3D micro-ct analysis of cancellous bone architecture

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1 3D micro-ct analysis of cancellous bone architecture Massimo Rossi *a, Franco Casali *a, Davide Romani *a, Maria Letizia Carabini *a a Physics Department, University of Bologna ABSTRACT Osteoporosis is the most common type of metabolic bone disease generating morphological alterations of the bone structure. Therefore, an intensive and accurate study of cancellous bone architecture is required to individuate the origin of the disease and to estimate its evolution. For this purpose, a specific project is in progress at the Physics Department of Bologna to carry out micro-tomography analysis in vitro of animal bone samples. The inspection system mainly consists of an X-ray microfocus source to irradiate the sample and of a CCD-based detector for the detection of the radiographic image. A sample with a diameter up to 30 mm and a height of 15 mm can be investigated over its whole volume. Three-dimensional images of the bone structure can be obtained with a spatial resolution variable from 30 up to few microns. A pig bone specimen has been investigated to test the equipment and to carry out intensive analysis and modeling of bone architecture. Moreover, a specific volume data processing procedure has been developed and tested to extract morphometric measurements of the bone structure. Keywords: computed tomography, nondestructive technique, digital detector, X-ray, bone, osteoporosis 1. INTRODUCTION Osteoporosis is a major underlying cause of bone fractures in postmenopausal women and older persons in general. It is a condition in which bone mass decreases, causing bones to be more susceptible to fracture. A fall, blow, or lifting action that would not bruise or strain the average person can easily cause one or more bones to break in a person with severe osteoporosis. Osteoporosis is a major public health problem. Although all bones are affected, fractures of the spine, wrist, and hip are typical and most common. The risk of developing osteoporosis increases with age and is higher in women than in men and in whites than in blacks. Its cause appears to reside in the mechanisms underlying an accentuation of the normal loss of bone, which follows the menopause in women and occurs in all individuals with advancing age. There are no laboratory tests for defining individuals at risk or those with mild osteoporosis. The diagnosis of primary osteoporosis is established by documentation of reduced bone density or mass in a patient with a typical fracture syndrome after exclusion of known causes of excessive bone loss. Prevention of fracture in susceptible patients is the primary goal of intervention. Strategies include assuring estrogen replacement in postmenopausal women, adequate nutrition including an elemental calcium intake of 1,000-1,500 mg a day, and a program of modest weight-bearing exercise. There is great need for additional research on understanding the biology of human bone, defining individuals at special risk, and developing safe, effective, low-cost strategies for fracture prevention. * Massimo.Rossi@bo.infn.it; phone ; fax ; Physics Department, University of Bologna, Viale Berti Pichat 6/2, Bologna, Italy Investigation supported by University of Bologna. Funds for selected research topics.

