| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
|
|
|
|||||||
|
|||||||||
1
Department of Nuclear Medicine, Concord Hospital, Hospital Rd., Concord, New
South Wales 2139, Australia.
2
Department of Radiology, St. George Hospital, Belgrave St., Kogarah 2217,
Australia.
3
School of Anatomy, University of New South Wales, Anzac Pde., Kensington 2052,
Australia.
Received July 19, 1999;
accepted after revision October 21, 1999.
Supported by research grants JM000103 and PC000104 from the Nuclear
Medicine Research Foundation, Sydney, Australia.
Abstract
 Top Abstract IntroductionMaterials and MethodsResultsDiscussionReferences |
|---|
MATERIALS AND METHODS. By using information from suitably windowed human axial CT scans, combined with the information gained from the injection of color-coded dyes into the segmental bronchi of human cadaveric lungs, the lobar and segmental boundaries were added to the existing phantom. Further refinements were added from reports in the literature regarding the predominant pattern of subsegmental bronchi in a series of human cadavers, enabling the creation of subsegmental boundaries.
RESULTS. A digitized model of the segmental and subsegmental anatomy of the human lung was successfully created. External, or pleural, projections of the complex internal arrangement of the segments closely corresponded with the projections of the best available authorities on the subject.
CONCLUSION. The model provides the opportunity to address several issues germane to scintigraphy and important for diagnosing pulmonary embolic disease. In particular, the model allows the manipulation of three-dimensional data sets to explore issues of importance to tomographic lung scanning.
|
TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
|---|
Voxel-based anthropomorphic phantoms exist that could be used for clinical therapy planning [3]; however, these phantoms cannot be used to address questions regarding pulmonary embolic disease because adjacent segments within the lungs have not been differentiated in the models. In the existing models, organs are treated as homogeneous structures that lack internal subdivisions; these subdivisions are critical when considering the pathophysiology of embolic disease.
Zubal et al. [3, 4] created a tissue-segmented phantom based on transverse slices of CT data from a living human man whose height and weight were similar to the dosimetry standard mathematic phantom [5]. The phantom of Zubal et al. has the potential to transpose and encode pulmonary segmental and subsegmental divisions. Using the phantom as a basis for such delineation, we undertook a study to appropriately subdivide the lungs to create a segmental lung chart in surface views and axial cross-sections.
Instead of trying to represent the large variability in both the external shape and the internal arrangement of segments in human lungs, the intention of our study was to construct a plausible and representative model—that is, a model that can be used to draw general conclusions rather than one that attempts to apply to individuals in a population that is inherently variable.
|
TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
|---|
Data Preparation
All data processing and image manipulation was performed on an Intel
processor (Intel, Santa Clara, CA) workstation running Windows NT version 3.51
service pack 5 (Microsoft, Redmond, WA).
The anthropomorphic phantom of Zubal et al. [3] consists of a volume array of 246 axial slices (each slice, 128 x 128 voxels). Each voxel contained an integer index value representing the tissue type and a volume of 4 mm3. A subset of the phantom was created for use in later simulations. This subset contained only the thorax and resulted in a smaller data set, 128 x 128 x 64, which was used throughout the rest of the study.
A set of 64 slices, each 128 x 128, was then created with the lung tissue set to a value of one, and all other tissue types set to zero. Photoshop version 3.04 (Adobe, Mountain View, CA) was used for the image processing necessary to segment and subsegment the slices.
Manual interpolation was required to remove slice duplication present in the phantom of Zubal et al. [3]. Care was taken to ensure that the boundaries of the lung tissue did not overlap with other tissue types and that the total lung volume remained constant.
Throughout the segmentation and subsegmentation process, the adopted nomenclature was that of Jackson and Huber [11] for the segments and that of Ikeda [12] for the subsegments, as used in the work of Netter [13]. The nomenclature differs from that used by Boyden [10] for several of the segments, but is used more widely than Boyden's nomenclature [14,15,16,17].
Segmentation
An experienced radiologist marked the interlobar fissures that were visible
on a CT scan of the thorax with normal findings, windowed for lung parenchymal
tissue. This scan consisted of 26 slices at 10-mm intervals, with a total
height of lung tissue of 260 mm, from a man who was 54 years old, 182-cm tall,
and 73 kg. The approximate locations of the segmental boundaries were marked
on the slices of the CT scan with reference to anatomic texts
[13]. One slice from the CT
scan was calculated to correspond with two from the phantom of Zubal et al.
[3].
The CT scan was used for external landmarks, lobar fissures, and the boundaries transferred to the reduced data set of Zubal et al. [3]. Results obtained from the cadaveric human lung sections [6, 7] were used to match the interlobar fissures and segmental boundaries that were subsequently transferred to the reduced data set of Zubal et al.
The area within the segmental boundaries of each slice was filled with a unique color in Photoshop. Manual interpolation of the lobar and segmental boundaries was performed to ensure each was unique and provided a plausible and smooth transition between areas.
