83. Rajkowska G, Goldman-Rakic P (1995). Cytoarchitect- 97. onic Definition of Prefrontal Areas in the Normal Human Cortex: II. Variability in Locations of Areas 9 and 46 and Relationship to the Talairach Coordinate System, Cerebral Cortex 5(4):323-337. 98.
84. Roland PE, Zilles K (1994). Brain Atlases—A New Research Tool, Trends in Neurosciences 17(11):458-467.
85. Rizzo G, Gilardi MC, Prinster A, Grassi F, Scotti G, Cerutti S, Fazio F (1995). An Elastic Computerized Brain 99. Atlas for the Analysis of Clinical PET/SPET Data, Eur.
86. Salamon G, Raynaud C, Regis J, Rumeau C (1990). 100. Magnetic Resonance Imaging of the Pediatric Brain: An Anatomical Atlas, Lippincott-Raven Publishers, 101. Philadelphia.
87. Sandor SR, Leahy RM (1994). Matching Deformable Atlas Models to Pre-processed Magnetic Resonance Brain Images, 102. Proc. IEEE Conf. on Image Processing 3:686-690.
88. Sandor SR, Leahy RM (1995). Towards Automated 103. Labeling of the Cerebral Cortex using a Deformable Atlas.
In Bizais Y, Barillot C, Di Paola R (eds), Info. Proc. in Med. Imag., June 1995, 127-138. 104.
89. Schaltenbrand G, Bailey P (1959). Introduction to Stereotaxis with an Atlas of the Human Brain. Thieme, 104a. Stuttgart.
90. Schaltenbrand G, Wahren W (1977). Atlas for Stereotaxy of the Human Brain, 2nd edn, Thieme, Stuttgart. 105.
91. Schiemann T, Nuthmann J, Tiede U, Höhne KH (1996). Segmentation of the Visible Human for High-Quality Volume-Based Visualization, Vis. Biomed. Comp. 4:13-22.
Schwartz EL, Merker B (1986). Computer-Aided Neuroanatomy: Differential Geometry of Cortical Surfaces and an Optimal Flattening Algorithm, IEEE Comp. Graphics and Applications 6:36-44. Seitz RJ, Bohm C, Greitz T, Roland PE, Eriksson L, Blomqvist G, Rosenqvist G, Nordell B (1990). Accuracy and Precision of the Computerized Brain Atlas Programme for Localization and Quantification in Positron Emission Tomography, J. Cereb. Blood Flow. Metab. 10:443-457.
Sereno MI, Dale AM, Liu A, Tootell RBH (1996). A Surface-Based Coordinate System for a Canonical Cortex, Proc. 2nd Int. Conf. Hum. Brain Mapping, Boston, MA, Neuroimage 3(3):S252.
Smith GE (1907). A New Topographical Survey of the Human Cerebral Cortex, Being an Account of the Distribution of the Anatomically Distinct Cortical Areas and their Relationship to the Cerebral Sulci, J. Anatomy 41:237-254.
Spritzer V, Ackerman MJ, Scherzinger AL, Whitlock D (1996). The Visible Human Male: A Technical Report, J. Amer. Med. Informatics Assoc. 3(2):118-130. http:// www.nlm.nih.gov/extramural_research.dir/visible_hum an.html
Steinmetz H, Furst G, Freund H-J (1989). Cerebral Cortical Localization: Application and Validation of the Proportional Grid System in MR Imaging, J. Comp. Assist. Tomography 13(1):10-19.
Steinmetz H, Furst G, Freund H-J (1990). Variation of Perisylvian and Calcarine Anatomic Landmarks within Stereotaxic Proportional Coordinates, Amer. J. Neuro-radiol 11 (6):1123-1130.
Stensaas SS, Eddington DK, Dobelle WH (1974). The Topography and Variability of the Primary Visual Cortex in Man, J. Neurosurg 40:747-755.
Stewart JE, Broaddus WC, Johnson JH (1996). Rebuilding the Visible Man, Vis. Biomed. Comp. 4:81-86.
Subsol G, Roberts N, Doran M, Thirion JP, Whitehouse
GH (1997). Automatic Analysis of Cerebral Atrophy,
Magn. Reson. Imaging 15(8):917-927.
Swanson LW (1992). Brain Maps: Structure of the Rat
Talairach J, Szikla G (1967). Atlas dAnatomie Stereotaxique du Telencephale: Etudes Anatomo-Radiologiques. Masson & Cie, Paris. Talairach J, Tournoux P (1988). Co-planar Stereotaxic Atlas of the Human Brain. Thieme, New York. Thirion J-P (1995). Fast Non-Rigid Matching of Medical Images, INRIA Internal Report 2547, Projet Epidaure, INRIA, France.
