Plastic & Reconstructive Surgery:
Three-Dimensional Surface-Imaging Systems
Tzou, Chieh-Han John M.D.; Artner, Nicole M.Sc.; Kropatsch, Walter Dipl. Ing.; Frey, Manfred M.D.
Division of Plastic and Reconstructive Surgery, Department of Surgery, Medical University of Vienna (Tzou)
Institute of Computer Graphics and Algorithms, Pattern Recognition and Image-Processing Group, Vienna University of Technology (Artner, Kropatsch)
Division of Plastic and Reconstructive Surgery, Department of Surgery, Medical University of Vienna, Vienna, Austria (Frey)
Correspondence to Dr. Tzou, Division of Plastic and Reconstructive Surgery, Department of Surgery, Medical University of Vienna, Waehringer Guertel 18-20, A-1090 Vienna, Austria firstname.lastname@example.org
In the past decade, advances in optical-based three-dimensional imaging technologies, such as structured light1 and stereophotogrammetry,2 have gained popularity worldwide and offer multiple medical applications.3 Understanding the science of these technologies is critical so that surgeons can select the appropriate tool for their clinical practice or institution.
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Structured light analyzes the surface of an object by sequentially capturing calibrated patterns of light projected onto the object's surface. Distortions in the patterns are analyzed and processed to register the shape data as color-texture information, creating a three-dimensional model. The technology was initiated by the machine-vision industry for fast full-field measurements and was commercialized in 1995. This system was enhanced by Siemens and introduced into the medical field by Axis Three Ltd. (Belfast, United Kingdom) in 2006.4 Because this system was originally designed for use in engineering, where accuracy of measurement has high priority, the time of data acquisition is approximately 2 seconds.
Stereophotogrammetry, first introduced in 1944,5 is based on the principle of taking two pictures of the same object from two different viewpoints to create a stereo pair, recording depth to generate a composite three-dimensional model. It has proven to be an appropriate three-dimensional method for quantifying and detecting changes in facial morphology. Because of its instant three-dimensional capture, where data acquisition is between 1 and 8 msec, it is the prime three-dimensional surface-imaging technique used in pediatrics. In alphabetical order, 3dMD, Canfield Scientific, and Dimensional Imaging introduced their systems in 1997, 2005, and 2002, respectively.4 Stereophotogrammetry has three strategies: active, passive, and hybrid. Active stereophotogrammetry (Canfield CR 3D) projects a pattern onto the surface of the target object. Combining this projected and the visible natural (e.g., pores, freckles, and scars) patterns of the object's surface allows dense measurements of the reconstructed three-dimensional geometry. No additional lighting is needed for this strategy, which resists the effects of ambient lighting. In contrast, passive stereophotogrammetry (Canfield and Di3D) does not need projection of any patterns; it generates three-dimensional geometry on the basis of natural visible patterns. Because strong directional ambient light may cause glare and diminish the superficial details, passive stereophotogrammetry depends on the integrity of the pixels and requires carefully controlled lighting conditions for the three-dimensional reconstruction. Hybrid stereophotogrammetry (3dMD) combines active and passive stereophotogrammetry triangulation strategies into one system, fusing both technologies.
Three-dimensional imaging surface systems may enhance surgeons' communication with patients, surgical planning, and outcome evaluation. Besides these, the major advantages of these systems are the standardized imaging of patients, generation of two-dimensional photographic views with one capture, and three-dimensional surface measurements without direct contact. Technologies vary within the companies mentioned above (Table 1). We believe this information comparing three-dimensional imaging technologies can be useful to surgeons selecting a system to suit their individual needs, based on their fields of application and requirements.
Chieh-Han John Tzou, M.D.
Division of Plastic and Reconstructive Surgery, Department of Surgery, Medical University of Vienna
Nicole Artner, M.Sc.
Walter Kropatsch, Dipl. Ing.
Institute of Computer Graphics and Algorithms, Pattern Recognition and Image-Processing Group, Vienna University of Technology
Manfred Frey, M.D.
Division of Plastic and Reconstructive Surgery, Department of Surgery, Medical University of Vienna, Vienna, Austria
The authors have no financial interest to declare in relation to the content of this article. No external funding was received.
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