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A pulmonary nodule modeling tool as a diagnostic aid for lung HRCT images
Computerized Medical Imaging and Graphics 29 (2005) 631–637 www.elsevier.com/locate/compmedimag
A pulmonary nodule modeling tool as a diagnostic aid for lung HRCT images Laetitia Pastor*, Annie Pousse, Philippe Manzoni, Bruno Kastler Laboratoire Imagerie et Inge´nierie pour la Sante´, Place St Jacques, 25 000 Besanc¸on, France Received 13 August 2004; accepted 11 January 2005
Abstract A lung model and a software tool were developed with the aim to help the radiologist in understanding the underlying nodule distribution modes in the lung, in spreading nodules on lung sections according to predefined distribution modes. For educational purpose lung elements can be easily highlighted using false colors. The fast execution times which allow the radiologist to test different nodule distributions and to choose, by comparison with CT, the likeliest one, makes it a helpful tool to determine the real diagnosis. Connected to a database containing final diagnoses, it should be a help for research in lung pathology. q 2005 Elsevier Ltd. All rights reserved. Keywords: Lung; Nodule; Computed tomography; Diagnostic aid
1. Introduction Due to the complex structures present in the lung and to the diversity of all possible lesions, lung tomograms are particularly difficult to analyze. Radiologist diagnoses often remain inaccurate and incomplete [1,2]. Several image enhancement methods and pulmonary nodule computerized detections have been proposed as automatic analysis of lung CT [3–6]. In order to support differential diagnosis, 3D reconstruction and classification of pulmonary vessels and bronchi have also been proposed [4]. However, these works only deal with nodule detection and only give no or partial information on nodule location in the lung. According to nodule distribution mode (bronchial, lymphatic,.), nodule location, in the lung is different, and thus characterizes the underlying pathology [1,7,8]. For example, a lymphatic distribution indicates a carcinomatous lymphangitis or a sarcoı¨dosis. Peribronchovascular nodule location may be induced by bronchial tuberculosis. Therefore, nodule location determination, especially regarding * Corresponding author. Tel.: C33 3 81 66 56 09; fax: C33 3 81 66 56 11. E-mail address: [email protected] (L. Pastor).
0895-6111/$ - see front matter q 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.compmedimag.2005.01.002
vessels and lymphatic system, is essential to establish the diagnosis from CT images. The purpose of the present work is to help the radiologist to understand the nodule distribution way in the lung. With this aim, a model of lung was created. This model contains lung lobules, bronchial tree and blood vessels. Then nodules shapes and distributions were modeled. Finally, a userfriendly software tool was build in order to mimic the CT images under analysis. 2. Materials and methods 2.1. Lung model The secondary lobule has been selected as basic pulmonary parenchymal element in order to model the lung. Several reasons lead to this choice. The lymphatic system is included in lobule septa, which is important in some pathologies [7,8]. Every lobule is supplied in blood by one main artery arriving in its center, and in air by one main bronchus located near the main artery. Then main vessels divide into smaller vessels to alveoli. The model only considers main vessels. In the same way, only one main vein coming out of the lobule and coursing along septa, is taken into account. As lobules are adjacent, veins are present all around every lobule. We theorize that the whole parenchymal lung model is constituted by a set of lobules, which are arranged in
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Fig. 1. On the left, one layer of the lung model with its corresponding slice, on the right the modeled slice with lobule septa exhibited.
horizontal layers, and that one lobule belongs to only one layer. Seven layers constituting the whole lung have been modeled. For every layer, a slice, corresponding to the cross-section of that layer with its horizontal median plane, has been modeled (Fig. 1). Thus, every secondary lobule is represented by polygon on one slice. Several decorative patterns (vertebra,.) allowing a better identification of the slice level, were also included in the computer slice display. The complexity of broncho-vascular trees and the need of knowing their precise location for nodule distributions, requires further explanation on how they have been build.
These trees are essentially 3D structures, which have been constructed up to the lobular level. In these structures, every tree node corresponds to a vessel junction or to a change in lobule layer, and every leaf points to one lobule (Fig. 2). However, the visualization is always 2D and thus, the whole pathway of a vessel in one layer is projected onto the corresponding 2D slice. In order to simplify the modelization, the same structure has been used for bronchi, arteries and veins. Obviously, node location depends on the tree nature to which the node belongs.