Thirion J-P, Calmon G (1997). Deformation analysis to detect and quantify active lesions in 3D medical image sequences, INRIA Internal Report 3101, Projet Epidaure, INRIA, France, Feb. 1997.
106. Thompson PM, Schwartz C, Toga AW (1996). HighResolution Random Mesh Algorithms for Creating a Probabilistic 3D Surface Atlas of the Human Brain, Neurolmage 3:19-34.
107. Thompson PM, Schwartz C, Lin RT, Khan AA, Toga AW (1996). 3D Statistical Analysis of Sulcal Variability in the Human Brain, J. Neurosci. 16(13):4261-4274.
108. Thompson PM, Toga AW (1996). A Surface-Based Technique for Warping 3-Dimensional Images of the Brain, IEEE Trans. Medical Imag. 15(4):1-16.
109. Thompson PM, MacDonald D, Mega MS, Holmes CJ, Evans AC, Toga AW (1997a). Detection and Mapping of Abnormal Brain Structure with a Probabilistic Atlas of Cortical Surfaces, J. Comp. Assist. Tomogr. 21(4):567-581.
110. Thompson PM, Toga AW (1997b). Detection, Visualization and Animation of Abnormal Anatomic Structure with a Deformable Probabilistic Brain Atlas based on Random Vector Field Transformations, Medical Image Analysis 1(4): 271-294; paper, with video sequences on CD-ROM with Journal Issue, November 1997.
111. Thompson PM, Moussai J, Khan AA, Zohoori S, Goldkorn A, Mega MS, Small GW, Cummings JL, Toga AW (1997). Cortical Variability and Asymmetry in Normal Aging and Alzheimer's Disease, Cerebral Cortex 8(6):492-509.
112. Thompson PM, Toga AW (1998). Anatomically-Driven Strategies for High-Dimensional Brain Image Registration and Pathology Detection, in: Brain Warping, Toga AW (ed.), Academic Press, 311-336.
113. Thompson PM, Giedd JN, Woods RP, MacDonald D, Evans AC, Toga AW (2000). Growth Patterns in the Developing Human Brain Detected Using Continuum-Mechanical Tensor Mapping. Nature, 404:190-193.
114. Thompson PM, Mega MS, Woods RP, Blanton RE, Moussai J, Zoumalan CI, Aron J, Cummings JL, Toga AW (2000). A Probabilistic Atlas of the Human Brain in Alzheimer's Disease. Submitted.
115. Thompson PM, Mega MS, Woods RP, Blanton RE, Moussai J, Zoumalan CI, Aron J, Cummings JL, Toga AW (1999). A Probabilistic Atlas of the Human Brain in Alzheimer's Disease: Emerging Patterns of Variability, Asymmetry and Degeneration, 5th International Conference on Functional Mapping of the Human Brain, Dusseldorf, Germany, June 1999.
116. Thurfjell L, Bohm C, Greitz T, Eriksson L (1993). Transformations and Algorithms in a Computerized Brain Atlas, IEEE Trans. Nucl. Sci. 40(4), pt. 1:1167-1191.
117. Tiede U, Bomans M, Hohne KH, Pommert A, Riemer M, Schiemann T, Schubert R, Lierse W (1993). A Computerized 3D Atlas of the Human Skull and Brain, Am. J. Neuroradiol. 14:551-559.
118. Toga AW, Ambach K, Quinn B, Hutchin M, Burton JS (1994). Postmortem Anatomy from Cryosectioned Whole Human Brain, J. Neurosci. Meth. 54(2):239-252.
119. Toga AW (1994). Visualization and Warping of Multimodality Brain Imagery. In: Functional Neuroimaging: Technical Foundations, Thatcher RW, Hallett M, Zeffiro T, John ER, Huerta M (eds.), 171-180.
120. Toga AW, Thompson PM, Payne BA (1996). Modeling Morphometric Changes of the Brain during Development, In: Developmental Neuroimaging: Mapping the Development of Brain and Behavior, Thatcher RW, Lyon GR, Rumsey J, Krasnegor N, (eds.), Academic Press.
121. Toga AW, Mazziotta JC (1996). Brain Mapping: The Methods, Academic Press.
122. Toga AW, Thompson PM (1997). Measuring, Mapping, and Modeling Brain Structure and Function, SPIE Med Imag 3033:104-115.
