Bio-CAD modeling and its applications in computer-aided tissue engineering

Bio-CAD modeling and its applications in computer-aided tissue engineering
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  Bio-CAD modeling and its applicationsin computer-aided tissue engineering W. Sun * , B. Starly, J. Nam, A. Darling  Department of Mechanical Engineering and Mechanics, Drexel University, 3141 Chestnut Street, Philadelphia, PA 19104, USA Accepted 2 February 2005 Abstract CAD has been traditionally used to assist in engineering design and modeling for representation, analysis and manufacturing. Advances inInformation Technology and in Biomedicine have created new uses for CAD with many novel and important biomedical applications,particularly tissue engineering in which CAD based bio-tissue informatics model provides critical information of tissue biological,biophysical, and biochemical properties for modeling, design, and fabrication of complex tissue substitutes. This paper will present somesalient advances of bio-CAD modeling and application in computer-aided tissue engineering, including biomimetic design, analysis,simulation and freeform fabrication of tissue engineered substitutes. Overview of computer-aided tissue engineering will be given.Methodology to generate bio-CAD models from high resolution non-invasive imaging, the medical imaging process and the 3Dreconstruction technique will be described. Enabling state-of-the-art computer software in assisting 3D reconstruction and in bio-modelingdevelopment will be introduced. Utilization of the bio-CAD model for the description and representation of the morphology, heterogeneity,and organizational structure of tissue anatomy, and the generation of bio-blueprint modeling will also be presented. q 2005 Elsevier Ltd. All rights reserved. Keywords:  CAD; Bio-CAD; Biomodeling; Computer-aided tissue engineering; Tissue scaffold design 1. Overview of computer-aided tissue engineering Recent advances in computing technologies both interms of hardware and software have helped in theadvancement of CAD in applications beyond that of traditional design and analysis. CAD is now being usedextensively in biomedical engineering in applicationsranging from clinical medicine, customized medicalimplantdesign to tissue engineering [1–4]. This has largely been made possible due to developments made in imagingtechnologies and reverse engineering techniques supportedequally by both hardware and software technology advance-ments. The primary imaging modalities that are made use of in different applications include, computed tomography(CT), magnetic resonance imaging (MRI), opticalmicroscopy, micro CT, etc. each with its own advantagesand limitations as described in [1]. Using data derived fromthese images, computer models of human joints for stressanalysis, dynamic force analysis and simulation; design of implants and scaffolds etc. have been reported in publishedliterature [5–7]. This effort to model human body parts in a CAD based virtual environment is also referred to as Bio-CAD modeling.Utilization of computer-aided technologies in tissueengineering research and development has evolved adevelopment of a new field of Computer-Aided TissueEngineering (CATE). CATE integrates advances inBiology, Biomedical Engineering, Information Technology,and modern Design and Manufacturing to Tissue Engineer-ing application. Specifically, it applies enabling computer-aided technologies, including computer-aided design(CAD), medical image processing, computer-aided manu-facturing (CAM), and solid freeform fabrication (SFF) formulti-scale biological modeling, biophysical analysis andsimulation, and design and manufacturing of tissue andorgan substitutes. In a broad definition, CATE embracesthree major applications in tissue engineering: (1) compu-ter-aided tissue modeling, including 3D anatomic visual-ization, 3D reconstruction and CAD-based tissue modeling,and bio-physical modeling for surgical planning and Computer-Aided Design 37 (2005) 1097–$ - see front matter q 2005 Elsevier Ltd. All rights reserved.doi:10.1016/j.cad.2005.02.002 *  Corresponding author. Tel.: C 1 215 895 5810; fax: C 1 215 895 2094. E-mail address: (W. Sun).  simulation; (2) computer-aided tissue scaffold informaticsand biomimetic design, including computer-aided tissueclassification and application for tissue identification andcharacterization at different tissue hierarchical levels,biomimetic design under multi-constraints, and multi-scalemodeling of biological systems; and (3) Bio-manufacturingfor tissue and organ regeneration, including computer-aidedmanufacturing of tissue scaffolds, bio-manufacturing of tissue constructs, bio-blueprint modeling for 3D cell andorgan printing. An overview of CATE is outlined in Fig. 1.Details of the applications and developments were reportedin [1,5,8], respectively. The extracellular matrix(ECM) that tissue scaffoldsattempt to emulate are of great complexity, for integratedwithin the ECM are instructions that direct cell attachment,proliferation,differentiation,andthegrowthofnewtissue.Inorder to fulfill its function, an ideal tissue scaffold should bedesigned to mimic the appropriate structure