1. Field of the Invention
The present methods, devices and systems relate generally to the fields of disease assessment and treatment planning.
2. Description of Related Art
Pulmonary emphysema (Russi and Weder, 2002; Sabanathan et al., 2003) is a chronic, common, debilitating, insidious, yet fatal, progressive disorder of the lungs, that is often related to smoking, but also has a strong familial association (Yim et al., 2004; Bolliger et al., 2004). A 1997 NIH health survey estimated that 3.2 million Americans have been diagnosed with emphysema (Noppen et al., 2004). Emphysema is associated with an expansion of alveolar macrophages in the peripheral lung, a classical marker of chronic inflammation. There is conflicting evidence as to whether emphysema develops because of a simple protease-antiprotease imbalance (Ernst, 2004; Ferson and Chi, 2005). Emphysema presents clinically very late in its course with severe breathlessness, at a time when useful preventative action cannot be undertaken. The disease begins when the patients are in their late 20's, yet clinically is detected in the 40-70 year old age group. In spite of the huge impact of this disease on the community, research regarding epidemiology, or therapeutic strategies, has been slowed because of the lack of a clinical package for objectively assessing the regional characteristics of the lung tissue.
Emphysema is defined as a condition of the lung which is characterized by abnormal, permanent enlargement of air spaces distal to the terminal bronchiole, with destruction of the alveolar walls, and with-out any obvious fibrosis. Destruction in emphysema is defined as non-uniformity in the pattern of respiratory air space enlargement so that the orderly appearance of the acinus and its components is disturbed and may be lost (Henschke et al., 1999). Emphysema has historically been identified and classified according to the macroscopic architecture of the removed, inflated and fixed at a standard pressure, whole lung (Henschke and Yankelevitz, 2000). Such patterns of destruction are clearly a target for objective tissue characterization methodologies using multidetector-row computed tomography (MDCT) imaging, either using histogram methods or more complex measures such as the Adaptive Multiple Feature Method (Aberle et al., 2001; Ellis et al., 2001; Swensen et al., 2005) for lung parenchymal assessment. Certain quantitative approaches have utilized only single first order measures and have largely been limited to mean lung density and assessment of the location of the lower 5th percentile cut-off on the lung density histogram (plotting number of pixels vs. Hounsfield units), and have not been available in an easily usable format.
A widely publicized therapy for pulmonary emphysema is lung volume reduction surgery (LVRS) (Labadi et al., 2005; Conforti and Shure, 2000). Prior to LVRS, therapy was purely supportive care. There is a large national trial (the National Emphysema Treatment Trial, or NETT, the results of which were published in The New England Journal of Medicine in May 2003) that has looked at this modality, with it being unclear as to how to consistently pick the region of the lung to treat using this approach. With LVRS, the obvious emphysematous regions of the lung are removed, while the patient is under anesthesia, using a variety of different techniques and relying on different levels of surgeon skill to decide where and how much lung to remove. MDCT, combined with the identification of the emphysematous regions using histogram analysis, can objectively designate the region of lung that has the most emphysema, and guide the surgeon. Further approaches, using endoscopic lung volume reduction surgery through the airways are currently coming into vogue. Some use aspiration of the affected lung through the airway, and others use some sort of implanted airway valve (Berlin, 2003).
Broadly, lung or pulmonary diseases are the second most common cause of death and morbidity, and there has been a necessary focus on new therapy, including both pharmacologic as well as surgical. A growing number of therapies proceed via endo- and trans-bronchial approaches within the lung, facilitated by rapid advances in optical-based tools and their miniaturization, and by the realization that lung segmental diseases are best treated by segmental or lobar approaches where at all possible. These new therapies include the placement of one-way valves or airway wall fenestrations as an alternative to lung volume reduction surgery for late state emphysema, the placement of stents to resolve airway obstructions, and radiofrequency ablation of airway smooth muscle as a therapy for severe asthma, or of lung tumor nodules.
In a broad respect, the invention relates to treatment planning methods and to techniques that can be used during the treatment planning process for diseases such as emphysema and lung cancer, although other diseases and associated tissues are applicable. The invention may take the form of treatment planning methods, devices (such as computer readable media) that may be used as part of the treatment planning process, and systems (such as computer systems) that may be used as part of the treatment planning process.
Some embodiments of the present treatment planning methods comprise displaying at least a portion of a set of lungs and one or more potential treatment regions; receiving a selection of a target treatment region from among the one or more potential treatment regions; and displaying one or more locations within the portion that are therapeutically linked to the target treatment region.
Other embodiments of the present treatment planning methods comprise displaying an airway tree segmented from a volumetric dataset of images; receiving a selection of a portion of the airway tree; and providing a display that includes the portion in straightened form and a dimensional attribute corresponding to a user-selectable location along the portion.
Different aspects of these methods, as well as other treatment planning methods, devices and systems, are described below.
The following drawings illustrate by way of example and not limitation. The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawings will be provided by the Office upon request and payment of the necessary fee.
FIGS. 1A-1D: Lung segmentation example according to some embodiments for an image of a normal subject.
FIGS. 1F-1G: Lung smoothing.
FIGS. 1H-1I: Lobe segmentation.
FIGS. 1M—1N: Watershed segmentation result for (1M) right lung sagittal slice, (1N) left lung sagittal slice.
FIGS. 1O-1P: (1O) Fissure ROI, (1P) Angle of rotation.
FIGS. 2A-2C: 2D example showing separation of lung into core and rind. Morphological erosion is used to peel away boundary pixels.
FIGS. 3A and 3B: Example of segmentation and skeletonization applied to an airway tree phantom.
FIGS. 4A and 4B: Example of anatomical labeling.
FIGS. 24A-24C: Results of valve experiment.
The terms “comprise” (and any form of comprise, such as “comprises” and “comprising”), “have” (and any form of have, such as “has” and “having”), “include” (and any form of include, such as “includes” and “including”) and “contain” (and any form of contain, such as “contains” and “containing”) are open-ended linking verbs. Thus, a method “comprising” displaying at least a portion of a set of lungs and one or more potential treatment regions; receiving a selection of a target treatment region from among the one or more potential treatment regions; and displaying one or more locations within the portion that are therapeutically linked to the target treatment region is a method that includes at least these recited steps, but is not limited to only possessing these recited steps.
Similarly, a computer readable medium “comprising” machine readable instructions for performing certain steps is a computer readable medium that has machine readable instructions for implementing at least the recited steps, but also covers media having machine readable instructions for implementing additional, unrecited steps.
The terms “a” and “an” are defined as one or more than one, unless this application expressly requires otherwise. The term “another” is defined as at least a second or more.
In some embodiments, the lungs and the bronchial tree may comprise a region or volume of interest. In such embodiments, lung segmentation may be performed with the goal of separating the voxels corresponding to lung tissue from the voxels corresponding to the surrounding anatomy. The dataset in which the voxels appear may be created using any suitable imaging modality, such as MDCT, computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), confocal microscopy, or volumetric ultrasound, to name a few. The dataset may be characterized as a volumetric dataset of images, which is a dataset comprised of a series of cross-sectional images of the region of interest (e.g., the mediastinum). Following an approach that has been used for segmentation in normal subjects (Hu et al., 2001), a combination of optimal thresholding, region connectivity and topology analysis may be used to identify the lungs. After segmentation, the left and right lungs may be separated using dynamic programming.