123. Toga AW (1998). Brain Warping, Academic Press.
124. Van Buren JM, Maccubin D (1962). An Outline Atlas of Human Basal Ganglia and Estimation of Anatomic Variants, J. Neurosurg. 19:811-839.
125. Van Buren JM, Borke RC (1972). Variations and Connections of the Human Thalamus, Vols. 1 & 2. Springer, New York.
126. Van Essen DC, Maunsell JHR (1983). Hierarchical Organization an Functional Streams in the Visual Cortex, Trends Neurol. Sci. 6:370-375.
127. Viola PA, Wells WM (1995). Alignment by Maximization of Mutual Information, 5th IEEE Int. Conf. on Computer Vision, 16-23, Cambridge, MA.
128. Warfield S, Dengler J, Zaers J, Guttmann CRG, Wells WM, Ettinger GJ, Hiller J, Kikinis R (1995). Automatic Identification of Gray Matter Structures from MRI to Improve the Segmentation of White Matter Lesions, Proc. Med. Robotics & Comp. Assist. Surg (MRCAS), Nov. 4-7 1995, 55-62.
129. Watson JDG, Myers R, Frackowiak RSJ, Hajnal JV, Woods RP, Mazziotta JC, Shipp S, Zeki S (1993). Area V5 of the Human Brain: Evidence from a Combined Study using Positron Emission Tomography and Magnetic Resonance Imaging, Cereb. Cortex 3:79-94.
130. Wells WM, Viola P, Atsumi H, Nakajima S, Kikinis R (1997). Multi-Modal Volume Registration by Maximization of Mutual Information, Medical Image Analysis 1(1):35-51.
131. Woods RP, Cherry SR, Mazziotta JC (1992). Rapid Automated Algorithm for Aligning and Reslicing PET Images, J. Comp. Assist. Tomogr. 16:620-633.
132. Woods RP, Mazziotta JC, Cherry SR (1993). MRI-PET Registration with Automated Algorithm, J. Comp. Assist. Tomogr. 17:536-546.
133. Woods RP (1996). Modeling for Intergroup Comparisons of Imaging Data, NeuroImage 4(3):84-94.
134. Woods RP, Grafton ST, Watson JDG, Sicotte NL, Mazziotta JC (1998). Automated Image Registration: II.
Intersubject Validation of Linear and Nonlinear Models, J. Comp. Assist. Tomog. 22(1):153-165. 135. Woods RP, Thompson PM, Mega MS, Toga AW, Mazziotta JC (2000). Polynomial Warping to a Brain Atlas: A Tool for the Study of Alzheimer's Disease (submitted).
136, Yoon H, Shin YS, Lee KC, Park HW (1997), Morphological Characteristics of the Developing Human Brain during the Embryonic Period, Yonsei Med J 38(1):26-32,
137, Zijdenbos AP, Dawant BM (1994), Brain Segmentation and White Matter Lesion Detection in MR Images, Crit, Rev, Biomed, Eng, 22(5-6):401-465,
41 Visualization Pathways in Biomedicine Meiyappan Solaiyappan 659
42 Three-Dimensional Visualization in Medicine and Biology Richard A. Robb 685
43 Volume Visualization in Medicine Arie E. Kaufman 713
44 Fast Isosurface Extraction Methods for Large Image Data Sets Yarden Livnat,
Steven G. Parker, and Christopher R. Johnson 731
45 Morphometric Methods for Virtual Endoscopy Ronald M. Summers 747
Richard A. Robb
Visualization has played an increasingly important role in biomedical applications in the past decade. In the intervening period, formulation of new theories and refinement of algorithms, development and implementation of comprehensive tools and systems, and extension of these capabilities to a wide variety of biological and medical applications have accelerated at a remarkable pace. Visualization in biological and medical research has emerged as a unique and significant discipline aimed at developing approaches and tools to allow researchers and practitioners to "see into" and comprehend the living systems they are studying. Topics of investigation and development in the discipline span from basic theory through tools and systems to complete applications. Biomedical visualization depends on computing environments, graphics hardware, and software tools that facilitate human-machine-data interaction for exploration and analysis of complex biological systems. Approaches to biomedical visualization include generation of realistic displays for presentation of images and related information in three or more dimensions, development of interactive and automated methods for manipulation of multidimensional images and associated parametric data, implementation of measurement tools for quantitative evaluation and assessment of images and image-derived data, and design and validation of models and paradigms that enhance interpretive and decision-making processes in biomedical applications.