Fig. 2. Piece of arterial tree structure and its display on template slice. Every tree node is named according to its position (B1DZmain right artery); every leaf is named by the lobule number; squares ( ) with identical patterns indicate that corresponding lobule belong to the same cluster.
L. Pastor et al. / Computerized Medical Imaging and Graphics 29 (2005) 631–637
Vessel diameters were automatically computed from the two following hypotheses: vessels have the same diameters at lobular level, and at every vessel junction, the sum of output flows is equal to input flow. The arterial tree is used to determine segmental, subsegmental and lobule cluster lung areas. 2.2. Nodule distributions The main goal of this work is to help the radiologist to define the true underlying nodule distribution present in the HRCT under analysis. In numerous cases, one nodule distribution mode characterizes one kind of pathology [1,7]. 2.2.1. Nodules A nodule is defined by its size, shape, limits and the materials from which it is composed (Fig. 3). Six nodule shapes were modeled: round, spicular, polycyclic, umbilical, bumpy and oblong. After precise examination, polygonal shape has been considered as totally filled lobules and not as nodules. Four mean nodule radius sizes have been predefined: micro (1 mm), small (2.5 mm), medium (4.5 mm) or great (7.5 mm). Every nodule radius is then randomly determined according to Gauss law [3] with narrow (0.2–0.8 mm depending on radius selected), medium (0.6–2.4 mm) or wide (2–4 mm) standard deviation. Radius can be manually defined, for big solitary nodule. Three characteristics have been defined for nodule limits: well defined, blurred and spicular. The two last ones can be combined. For blurred nodule description, two different radii are used: one characterizes its core area, the other one characterizes its blurred area. Blurred expanse is chosen between small (2.5 mm), medium (4.5 mm) or large (7.5 mm). Blurred limit is simulated by drawing the nodule shape with a radius increasing from nodule radius to blur radius with a color going from nodule material color to parenchyma color. Nodules may be simple or cavitated. Cavitated nodules are composed of only two different materials. Cavity may be: small (20% of nodule), medium (40%), large (60%) or maximal (90%), and its shape is defined as regular or irregular.
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2.2.2. Distribution modes For nodule spreading, the slices included in the model presented here are considered as high resolution ones. Indeed, one goal is to study small and micro nodule distributions, which are only visible in thin slices. Thus, whatever the distribution, nodules are not allowed to overlap. The main known modes of nodule distribution have been modeled. In lung pathology some of them are considered as systematic (bronchial, arterial, pleural, lymphatic, peripheral), the other ones as non-systematic (homogenous, centrolobular). Distribution mode is characterized by the area where nodules can be spread. As long as homogenous mode is concerned, nodules are uniformly spread on the selected lung area. In order to simulate, some others well-known pathologies, a gradient can be superimposed to homogenous distribution mode following either anterio-posterior, or up–down direction or both. Centrolobular mode is realized as homogenous mode, however nodules, which overlap lobule septa, are rejected. As thin slices are considered, nodules superimposed on vessel or bronchi are also rejected. Likewise, peripheral mode is a homogenous distribution mode restricted to peripheral lobules. These definitions imply that for homogenous, centrolobular and peripheral modes, nodule distribution zone is 2D area. Thus, a specific distribution is completed when the sum of generated nodule areas equals the desired percent of allowed zone which define the nodule density. In pleural mode, nodules are spread tangential to pleura. In lymphatic mode, they are spread near the lymphatic system constituted by lobule septa and/or pleura. To simulate bronchial and arterial mode, nodules are spread distal to the chosen tree node. The spreading starts from this node up to all following lobules. The choice of the starting node is performed on the tree structure. For all these modes, the places where nodules are allowed are line segment. Thus, the nodule density is defined here as the ratio between the sum of all nodule diameters and the total lung lengths on which then can be spread. Any distribution may be restricted to one segment or to one lobe either in all the slices concerned or only in the displayed slice.
Fig. 3. Available nodule shapes: (a) round, (b) spicular, (c) polycyclic, (d) umbilicate, (e) bumpy and (f) oblong. Available nodule limits: (g) blurred, (h) spicular and (i) blurred and spicular. Available nodule cavities: (j) small regular, (k) large irregular.