and character-istics of the desired tissue in terms of biocompatibility,architecture, environment, and chemical composition. Inaddition, the construction of the scaffoldmustbe achieved atmultiple organizational levels, spanning from the micro-scale for cell-printing, to the macro-scale for organ-printing.The scaffold must also have incorporated within it,heterogeneous characteristics in the form of scaffoldmaterials, a controlled spatial distribution of growth factors,and an embedded microarchitectural vascularization forcellularnutrition,movement,andchemotaxis.Considerationof these multiple biological,biomechanical and biochemicalissues can be represented by a comprehensive ‘scaffoldinformatics’ model. The biological implications of thedeveloped technique and the scaffold informatics modelcould be significant-ranging from the controlled release of growth factors within a 3D scaffold, to the design andintroduction of tissue angiogenesis, creation of a multipletissue assembly, to the formation of a complex hetero-geneous tissue scaffold for soft-hard tissue interface andapplications. Central to CATE approach is in its ability of representing such a bio-tissue scaffold informatics model.Bio-CAD modeling plays an important role in this scaffoldinformatics modeling development by providing the basicmorphology,anatomyandorganizationoftheto-be-replacedtissue onwhich the pertinentbiological design intents canbeintroduced. For example, the definition of the cell-specificscaffolding biomaterials (for cell attachment), the materialcompositions(forscaffoldcontrolleddegradation),poresize,pore shape and ideal topology for inter-architecturalconnectivity (for cell proliferation, differentiation and newtissue growth), and the prescribed surface chemistry andtopography (for cell mechanosensation). 2. Image based bio-CAD modeling technique Construction of a Bio-CAD model for a specific tissueoften starts from the acquisition of anatomic data from anappropriate medical imaging modality. This is referred to asimage-based Bio-CAD modeling in which the imagingmodality must be capable of producing three-dimensionalviews of anatomy, differentiating heterogeneous tissuetypes and displaying the vascular structure, and generatingcomputational tissue models for other down streamapplications, such as analysis and simulation. In general,an image based bio-CAD modeling process involvesfollowing three major steps: (1) non-invasive imageacquisition; (2) imaging process and three-dimensional Fig. 1. Overview of computer-aided tissue engineering. W. Sun et al. / Computer-Aided Design 37 (2005) 1097–1114 1098  reconstruction (3DR) to form voxel-based volumetric imagerepresentation; and (3) construction of CAD-based model. 2.1. Non-invasive imaging data acquisition The primary imaging modalities used in tissue modelingare CT, MRI, and optical microscopy, each with its ownadvantages and limitations as briefly described as follows.Detailed discussions on using CT and MRI can be found in[1,8]. CT or  m CT scans require exposure of a sample tosmall quantities of ionizing radiation, the absorption of which is detected and imaged. This results in a series of 2Dimages displaying a density map of the sample. Stackingthese images creates a 3D representation of the scannedarea. The latest development of micro-CT technology hasbeen successfully used to quantify the microstructure-function relationship of tissues and the designed tissuestructures, include to characterize micro-architecture of tissue scaffolds [9,10], to help the design and fabrication of  tailored tissue microstructures [11,12], to quantify the bone tissue morphologies and internal stress-strain behavior[13–15], and to non-destructively evaluate the porous biomaterials [16], and to model lung tissue at 10–50 micronresolution [17]. The main advantage of CT and micro-CT asan imaging modality for tissue engineering purposes isreasonably high resolution. MRI provides images for softtissues as well as for hard tissues, and as such is vastlysuperior in differentiating soft tissue types and recognizingborder regions of tissues of similar density. Dhenain et al.performed micro-MRI scans on mouse embryos andresolution achieved was 20–80 micron voxels. The resultingsegmentation isolated each of the major developing organsin the embryo [18]. Using simple region growing techniquesand Mimics software [19], the author’s group developed a 3D representation for the central nervous system, heart, andkidneys of the subject as reported in [1].Optical microscopy has limited applications to 3D bio-tissue modeling due to the intensive data manipulation. Forexample, to examine a sample with high resolution usingoptical microscopy, it must be physically sectioned to athickness of between 5 and 80 microns and placed ontoslides, providing a square sample perhaps 1 cm ! 