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3 March 2007 Active index model: a unique approach for regional quantitative morphometry in longitudinal and cross-sectional studies
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Abstract
Recent advancements in digital medical imaging have opened avenues for quantitative analyses of different volumetric and morphometric indices in response to a disease or a treatment. However, a major challenge in performing such an analysis is the lack of a technology of building a mean anatomic space (MAS) that allows mapping data of a given subject onto MAS. This approach leads to a tool for point-by-point regional analysis and comparison of quantitative indices for data coming from a longitudinal or transverse study. Toward this goal, we develop a new computation technique, called Active Index Model (AIM), which is a unique tool to solve the stated problem. AIM consists of three building blocks - (1) development of MAS for a particular anatomic site, (2) mapping a specific data onto MAS, (3) regional statistical analysis of data from different populations assessing regional response to a disease or treatment progression. The AIM presented here is built at the training phase from two known populations (e.g., normal and diseased) which will be immediately ready for diagnostic purpose in a subject whose clinical status is unknown. AIM will be useful for both cross sectional and longitudinal studies and for early diagnostic. This technique will be a vital tool for understanding regional response of a disease or treatment at various stages of its progression. This method has been applied for analyzing regional trabecular bone structural distribution in rabbit femur via micro-CT imaging and to localize the affected myocardial regions from cardiac MR data.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
P. K. Saha, H. Zhang, M. Sonka, G. E. Christensen, and C. S. Rajapakse "Active index model: a unique approach for regional quantitative morphometry in longitudinal and cross-sectional studies", Proc. SPIE 6512, Medical Imaging 2007: Image Processing, 65121B (3 March 2007); https://doi.org/10.1117/12.709022
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