Biomedical visualization has its roots in several traditional disciplines of biological and medical science that are significantly grounded in the observation of living structures and in the measurement of various properties of these structures (e.g., their functions). These observations and measurements are often recorded as images. Ever since the invention of the microscope and the discovery of X-rays, physicians, surgeons, and life scientists have been using images to diagnose and treat disease and to better understand basic anatomy, physiology, and biology. The value of biomedical images depends upon the context in which they are obtained, and the scientific or medical interests and goals that motivate their production and use. These contexts range over several traditional disciplines of biological science and medical care, from biophysics, biochemistry, physiology, and molecular and cellular biology, to diagnostic medicine and clinical treatment. In each discipline, the information derived from or conveyed by images is quite different, as are the imaging devices that produce them. But common to all is some form of visualization, which includes, in its broadest definition, both display and analysis of images.
The imaging modalities used in biology and medicine are based on a variety of energy sources, including light, electrons, lasers, X-rays, radionuclides, ultrasound, and nuclear magnetic resonance. The images produced span orders of magnitude in scale, ranging from molecules and cells to organ systems and the full body. The advantages and limitations of each imaging modality are primarily governed by the basic physical and biological principles that influence the way each energy form interacts with tissues, and by the specific engineering implementation for a particular medical or biological application. Disease processes and abnormalities affecting all regions of the human body are so numerous and different that each imaging modality possesses attributes that make it uniquely helpful in providing the desired understanding and/or discrimination of the disease or abnormality, and therefore no single method has prevailed to the complete exclusion of others. Even though significant disparity in scale and/or characteristic features separate the imaging realms, striking parallels and common approaches exist in visualization and analysis of these images. In general, the methodologies are complementary, together providing a powerful and synergistic armamentarium of clinical diagnostic, therapeutic, and biomedical research capabilities that has potential to significantly advance the practice of medicine and the frontiers of biological understanding.
The process of forming an image involves the mapping of an object, and/or some property of an object, into or onto what maybe called "image space." This space is used to visualize the object and its properties and may be used to quantitatively characterize its structure and/or its function. Imaging and visualization science may be defined as the study of these mappings and development of ways to better understand them, to improve them, and to productively use them. Generally, the steps involved in imaging and visualization procedures include image-data acquisition, image display and analysis, and image interpretation (i.e., perception, cognition, and understanding). Most modern imaging devices are digital computer-based and produce images in the form of arrays of 2D picture elements (pixels) or 3D volume elements (voxels). The numbers associated with these pixels and voxels represent the mappings of object properties that can be detected and localized spatially and/or functionally. The agenda for the field of imaging science is to develop a comprehensive understanding of all of the steps involved in the biomedical imaging process, and the complex ways in which they are interdependent, so that parameters pertaining to each step can be optimized to improve the basic scientific, diagnostic, and therapeutic value of biomedical images.
The particular challenge of biomedical visualization is to define and formulate a rationale basis and associated capabilities for acquisition, processing, display, and quantitative analysis that will significantly increase the faithful extraction of both scientific and clinical information contained in biomedical images. This is a formidable task, one which consistently suggests that continued advances are required to address it effectively. The need for new approaches to image visualization and analysis will become increasingly important and pressing as improvements in imaging technology enable more complex objects and processes to be imaged and simulated. The revolutionary capabilities of new 3D and 4D medical imaging modalities and 3D scanning microscope technologies based on computed tomography, along with computer reconstruction and rendering of multidimensional medical and histologic volume image data, obviate the need for physical dissection or abstract assembly and provide powerful new data display and analysis tools for both biologists and physicians.
Virtual reality (VR) refers to a human-computer interface that facilitates highly interactive visualization and control of computer-generated 3D scenes and their related components with sufficient detail and speed so as to evoke sensorial experience similar to that of real experience. VR technology opens new realms in the teaching and practice of medicine and biology by permitting the computed 3D images obtained from medical and biologic imaging systems to be manipulated with intuitive immediacy similar to that of real objects, by allowing the viewer to "enter" the visualizations to take up any viewpoint, by enabling the objects to be dynamic-either in response to viewer actions or to illustrate normal or abnormal motion-and by engaging other senses such as touch and hearing (or even smell) to enrich the simulation. Applications extend across a vast range of scale from individual molecules and cells through the varieties of tissue to organs and organ systems, including functional attributes of these systems, such as biophysical and physiological properties. Medical applications include anatomy instruction, enhanced diagnosis, and treatment planning and rehearsal. The greatest potential for revolutionary innovation in the teaching and practice of medicine and biology lies in dynamic, fully immersive, multisensory fusion of real and virtual information data streams. Although this technology is still under development, and not yet generally available to the medical practitioner, modern computer technology has facilitated major advances, and there are several practical applications involving varying levels of interactivity and immersion that are possible. These applications will have a positive impact on medicine and biology now and in the near future. The most complex and challenging applications, those that show the greatest promise of significantly changing the practice of medical research, diagnosis, and treatment, require an intimate and immediate union of patient-specific images and models with other real-world, real-time data. It may well be that the ultimate value of 3D visualization and VR in medicine will derive more from the sensory enhancement of real experience than from the simulation of normally sensed reality. Preliminary results suggest that virtual procedures can provide accurate, reproducible, and clinically useful visualizations and minimally invasive interventions. These results will help drive improvements in and lend credibility to virtual procedures and simulations as routine clinical tools in the next millennium.