2 Early diagnosis and assessment of osteoporosis is one of the most important goals of medical research. Advanced noninvasive techniques are essential to obtain information on bone microstructure of specific parts of the skeleton and determine its evolution with age and progress of disease. In particular, microanalysis and classification of cortical and cancellous parts of bone are fundamental to estimate mass losses and density variations. Radiographic techniques and laboratory tests such as biopsy are commonly used for diagnosis but results are limited to 2Dimage measurements which do not assess completely the complex three-dimensional structure of bone. Computed tomography (CT), as well as nuclear magnetic resonance (NMR), offers the opportunity to obtain 3D maps of bone structure but, at present, conventional devices are limited in spatial resolution. For this purpose, great effort is devoted to the development of advanced diagnostic tools to obtain bone microanalysis. A promising technique is micro-computed tomography (micro-ct) 1, which provides 3D high spatial resolution results of samples inspected. This technique arose and developed for industrial application but it has been tested successfully also for biomedical application and in particular for bone analysis 2,3,4,5. At present, this technique operates in vitro but it could be implemented in the future for in vivo examination. Although micro-ct images considerably increase information about the shape and compactness of bone, a morphometric study of its trabecular structure is essential to determine biomedical and biomechanical parameters, which can be used to assess the status of disease and the treatment effectiveness. The Physics Department is at present involved in a special project of the University of Bologna concerning the set-up of microanalysis techniques for the assessment of osteoporosis on animal bone samples. The project concerns the analysis in vitro of small specimens with micro-ct and NMR techniques in order to obtain high-resolution modeling of the bone structure. In this framework, an experimental micro-ct device has been developed and is now under test. Moreover, a volume data processing procedure has been investigated and tested in order to extract morphometric measurements of the bone structure. 2. THE MICRO-CT SYSTEM The inspection system has been arranged in a shielded laboratory of Physics Department where a 200 kvp, 3 ma, microfocus X-ray tube and a high precision programmable stage are operating. Figures 1 and 2 report respectively a scheme of the experimental set-up and pictures of the micro-ct system. The detector has been assembled with several parts selected to optimize performance and costs. Basically, it consists of a thin Gd 2 O 2 S:Tb phosphor layer (30 mg/cm 2 ) deposited on a 2:1 fiberoptic taper (FOT) optically coupled to a cooled charged coupled device (CCD) camera. The useful detection area is 30x15 mm 2 with a pixel size of about 30 microns. The system acquires 12 bits digital images (1024x512 pixels) in real time. In order to improve the signal-to-noise ratio all measurements reported in this paper have been obtained averaging 4 frames with an exposition time of the detector variable in the range 1-4 seconds. The detector was hardly tested with regards to its performance and results are reported in detail in another paper of these proceedings 6. This paper reports preliminary results obtained in the analysis of the first biological specimen; tests have considered the system ability to show clearly the bone structure by means of 3D images and the opportunity offered by digital data processing for the extraction of trabecular elements.

3 Figure 1: Scheme of the experimental set-up (A) (B) Figure 2: Pictures of the experimental set-up. (A) The X-ray detector. (B) View of the micro-ct system; it can be recognized: the microfocus X-ray tube (left), the object handler and rotation stage (center), and the detector (right). 3. MICROANALYSIS OF AN ANIMAL BONE SAMPLE The sample considered for preliminary test of micro-ct system is a pig bone specimen extracted from the proximal part of the femur. The object has a cylinder shape of size 7.1 mm (Ø) x 6.99 mm (h) and has been previously submitted to a drying treatment for allowing also NMR tests (Figure 3). The specimen presents a clear trabecular structure over all the volume with a cortical region in one of the extremities corresponding to the extraction point. The X-ray tube has been set with a molybdenum anode at 30 kv, 0.5 mas with an aluminum filter of about 0.5 mm. The source-detector distance was 17 cm with an object magnification of 1.1.

4 The tomographic analysis of the sample has been carried out by measuring 720 radiographic projections over 360 with an angular sampling of 0.5. Therefore, the radiographic data-set consists of about 700 Mb then reconstructed on 320 slices of 400x400 pixels by means of Feldkamp algorithm 7. The CT voxel size is 27 microns. A radiographic projection of the sample is reported in Figure 4; the trabecular structure is clearly evident as well as the cortical component in the lower part of the sample. On the other hand, the superimposition in the radiographic image of the various layers of the object prevents the opportunity to extract quantitative information about size, shape and direction of different trabecular elements. Figure 5 shows a CT reconstructed image of the bone. The cancellous composition and its geometrical distribution are now simply detectable as well as the marrow among the bone. In order to allow a detailed analysis of the bony component, a segmentation of the data-set has been applied to remove the marrow. The segmentation process is a crucial point of the morphometric procedure and it shall be described in detail in the next section. Figure 3: Picture of the pig bone sample. Figure 4: Radiographic projection of the specimen. (A) (B) Figure 5: CT slice of the pig bone sample extracted in the mid-position of the inspected volume. (A): original slice with tissue visible inside the trabecular structure. (B): same slice after segmentation for bone data selection. Then, all the slices were packed and the volume obtained was displayed by means of rendering techniques. Figure 6 shows a 3D visualization of part of the data-set (left) and the geometrical extraction of a region-of-interest (right) in order to show the inner parts of the specimen. The trabecular structure is manifest and clearly displayed. In this way, a complete qualitative analysis of the bone can be carried out quickly to assess the status of the sample and results can be integrated with biomedical information. Extraction and display of parts of the data-set (Figure 7) can be optimized to obtain a sort of zoom of details; the high spatial resolution of CT results allows to obtain the extraction and visualization of single trabecular elements. Next section describes the procedure adopted for the real extraction and analysis of trabeculae and for measurements of morphometric parameters.