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Fig. 4. The seven modeled slices displayed according to several representations. (a) Slice 1 with vein and artery projection, (b) slice 2 with artery projection (dark gray) and lobule connection (shade of gray), (c) slice 3 with artery projection (dark gray) and segment limits (black), (d) slice 4 with artery (dark gray), vein and bronchus (hallow line) projections, (e) slice 5 with sub-segments (shade of gray) and segment limits (black), (f) slice 6 with peripheral lobule position, (g) slice 7 with anterio-posterior lobule position.
3. Results Anatomical elements taken into account in the lung model section have been successfully translated into computing structures and needed acquisition procedures have been developed [9]. Seven slices have been modeled, and the corresponding layers, combined with bronchial, arterial and venous trees, form the lung model (Fig. 4).
For educational purpose, the different lung areas (segment, lobule connection,.) may be easily exhibited. In any slice, bronchi, arteries and veins included in the corresponding layer can be visualized as well as fissure, lobule septa and segment boundaries. They are all drawn on slices with false colors. Lobules can be displayed with the color-coding corresponding to the sub-area to which it belongs in the selected representation (Fig. 4(b), (e)–(g)).
Fig. 5. Software window. (a) Slice resolution panel, (b) panel for selection of highlighted elements, (c) history of performed distribution, (d) number of displayed slices, (e) gray scale color palette, (f) panel of nodule characterization with image of resulting nodule, (g) distribution characteristics, (h) panel for selection of lung area concerned.
L. Pastor et al. / Computerized Medical Imaging and Graphics 29 (2005) 631–637
Fig. 6. Example of appropriate artery tree modeling. (a) Scan with segment 8 and 9 exhibited, (b) slice 6 with highlighted segments.
Fig. 5 shows the software window used to visualize lung slices and to spread nodules. Panel b in Fig. 5, allows to select the required display as shown in Fig. 4. Decorative patterns situated outside the lung are drawn with colors not belonging to the predefined palette. When no special representation is required, to be in accordance with CT images, all shades of gray, used to represent parenchyma and material constituting nodules in synthesized slices, are defined in relation to Hounsfield’s scale. A gray-scale palette (Fig. 5(e)) allows to easily modify maximal contrast zone, extent of displayed Hounsfield’s scale and to switch between positive and negative visualization. Slice visualization panel (Fig. 5(a)) allows to simultaneously display one, four or nine slices according to radio
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buttons (Fig. 5(d)), in order to facilitate comparison between successive slices, and the active slice is framed. Characteristics of nodule shape, size, composition and limits are chosen using scrolling lists (Fig. 5(f)). As a help in choosing right values, a small image shows a model of resulting nodule. Characteristics of nodule distribution are selected among closed sets of choices using scrolling lists (Fig. 5(g)) and radio buttons (Fig. 5(h)). Every performed distribution is added to a history list containing main distribution characteristics (Fig. 5(c)). Any previously performed distribution may be canceled or repeated in order to increase the number of generated nodules. Moreover, every nodule can be manually moved, with the mouse, providing that its final position is in accordance with the distribution used for its creation. Obtained results are stored in a patient file. Some types of pathology as that one shown in Fig. 6(a) outline particular lung segments, here segments 8 and 9. Template segments 8 and 9, exhibited in Fig. 6(b), show a satisfying correspondence with the scan. Such a result confirms the quality of arterial tree modeling. In order to exemplify the software ability to simulate most of the known nodule distributions, Fig. 7 shows some nodule distributions performed with the predefined nodule distribution modes developed. For every distribution, two images have been drawn: the first one displays only nodules, the second one displays in addition the anatomical lung elements concerned by the distribution. Fig. 7(a) exemplifies an arterial micro-nodule distribution and Fig. 7(b) a lymphatic micro-nodule one. In Fig. 7(c), a homogenous micro-nodule distribution, in which a anterio-posterior gradient direction is superimposed, is
Fig. 7. Example of several nodule distribution modes. On the top: nodule distribution. On the bottom: nodule distribution and lung elements concerned by the distribution. (a) Arterial micro-nodule distribution mode with 20% nodule density, (b) lymphatic micro-nodule distribution on lobule septa and pleura, with 20% nodule density, (c) homogenous micro-nodule distribution with anterio-posterior gradient, 5% nodule density, (d) peripheral small nodule distribution with 5% nodule density.