Optimal thresholding is an automatic threshold selection method that allows the user to accommodate the normal variations in tissue density expected across a population of subjects. The segmentation threshold may be selected through an iterative procedure. For example, let Ti be the segmentation threshold at step i. To choose a new segmentation threshold, apply Ti to the image to separate the voxels into body and non-body voxels. Let μb and μn be the mean gray level of the body voxels and non-body voxels after segmentation with threshold Ti. Then the new threshold for step i+1 is: Ti+1=½=(μb+μn). This iterative threshold update procedure may be repeated until there is substantially no change in the threshold, e.g., Ti+1=Ti. The initial threshold T0 may be selected based on the CT number for pure air (−1000 HU) and the CT number for voxels within the chest wall/body (>0 HU).
After applying the optimal threshold, 3D connected components labeling may be used to identify the lung voxels. The background air may be eliminated by deleting regions that are connected to the border of the image. Small, disconnected regions may be discarded if the region volume is too small. To identify the lungs, only the two largest components in the volume may be retained, with the additional constraint that each component must be larger than a predetermined minimum volume. The high-density blood-filled vessels in the lung may be labeled as body voxels during the optimal thresholding step. As a result, the 3D lung regions will contain unwanted interior cavities. Topological analysis, similar to that used in (Hu et al., 2001; Reinhardt and Higgins, 1998), may be used to fill the lung regions and eliminate the interior cavities.
The trachea and left and right mainstem bronchi may be identified in the original gray level image data using a closed-space dilation with a unit radius kernel (Masutani et al., 1996). This procedure is equivalent to directed slice-by-slice region growing. To initialize the closed-spaced dilation, the location of the trachea may be automatically identified by searching for the large, circular, air-filled region near the center of the first few slices in the data set. Regions in the current slice provide potential seed point positions for the next slice. The slice-by-slice growing procedure may be stopped when the size of the region on a new slice increases dramatically, indicating that the airways have merged into the low-density lung tissue.
When viewed on transverse CT slices, the anterior and posterior junctions between the left and right lungs may be very thin with weak contrast. Grayscale thresholding may not separate the left and right lungs near these junctions. The left and right lungs may be separated using a 2D dynamic programming technique applied on the transverse slices (Hu et al., 2001; Brown et al., 1997), driven by a graph containing weights proportional to pixel gray level. The maximum cost path corresponds to the junction line position.
The lung regions near the mediastinum contain the radio-dense pulmonary arteries and veins. By the gray level processing described above, these vessels may be excluded from the lung regions. But when manually tracing the lung contours, a manual analyst may “cut” across the large pulmonary vessels and group them with the lung regions, yielding a smooth lung contour. A similar situation occurs as the mainstem bronchi merge into the lungs. A lung boundary shape smoothing step may be included to give a smoothed lung boundary that more closely-mimics the borders defined by some manual analysts. The smoothing step may use operations from binary mathematical morphology to address three shape-related issues: boundary indentations caused by the large pulmonary vessels, large boundary bulges caused by the left and right mainstem bronchi merging into the lungs, and small boundary bulges caused by small airways near the lung borders. Results of the initial grayscale segmentation, left and right lung separation, and smoothing steps are shown for a normal subject in
The lung segmentation technique described below is one manner of carrying out lung segmentation consistent with some embodiments of the present methods, devices and systems:
1.1 Lung Segmentation Introduction
Lung segmentation is the automatic identification of the left and right lungs from chest CT scans.
1.1.1 Inputs
16-bit X-ray CT image object.
1.1.2 Outputs
8-bit volume with separate labels for right and left lung.
1.1.3 Orientation
The lungs are assumed to be oriented such that, in the memory representation, X increases from the right of the patient to the left, Y increases from the front of the patient to the back, and Z increases from the head to the feet.
1.2 Algorithm
The method described below is referred to by (Hu et al., 2001). It comprises the following principal steps:
For thresholding the CT data, an optimal threshold selection procedure is used (Sonka et al., 1999). Optimal thresholding is an automatic threshold selection method that allows us to accommodate the small variations in tissue density expected across a population of subjects. For this step, we assume that the image volume contains only two types of voxels: 1) voxels within the very dense body and chest wall structures (the body voxels); and 2) low-density voxels in the lungs or in the air surrounding the body of the subject (the non-body voxels). We will use optimal thresholding to select a segmentation threshold to separate the body from the non-body voxels, and then identify the lungs as the low-density cavities inside of the body.
The segmentation threshold is selected through an iterative procedure. Let Ti be the segmentation threshold at step i. To choose a new segmentation threshold, we apply Ti to the image to separate the voxels into body and non-body voxels. In this case all voxels lesser than Ti are assumed to be non-body. Let μb and μn be the mean gray-level of the body voxels and non-body voxels after segmentation with threshold Ti. Then the new threshold for step i+1 is:
This iterative threshold update procedure is repeated until there is no change in the threshold, i.e., Ti+1−Ti<tolerance. We set a tolerance value equal to 1. The algorithm is initialized by computing μn from the eight corner voxels in the 3-D volume, and μb from the remaining voxels.
1.2.2 Connectivity and Topological Analysis
After applying the optimal threshold, the non-body voxels (foreground) will correspond to the air surrounding the body, the lungs, and other low-density regions within the image volume (i.e., gas in the bowel). The following steps are then used to retain only the lungs as the solitary binary objects.
Filling of Holes
The high-density vessels in the lung will be labeled as body voxels during the optimal thresholding step. As a result, the 3-D lung regions will contain unwanted interior cavities. Topological analysis is used to fill the lung regions and eliminate the interior cavities:
The above method ensures that all the small 3-D holes inside the lung are filled. A 2-D hole filling procedure is not used because it sometimes leads to filling in of the region near the heart on some lungs, causing problems during lung separation later.
Delete Background
We need to delete the background air to ensure that the lungs are the biggest objects left. For every transverse slice we do seeded region growing from the four comers of the slice, and set all connected voxels to the background label.
Segmentation of the Large Airways
The trachea and left and right mainstem bronchi are identified in the original gray-level image data using a closed-space dilation (Masutani et al., 1996):
After the closed-space dilation, we use 3-D connected component analysis to extract all components larger than Dataset.volume*LUNG_VOLUME_RATIO—the ratio pre-defined as 0.011. On most datasets the 2 lungs are connected and we have to separate the right and left lungs. Before right and left lung separation, we process every transverse slice to delete all foreground objects smaller than 0.1*(Size of biggest object). During this step we also detect those slices on which the lungs are connected, by checking the area of the lungs. If there is one big connected object larger than 0.038*Slice_volume, the slice is put on the stack of connected slices.
Filling of Holes—2D
At this step a copy of the lung mask image is made, LungCopy. A 2-D hole filling operation is done on the lung mask image. For each transverse slice all the holes in the foreground are filled in, similar to the hole filling procedure described above, in 3-D. This step is undertaken to take care of the cases where the region near the heart appears as a hole.
1.2.3 Left and Right Lung Separation
For the cases in which the lungs are connected, separation is performed using a 2 step process. First the approximate joining region is found using morphological operations. Then dynamic programming is performed slice by slice to actually separate the lungs.
Finding Connection Region
For all slices determined to have connected lungs, as described above, the connection region is determined by the following method:
MinLungVolume=0.01*Slice_volume. The unit disk structuring element, B4, is shaped like the 4-connected neighborhood of a voxel.
