Bio-CAD modeling and its applications in 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 in Information 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 some salient 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 3D reconstruction technique will be described. Enabling state-of-the-art computer software in assisting 3D reconstruction and in bio-modeling development 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 in terms of hardware and software have helped in the advancement of CAD in applications beyond that of traditional design and analysis. CAD is now being used extensively in biomedical engineering in applications ranging from clinical medicine, customized medical implant design to tissue engineering [1–4]. This has largely been made possible due to developments made in imaging technologies and reverse engineering techniques supported equally by both hardware and software technology advancements. The primary imaging modalities that are made use of in different applications include, computed tomography (CT), magnetic resonance imaging (MRI), optical microscopy, micro CT, etc. each with its own advantages and limitations as described in [1]. Using data derived from these images, computer models of human joints for stress analysis, dynamic force analysis and simulation; design of implants and scaffolds etc. have been reported in published literature [5–7]. This effort to model human body parts in a CAD based virtual environment is also referred to as BioCAD modeling. Utilization of computer-aided technologies in tissue engineering research and development has evolved a development of a new field of Computer-Aided Tissue Engineering (CATE). CATE integrates advances in Biology, Biomedical Engineering, Information Technology, and modern Design and Manufacturing to Tissue Engineering application. Specifically, it applies enabling computeraided technologies, including computer-aided design (CAD), medical image processing, computer-aided manufacturing (CAM), and solid freeform fabrication (SFF) for multi-scale biological modeling, biophysical analysis and simulation, and design and manufacturing of tissue and organ substitutes. In a broad definition, CATE embraces three major applications in tissue engineering: (1) computer-aided tissue modeling, including 3D anatomic visualization, 3D reconstruction and CAD-based tissue modeling, and bio-physical modeling for surgical planning and Computer-Aided Design 37 (2005) 1097–1114 www.elsevier.com/locate/cad 0010-4485//$ – see front matter q 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.cad.2005.02.002 * Corresponding author. Tel.: C1 215 895 5810; fax: C1 215 895 2094. E-mail address: sunwei@drexel.edu (W. Sun). simulation; (2) computer-aided tissue scaffold informatics and biomimetic design, including computer-aided tissue classification and application for tissue identification and characterization at different tissue hierarchical levels, biomimetic design under multi-constraints, and multi-scale modeling of biological systems; and (3) Bio-manufacturing for tissue and organ regeneration, including computer-aided manufacturing of tissue scaffolds, bio-manufacturing of tissue constructs, bio-blueprint modeling for 3D cell and organ printing. An overview of CATE is outlined in Fig. 1. Details of the applications and developments were reported in [1,5,8], respectively. The extracellular matrix(ECM) that tissue scaffolds attempt to emulate are of great complexity, for integrated within the ECM are instructions that direct cell attachment, proliferation, differentiation, and the growth of new tissue. In order to fulfill its function, an ideal tissue scaffold should be designed to mimic the appropriate structure and characteristics of the desired tissue in terms of biocompatibility, architecture, environment, and chemical composition. In addition, the construction of the scaffold must be achieved at multiple organizational levels, spanning from the microscale for cell-printing, to the macro-scale for organ-printing. The scaffold must also have incorporated within it, heterogeneous characteristics in the form of scaffold materials, a controlled spatial distribution of growth factors, and an embedded microarchitectural vascularization for cellular nutrition, movement, and chemotaxis. Consideration of these multiple biological, biomechanical and biochemical issues can be represented by a comprehensive ‘scaffold informatics’ model. The biological implications of the developed technique and the scaffold informatics model could be significant-ranging from the controlled release of growth factors within a 3D scaffold, to the design and introduction of tissue angiogenesis, creation of a multiple tissue assembly, to the formation of a complex heterogeneous tissue scaffold for soft-hard tissue interface and applications. 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 scaffold informatics modeling development by providing the basic morphology, anatomy and organization of the to-be-replaced tissue on which the pertinent biological design intents can be introduced. For example, the definition of the cell-specific scaffolding biomaterials (for cell attachment), the material compositions (for scaffold controlled degradation), pore size, pore shape and ideal topology for inter-architectural connectivity (for cell proliferation, differentiation and new tissue growth), and the prescribed surface chemistry and topography (for cell mechanosensation). 2. Image based bio-CAD modeling technique Construction of a Bio-CAD model for a specific tissue often starts from the acquisition of anatomic data from an appropriate medical imaging modality. This is referred to as image-based Bio-CAD modeling in which the imaging modality must be capable of producing three-dimensional views of anatomy, differentiating heterogeneous tissue types and displaying the vascular structure, and generating computational tissue models for other down stream applications, such as analysis and simulation. In general, an image based bio-CAD modeling process involves following three major steps: (1) non-invasive image acquisition; (2) imaging process and three-dimensional Fig. 1. Overview of computer-aided tissue engineering. 1098 W. Sun et al. / Computer-Aided Design 37 (2005) 1097–1114 reconstruction (3DR) to form voxel-based volumetric image representation; and (3) construction of CAD-based model. 2.1. Non-invasive imaging data acquisition The primary imaging modalities used in tissue modeling are CT, MRI, and optical microscopy, each with its own advantages and limitations as briefly described as follows. Detailed discussions on using CT and MRI can be found in [1,8]. CT or mCT scans require exposure of a sample to small quantities of ionizing radiation, the absorption of which is detected and imaged. This results in a series of 2D images displaying a density map of the sample. Stacking these images creates a 3D representation of the scanned area. The latest development of micro-CT technology has been successfully used to quantify the microstructurefunction relationship of tissues and the designed tissue structures, 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 micron resolution [17]. The main advantage of CT and micro-CT as an imaging modality for tissue engineering purposes is reasonably high resolution. MRI provides images for soft tissues as well as for hard tissues, and as such is vastly superior in differentiating soft tissue types and recognizing border regions of tissues of similar density. Dhenain et al. performed micro-MRI scans on mouse embryos and resolution achieved was 20–80 micron voxels. The resulting segmentation isolated each of the major developing organs in the embryo [18]. Using simple region growing techniques and Mimics software [19], the author’s group developed a 3D representation for the central nervous system, heart, and kidneys of the subject as reported in [1]. Optical microscopy has limited applications to 3D biotissue modeling due to the intensive data manipulation. For example, to examine a sample with high resolution using optical microscopy, it must be physically sectioned to a thickness of between 5 and 80 microns and placed onto slides, providing a square sample perhaps 1 cm!1 cm for fine resolution. The division into these slides is a labor intensive process, and the resulting images of the target organ would be thousands of 2D images that must be both digitally stacked into 3D columns as in CT and MRI and arranged in correct X and Y positions. This is computationally and a memory intensive process but within the capabilities of many computer modeling programs. From a practicality point of view, pathologists cannot be expected to examine thousands of individual slides of an entire organ and identify each and every cell in the image. Therefore, it will be a significant challenge to train computers to identify individual cells by their visual characteristics, even with the aid of complex staining. However, differentiating tissue down to the level of the individual cell may still be only possible by using optical microscopy. Differentiation of tissue in CT scans is accomplished through contrast segmentation, the grayscale value of each voxel determined solely by tissue density. As such, CT is inferior to both MRI and optical microscopy in differentiating soft tissues of similar density. It is much more effective in the modeling of hard tissues and sharply defined density changes, such as the interface between bone and soft tissues. Sometimes, the disadvantag

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