictive value of 66%.
In most magnetic resonance imaging (MRI) clinical examinations, the orientation and position of diagnostic scans are
manually defined by MRI operators. To accelerate the workflow, algorithms have been proposed to automate the
definition of the MRI scanning planes. A mid-sagittal plane (MSP), which separates the two cerebral hemispheres, is
commonly used to align MRI neurological scans, since it standardizes the visualization of important anatomy. We
propose an algorithm to define the MSP automatically based on lines separating the cerebral hemispheres in 2D coronal
and transverse images. Challenges to the automatic definition of separation lines are disturbances from the inclusion of
the shoulder, and the asymmetry of the brain. The proposed algorithm first detects the position of the head by fitting an
ellipse that maximizes the image gradient magnitude in the boundary region of the ellipse. A symmetrical axis is then
established which minimizes the difference between the image on either side of the axis. The pixels at the space between
the hemispheres are located in the adjacent area of the symmetrical axis, and a linear regression with robust weights
defines a line that best separates the two hemispheres. The geometry of MSP is calculated based on the separation lines
in the coronal and transverse views. Experiments on 100 images indicate that the result of the proposed algorithm is
consistent with the results obtained by domain experts and is significantly faster.
KEYWORDS: Arteries, Image segmentation, 3D visualizations, 3D modeling, Computed tomography, Lung, Visualization, 3D vision, 3D image processing, Interfaces
Pulmonary embolism (PE) is a life-threatening disease, requiring rapid diagnosis and treatment. Contrast enhanced computed tomographic (CT) images of the lungs allow physicians to confirm or rule out PE, but the large number of images per study and the complexity of lung anatomy may cause some emboli to be overlooked. We evaluated a novel three-dimensional (3D) visualization technique for detecting PE, and compared it with traditional 2D axial interpretation. Three readers independently marked 10 cases using the 3D method, and a separate interpretation was performed at a later date using only source axial images. An experienced thoracic radiologist adjudicated all marks, classifying clots according to location and confidence. There were a total of 8 positive examinations with 69 validated emboli. 44 (64%) of the clots were segmental while 12 (17%) proved subsegmental. Using the traditional 2D method for examination, readers detected a mean of 45 PE for 66% sensitivity. Using the 3D method, readers detected a mean of 35 PE (50% sensitivity). Combining both methods, readers detected a mean of 51 PE (74% sensitivity), significantly higher than either single method (p<0.001). Considered by arterial level, significant improvement was observed for detection of segmental and subsegmental clots (p<0.001) when comparing combined reading with either single method. The mean number of false positives per patient was 0.23 for both 2D and 3D readings and 0.4 for combined reading. 3D visualization of pulmonary arteries allowed readers to detect a significant number of additional emboli not detected during 2D axial interpretations and thus may lead to a more accurate diagnosis of PE.
Many medical imaging techniques use mathematical morphology (MM), with discs and spheres being the structuring elements (SE) of choice. Given the non-linear nature of the underlying comparison operations (min, max, AND, OR), MM optimization can be challenging. Many efficient methods have been proposed for various types of SE based on the ability to decompose the SE by way of separability or homotopy. Usually, these methods are only able to approximate disc and sphere SE rather than accomplish MM for the exact SE obtained by discretization of such shapes. We present a method that for efficiently computing MM for binary and gray scale image volumes using digitally convex and X-Y-Z symmetric flat SE, which includes discs and spheres. The computational cost is a function of the diameter of the SE and rather than its volume. Additional memory overhead, if any, is modest. We are able to compute MM on real medical image volumes with greatly reduced running times with increasing gains for larger SE. Our method is also robust to scale: it is applicable to ellipse and ellipsoid SE which may result from discretizing a disc or sphere on an anisotropic grid. In addition, it is easy to implement and can make use of existing image comparison operations. We present performance results on large medical chest CT datasets.