Constrained Dilation
Constrained dilation is a morphological operation defined by the following algorithm:
Dynamic programming is applied to find the maximum cost path through a graph with weights equal to pixel gray-level, similar to the method described in (Brown et al., 1997). The maximum cost path corresponds to the junction line position.
For slices with hole filling problem, do the following steps:
After separation the lungs are separated on all transverse slices, but they might still be connected in the 3-D connectivity sense. The labeling is done in a slice by slice approach. We use the fact that on transverse slices, the right lung is in the left half of the dataset and vice-versa. First, a seed point is detected for each lung, in the following manner:
repeat above procedure for slices above and below middle slice
(center point—mean x and y coordinates for a region)
After the initial seeds have been found, the labels are propagated slice by slice. The basic assumption is that the center points for a lung on one slice will lie inside the corresponding lung on an adjacent slice. The algorithm can be described as follows:
For the right lung, the search window is of size XDim/5 to the left of current center point X coordinate. For the left lung, the search window of size XDim/5 to the right of current center point X coordinate. XDim is the size of the image object in X. Finally, any unlabelled voxels left are labeled according to the labels of connected voxels.
The lung smoothing technique described below is one manner of carrying out lung smoothing consistent with some embodiments of the present methods, devices and systems:
2.1 Lung Smoothing Introduction
Lung smoothing is a post processing step after lung segmentation, to smooth the contours of the lungs, especially on the inner surface near the mediastinum.
2.1.1 Inputs
8-bit lung segmentation image object, 8-bit airway tree image object.
2.1.2 Outputs
8-bit volume with separate labels for right and left lung.
2.1.3 Orientation
The lungs are assumed to be oriented such that, in the memory representation, X increases from the right of the patient to the left, Y increases from the front of the patient to the back, and Z increases from the head to the feet.
2.2 Algorithm
The unsmoothness in the contour of the lungs is caused due to 2 principal reasons.
After the first step, we are left with a lung mask with indentations on the lung surface corresponding to the vessels.
In some embodiments, once the lungs have been identified, the lung regions may be divided into surgically-accessible and surgically-inaccessible subregions using 3D “rind” and “core” algorithms, respectively (Guo et al., 2002). The core and rind may be useful for surgical planning for an intervention such as LVRS. Operations from mathematical morphology may be used to “peel” away a prescribed thickness of boundary pixels to identify the rind, leaving only the core.
In some embodiments, airway segmentation takes advantage of the relatively high contrast in, for example, CT images between the center of an airway and the airway wall. A seeded region growing may be used, starting from an automatically-identified seedpoint within the trachea, similar to the method used in (Hu et al., 2001). New voxels may be added to the region if they have a similar X-ray density as a neighbor voxel that already belongs to the region. The tree may be segmented multiple times using more and more aggressive values for measuring the similarity between neighboring voxels. At some point, the segmentation may start to “leak” into the surrounding lung tissue. This may be detected by measuring the volume growth between iterations. A sudden big increase in volume indicates a leak and the result from the previous iteration step may be taken as end-result. A breadth-first search (Silvela and Portillo, 2001) may be used, which allows a fast and memory-friendly implementation. After airway segmentation, a binary subvolume may be formed that represents the extracted airway tree.
In some embodiments, the segmented airway tree may be skeletonized to identify the three-dimensional centerlines of individual branches and to determine the branchpoint locations. A sequential 3D thinning algorithm reported by Palágyi et al. (2001) may serve as the basis for such skeletonization. To obtain the skeleton, a thinning function may be used to delete border voxels that can be removed without changing the topology of the tree. Such a thinning step may be applied repeatedly until no more points can be deleted. The thinning may be performed symmetrically and the resulting skeleton will lie in the middle of the cylindrically shaped airway segments.
After completion of the thinning step, the skeleton may be smoothed (see Palágyi et al.), false branches may be pruned, the location of the branchpoints may be identified, and the complete tree may be converted into a graph structure using an adjacency list representation (see Palágyi et al.). The pruning and branchpoint location identification techniques described in U.S. patent application Ser. No. 11/122,924 filed May 5, 2005 and entitled “Methods and Devices for Labeling and/or Matching” may be used in this regard, which co-pending application is expressly incorporated by reference.
In some embodiments, minor and major airway diameter, cross-sectional area, and wall-thickness are measured. The techniques described in section 5.4 “Methods” of Tschirren (which section is incorporated by reference) may be used in this regard. Measurements may be taken at every centerline voxel position. The inner and outer airway wall may be segmented simultaneously using 3D optimal surface detection based on the graph searching algorithm of Wu and Chen (2002). While inner wall surfaces are generally visible in CT images, outer airway wall surfaces are very difficult to segment due to their blurred and discontinuous appearance. Adjacent blood vessels further increase the difficulty of this task. By optimizing the inner and outer wall surfaces and considering the geometric constraints, a coupled-surfaces segmentation approach (Li et al., 2004) may produce good segmentation results for both airway wall surfaces in a robust manner. Unlike other optimal surface detection methods, Wu and Chen's algorithm does not suffer from exponentially growing computational complexity, which makes it run efficiently. A complete airway tree can be measured in about 3 minutes on a dual CPU 3 GHz machine. All measurement results may be stored in a hierarchical XML file that holds information about airway geometry, topology, and anatomical labels. This allows the user to easily access results for further processing.
In some embodiments, the segmented airway tree may be anatomically labeled, such that 33 commonly used labels are assigned to the airway tree.
The labels may be assigned by matching the graph representation of the tree against a population average. This population average may be created by averaging a set of trees that are hand-labeled by a human expert. The population average may incorporate the natural variability of the airway geometry and topology as it can be observed across the population. This will make the automated labeling robust against such variations. This labeling technique is described in U.S. patent application Ser. No. 11/122,924 filed May 5, 2005 and entitled “Methods and Devices for Labeling and/or Matching,” and that description is expressly incorporated by reference.
Not all false branches can automatically be eliminated during the skeletonization. However, the anatomical labeling module (or algorithm) may be aware of that and be able to tolerate false branches in almost all cases.
The human lungs are divided into five distinct anatomic compartments called lobes. The physical boundaries between the lobes are called the lobar fissures. The lobes can also be distinguished by the separate vascular and airway sub-trees supplying each lobe.
In other embodiments, another way to segment the lobes includes segmenting the vascular tree, and computing a watershed transform on a distance map of the segmented vascular tree. The distance map may be determined (e.g., created) by assigning to every background voxel its distance to the closest foreground voxel, where “foreground” voxels belong to a vessel and “background” voxels are any other voxels within the lung. With the vascular tree segments corresponding to the topographical basins of the gray level image, the lobes may be segmented by placing seed points on the corresponding vascular sub-trees. Because the airway tree segments run close to the vascular tree segments for a number of generations, the seed point placement may be automated by using the anatomical description of the airway tree. Vascular tree points may be identified in a window (e.g., a window appearing on a display device) near the airway sub-tree branchpoints and endpoints, and those points may be used as seeds.
Using the approach to lobe segmentation described in the previous paragraph, a close approximation to the lobar fissures should be achieved (see
The lobe segmentation technique below is one manner of carrying out lobe segmentation consistent with some embodiments of the present methods, devices and systems.