5 Figure 6: 3D reconstruction of part of the data-set. Left: rendering of the volume. Right: geometrical extraction of a region-of-interest to allow the inspection of the internal trabecular structure. Figure 7: 3D reconstruction of the overall volume with increasing zoom of the data-set (left to right). It must be remarked that the high spatial resolution of the CT system offers the opportunity to obtain images of single trabecular elements. 4. VOLUME DATA PROCESSING This section describes the procedure adopted for the identification and geometrical extraction of single trabecular elements. This technique represents the first attempt to individuate a method useful to locate and separate trabeculae in the volume and to perform statistical analysis of results. At present, the procedure is semi-automatic but work is in progress to improve and fully automatize the method. Literature reports a large number of parameters defined to assess histomorphometric characteristics of bone sample but only few are generally accepted as interesting parameters for the early detection of osteoporosis. This work has been devoted to assess a CT data processing procedure for analysis of single trabecular elements. Therefore, thickness (Tb.Th), number (Tb.N), separation (Tb.Sp), and direction of trabeculae have been considered. In summary, the technique first segments the volume data-set for extraction of bone and then asks the user to draw a regionof-interest (ROI) around a trabecular region. The contour of the trabecula is determined in the slice and traced in the next

6 contiguous slices. Geometrical criteria are defined to evaluate if the trabecula connects to a node and, thus if the same ROI must be accepted or refused in next slices. The algorithm stops when selection criteria are not satisfied. Therefore, slices identified are packed together to create the volume-of-interest of the trabecula from which morphometric parameters will be derived. 4.1 The segmentation process The first step of the method consists in the segmentation process. Due to beam hardening and noise, CT data histogram is not described by isolated peaks corresponding to sharp density distribution of various materials, but by continuous and complicated distribution. In general, this condition requires to set manually thresholds on the histogram for material selection. Our procedure tries to individuate these thresholds by means of an automatic process. The data-set histogram (Figure 8 left) was processed by first (Figure 8 right) and second derivative to individuate thresholds among different material regions. Transition points are calculated as zeros of derivative functions and values obtained are used as thresholds of the original data-set. In the plot, arrow 1 points to the threshold for background; arrow 2 to the marrow peak, and arrow 3 to the threshold marrow-bone. Then, a threshold of about 0.22 mm -1 has been set to create the bone (alone) data-set Arbitrary units 3 Arbitrary units 3 Attenuation coefficient (mm -1 ) Attenuation coefficient (mm -1 ) Figure 8: Segmentation method. The data-set histogram (left) was processed by first (right) and second derivative to individuate thresholds among different material regions. 1: threshold for background; 2: marrow peak; 3: threshold for transition marrow-bone. 4.2 Extraction of trabecular elements After the segmentation process, the tomographic data-set consists only of values related to bone (with attenuation coefficient more than zero) and to background (zeroed). It s now possible to select a single trabecular structure over a 2D slice of the CT data-set and to track its section over the next contiguous slices. The user chooses a slice in the data-set (we call this slice: reference slice) and selects a ROI (reference ROI) around a trabecular element (reference trabecular section). Then, the same ROI is extracted, in the same position, on all the contiguous slices of the data-set. Due to its complex and irregular shape, trabecula can expand itself in any direction of the