Tree matching methods have numerous applications in medical imaging, including registration, anatomical labeling, segmentation, and navigation of structures such as vessels and airway trees. Typical methods for tree matching rely on conventional graph matching techniques and therefore suffer potential limitations such as sensitivity to the accuracy of the extracted tree structures, as well as dependence on the initial alignment. We present a novel path-based tree matching framework independent of graph matching. It is based on a point-by-point feature comparison of complete paths rather than branch points, and consequently is relatively unaffected by spurious airways and/or missing branches. A matching matrix is used to enforce one-to-one matching. Moreover our method can reliably match irregular tree structures, resulting from imperfect segmentation and centerline extraction. Also reflecting the nature of these features, our method does not require a precise alignment or registration of tree structures. To test our method we used two thoracic CT scans from each of ten patients, with a median inter-scan interval of 3 months (range 0.5 to 10 months). The bronchial tree structure was automatically extracted from each scan and a ground truth of matching paths was established between each pair of tree structures. Overall 87% of 702 airway paths (average 35.1 per patient matched both ways) were correctly matched using this technique. Based on this success we also present preliminary results of airway-to-artery matching using our proposed methodology.
Pulmonary diseases such as bronchiectasis, asthma, and emphysema are characterized by abnormalities in airway dimensions. Multi-slice computed tomography (MSCT) has become one of the primary means to depict these abnormalities, as the availability of high-resolution near-isotropic data makes it possible to evaluate airways at oblique angles to the scanner plane. However, currently, clinical evaluation of airways is typically limited to subjective visual inspection only: systematic evaluation of the airways to take advantage of high-resolution data has not proved practical without automation. We present an automated method to quantitatively evaluate airway lumen diameter, wall thickness and broncho-arterial ratios. In addition, our method provides 3D visualization of these values, graphically illustrating the location and extent of disease. Our algorithm begins by automatic airway segmentation to extract paths to the distal airways, and to create a map of airway diameters. Normally, airway diameters decrease as paths progress distally; failure to taper indicates abnormal dilatation. Our approach monitors airway lumen diameters along each airway path in order to detect abnormal profiles, allowing even subtle degrees of pathologic dilatation to be identified. Our method also systematically computes the broncho-arterial ratio at every terminal branch of the tree model, as a ratio above 1 indicates potentially abnormal bronchial dilatation. Finally, the airway wall thickness is computed at corresponding locations. These measurements are used to highlight abnormal branches for closer inspection, and can be summed to compute a quantitative global score for the entire airway tree, allowing reproducible longitudinal assessment of disease severity. Preliminary tests on patients diagnosed with bronchiectasis demonstrated rapid identification of lack of tapering, which also was confirmed by corresponding demonstration of elevated broncho-arterial ratios.
The ribs within computed tomography (CT) images form curved structures intersecting the axial plane at oblique angles. Rib metastases and other pathologies of the rib are apparent in CT images. Analysis of the ribs using conventional 2D axial slice viewing involves manually tracking them through multiple slices. 3D visualization of the ribs also has drawbacks due to occlusion. Examination of a single rib may require repositioning the viewpoint several times in order to avoid other ribs. We propose a novel visualization method that eliminates rib curvatures by straightening each rib along its centerline. This reduces both 2D and 3D viewing complexities. Our method is based upon first segmenting and extracting the centerlines of each rib. These steps are done through a tracing based segmentation. Next, the centerlines are refined to a smoother contour. Each centerline is then used to resample and digitally straighten each rib. The result is a simplified volume containing only the straightened ribs, which can be quickly examined both in 3D and by scrolling through a series of about 40 slices. Additionally, a projection of the image can yield a single 2D image for examination. The method was tested on chest CT images obtained from patients both positive and negative for rib metastases. Running time was less than 15 seconds per dataset. Preliminary results demonstrate the effectiveness of the visualization in detecting and delineating these metastases.
Given the nature of pulmonary embolism (PE), timely and accurate diagnosis is critical. Contrast enhanced high-resolution CT images allow physicians to accurately identify segmental and sub-segmental emboli. However, it is also important to assess the effect of such emboli on the blood flow in the lungs. Expanding upon previous research, we propose a method for 3D visualization of lung perfusion. The proposed method allows users to examine perfusion throughout the entire lung volume at a single glance, with areas of diminished perfusion highlighted so that they are visible independent of the viewing location. This may be particularly valuable for better accuracy in assessing the extent of hemodynamic alterations resulting from pulmonary emboli. The method also facilitates user interaction and may help identify small peripheral sub-segmental emboli otherwise overlooked. 19 patients referred for possible PE were evaluated by CT following the administration of IV contrast media. An experienced thoracic radiologist assessed the 19 datasets with 17 diagnosed as being positive for PE with multiple emboli. Since anomalies in lung perfusion due to PE can alter the distribution of parenchymal densities, we analyzed features collected from histograms of the computed perfusion maps and demonstrate their potential usefulness as a preliminary test to suggest the presence of PE. These histogram features also offer the possibility of distinguishing distinct patterns associated with chronic PE and may even be useful for further characterization of changes in perfusion or overall density resulting from associated conditions such as pneumonia or diffuse lung disease.