3.1 Lobe Segmentation Introduction
Lobe segmentation is the automatic identification of the five lung lobes: left upper, left lower, right upper, right middle and right lower lobes from chest CT scans. The right upper and right middle lobes are separated by the horizontal fissure. The right upper and right middle lobes together are separated from the right lower lobe by the right oblique fissure. The left upper lobe is separated from the left lower lobe by the left oblique fissure. See
3.1.1 Inputs
16-bit CT image object, 8-bit lung smoothing image object, 8-bit vascular tree segmentation image object, 8-bit airway tree segmentation image object, Airway tree XML file.
3.1.2 Outputs
Analyze 8-bit volume with separate labels for each lobe.
3.1.3 Orientation
The lungs are assumed to be oriented such that, in the memory representation, X increases from the right of the patient to the left, Y increases from the front of the patient to the back, and Z increases from the head to the feet.
3.2 Algorithm
A lobe segmentation flow chart appears in
The lobe segmentation algorithm is based on computing a watershed transform on a distance map of the vessel segmentation. The guiding principle is that the fissures are characterised by an absence of vessels, and hence can be thought of as local maxima in the distance map. If we consider the vessels and airways as the basins of the image topography and spread labels from these basins, we can expect that the labels corresponding to the different lobes to meet at or near the fissures. So our distance map image should correspond to basins at the vessels and airways, and peaks corresponding to the fissures. It is calculated as follows:
The above method ensures that the airways and vessels correspond to the basins and the fissures are the peaks or watersheds(also called merging candidates), of the 3-D topography of the image.
3.2.2 Watershed Transform
The watershed transform algorithm used is the Interactive Watershed Transform (IWT) algorithm proposed by (Hahn and Peitgen, 2003), for its efficiency and real-time interactivity. It computes the basins and merges from the input image and arranges them hierarchically in a tree.
Initialize: 0,∀p ∈ D
0 //basin counter
0 //merging counter
S[v]
{bk ∈ B| ∃q ∈ N(p)
W[q] = k} //neighboring basins
β + 1
gray level value of p
β
β
arg min{BASE(k) | bk ∈ Bp}|
α //basin counter
r[BASE(α)]
μ + 1
k
α
gray level value of p
BASE(r[k])
Once the watershed transform has been computed, the basins can be merged according to selected markers in real-time, using the following algorithm:
Require: hpf, the pre-flooding height to control basin merging, and the set of markers MARK. 0
k
W[m]
label of m
BASE(k[μ])
BASE(a[μ])
W[p]
r[k] ≠ k then
l[BASE(k)]
NOTE: The basin and merging tables, referred to in the algorithm above, have three different fields for each basin and merging index. The tables were implemented as vector of vectors (C++ Standard Template Library), thus dynamic allocating memory for each new basin and merging candidate.
3.2.3 Marker Selection
The IWT requires markers for basin merging. The markers for basin merging are selected automatically, from the airway tree anatomical labeling. The airway tree XML file is read and the tree structure is extracted. The airway branchpoints corresponding to the roots of the different lobar sub-trees are TriLUL, LLB6, TriRUL, RB4+5 and TriEndBronInt, which correspond to the left upper lobe, left lower lobe, right upper lobe, right middle lobe and the right lower lobe respectively (
Additionally select some extra markers from a-priori shape information for the lobes. Take transverse slice number 30 from the top of the lung and pick all vascular segments as markers for the upper lobes for each lung.
3.2.4 Watershed Reconstruction/Basin Merging
After selecting all markers, the lobes can be segmented using the basin merging procedure described above. It should be noted that if the segmentation is not satisfactory, manually selected markers can be placed and the basin merging redone interactively and in real-time.
The oblique fissure segmentation technique and the horizontal fissure segmentation technique described below represent suitable, example ways of carrying out oblique and horizontal fissure segmentation (more broadly, fissure segmentation) consistent with some embodiments of the present methods, devices and systems:
4.1 Oblique Fissure Segmentation Introduction
Oblique fissure segmentation is the automatic identification of the right and left oblique fissures. The fissure ROI is determined from the approximate lobar boundaries (
4.1.1 Inputs
16-bit CT image object, 8-bit smoothed lung image object, 8-bit segmented lobe image object (chapter 6), 8-bit output image object.
4.1.2 Outputs
Analyze 8-bit volume with separate labels for upper and lower lobes (for the right lung, the upper and middle lobes are combined under a single upper lobe label).
4.1.3 Orientation
The lungs are assumed to be oriented such that, in the memory representation, X increases from the right of the patient to the left, Y increases from the front of the patient to the back, and Z increases from the head to the feet.
4.2 Algorithm
The oblique fissure segmentation algorithm comprises the following main steps:
The fissure ROI is found based on the lobe segmentation result obtained by the method described in sections 3.1-3.2.4 above. First, we extract all upper lobe (and middle lobe, for right lung) voxels with at least one 6-connected lower lobe voxel. This constitutes our approximate fissure point set. From this point set we compute an approximate euclidean distance map as described in (Borgefors, 1986), which gives us the distance of each background voxel to the closest foreground voxel. We then compute a fissure mask with three labels: ROI1, ROI2 and LUNG. ROI1 refers to all lung voxels which are at a distance≦d1, and LUNG refers to all other lung voxels. ROI2 refers to all non-lung voxels which are at a distance≦d2. We use the values d1=6.23538 mm and d2=4.1569 mm. The approximate euclidean distance map values are converted to metric values by assuming that √{square root over (xi2+yi2+zi2)} mm˜5 Chamfer distance units. xi, yi and zi are the image voxel dimensions.
4.2.2 Rotate ROI Image
The oblique fissure is tilted by a certain angle away from the Z(vertical) axis, as can be seen in
To rotate the raw image, we use tri-linear interpolation, along with re-sampling to make the voxels isotropic. Let
be the forward rotation matrix, and
be the inverse rotation matrix. If xi, yi and zi are the original voxel dimensions, set new voxel dimensions for rotated image as xo=yo=zo=min(xi, yi, zi). The rotation scheme can then be described as follows:
Require: Let I be original image, F input mask image, and O be output raw image and M be the output mask image (y dim×yi)/2
(z dim×zi)/2
(Rinv[0][0]×yc+Rinv[0][1]×zc−yc)
(Rinv[1][0]×yc+Rinv[1][1]×zc−zc)
y*yo
z*zo
x*xo
(Rinv[0][0]×ym+Rinv[0][1]×zm−yoff)
(Rinv[1][0]×ym+Rinv[1][1]×zm−zoff)
floor(X/xi)
floor(Y/yi)
floor(Z/zi)
X−xf×xi
Y−yf×yi
Z−zf×zi
(1−c)*(1−b)*(1−a)*I(xf,yf,zf)+(1−c)*(1−b)*(a)*I(xf+1,yf,zf)+
(int)(X/xi=0.5)
(int)(Y/yi=0.5)
(int)(X/zi=0.5)
F(xx,yy,zz)
After rotation, we find the bounding-box for the label ROI1, as shown in
4.2.3 Ridgeness Map
We enhance the fissures by computing the ridgeness value. The ridge detector is based on the MLSEC-ST (Multilocal level set extrinsic curvature and its enhancement by structure tensors) (Li et al. II, 2004). The ridgeness map is calculated for all XY slices of the fissure bounding box.
4.2.4 Cost Function
The cost function is defined in the following manner:
I refers to the rotated input CT image. R is the ridgeness map of I.