1 cm forfine resolution. The division into these slides is a laborintensive process, and the resulting images of the targetorgan would be thousands of 2D images that must be bothdigitally stacked into 3D columns as in CT and MRI andarranged in correct  X   and  Y   positions. This is computation-ally and a memory intensive process but within thecapabilities of many computer modeling programs. Froma practicality point of view, pathologists cannot be expectedto examine thousands of individual slides of an entire organand identify each and every cell in the image. Therefore, itwill be a significant challenge to train computers to identifyindividual cells by their visual characteristics, even with theaid of complex staining. However, differentiating tissuedown to the level of the individual cell may still be onlypossible by using optical microscopy.Differentiation of tissue in CT scans is accomplishedthrough contrast segmentation, the grayscale value of eachvoxel determined solely by tissue density. As such, CT isinferior to both MRI and optical microscopy in differentiat-ing soft tissues of similar density. It is much more effectivein the modeling of hard tissues and sharply defined densitychanges, such as the interface between bone and soft tissues.Sometimes, the disadvantage of poor soft tissue differen-tiation can be addressed with the help of using contrastagents [20,21]. MRI, on the other hand, despite the high tissue differentiation capacity, the resolution is consistentlyworse than both CT and optical microscopy. However, MRIhas been of great use in assembling anatomic atlases of increasingly fine resolution as the technology matures, andfind more clinical applications because it does not exposethe patient to ionizing radiation.A hybrid modality approach may be appropriate fordetermining a more precise 3D model on the same specimento correct for deficiencies in any single modality. Forinstance, 3D models derived from MRI and CT could becombined to display heterogeneous soft tissue, for whichMRI is excellent, within a high-resolution bone structuresuch as the skull, for which CT is better suited. Acombination of CT and PET has been studied as a meansto provide both structural and metabolic information forclinical applications such as precise localization of cancer inthe body [22]. A CT/optical microscopy combination mightbe of use in correcting the histological distortion from thephysical sectioning required for optical microscopy, other-wise an ideal modality for high resolution, high tissuedifferentiation imaging. The CT angiography-derived vas-cular tree may be a means to help correct this histologicaldistortion in the final model. The optical microscopymethod would image the vasculature just as the CT scanwould, but the individual vessels might be moved due tocutting distortion in any given slide. By comparing theoptical vascular model to the CT-derived vascular modelwhich does not require significant cutting, the histologicaldistortion might be correctable. At the very least, thecomparison could determine whether the final modelderived from optical microscopy was grossly distorted. Anillustration of our study for developing hybrid micro-CT/ optical microscopy 3D model, which uses the vascular treefrom a micro-CT angiograph to correct distortion in imagesfrom optical microscopy sections, is presented in Fig. 2. 2.2. Reconstruction for 3D image representation A roadmap of the reconstruction of three-dimensionalanatomic model from CT/MRI is described in Fig. 3. In theprocess shown in the roadmap, the CT/MRI images areintegrated using 2D segmentation and 3D region growth andthis volumetric image data extracts more meaningful,derivative images via three-dimensional anatomic view. W. Sun et al. / Computer-Aided Design 37 (2005) 1097–1114  1099  The three-dimensional anatomic view produces novel viewsof patient anatomy while retaining the image voxelintensities that can be used for volume rendering, volu-metric representation and three-dimensional image rep-resentation. These three-dimensional images lead to thegeneration of anatomic modeling. Anatomic modeling isused for contour based generation and 3D shaded surfacerepresentation of the CAD based medical models. Theshaded surface display of 3D objects can involvewidespread processing of images to create computerrepresentations of objects. Several visualization issues thatcannot be resolved by CAD models provide motivation forthe construction of a prototype model. Prototype modelingis done through additive/constructive processes as opposedto subtractive processes. Model slicing and model proces-sing lead to model assisted applications like in surgicalplanning, preoperative planning, intra-operative planning incomputer assisted surgery. Fig. 2. An illustration of the micro-CT/optical microscopy hybrid model.Fig. 3. Roadmap from CT/MRI to 3D reconstruction [8]. W. Sun et al. / Computer-Aided Design 37 (2005) 1097–1114 1100  Three-dimensional anatomical image and representationis usually constructed through either segmentation orvolumetric representation. 2D segmentation is extractionof the geometry of the CT scan data set [23]. Each slice is processed independently leading to the detection of theinner and outer contours of the living tissue, e.g. using aconjugate gradient (CG) algorithm [24,25]. The contours are stacked in 3D and used as reference to create a solidmodel usually through skinning operations. 