We propose a new quantitative method for detailed analysis of the major airways. Using a 3D MDCT chest image as input, the method involves three major steps: (1) segmentation of the airway tree, (2) extraction of the central-axis structure of the major airways, and (3) a novel improvement on the standard full-width half-maximum approach for airway-wall delineation. The method produces measurements for all defined tree branches. These measurements include various airway diameters and cross-sectional area values. To facilitate the examination of these measurements, we also demonstrate an integrated virtual-bronchoscopic analysis system that enables flexible interrogation of the airways. Of particular note are techniques for unraveling and viewing the topography of selected airways. A large series of phantom and human tests confirm the efficacy of our methods.
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.
Pulmonary embolism (PE) detection via contrast-enhanced computed tomography (CT) images is an increasingly important topic of research. Accurate identification of PE is of critical importance in determining the need for further treatment. However, current multi-slice CT scanners provide datasets typically containing 600 or more images per patient, making it desirable to have a visualization method to help radiologists focus directly on potential candidates that might otherwise have been overlooked. This is especially important when assessing the ability of CT to identify smaller, sub-segmental emboli. We propose a cartwheel projection approach to PE visualization that computes slab projections of the original data aided by vessel segmentation. Previous research on slab visualization for PE has utilized the entire volumetric dataset, requiring thin slabs and necessitating the use of maximum intensity projection (MIP). Our use of segmentation within the projection computation allows the use of thicker slabs than previous methods, as well as the ability to employ visualization variations that are only possible with segmentation. Following automatic segmentation of the pulmonary vessels, slabs may be rotated around the X-, Y- or Z-axis. These slabs are rendered by preferentially using voxels within the lung vessels. This effectively eliminates distracting information not relevant to diagnosis, lessening both the chance of overlooking a subtle embolus and minimizing time on spent evaluating false positives. The ability to employ thicker slabs means fewer images need to be evaluated, yielding a more efficient workflow.
Although Pulmonary Embolism (PE) is one of the most common causes of unexpected death in the U.S., it may also be one of the most preventable. Images acquired from 16-slice Computed Tomography (CT) machines of contrast-injected patients provide sufficient resolution for the localization and analysis of emboli located in segmental and sub-segmental arteries. After a PE is found, it is difficult to assess the local characteristics of the affected arterial tree without automation. We propose a method to compute characteristics of the local arterial tree given the location of a PE. The computed information localizes the portion of the arterial tree that is affected by the embolism. Our method is based on the segmentation of the arteries and veins followed by a localized tree computation at the given site. The method determines bifurcation points and the remaining arterial tree. A preliminary segmentation method is also demonstrated to locally eliminate over-segmentation of the arterial tree. The final result can then be used assess the affected lung volume and arterial supply. Initial tests revealed a good ability to compute local tree characteristics of selected sites.
Pulmonary Embolism (PE) is one of the most common causes of unexpected death in the US. The recent introduction of 16-slice Computed Tomography (CT) machines allows the acquisition of very high-resolution datasets. This has made CT a more attractive means for diagnosing PE, especially for previously difficult to identify small subsegmental peripheral emboli. However, the large size of these datasets makes it desirable to have an automated method to help radiologists focus directly on potential candidates that might otherwise be overlooked. We propose a novel method to highlight potential PEs on a 3D representation of the pulmonary arterial tree. First lung vessels are segmented using mathematical morphology techniques. The density values inside the vessels are then used to color the outside of a Shaded Surface Display (SSD) of the vessel tree. As PEs are clots of significantly lower Hounsfield Unit (HU) values than surrounding contrast-enhanced blood, they appear as salient contrasted patches in this 3D rendering. During preliminary testing on 6 datasets 19 PEs out of 22 were detected (sensitivity 86%) with 2 false positives for every true positive (Positive Predictive Value 33%).