4.2.5 3-D Graph Search
After defining the cost function, we find the optimal 3-D surface f(X, Z), within the graph defined by the image −C. The algorithm proposed by (Li et al. II, 2004) is used, which finds the optimal surface by transforming the problem to computing the minimum s-t cut in a derived graph. Detailed description of the algorithm is presented in (Li et al. II, 2004). The different parameters for the optimal surface search, that are used for this application are:
The optimal surface search returns a single voxel wide connected surface with label 255, and 0 elsewhere. For a detected fissure point (xf, yf, zf), all lung voxels (xf, y, zf)|y<=yf are assigned to the upper lobe. Similarly, all lung voxels (xf, y, zf)|y>yf belong to the lower lobe. Using this information, the following steps are performed to label the lobes:
Next, the labels are rotated back to the initial orientation, using nearest neighbor interpolation. Any remaining unlabelled lung voxels are labeled using the same algorithm as above.
5.1 Horizontal Fissure Segmentation Introduction
Horizontal fissure segmentation is the automatic identification of the right and left oblique fissures. The fissure ROI is determined from the approximate lobar boundaries (
5.1.1 Inputs
16-bit CT image object, 8-bit segmented oblique fissure image object (sections 4.1-4.2.6), 8-bit segmented lobe image object (sections 3.1-3.2.4), 8-bit output image object.
5.1.2 Outputs
Analyze 8-bit volume with separate labels for upper, middle and lower lobes.
5.1.3 Orientation
The lungs are assumed to be oriented such that, in the memory representation, X increases from the right of the patient to the left, Y increases from the front of the patient to the back, and Z increases from the head to the feet.
5.2 Algorithm
The oblique fissure segmentation algorithm comprises the following main steps:
The fissure ROI is found based on the lobe segmentation result obtained by the method described in sections 3.1-3.2.4 above. First, we extract all right upper lobe voxels with at least one 6-connected middle lobe voxel. This constitutes our approximate fissure point set. From this point set we compute an approximate euclidean distance map as described in (Borgefors, 1986), which gives us the distance of each background voxel to the closest foreground voxel. We then compute a fissure mask with three labels: ROI1, ROI2 and LUNG. ROI1 refers to all lung voxels which are at a distance≦d1, and LUNG refers to all other lung voxels. ROI2 refers to all non-lung voxels which are at a distance≦d2. We use the values d1=6.23538 mm and d2=4.1569 mm. The approximate euclidean distance map values are converted to metric values by assuming that √{square root over (xi2′+yi2+zi2)} mm˜5 Chamfer distance units. xi, yi and zi are the image voxel dimensions.
5.2.2 Re-sample ROI Image
If the input CT volume is non-isotropic, we resample the dataset to make it isotropic. If xi, yi and zi are the original voxel dimensions, set new voxel dimensions for rotated image as xo=yo=zo=min(xi, yi, zi). The interpolation scheme can then be described as follows:
Require: Let I be original image, F input mask image, and O be output raw image and M be the output mask image x*xo
y*yo
z*zo
floor(X/xi)
floor(Y/yi)
floor(Z/zi)
X−xf×xi
Y−yf×yi
Z−zf×zi
(1−c)*(1−b)*(1−a)*I(xf,yf,zf)+(1−c)*(1−b)*(a)*I(xf+1,yf,zf)+
(int)(X/xi=0.5)
(int)(Y/yi=0.5)
(int)(Z/zi=0.5)
F(xx,yy,zz)
After re-sampling, we find the bounding-box for the label ROI1, as shown in
5.2.3 Ridgeness Map
We enhance the fissures by computing the ridgeness value. The ridge detector is based on the MLSEC-ST (Multilocal level set extrinsic curvature and its enhancement by structure tensors) (Lopez et al., 2000). The ridgeness map is calculated for all YZ slices of the fissure bounding box.
5.2.4 Cost Function
The cost function is defined in the following manner:
I refers to the rotated input CT image. R is the ridgeness map of I.
5.2.5 3-D Graph Search
After defining the cost function, we find the optimal 3-D surface f(X, Y), within the graph defined by the image −C. The algorithm proposed by (Li et al. II, 2004) is used, which finds the optimal surface by transforming the problem to computing the minimum s-t cut in a derived graph. Detailed description of the algorithm is presented in (Li et al. II, 2004). The different parameters for the optimal surface search, that are used for this application are:
The optimal surface search returns a single voxel wide connected surface with label 255, and 0 elsewhere. For a detected fissure point (xf, yf, zf), all lung voxels (xf, yf, z)|z<zf are assigned to the upper lobe. For a detected fissure point (xf, yf, zf), all lung voxels (xf, yf, z)|z<zf are assigned to the upper lobe. Similarly, all lung voxels (xf, yf, z)|z>=zf belong to the middle lobe. Using this information, the following steps are performed to label the lobes:
Next, the labels are transferred to the original volume, using nearest neighbor interpolation. Any remaining unlabelled lung voxels are labeled using the same algorithm as above.
In some embodiments (e.g., those in which emphysematic tissue is the target tissue), low attenuation clusters (LACs) representative of disease may be found by thresholding the gray level images in a dataset of images making up an anatomical region of interest (e.g., the lungs). A low attenuation cluster is one type of potential treatment region and may, if selected as described below in conjunction with Pathfinder, be characterized as a target treatment region. The dataset of images may be obtained using any of the imaging modalities discussed above. When some form of CT is used, the left and right lung may be distinguished by masking the CT volume with the lung segmentation result. The threshold result may be traversed and one-voxel bridges that connect neighboring clusters may be removed. The volume of each cluster may then be measured and the histogram of the cluster sizes may be computed. Taking the logarithm of both the ordinate (count) and abscissa (cluster size) leads to a linearization of the histogram (see
Some embodiments include finding a feeding airway to a region t of the lung. This may be done for different reasons, including: 1) to determine which airway path is supplying air to region t; and 2) to determine which airway path is leading closest to region t such that the region can be accessed by a medical device capable of traversing the airway tree (e.g., an endoscope or a bronchoscope). These two reasons are examples of conditions that qualify as therapeutically linking a potential location for a therapeutic device (e.g., a one-way valve or a stent) to a target treatment region (e.g., a LAC representing emphysematic lung tissue).
The target t may be any point of interest inside a given lung, e.g., a low attenuation cluster (in the case of emphysema), a suspected or confirmed lung nodule, a lymph node, or any other location within the lung that can be specified by a coordinate. The target t may be specified by a single point (e.g., the center of a lung nodule), or it may be specified by a set of coordinates (e.g., the coordinates of all the voxels that are located inside a nodule, or the coordinates of all the voxels that are located on the surface of a nodule). The set T may be defined to contain all the coordinates of the points that are contained within region t.