Iterative corrections were made to the segmented data set to more closely resemble the external landmarks described by Netter [13]. A set of six external views of the lungs, anterior, posterior, both lateral, and both medial projections, were created for visual comparison with the work of Netter.
Subsegmentation
Subsegmentation was performed on a segment-by-segment basis, using
information from studies of the pattern of subsegments present in cadaveric
human lungs
[8,9,10].
In each case, the predominant pattern present in the examined lungs was used
as the basis for partitioning the segments within the previously established
boundaries.
Initially, the number of subsegments in each segment was determined, taking into consideration the overall size of the segment and the predominant anatomic pattern. Thus, larger segments would be divided by the equivalent of a further generation of bronchial subdivisions.
The spatial distribution of the division of each segment was found to be possible in only six different ways, almost completely defined by the anatomic location of the segment, its boundaries, and the predominant pattern of branching of the higher order bronchi and bronchioles (Fig. 1). Rotation of the six different patterns of subdivisions accounted for all the segments.
|
The subsegmental boundaries were added to the individual slices in the data set of Zubal et al. [3] and the resulting boundaries were filled with unique colors in Photoshop that were chosen to represent the subsegments. Views of the data set were used to iteratively improve the subsegmentation to more closely match the predominant patterns reported in the literature and to improve the consistency of external boundaries, which were otherwise difficult to maintain when manipulating individual slices.
|
TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
|---|
|
A small selection of axial slices through the reduced data set of Zubal et al. [3] shows the three stages of the segmentation process with segmental and subsegmental boundaries included in the lower rows (Fig. 3). A complex arrangement of segments and subsegments is apparent within the lower lobes in particular, because of the density of segments. This highlights the presence of two radially arranged rows of segments in the right lower lobe, in comparison with the single row in the base of the left lung.
|
The left lung was found to occupy 52% and the right to occupy 48% of the total volume of lung tissue.
|
TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
|---|
Visual comparison of the current model with the segmental boundaries presented in the report by Netter [13] shows good correspondence (Fig. 2). The nature of the three-dimensional data set was such that only variations within narrow limits produced segmental boundaries approximating those shown by Netter and other anatomic texts [14,15,16] as the predominant pattern. Iterative corrections rapidly converged on the patterns required.
In no case was the subsegmentation taken past a second generation of airway branching. Consideration of the size of subsegments in contrast to their generation is reasonable because the relationship between the size of the bronchus and the volume of lung tissue supplied remained largely constant. This constancy is despite the imposition of a rigid nomenclature and should not be taken to imply that all subsegments of equal order are necessarily of equal volume.
Examination of the model showed that a number of segments, particularly those in the lower lobes, never extend to the pleural surface of the lungs. Such a situation was implied by the anatomic distribution of segments and their predominant pattern of branching [10].
Unfortunately, the current model represents neither all variations on segmental and subsegmental anatomy nor the gross variations in size and shape of the human thorax and the variations that would be present as a result of different disease states, such as hyperinflation with emphysema or displacement of the lung volume by gross cardiomegaly.
Despite the limitations, our model could be applied in many ways to the interpretation of scintigraphic images and could be a valuable resource for teaching segmental and subsegmental anatomy and the cross-sectional anatomy of the lungs. The use of our model could be further extended to the interpretation of planar and tomographic scintigraphic imaging; currently, no standard for the interpretation of tomographic perfusion images exists. The clinical usefulness of the model is illustrated in Figure 4, where the segments involved by pulmonary embolism are clearly delineated. This model could help define the patterns of segmental and subsegmental perfusion defects.
|
A number of crucial problems have been identified in both planar and tomographic scintigraphic imaging that could potentially be solved by the judicious use of such a model. These problems include the investigation of the usefulness of the various planar projections [18], planar versus tomographic lung imaging and the nature of attenuation, and scatter and shine-through activity in pulmonary scintigraphic imaging [19].
In conclusion, a methodology was developed for the segmentation and subsegmentation of the lungs within an existing anthropomorphic whole-body phantom. This proceeded in several stages, introducing first the lobar, then the segmental, and finally the subsegmental boundaries.
Reference to and comparison with the literature suggests that this methodology has provided a plausible and consistent example of segmentation and subsegmentation, designed to represent a majority of the population. This model can by no means represent every individual, but will nonetheless prove useful for interpreting planar and tomographic scintigraphic images of the lungs.
|
TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
|---|
CiteULike 
Complore 
Connotea 
Del.icio.us 
Digg 
Reddit 
Technorati What's this?
This article has been cited by other articles:
|
|
 |
|
  A. Kluge, W. Luboldt, and G. Bachmann Acute pulmonary embolism to the subsegmental level: diagnostic accuracy of three MRI techniques compared with 16-MDCT. Am. J. Roentgenol., July 1, 2006; 187(1): W7 - 14. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
D. T. Boll, R. C. Gilkeson, T. R. Fleiter, K. A. Blackham, J. L. Duerk, and J. S. Lewin Volumetric Assessment of Pulmonary Nodules with ECG-Gated MDCT Am. J. Roentgenol., November 1, 2004; 183(5): 1217 - 1223. [Abstract] [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