7 volume but it can be supposed that, in the adjacent slices above and below that one of reference, the selected trabecula can be still individuate. In order to determine automatically if the same trabecula is present in the adjacent slices, we calculate two parameters: the area of the reference trabecular section and the number of intersections with a reference grid. The changing of the trabecula area in adjacent slices could be a useful parameter to determine if trabecula region increases or decreases in contiguous regions (and therefore to estimate if trabecula connects to a node). On the other hand, this parameter alone cannot assess the changing of the trabecula shape. For this reason, the number of bone-background intersections is calculated to consider also the shape variations. The area is calculated as the number of pixels with attenuation coefficient more than zero multiplied by the area of each pixel (about 730 µm 2 ). The number of intersections is estimated by means of a reference grid drawn in the ROI (Figure 9) and considering the gradient function applied to data values along these lines. Since the resulting points of maximum and minimum are located in correspondence of the bone crossing background (and vice versa), their total number gives information about material-background intersections. Figure 9: A grid is plotted on the reference ROI for mapping the trabecula. Intersections of the grid lines with trabecula region are used for determining the shape variation of the trabecula in contiguous CT slices. The procedure assumes that adjacent slices contain the section of the same trabecular element only if the values of the two parameters vary inside a predetermined range. This interval is estimated by the values of area and intersection number calculated in the reference ROI and by statistical preprocessing of ROIs randomly chosen. The procedure stops when slices are rejected in both directions due to the crossing with a node or because the trabecula is broken. The slices of interest are marked and packed together to form the volume-of-interest related to the trabecula. Selection criteria are very difficult to fix due the large variation of size, shape, and direction of trabeculae. The complex structure of bone limits the opportunity to determine simply the fluctuation range of parameters considered. Thus, a preliminary analysis has been conducted on a short sample of trabeculae to investigate their variation. For both the parameters, a range of about 40% has been set with regards to the value calculated in the reference ROI. The large value fixed for parameter variation accounts for the very complex structure of cancellous bone which strongly varies with regards to location in the skeleton and to different animal species. Anyway, it must be pointed out that this data processing technique is still under test for checking the procedure effectiveness and more rigorous tests are in progress to reach better results. Table 1 reports an example of application of the procedure applied to a reference ROI. It is clear how the two parameters must simultaneously satisfy the selection criteria. For instance, the method stops on both directions when trabecula connects to a node even if the number of intersections still remains inside the useful range.

8 Area Number of intersections Previous slice Link to a node Reference ROI 17 8 Reference ROI Subsequent slice 15 8 Correct adjacent slice Second subsequent slice 16 8 Correct adjacent slice Third subsequent slice Link to a node Table 1: Example of application of the data processing procedure to a ROI. Starting from a reference slice, the technique tracks contiguous slices to determine the extension of the trabecula among nodes. For both the parameters, a range of about 40% has been set with regards to the value calculated in the reference ROI. In this case, selection criteria are 17±7 for area and 8±3 for number of intersections. It is clear how both the parameters must vary inside the range to determine the correct classification of the trabecula. This procedure allows also to determine the spatial location of the single trabecular elements in the original volume. Figure 10 shows the position of each trabecula inside the specimen volume. In this way, trabeculae can be assessed, measured, and compared taking into account also their position. Figure 10: 3D extraction of single trabecular elements. Left: the location of the selected trabeculae is clearly defined in the original bone volume (the box). Right: volume data processing allows to view clearly the structure of a single element and to compare it with that one of other trabeculae. To improve display, also the link of trabeculae to nodes is shown.