An example set of steps that may be performed to identify the feeding airway segments to a target region within a lung comprise:
a) Identifying the lung lobe(s) in which the coordinates in T are located (described below as “lobe(s) of interest”). The lobe(s) may be defined by the result of the lobar segmentation described above. The lobar segmentation algorithm may assign every voxel within the volumetric image scan (e.g., the dataset of images) to an anatomical lobe (or several anatomical lobes if no definitive assignment can be made due, for example, to an incomplete fissure line).
b) Finding all airway-tree segments (ats) that have the majority of their volume located within the lobe or lobes identified in step a). This step may be conducted by traversing the segmented airway branch-by-branch and determining if a given branch (up to all branches) is located in one of the lobes of interest.
c) For every airway-tree segment ats identified in step b), computing the distance to every point in T. Each such distance may be computed from only one single point within a given ats, or, alternatively, distances may be computed from several or all points within a given ats. When the “only one point” technique is used, the endpoint of every terminal branch may be used to compute the distance to the target t in order to find the terminal branch with the shortest associated distance. Computing the distance from multiple points within a given ats may be used to find the closest point on the surface of any ats to a given target t in cases of, for example, planning for a needle biopsy of that target. Every distance that is computed (e.g., found or determined) may be stored together with its associated ats and its associated point within that ats.
Such distances may be computed in any suitable way. For example, if the goal of the procedure is the planning of, for example, a needle-biopsy, then an Euclidean distance measure may be used. Alternatively, for better computational performance, an integer-based measure such as a city-block distance may be used instead of the Euclidean distance measure. The gray-value of the volumetric image scan of the lung may be factored into each distance measure such that gray-values that indicate a high air content represent shorter distances. In this regard, air-rich tissue may be favored over tissue with a low air content.
d) Sorting the distances determined in step c) and finding the N shortest ones. The value of N depends on the application and may be adjusted by the user.
e) For each point p within the airway tree that is associated with the N shortest distances found in step d), finding the shortest path between the points q and p, where point q may be the origin of the airway tree (the top of the trachea), or any other user-defined point.
Some (up to all) of the steps described in the sections above may be implemented in the form of software. The software may be stored on any suitable computer readable media, such as any suitable form of memory or data storage device, including but not limited to hard disks, removable magnetic disks, CDs, DVDs, optical disks, magnetic cassettes, flash memory cards, Bernoulli cartridges, USB drives, and the like. In such embodiments, the invention may be characterized as computer readable media having machine readable instructions for performing certain step(s).
Some (up to all) of the steps described in the sections above may be implemented using a computer having a processor (e.g., one or more integrated circuits) programmed with firmware and/or running software. Some (up to all) of the steps described in the sections above may be implemented using a distributed computing environment. In a distributed computing environment (e.g., a computer system), multiple computers may be used, such as those connected by any suitable number of connection mediums (e.g., a local area network (LAN), a wide area network (WAN), or other computer networks, including but not limited to Ethernets, enterprise-wide computer networks, intranets and the Internet).
Each step or group of steps identified by a bolded heading above may be coded as a standalone plug-in (e.g., a module) having a well-defined interface that may be integrated with other modules, and/or with a user interface (e.g., a graphical user interface). Standard-conforming C++ may be used. Well-established cross-platform libraries such as OpenGL and Boost may be utilized to execute embodiments of the present methods, devices and systems. Multi-threading may be used wherever applicable to reduce computing time on modem multiprocessor based hardware platforms. All modules may be written to enable them to be compiled on all common platforms (e.g., Microsoft®, Linux®, Apple Macintosh® OS X, Unix®).
One example software package, or application, that may be used to practice some embodiments of the steps described above will be referred to as “Pulmonary Workstation” in this disclosure. Those of ordinary skill in the art having the benefit of this disclosure will be able to write code (machine readable instructions) without undue experimentation for accomplishing the features of Pulmonary Workstation (including the graphical user interface) described below and shown in the figures. Pulmonary Workstation may be configured using the techniques disclosed in sections 1.1-5.2.6 above and the following sections pertaining to “Display,” “Regression Testing,” and “Coordinate System” disclosed below:
6 Display
6.1 Gamma Correction & 16 Bit to 8 Bit Conversion
Given an input voxel vi (12 bit grayvalue) with a minimum grayvalue a and a maximum grayvalue b. Convert to a 8 bit grayvalue va using gamma correction γ:
with explanations:
Regression testing validates the output of an algorithm against a reference output. The current output of the algorithm is compared with the reference output, and a decision is made if the two outputs are identical. If the two outputs are identical within a pre-defined tolerance then the regression test returns as passed, otherwise it returns as failed. The passed/failed decision is made automatically, without any human intervention.
Every algorithm (in particular, every interface of every plugin) has an own regression test defined. Regression tests can be run in two different modes:
Manually executed local test. When working on a particular interface the developer can execute a regression test on that particular interface. The result of the regression test is printed in a human readable form to terminal window. This allows the developer to repeatedly and quickly check the performance of an alogorithm while doing changes on it.
Automatically executed global test. An automatically executed global test may for example be scheduled to run on a nightly basis. This test usually executes many different regression tests (e.g., on pre-configured interfaces). Each individual regression test outputs its results in a machine-readable format. The controlling instance gathers the individual results and combines them into one report, e.g., a webpage comparable to the “Dashboard” used by the ITK project.
7.2 Regression Test Executable
The test and reference datasets are organized in the following directory structure/naming scheme:
For each test case . . .
This section 8.1 is copied from “Medical Image Format FAQ—Part 2” (available on the World Wide Web at faqs.org/faqs/medical-image-faq/part2/), Section 2.2.2. The bracketed “Projection radiographs” and “Cross-sectional images” and the italics (for emphasis) have been added.
Another question that is frequently asked in comp.protocols.dicom is how to determine which side of an image is which (e.g. left, right) and so on. The short answer is that for projection radiographs this is specified explicitly using the Patient Orientation attribute, and for cross-sectional images it needs to be derived from the Image Orientation (Patient) direction cosines. In the standard these are explained as follows:
Some simple code to take one of the direction cosines (vectors) from the Image Orientation (Patient) attribute and generate strings equivalent to one of the values of Patient Orientation looks like this (noting that if the vector is not aligned exactly with one of the major axes, the resulting string will have multiple letters in as described under “refinements” in C.7.6.1.1.1):
Pulmonary Workstation may be configured (e.g., code may be written) such that starting the application brings the user to a startup screen as illustrated in
Choosing a Patient from the Database
Pulmonary Workstation may be configured such that all the patient data and analysis it performs may be stored in a database or some other data storage device. Pulmonary Workstation may be configured such that choosing the “Review/Analyze Screen” link brings up a “Load Patient” dialog box, such as the one shown in
Loading New Patients into the Database
Pulmonary Workstation may be configured such that new patients/cases may be loaded by pressing the “New Patient” button in the lower left of the
Patient Administration
Pulmonary Workstation may be configured such that clicking the “Administer Patients” button on the main screen (
Airway Analysis
Pulmonary Workstation may be configured such that when the user selects a scan, the status of that scan is displayed in the “Analyze Study” window as shown in
Pulmonary Workstation may be configured such that there are several options available for the airway measurement workflow. For example, Pulmonary Workstation may be configured such that if the user chooses the “Measure User Selected Paths” option (
Pulmonary Workstation may be configured such that pressing the “Analyze” button shown in
The Bronchial Airway Tree Window
Pulmonary Workstation may be configured such that the following controls are available to the user:
Editing the Airway Nomenclature
Pulmonary Workstation may be configured such that once an individual airway segment is selected via a double click, a right click on that segment will bring up a menu for changing the airway's automatically selected name, as seen in
Airway Measurements
Pulmonary Workstation may be configured such that once a path from the trachea to a selected segment is highlighted as described above, or when one or more individual segments are highlighted as described above, clicking the “measure” button on the upper left of the screen (see, e.g.,
Pulmonary Workstation may be configured such that if the measurements have not been done yet, the measurements are done dynamically for each path as the paths are examined in the “Lumen View” window; however, the results are stored in a database so that for each case, no measurements need be taken twice. Pulmonary Workstation may be configured such that each segment from the whole tree may be measured at once, as indicated in the radio buttons in the “Analyze Study” window shown in
The Lumen Detail Screen
Pulmonary Workstation may be configured with the “Lumen View” screen depicted in
Pulmonary Workstation may be configured such that the selected path, segment or group of segments (segments that are not aligned will appear in the
Pulmonary Workstation may be configured such that the straightening comprises taking any curve or curves out of the segment, segments or path. This configuration may be achieved, and the resulting straightened section depicted, as follows. A centerline may be assigned to the path to be straightened (which, for this purpose may include only one segment). The centerline may be characterized as extending between the beginning (e.g., point A) of the path to be straightened and the end of the path to be straightened (e.g., point B). The centerline may be divided into small steps. The small steps may be defined by the closest image voxels. However, any other subdivision of the centerline may be used. The centerline may be divided into even or uneven steps. At each step location along the centerline, the gray-values of the original image volume are sampled along a line L that is positioned such that it runs perpendicular to the tangent at the current centerline position. The tangent may be determined by taking the first derivative of the spline representation of the centerline. The rotational position of line L (e.g., sample line L0) and point A may be determined by the user (the rotational position may be defined by an angle in the range of between 0 and 360 degrees). Each subsequent sample line L1, L2, . . . , Ln may be positioned such that the angle between two neighboring sample lines Ln and Ln+1 is minimized. The gray-values sampled from the image volume along the lines L may be visualized in the longitudinal view shown in the profile window near the top
Pulmonary Workstation may be configured such that the outlines in red around the straightened portion of the airway tree represent the inner and outer walls of that portion, as determined according to the techniques discussed above in the Measurements of Airway Tree Geometry section.