3D segmenta-tion [26] of the CT data set are able to identify, within theCT data set, voxels bounding the bone and extract a ‘tiledsurface’ from them. A tiled surface is a discreterepresentation made of connected polygons (usuallytriangles). The most popular algorithm is the marchingcube algorithm [27,28]. In its original formulation the marching cube method produces tiled surfaces withtopological inconsistencies (such as missing triangles)and usually a large number of triangle elements. Thismethod decomposes the complex geometries in ‘finiteelements’ and approximations to the behavior of thesystem and the quality of approximation depends on thenumber of these elements and the order of the approxi-mation over each element. In the visualization processing,each triangle is treated as separated polygonal entity andthe computational requirements scale up exponentiallywith the number of triangles. To overcome thesedifficulties, a new algorithm, Discretized Marching Cube(DMC) algorithm is developed for the 3D segmentation of the CT data set. This algorithm was reported to be able toresolve most topological inconsistencies and maintaining ahigh level of geometric accuracy through implementingvarious disambiguation strategies [29].Beyond the simple reforming of CT scans or MR imagesinto new views [30], three-dimensional modeling andreconstruction provides a new way of viewing the 3Danatomy of the patient. These derived imaging’s go beyondsimple reformatting to provide a view that integrates acrossslices to produce ‘snapshots’ of entire organs or bones. Arealistic tissue model is desirable for virtual reality surgerytraining simulators, mechanical tool design and controllerdesign for safe and effective tissue manipulation. Theanatomic tissue modeling should result in efficient andrealistic estimation of tissue behavior and interaction forces.The construction of anatomic modeling by either Contour-based method or 3D shaded surface extraction is describedin [8]. 2.3. Construction of CAD based biomodeling Although non-invasive modalities, such as CT, Micro-CT, MRI and Optical Microscopy can be used to produceaccurate 3D tissue descriptions, however, the voxel-basedanatomical imaging representation cannot be effectivelyused in many biomechanical engineering studies. Forexample, 3D surface extraction requires either a largeamount of computational power or extreme sophistication indata organization and handling; and 3D volumetric modelon the other hand, while producing a realistic 3D anatomicalappearance, does not contain geometric topological relation.Although they are capable of describing the anatomicalmorphology and are applicable to rapid prototyping througha converted STL format, neither of them is capable of performing anatomical structural design, modeling-basedanatomical tissue biomechanical analysis and simulation. Ingeneral, activities in anatomical modeling design, analysisand simulation need to be carried out in a vector-basedmodeling environment, such as using Computer-AidedDesign system and CAD-based solid modeling, which isusually represented as ‘boundary representation’ (B-REP)and mathematically described as Non-Uniform RationalB-Spline (NURBS) functions. Unfortunately, the directconversion of the medical imaging data into its NURBSsolid model is not a simple task. In last few years somecommercial programs, for example, SurgiCAD by IntegraphISS, USA, Med-Link, by Dynamic Computer Resources,USA, and Mimic and MedCAD, by Materialise, Belgium,were developed and used to construct a CAD-based modelfrom medical images. However, none of these programs hasbeen efficiently and widely adopted by the biomedical andtissue engineering community due to the inherent complex-ity of the tissue anatomical structures. Effective methods forthe conversion of CT data into CAD solid models still needto be developed.We have evaluated and compared following threedifferent process paths for generating a CAD model frommedical imaging data: (1) MedCAD interface approach, (2)reverse engineering interface approach, and (3) STL-triangulated model converting approach. The outline of the processes is presented in Fig. 4. The comparison andcomments of these three process paths for a case study of femur model generation are listed in Table 1. 2.3.1. Process path 1: MedCAD interface The MedCAD interface, normally as a standard moduleof medical imaging process software, is intended to bridgethe gap between medical imaging and CAD designsoftware. The MedCAD interface can export data from theimaging system to the CAD platform and vice versa througheither IGES (International Graphics Exchange Standard),STEP (Standard for Exchange of Product (STEP) or STLformat. The interface provides for the fitting of primitivessuch as cylinders, planes, spheres etc at the imaging 2Dsegmentation slices. It also provides the limited ability tomodel a freeform surface (such as B-spline surfaces). In theexample given below, we have used both primitives andfreeform shapes to model a femur bone anatomy and reportthe in Fig. 5. The limitation of using MedCAD interface isthe incapability to capture detail and complex tissueanatomical features, particularly for features with complexgeometry. W. Sun et al. / Computer-Aided Design 37 (2005) 1097–1114  1101
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