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Fig. 8. Lymphatic distribution on lobule septa. (a) Real HRCT scan on which lobule septa are visible; (b) modeled image with lymphatic distribution in septa and pleura.
shown. And a peripheral small nodule distribution is drawn in Fig. 7(d). Figs. 8 and 9 exemplify the consistency of synthesized slices with real CT scan. The CT scan in Fig. 8(a), shows lobule septa in which well-defined nodules appear. The modeled lymphatic distribution performed in Fig. 8(b), obviously shows nodules superimposed on the septa and also, as in the original CT, large areas free of nodules. In Fig. 9(a), blurred round nodules appear on the real CT. On the corresponding modeled slice (Fig. 9(b) and (c)), a centrolobular distribution has been performed. Several tests were realized with different nodule amounts, and a 7% density gives the best result. Large areas appear free of nodules due to vessel concentration, as in the original scan.
4. Discussion The lung model described allows to display anatomical lung elements on template lung slices for radiologist education and training. It permitted of building a friendly software for representing CT scans. The software presented here relies on a new approach. Indeed, instead of extracting nodule information from
HRCT images as in automatic detection systems, it allows to spread nodules in the lung according to numerous predetermined modes. The lobule structures used to build the developed lung model, permit to perform nodule distribution, even along lymphatic ways. Known nodule distributions appearing on acquired CT images are well mimicked by developed procedures. Thus the way by which nodules appear in lung, is naturally deduced from the distribution mode used, and a more precise diagnosis may be determined. Moreover to strengthen his opinion, the radiologist can highlight lung elements visible as well on thin as on thick CT slices. Similar procedures will be developed for other pulmonary lesions such as ground glass. Not enough tests were yet realized to assert that this software will lead to a more precise diagnosis. But it is able to mimic known nodule distribution. And, fast execution times allow the physician to try several distribution modes and compare the modeled results to the original CT slices until the synthesized results mimic the observed slices. This try and compare process helps to determine the true underlying distribution mode and should lead to a more accurate diagnosis. The system developed and presented here is actually the only one following this approach. It is not only a potential
Fig. 9. Centrolobular distribution. (a) Real lung section with centrolobular small blurred nodule distribution, (b) and (c) simulated slice with centrolobular small blurred nodule distribution, with artery display on (b).
L. Pastor et al. / Computerized Medical Imaging and Graphics 29 (2005) 631–637
powerful tool for radiologist education and training, and a help for diagnosis determining, but it may represent also a help for research in lung pathology when it will be connected to a large data base containing information on final diagnosis.
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several analyses level, and possibility to illustrate practical cases.
References 5. Summary The complex structures present in the lung and the diversity of all possible lesions make particularly difficult, for the radiologist, determination of a diagnostic from lung tomograms. The complex information contained in HRCT images make automatic analysis methods few efficient; therefore, we proposed a new approach for diagnostic aid. Instead of extracting nodule information from HRCT images, nodule pathologies were reproduced on a lung model to mimic CT scan under analysis. In this aim, a model of lung, including several slices and bronchial tree and blood vessels, was modeled. Nodular pathologies were modeled with underlying notions giving information about pathology origin. The structure used to build developed model permit to perform nodule distribution, even along lymphatic ways. Efficient tools were developed which permit of building friendly software for representing CT scan. Thus the physician may test several diagnosis hypotheses by image generation and by comparison with original HRCT images. Moreover, to strengthen his opinion, the radiologist can highlight lung elements. To obtain an educational tool, the model was build so as to obtain a didactic display which permit to combine
[1] Fraser RS, Mu¨ller NL, Coleman N, Pare´ PD. Pulmonary infection: general features of pulmonary infection. 4th ed. Diagnosis of diseases of the chest, vol. 2. Philadelphia: W.B. Saunders; 1999. p. 695–734. [2] Mac Donald SL, Rubens MB, Hansell DM, Copley SJ, Desai SR, du Bois RM, et al. Non specific interstitial pneumonia and usual interstitial pneumonia: comparative appearances at and diagnostic accuracy of thin-section CT. Radiology 2001;221(3):583–4. [3] Guyang-Zong Y, Hansell DM. CT image enhancement with wavelet analysis for the detection of small airways disease. IEEE Trans Med Imaging 1997;16:953–61. [4] Tozaki T, Kawata Y, Niki N, et al. Pulmonary organs analysis for differential diagnosis based on thoracic thin-section CT images. IEEE Trans Nucl Sci 1998;45:3075–82. [5] Lee Y, Hara T, Fujita H, Itoh S, Ishigaki T. Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique. IEEE Trans Med Imaging 2001;20(7): 595–604. [6] Grunden J, Webb R, Naidich D, McGuinness Georgeann. Multinodular disease: anatomic localization at thin-section CT—multireader evaluation of a simple algorithm. Radiology 1999;210:711–20. [7] Zerhouni EA, Naidich DP, Stitik KP, Knonri NF, Siegelman SS. Computed tomography of the pulmonary parenchyma. II. Interstitial disease. J Thorac Imaging 1985;1:54–64. [8] Remy-Jardin M, Beuscart R, Sault MC, Marquette CH, Remy J. Subpleural micronodules in diffuse infiltrative lung disease: evaluation with thin-section CT scans. Radiology 1990;177:133–9. [9] Pastor L, Pousse A, Manzoni P, Parmentier M, Kastler B. Lung modeling for nodule analysis in HRCT. IEEE Med Imaging Conf Record 2001.