Pulmonary Workstation may be configured such that the position of the viewing plane for the straightened path is seen in the lower left panel, which is an orthogonal view of the airway lumen and walls. Pulmonary Workstation may be configured such that the green arrow that bisects the orthogonal view shows the orientation of the path cross-section shown in the top
Pulmonary Workstation may be configured such that the orthogonal view in the lower left of
Pulmonary Workstation may be configured such that the Path Location Display (labeled as such in
Pulmonary Workstation may be configured such that the “Lumen View” screen includes an airway measurements window beneath the profile window (see
Pulmonary Workstation may be configured such that if an additional or different airway segment or path needs to be examined, a user can (using the folder tabs at the top of the screen) return to the “Bronchial Tree” page and select a new path/segment(s), using the technique described above.
Pulmonary Workstation may be configured such that the “Window/Level” settings of the gray-level images may be changed, as shown in the upper left-hand corner of the
Saving the Data to Another Format
Pulmonary Workstation may be configured such that clicking the folder tab labeled “Report” allows the measurement data that has been determined to be outputted to, for example, a CVS format text file. Pulmonary Workstation may be configured such that the default setting creates a space (ASCII 32) delimited file, while other delimiters can be chosen. Pulmonary Workstation may be configured such that when loaded into Microsoft® Excel, the file will trigger the autoparse function, which allows the user to choose “delimited” parsing, which arranges the columns based on the delimiter character chosen. Pulmonary Workstation may be configured such that the resulting spreadsheet, with some formatting of the labels, looks like the spreadsheet depicted in
As
average minor diameter (labeled as “avgMinorDiam”), which comprises the arithmetic average of individual minor diameter measurements conducted at different locations along the segment;
average major diameter (labeled as “avgMajorDiam”), which comprises the arithmetic average of individual major diameter measurements conducted at different locations along the segment;
average cross-sectional area (labeled as “avgArea”), which comprises the arithmetic average of individual cross-sectional area measurements conducted at different locations along the segment;
average value of average wall thickness (labeled as “avgAvgWallThickness”), which comprises the arithmetic average of individual average wall thickness measurements conducted at different locations along the segment;
average minimum wall thickness (labeled as “avgMinWallThickness”), which comprises the arithmetic average of individual minimum wall thickness measurements conducted at different locations along the segment;
average maximum wall thickness (labeled as “avgMaxWallThickness”), which comprises the arithmetic average of individual maximal wall thickness measurements conducted at different locations along the segment;
minimum average minor diameter (labeled as “minAvgMinorDiam”), which comprises the minimum value of individual average minor diameter measurements conducted at different locations along the segment;
minimum average major diameter (labeled as “minAvgMajorDiam”), which comprises the minimum value of individual average major diameter measurements conducted at different locations along the segment;
minimum average cross-sectional area (labeled as “minAvgArea”), which comprises the minimum value of individual average cross-sectional area measurements conducted at different locations along the segment;
maximum average cross-sectional area (labeled as “maxAvgArea”), which comprises the maximum value of individual average cross-sectional area measurements conducted at different locations along the segment;
minimum average wall thickness (labeled as “minAvgWallThickness”), which comprises the minimum value of individual average wall thickness measurements conducted at different locations along the segment;
maximum average wall thickness (labeled as “maxAvgWallThickness”), which comprises the maximum value of individual average wall thickness measurements conducted at different locations along the segment;
minimum value of minimum wall thickness (labeled as “minMinWallThickness”), which comprises the minimum value of individual minimal wall thickness measurements conducted at different locations along the segment;
maximum value of minimum wall thickness (labeled as “maxMinWallThickness”), which comprises the maximum value of individual minimal wall thickness measurements conducted at different locations along the segment;
minimum value of maximum wall thickness (labeled as “minMaxWallThickness”), which comprises the minimum value of individual maximal wall thickness measurements conducted at different locations along the segment; and
maximum value of maximum wall thickness (labeled as “maxMaxWallThickness”), which comprises the maximum value of individual maximal wall thickness measurements conducted at different locations along the segment.
The preceding parameters are examples of dimensional attributes of a given selected portion of an airway tree.
Another example software package that may be used to practice some embodiments of the steps described above will be referred to as “Pathfinder” in this disclosure. Those of ordinary skill in the art having the benefit of this disclosure will be able to write code (machine readable instructions) without undue experimentation for accomplishing the features of Pathfinder (including the graphical user interface) described below and shown in the figures.
Pathfinder may be configured to produce the graphical user interface (GUI) shown in
As
Pathfinder may be configured such that the bronchial tree may be manipulated in a similar or identical manner to the way in which the bronchial tree depiction in the “Bronchial Tree” screen of Pulmonary Workstation may be manipulated. Thus, and as an example, Pathfinder may be configured such that:
dragging a mouse with the left mouse key held down rotates the tree (
dragging the mouse with the middle mouse key held down (or, e.g., a roller wheel) translates the position of tree; and
dragging the mouse with the right mouse key held down zooms.