9 Moreover, the direction of the extracted trabecula element has been estimated calculating the mass center coordinates of each trabecular section. In this way, the punctual direction of each trabecula is given slice by slice in the 3D data volume. Figure 11 reports 3D plots stating directions of four trabeculae. A more detail representation of trabeculae direction shall be obtained increasing the statistics related to the number of trabeculae processed by this technique; in this way, a classification of trabecula direction can be achieved for supporting biomedical studies of the sample examined. Figure 11: Direction of four trabecular elements is obtained by fitting for each slice the center of mass of trabecula. 4.3 Estimation of morphometric parameters Data related to trabeculae extracted from the bone volume are then processed for estimating morphometric parameters 8. Measurements have been considered only on the following parameters according Feldkamp's definition and method 9 : Trabecular thickness (mm): Tb.Th = 2 BS / BV P = P p L (1) Number of trabeculae (mm -1 ): Tb.N = BV / TV Tb. Th = PL (2) Trabecular separation (mm): Tb.Sp = 1 1 P TbTh. = Tb. N P L P (3) where P P and P L are respectively an estimation of area and spatial frequency of trabeculae. Some results obtained on three different slices and for a larger ROI are reported in Table 2. Values vary in expected range for a typical sample and accord with 3D images reported above. Morphometric parameters Tb.Th ( mm ) Tb.N ( mm -1 ) Tb.Sp ( mm ) Slice Mean value ± sigma 0.24 ± ± ± 0.1 Table 2: Morphometric parameters measured on 3 different slices.

10 5. CONCLUSIONS A micro-ct experimental device has been tested for application in the study of bone samples and assessment of osteoporosis disease. Preliminary results confirm the opportunity offered by 3D data processing of CT values to obtain highresolution images of the sample showing the bone structure and its morphology. Moreover, a semi-automatic data processing technique has been developed and preliminary tested to detect and assess single trabecular elements, and to measure biomedical morphological parameters. Results are promising and a procedure refinement is in progress to optimize the technique and to obtain a full automatic method to calculate histomorphometric parameters of the specimen. ACKNOWLEDGMENTS The authors would like to thank P. Fantazzini and C. Garavaglia of Physics Department of University of Bologna for supporting this research. REFERENCES 1. B.P.Flannery, H.W. Deckman, W.G. Roberge, K. D Amico, Three-dimensional X-ray microtomography, Science, 237, pp , Rüegsegger P., Koller B., Muller R.: A microtomographic system for the non-destructive evaluation of bone architecture, Calcified Tissue International, 58, pp , R. Muller, M. Hahn, M. Vogel, G. Delling, P. Rüegsegger, Morphometric analysis of noninvasively assessed bone biopsies: Comparison of high-resolution CT and histological sections, Bone, 18, pp , M. Salome, F. Salome, P. Cloetens, C. Odet, A.M. Laval-Jeantet, J. Baruchel, P. Spanne, A synchrotron radiation microtomography system for the analysis of trabecular bone samples, Medical Physics, 26, pp , Engelke K., Karolczak M., Lutz A., Seibert U., Schaller S., Kalender W.: Micro-CT: Technology and applications for assessing bone structure, Radiologe, 39(3), pp , M.Rossi et al., An experimental micro-ct system for X-ray NDT, Proceedings of SPIE's 46th Annual Meeting, Conference: Developments in X-Ray Tomography III (AM138), July 29 - Agoust , S.Diego (CA), USA. 7. L.A. Feldkamp, L.C. Davis, J.W. Kress, Practical Cone-Beam Algorithm, J.Opt.Soc.Am. A, 1 (6), pp , A. Micheal Parfitt, Marc K. Drezner, Francis H. Glorieux, John A. Kanis, Hartmut Malluche, Pierre J. Meunier, Susan M. Ott, and Robert R. Recker, Bone histomorphometry: standardization of nomenclature, symbols, and units, Journal of Bone and Mineral Research, 2(6), pp , L.A. Feldkamp, S.A. Goldstein, A.M. Parfitt, G. Jesion, and M. Kleerekoper, "The direct examination of threedimensional bone architecture in vitro by computed tomography", Journal of Bone and Mineral Research, 4(1), pp.3-11, 1989.