A pulmonary nodule modeling tool as a diagnostic aid for lung HRCT images Laetitia Pastor*, Annie Pousse, Philippe Manzoni, Bruno Kastler Laboratoire Imagerie et Inge´nierie pour la Sante´, Place St Jacques, 25 000 Besanc¸on, France Received 13 August 2004; accepted 11 January 2005
Abstract A lung model and a software tool were developed with the aim to help the radiologist in understanding the underlying nodule distribution modes in the lung, in spreading nodules on lung sections according to predefined distribution modes. For educational purpose lung elements can be easily highlighted using false colors. The fast execution times which allow the radiologist to test different nodule distributions and to choose, by comparison with CT, the likeliest one, makes it a helpful tool to determine the real diagnosis. Connected to a database containing final diagnoses, it should be a help for research in lung pathology. q 2005 Elsevier Ltd. All rights reserved. Keywords: Lung; Nodule; Computed tomography; Diagnostic aid
1. Introduction Due to the complex structures present in the lung and to the diversity of all possible lesions, lung tomograms are particularly difficult to analyze. Radiologist diagnoses often remain inaccurate and incomplete [1,2]. Several image enhancement methods and pulmonary nodule computerized detections have been proposed as automatic analysis of lung CT [3–6]. In order to support differential diagnosis, 3D reconstruction and classification of pulmonary vessels and bronchi have also been proposed [4]. However, these works only deal with nodule detection and only give no or partial information on nodule location in the lung. According to nodule distribution mode (bronchial, lymphatic,.), nodule location, in the lung is different, and thus characterizes the underlying pathology [1,7,8]. For example, a lymphatic distribution indicates a carcinomatous lymphangitis or a sarcoı¨dosis. Peribronchovascular nodule location may be induced by bronchial tuberculosis. Therefore, nodule location determination, especially regarding * Corresponding author. Tel.: C33 3 81 66 56 09; fax: C33 3 81 66 56 11. E-mail address: [email protected] (L. Pastor).
0895-6111/$ - see front matter q 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.compmedimag.2005.01.002
vessels and lymphatic system, is essential to establish the diagnosis from CT images. The purpose of the present work is to help the radiologist to understand the nodule distribution way in the lung. With this aim, a model of lung was created. This model contains lung lobules, bronchial tree and blood vessels. Then nodules shapes and distributions were modeled. Finally, a userfriendly software tool was build in order to mimic the CT images under analysis. 2. Materials and methods 2.1. Lung model The secondary lobule has been selected as basic pulmonary parenchymal element in order to model the lung. Several reasons lead to this choice. The lymphatic system is included in lobule septa, which is important in some pathologies [7,8]. Every lobule is supplied in blood by one main artery arriving in its center, and in air by one main bronchus located near the main artery. Then main vessels divide into smaller vessels to alveoli. The model only considers main vessels. In the same way, only one main vein coming out of the lobule and coursing along septa, is taken into account. As lobules are adjacent, veins are present all around every lobule. We theorize that the whole parenchymal lung model is constituted by a set of lobules, which are arranged in
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L. Pastor et al. / Computerized Medical Imaging and Graphics 29 (2005) 631–637
Fig. 1. On the left, one layer of the lung model with its corresponding slice, on the right the modeled slice with lobule septa exhibited.
horizontal layers, and that one lobule belongs to only one layer. Seven layers constituting the whole lung have been modeled. For every layer, a slice, corresponding to the cross-section of that layer with its horizontal median plane, has been modeled (Fig. 1). Thus, every secondary lobule is represented by polygon on one slice. Several decorative patterns (vertebra,.) allowing a better identification of the slice level, were also included in the computer slice display. The complexity of broncho-vascular trees and the need of knowing their precise location for nodule distributions, requires further explanation on how they have been build.