Pathfinder may be configured such that double-clicking on a sphere causes the pathway through the bronchial tree to a location within the bronchial tree that is closest to that sphere to be determined (e.g., computed) according, for example, to the techniques discussed above in the Identifying Relevant Airway Segments section and displays that pathway, such as with a red line as shown in
Pathfinder may be configured such that double-clicking on an airway segment effectively constitutes placing a one-way valve (or some other therapeutic device, such as a stent) in that segment because, in some embodiments, it triggers a determination of the LAC or LACs that are downstream of (or fed by) the double-clicked segment, and highlights only those spheres (such that the target sphere may be de-highlighted if it is not one of those downstream LACs, as in
Pathfinder also may be configured such that when a given airway segment is double-clicked, the percentage of lung volume that is ventilated by the selected airway segment (e.g., the portion of the airway tree that is downstream from the selected segment) may be determined (e.g., computed) and displayed in another window (see the “Percent of lung volume ventilated by airways downstream from selected segment” designation in the upper right-hand window of
As
Lung Segmentation: Low-dose datasets (120 kV, 50 mAs) from 10 different emphysema patients were available. The resolution of the scans was 0.66×0.66×0.6 mm3. A human expert hand-traced the lung borders in 6 randomly selected axial slices. For each border point in the automatically-obtained lung segmentation result, the closest point in the reference dataset was determined and the 3D Euclidean distance between each such pair of points was measured. Table 1 summarizes the results of the error measurements, showing all values in millimeters:
Table 1 shows the root mean squared (RMS) errors for the left, right, and both lungs; the signed errors and unsigned errors showed similar results. In all cases the signed error is relatively close to zero and well below the CT scanner resolution, which indicates that no significant systematic error is present.
Lobe Segmentation: Low-dose datasets (120 kV, 50 mAs) from 10 different emphysema patients were available. The resolution of the scans was 0.66×0.66×0.6 mm3. A human expert hand-traced each of the 3 fissures (left oblique, right oblique, right horizontal) in 6 randomly selected sagittal slices. For each fissure point in the automatically obtained lobe segmentation result, the closest point in the reference dataset was determined and the 3D Euclidean distance between each such pair of points was measured. Table 2 summarizes the results of the error measurements.
The root mean squared (RMS) errors is listed separately for the left oblique fissure, the right oblique fissure, and the right horizontal fissure. Results for the signed errors and unsigned errors were similar. The detection of the left oblique fissure in subject brpild005tlc and the right horizontal in subject brpild008tlc failed because of badly deteriorated fissure lines (due to disease). For the two oblique fissures, the signed error is relatively close to zero, which indicates that no significant systematic error is present. The signed error for the right horizontal fissure is bigger because the right horizontal fissure mostly lies within the scan plane and due to that it exhibits a relatively low contrast. Also the scanner resolution is coarser in scan-direction, which additionally contributes to this increased error.
LAC Analysis
Scan from emphysema subjects and non-emphysema (normal) subjects were analyzed and the values for the slope α from the LAC log-log-plots between these two groups were compared. A total of 11 scans from emphysema subjects and 10 scans from normal subjects were available.
Anatomical Labeling: Anatomical labeling was validated on a total of 10 normal subjects and 7 diseased subjects. A human expert provided a gold-standard by hand-labeling all 17 trees. On average, 27 labels were assigned correctly per tree. In contrast, an average of 0.82 labels were wrong per tree. This amounts to 97.1% correct labels vs. 2.9% incorrect labels. The worst case was one of the normal subjects with 15 correctly-assigned labels vs. 4 incorrectly-assigned labels.
Airway Measurements: The automated airway measurements for the inner airway wall were validated with the help of a PLEXIGLAS airway phantom containing tubes with diameters of 1.98 mm to 19.25 mm. The validation on a phantom was acceptable, but not ideal—an in-vivo gold standard is preferred. Unfortunately, such a standard is not available because it is impossible to take sub-millimeter reference measurements on a living organism. Table 3 lists the results of the error measurements.
The absolute average deviation from the nominal diameter was never greater than 0.26 millimeters. Compared with the voxel dimension of 0.391×0.391×0.6 mm3, sub-voxel accuracy can be claimed for the average values. The same accuracy was achieved for all airway orientations, even for those airways situated within the scanner plane.
The segmentation of the outer airway wall was more realistically tested in in-vivo images because discontinuities caused by vessels are not represented in the PLEXIGLAS phantom that was available for validation. For that reason, a human expert hand-traced both the inner and the outer airway borders. The hand-drawn results were then compared with the automatically obtained borders. A qualitative comparison of the results (
Planning valve placement and verifications: Sheep Experiment: Three sheep were placed supine in the I-CLIC Siemens Sensation 64 scanner, held apneic with air-way pressure of 25 cm H2O during scanning. A thin slice (0.6 mm thick) spiral scan was obtained using 120 kV, 100 mAs. The Pulmonary Workstation (as described above) was used to segment the airway tree while simulated emphysema regions (identified by a technician) were shown as 3D spheres (see
Descriptions of well known processing techniques, components and equipment have been omitted so as not to unnecessarily obscure the present methods, devices and systems in unnecessary detail. The descriptions of the present methods, devices and systems are exemplary and non-limiting. Certain substitutions, modifications, additions and/or rearrangements falling within the scope of the claims, but not explicitly listed in this disclosure, may become apparent to those or ordinary skill in the art based on this disclosure.
For example, while this disclosure focuses on methods, devices and systems configured to help plan treatment for emphysema, there are wider applications for the present methods, devices and systems. For example, the functionality of the embodiment of Pathfinder discussed above may be used to assist a doctor in planning where to place a stent or some other type of patency-maintaining device, such as those disclosed in U.S. Patent Application Publication Nos. 2005/0137712 and 2005/0192526. As another example, the lobar segmentation techniques discussed above may be combined with lung nodule detection and characterization—which detection and characterization may be achieved either manually (e.g., a human operator uses a computer mouse to click on a suspected nodule), or automatically, using an algorithm known in the art—and used to plan surgical treatment for lung cancer. More specifically, the closest feeding airway to the area containing a lung nodule may be determined. The diameter and/or turn-radius characteristic(s) of a suitable therapeutic device—such as an radio frequency (RF) ablation catheter (and carrying bronchoscope), liquid nitrogen ablation device (and carrying bronchoscope) or radioactive bead insertion device—may also be determined. Additionally, certain dimensional parameters of the relevant portion of the airway tree may be determined. These data (e.g., in the form of inputs) may then be processed to identify (e.g., plot) a suitable (e.g., an optimal) route to the target treatment location.
As another example, the techniques discussed above in the Measurements of Airway Tree Geometry section that allow for the determination of certain airway tree measurements may be used to study airway reactivity and asthma, and to conduct longitudinal trials for asthma interventions, by comparing data on an anatomical basis (e.g., by lobe, named sub-lobar segment, named airway segment, and/or area distal to named airway segment). Such an anatomical-comparison technique may be important if a therapy is being measured that changes the geometry of the lung, such as pre- and post-asthma treatment comparison of airway wall thickness; direct measurement of lung volume distal to a lung valve or valves; and direct measurement of air trapping distal to an airway.
The appended claims are not to be interpreted as including means-plus-function limitations, unless such a limitation is explicitly recited in a given claim using the phrase(s) “means for” and/or “step for,” respectively.
The following references, to the extent that they provide exemplary procedural or other details supplementary to those set forth, are specifically incorporated by reference at the locations in this disclosure where they are respectively cited.
This application claims priority to U.S. Provisional Patent Application Ser. No. 60/722,176, filed Sep. 30, 2005, the entire contents of which are expressly incorporated by reference.
This invention was made at least partially with government support under National Institute of Health grant number 1 R43 HL075953-01A1. The government has certain rights in the invention.
Number | Date | Country | |
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60722176 | Sep 2005 | US |