Novel and efficient visualization methods, including virtual reality (VR), are under development for processing of volumetric image data that will provide realistic, real-time surgery planning capabilities, as well as capabilities to effectively enhance surgical performance on-line. VR offers the promise of highly interactive, natural control of the visualization process, providing realistic simulations of surgery for training, planning, and rehearsal. Efficient methods have been developed for the production of accurate models of anatomic structures computed from patient-specific volumetric image data (such as CT or MRI). The models can be enhanced with textures mapped from photographic samples of the actual anatomy, and when used on a VR system, such models provide realistic and interactive capabilities for surgical training, surgery planning, and procedure rehearsal on specific patient data. Visualization technology can also be deployed in the operating room to provide the surgeon with on-line, intraoperative access to all preoperative planning data and experience, translated faithfully to the patient on the operating table. These preoperative data and models can be fused with real-time data in the OR to provide enhanced reality visualization during the actual surgical procedures. Additionally, the adjunctive value of full 3D imaging (e.g., looking "outside" of the normal field of view) during the virtual surgical procedure or endoscopic exam is being evaluated. Quantitative analyses of local geometric and densitometric properties obtained from the virtual procedures ("virtual biopsy") are being developed and compared with other direct measures. Preliminary results suggest that these virtual procedures can provide accurate, reproducible, and clinically useful visualizations and measurements.
Virtual endoscopy is an important subset of virtual reality surgery planning systems that provides adjunctive diagnostic capabilities, potentially fully noninvasive screening. Virtual endoscopy is the fountainhead of an entire generation of new diagnostic opportunities that will influence surgical practice. Theoretically, all internal body structures can be visualized noninvasively using this modality. Virtual endoscopy epitomizes a significant major realization of the remarkable promise of modern imaging and computing technologies. It uses computer processing of 3D image datasets (such as CD or MRI scans) to provide simulated visualizations of patient-specific organs similar or equivalent to those produced by standard endoscopic procedures. Conventional endoscopy is invasive and often uncomfortable for patients. It sometimes has serious side effects such as perforation, infection, and hemorrhage. Virtual endoscopic visualization avoids these risks and can minimize difficulties and decrease morbidity when used before actual endoscopic procedures. In addition, there are many body regions not compatible with real endoscopy that can be explored with virtual endoscopy. Eventually, virtual endoscopy may replace many forms of real endoscopy. Other potential applications of virtual reality in medicine include anesthesiology training, virtual histology, and virtual biology, all of which provide faithful virtual simulations for training, planning, rehearsing, and/or analyzing using medical and/or biological image data.
There remains a critical need to refine and validate advanced visualization procedures before they are acceptable for routine clinical use. The Visible Human Dataset from the National Library of Medicine has been extensively used to develop and test these procedures and to evaluate their use in a variety of clinical applications. Specific clinical protocols are under way to evaluate image-guided surgery and virtual surgery against surgical outcomes and to compare virtual endoscopy with real endoscopy. Such future technologies hold significant promise for optimizing many surgical procedures, for minimizing patient risk and morbidity, and for reducing health care costs. Practicing surgeons, physicians, and therapists, working with scientists and engineers, are committed to development, evaluation, and routine clinical implementation of advanced visualization and virtual reality systems for medical diagnosis and treatment.
This section features five complementary chapters that cover these topics and issues, from principles to practice. The first chapter by Solaiyappan provides an introductory overview and background. The chapter by Robb discusses both 2D and 3D methods and illustrates their use in a variety of representative applications. The chapter by Kaufman provides detailed explanations of the principal technologies used for image volume visualization. The chapter by Johnson addresses the important topic of efficient extraction of homologous surfaces from large image volumes for visualization of geometric models. Finally, the chapter by Summers describes virtual endoscopy and addresses the important problem of evaluating measurements made with such techniques. Although these chapters have some inevitable overlap, each one contributes unique information and focus, and together they represent a succinct and effective introduction to the basic principles and applications in biomedical visualization.
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