These trees are essentially 3D structures, which have been constructed up to the lobular level. In these structures, every tree node corresponds to a vessel junction or to a change in lobule layer, and every leaf points to one lobule (Fig. 2). However, the visualization is always 2D and thus, the whole pathway of a vessel in one layer is projected onto the corresponding 2D slice. In order to simplify the modelization, the same structure has been used for bronchi, arteries and veins. Obviously, node location depends on the tree nature to which the node belongs.
Fig. 2. Piece of arterial tree structure and its display on template slice. Every tree node is named according to its position (B1DZmain right artery); every leaf is named by the lobule number; squares ( ) with identical patterns indicate that corresponding lobule belong to the same cluster.
L. Pastor et al. / Computerized Medical Imaging and Graphics 29 (2005) 631–637
Vessel diameters were automatically computed from the two following hypotheses: vessels have the same diameters at lobular level, and at every vessel junction, the sum of output flows is equal to input flow. The arterial tree is used to determine segmental, subsegmental and lobule cluster lung areas. 2.2. Nodule distributions The main goal of this work is to help the radiologist to define the true underlying nodule distribution present in the HRCT under analysis. In numerous cases, one nodule distribution mode characterizes one kind of pathology [1,7]. 2.2.1. Nodules A nodule is defined by its size, shape, limits and the materials from which it is composed (Fig. 3). Six nodule shapes were modeled: round, spicular, polycyclic, umbilical, bumpy and oblong. After precise examination, polygonal shape has been considered as totally filled lobules and not as nodules. Four mean nodule radius sizes have been predefined: micro (1 mm), small (2.5 mm), medium (4.5 mm) or great (7.5 mm). Every nodule radius is then randomly determined according to Gauss law [3] with narrow (0.2–0.8 mm depending on radius selected), medium (0.6–2.4 mm) or wide (2–4 mm) standard deviation. Radius can be manually defined, for big solitary nodule. Three characteristics have been defined for nodule limits: well defined, blurred and spicular. The two last ones can be combined. For blurred nodule description, two different radii are used: one characterizes its core area, the other one characterizes its blurred area. Blurred expanse is chosen between small (2.5 mm), medium (4.5 mm) or large (7.5 mm). Blurred limit is simulated by drawing the nodule shape with a radius increasing from nodule radius to blur radius with a color going from nodule material color to parenchyma color. Nodules may be simple or cavitated. Cavitated nodules are composed of only two different materials. Cavity may be: small (20% of nodule), medium (40%), large (60%) or maximal (90%), and its shape is defined as regular or irregular.
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2.2.2. Distribution modes For nodule spreading, the slices included in the model presented here are considered as high resolution ones. Indeed, one goal is to study small and micro nodule distributions, which are only visible in thin slices. Thus, whatever the distribution, nodules are not allowed to overlap. The main known modes of nodule distribution have been modeled. In lung pathology some of them are considered as systematic (bronchial, arterial, pleural, lymphatic, peripheral), the other ones as non-systematic (homogenous, centrolobular). Distribution mode is characterized by the area where nodules can be spread. As long as homogenous mode is concerned, nodules are uniformly spread on the selected lung area. In order to simulate, some others well-known pathologies, a gradient can be superimposed to homogenous distribution mode following either anterio-posterior, or up–down direction or both. Centrolobular mode is realized as homogenous mode, however nodules, which overlap lobule septa, are rejected. As thin slices are considered, nodules superimposed on vessel or bronchi are also rejected. Likewise, peripheral mode is a homogenous distribution mode restricted to peripheral lobules. These definitions imply that for homogenous, centrolobular and peripheral modes, nodule distribution zone is 2D area. Thus, a specific distribution is completed when the sum of generated nodule areas equals the desired percent of allowed zone which define the nodule density. In pleural mode, nodules are spread tangential to pleura. In lymphatic mode, they are spread near the lymphatic system constituted by lobule septa and/or pleura. To simulate bronchial and arterial mode, nodules are spread distal to the chosen tree node. The spreading starts from this node up to all following lobules. The choice of the starting node is performed on the tree structure. For all these modes, the places where nodules are allowed are line segment. Thus, the nodule density is defined here as the ratio between the sum of all nodule diameters and the total lung lengths on which then can be spread. Any distribution may be restricted to one segment or to one lobe either in all the slices concerned or only in the displayed slice.