Modern micro-CT and multidetector helical CT scanners can produce high-resolution 3D digital images of various anatomical tree structures, such as the coronary or hepatic vasculature and the airway tree. The sheer size and complexity of these trees make it essentially impossible to define them interactively. Automatic approaches, using techniques such as image segmentation, thinning, and centerline definition, have been proposed for a few specific problems. None of these approaches, however, can guarantee extracting geometrically accurate multigenerational tree structures. This limits their utility for detailed quantitative analysis of a tree. This paper proposes an approach for accurately defining 3D trees depicted in large 3D CT images. Our approach utilizes a three-stage analysis paradigm: (1) Apply an automated technique to make a "first cut" at defining the tree. (2) Analyze the automatically defined tree to identify possible errors. (3) Use a series of interactive tools to examine and correct each of the identified errors. At the end of this analysis, in principle, a more useful tree will be defined. Our paper will present a preliminary description of this paradigm and give some early results with 3D micro-CT images.
This paper describes a new airway segmentation algorithm that improves the speed of morphological-based segmentation approaches. Airway segmentation methods based on morphological operators suffer from the indiscriminant application of all operators to a large area. Using the results of three-dimensional (3D) region growing, the discrete application of larger operators is possible. This change can greatly decrease the execution time of the algorithm. This hybrid approach typically runs 5 to 10 times faster than the original algorithm. 3D adaptive region growing, morphological segmentation, and the hybrid approach are then compared via data obtained from human volunteers using a Marconi MX8000 scanner with the lungs held at 85% TLC. Results show that filtering improves robustness of these techniques. The hybrid approach allows for the practical use of morphological operators to create a clinically useful segmentation. We also demonstrate the method's utility for peripheral nodule analysis in a human case.
Transbronchial needle biopsy is a common procedure for early detection of lung cancer. In practice, accurate results are difficult to obtain, since the bronchoscopy procedure requires a blind puncture into a region hidden behind the airway walls. This paper presents an image-guided endoscopy system for procedure preplanning and for guidance during bronchoscopy. Before the bronchoscopy, a 3D CT scan is analyzed to define guidance paths through the major airways to suspect biopsy sites. During subsequent bronchoscopy, the paths give the physician step-by-step guidance to each suspect site location. At a suspect site, a virtual CT image is registered to the bronchoscopic video. Then, the predefined biopsy site, from the prior CT analysis, is rendered onto the registered video. This gives the physician a reference for performing the needle biopsy. This paper focuses on our recent experiments with this system. These experiments involve a rubber phantom model of the human airway tree and in vivo animal tests. The experiments demonstrate the promise of our approach.
Anthony Sherbondy, Atilla Kiraly, Allen Austin, James Helferty, Shu-Yen Wan, Janice Turlington, Tao Yang, Chao Zhang, Eric Hoffman, Geoffrey McLennan, William Higgins
To improve the care of lung-cancer patients, we are devising a diagnostic paradigm that ties together three-dimensional (3D) high-resolution computed-tomographic (CT) imaging and bronchoscopy. The system expands upon the new concept of virtual endoscopy that has seen recent application to the chest, colon, and other anatomical regions. Our approach applies computer-graphics and image-processing tools to the analysis of 3D CT chest images and complementary bronchoscopic video. It assumes a two-stage assessment of a lung-cancer patient. During Stage 1 (CT assessment), the physician interacts with a number of visual and quantitative tools to evaluate the patient's 'virtual anatomy' (3D CT scan). Automatic analysis gives navigation paths through major airways and to pre-selected suspect sites. These paths provide useful guidance during Stage-1 CT assessment. While interacting with these paths and other software tools, the user builds a multimedia Case Study, capturing telling snapshot views, movies, and quantitative data. The Case Study contains a report on the CT scan and also provides planning information for subsequent bronchoscopic evaluation. During Stage 2 (bronchoscopy), the physician uses (1) the original CT data, (2) software graphical tools, (3) the Case Study, and (4) a standard bronchoscopy suite to have an augmented vision for bronchoscopic assessment and treatment. To use the two data sources (CT and bronchoscopic video) simultaneously, they must be registered. We perform this registration using both manual interaction and an automated matching approach based on mutual information. We demonstrate our overall progress to date using human CT cases and CT-video from a bronchoscopy- training device.
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