Fig. 3. Available nodule shapes: (a) round, (b) spicular, (c) polycyclic, (d) umbilicate, (e) bumpy and (f) oblong. Available nodule limits: (g) blurred, (h) spicular and (i) blurred and spicular. Available nodule cavities: (j) small regular, (k) large irregular.
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Fig. 4. The seven modeled slices displayed according to several representations. (a) Slice 1 with vein and artery projection, (b) slice 2 with artery projection (dark gray) and lobule connection (shade of gray), (c) slice 3 with artery projection (dark gray) and segment limits (black), (d) slice 4 with artery (dark gray), vein and bronchus (hallow line) projections, (e) slice 5 with sub-segments (shade of gray) and segment limits (black), (f) slice 6 with peripheral lobule position, (g) slice 7 with anterio-posterior lobule position.
3. Results Anatomical elements taken into account in the lung model section have been successfully translated into computing structures and needed acquisition procedures have been developed [9]. Seven slices have been modeled, and the corresponding layers, combined with bronchial, arterial and venous trees, form the lung model (Fig. 4).
For educational purpose, the different lung areas (segment, lobule connection,.) may be easily exhibited. In any slice, bronchi, arteries and veins included in the corresponding layer can be visualized as well as fissure, lobule septa and segment boundaries. They are all drawn on slices with false colors. Lobules can be displayed with the color-coding corresponding to the sub-area to which it belongs in the selected representation (Fig. 4(b), (e)–(g)).
Fig. 5. Software window. (a) Slice resolution panel, (b) panel for selection of highlighted elements, (c) history of performed distribution, (d) number of displayed slices, (e) gray scale color palette, (f) panel of nodule characterization with image of resulting nodule, (g) distribution characteristics, (h) panel for selection of lung area concerned.
L. Pastor et al. / Computerized Medical Imaging and Graphics 29 (2005) 631–637
Fig. 6. Example of appropriate artery tree modeling. (a) Scan with segment 8 and 9 exhibited, (b) slice 6 with highlighted segments.
Fig. 5 shows the software window used to visualize lung slices and to spread nodules. Panel b in Fig. 5, allows to select the required display as shown in Fig. 4. Decorative patterns situated outside the lung are drawn with colors not belonging to the predefined palette. When no special representation is required, to be in accordance with CT images, all shades of gray, used to represent parenchyma and material constituting nodules in synthesized slices, are defined in relation to Hounsfield’s scale. A gray-scale palette (Fig. 5(e)) allows to easily modify maximal contrast zone, extent of displayed Hounsfield’s scale and to switch between positive and negative visualization. Slice visualization panel (Fig. 5(a)) allows to simultaneously display one, four or nine slices according to radio
635
buttons (Fig. 5(d)), in order to facilitate comparison between successive slices, and the active slice is framed. Characteristics of nodule shape, size, composition and limits are chosen using scrolling lists (Fig. 5(f)). As a help in choosing right values, a small image shows a model of resulting nodule. Characteristics of nodule distribution are selected among closed sets of choices using scrolling lists (Fig. 5(g)) and radio buttons (Fig. 5(h)). Every performed distribution is added to a history list containing main distribution characteristics (Fig. 5(c)). Any previously performed distribution may be canceled or repeated in order to increase the number of generated nodules. Moreover, every nodule can be manually moved, with the mouse, providing that its final position is in accordance with the distribution used for its creation. Obtained results are stored in a patient file. Some types of pathology as that one shown in Fig. 6(a) outline particular lung segments, here segments 8 and 9. Template segments 8 and 9, exhibited in Fig. 6(b), show a satisfying correspondence with the scan. Such a result confirms the quality of arterial tree modeling. In order to exemplify the software ability to simulate most of the known nodule distributions, Fig. 7 shows some nodule distributions performed with the predefined nodule distribution modes developed. For every distribution, two images have been drawn: the first one displays only nodules, the second one displays in addition the anatomical lung elements concerned by the distribution. Fig. 7(a) exemplifies an arterial micro-nodule distribution and Fig. 7(b) a lymphatic micro-nodule one. In Fig. 7(c), a homogenous micro-nodule distribution, in which a anterio-posterior gradient direction is superimposed, is
Fig. 7. Example of several nodule distribution modes. On the top: nodule distribution. On the bottom: nodule distribution and lung elements concerned by the distribution. (a) Arterial micro-nodule distribution mode with 20% nodule density, (b) lymphatic micro-nodule distribution on lobule septa and pleura, with 20% nodule density, (c) homogenous micro-nodule distribution with anterio-posterior gradient, 5% nodule density, (d) peripheral small nodule distribution with 5% nodule density.
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Fig. 8. Lymphatic distribution on lobule septa. (a) Real HRCT scan on which lobule septa are visible; (b) modeled image with lymphatic distribution in septa and pleura.
shown. And a peripheral small nodule distribution is drawn in Fig. 7(d). Figs. 8 and 9 exemplify the consistency of synthesized slices with real CT scan. The CT scan in Fig. 8(a), shows lobule septa in which well-defined nodules appear. The modeled lymphatic distribution performed in Fig. 8(b), obviously shows nodules superimposed on the septa and also, as in the original CT, large areas free of nodules. In Fig. 9(a), blurred round nodules appear on the real CT. On the corresponding modeled slice (Fig. 9(b) and (c)), a centrolobular distribution has been performed. Several tests were realized with different nodule amounts, and a 7% density gives the best result. Large areas appear free of nodules due to vessel concentration, as in the original scan.
4. Discussion The lung model described allows to display anatomical lung elements on template lung slices for radiologist education and training. It permitted of building a friendly software for representing CT scans. The software presented here relies on a new approach. Indeed, instead of extracting nodule information from
HRCT images as in automatic detection systems, it allows to spread nodules in the lung according to numerous predetermined modes. The lobule structures used to build the developed lung model, permit to perform nodule distribution, even along lymphatic ways. Known nodule distributions appearing on acquired CT images are well mimicked by developed procedures. Thus the way by which nodules appear in lung, is naturally deduced from the distribution mode used, and a more precise diagnosis may be determined. Moreover to strengthen his opinion, the radiologist can highlight lung elements visible as well on thin as on thick CT slices. Similar procedures will be developed for other pulmonary lesions such as ground glass. Not enough tests were yet realized to assert that this software will lead to a more precise diagnosis. But it is able to mimic known nodule distribution. And, fast execution times allow the physician to try several distribution modes and compare the modeled results to the original CT slices until the synthesized results mimic the observed slices. This try and compare process helps to determine the true underlying distribution mode and should lead to a more accurate diagnosis. The system developed and presented here is actually the only one following this approach. It is not only a potential
Fig. 9. Centrolobular distribution. (a) Real lung section with centrolobular small blurred nodule distribution, (b) and (c) simulated slice with centrolobular small blurred nodule distribution, with artery display on (b).
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powerful tool for radiologist education and training, and a help for diagnosis determining, but it may represent also a help for research in lung pathology when it will be connected to a large data base containing information on final diagnosis.
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several analyses level, and possibility to illustrate practical cases.
References 5. Summary The complex structures present in the lung and the diversity of all possible lesions make particularly difficult, for the radiologist, determination of a diagnostic from lung tomograms. The complex information contained in HRCT images make automatic analysis methods few efficient; therefore, we proposed a new approach for diagnostic aid. Instead of extracting nodule information from HRCT images, nodule pathologies were reproduced on a lung model to mimic CT scan under analysis. In this aim, a model of lung, including several slices and bronchial tree and blood vessels, was modeled. Nodular pathologies were modeled with underlying notions giving information about pathology origin. The structure used to build developed model permit to perform nodule distribution, even along lymphatic ways. Efficient tools were developed which permit of building friendly software for representing CT scan. Thus the physician may test several diagnosis hypotheses by image generation and by comparison with original HRCT images. Moreover, to strengthen his opinion, the radiologist can highlight lung elements. To obtain an educational tool, the model was build so as to obtain a didactic display which permit to combine
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