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Active Index Model: A Technology For Regional Quantitative Medical Imaging Analysis

Detailed Technology Description

Researchers have developed a tool for analyzing medicalimaging data that can specifically identify regional anatomic changes in termsof structural and/or functional characteristics in an individual, or in aclinically defined population as compared to a reference population. ThisActive Index Model (AIM) is a method for computing statistical distributions orcharacteristics of one or more quantitative measures taken from one or moreclinically defined populations over a mean atomic space (MAS). AIM is capable of detecting regional physiologicaldifferences and changes in an anatomical structure, and uses the informationfor diagnostic purposes in a subject/population whose clinical status isunknown. This technology can be utilized in a wide range of medical imaging,and detecting early responses in osteoporosis, osteoarthritis, aneurysmalchanges in vasculature of the brain, etc.

*Abstract

Background Information

Medical imaging is thetechnique and process of creating visual representations of the interior of abody for clinical analysis/diagnostics. Medical images establish a database ofnormal anatomy and physiology to make it possible to identify abnormalities ina patient. Medical imaging methods generally analyze global changes inquantitative measures in response to disease or treatment. A major disadvantagefor this method is that the effects of the measured disease or treatment becomesdiluted due to global averaging, and early detection of abnormalities becomecompromised. Researchers at The University of Iowa have developed a novelanalytical tool which when fully implemented provides the necessary regionalstatistical reference in an anatomic coordinate space for quantitativediagnosis.

TechnologySummary

Researchers have developed a tool for analyzing medicalimaging data that can specifically identify regional anatomic changes in termsof structural and/or functional characteristics in an individual, or in aclinically defined population as compared to a reference population. ThisActive Index Model (AIM) is a method for computing statistical distributions orcharacteristics of one or more quantitative measures taken from one or moreclinically defined populations over a mean atomic space (MAS). AIM is capable of detecting regional physiologicaldifferences and changes in an anatomical structure, and uses the informationfor diagnostic purposes in a subject/population whose clinical status isunknown. This technology can be utilized in a wide range of medical imaging,and detecting early responses in osteoporosis, osteoarthritis, aneurysmalchanges in vasculature of the brain, etc.

Advantages

·        Region Specific Analysis: More accuratecharacterizations of disease conditions and/or regional responses toprogression of treatments.

·        Anatomic Coordinate Reference: The meananatomic space (MAS) serves as an excellent reference point which allowsaccurate comparisons and precise calculation of statistical distributions for apatient.

·        Diagnostic and Prognostic Tool:Individual or population patient dataset can be analyzed in comparison over theMAS to quickly assess the current condition of the region of interest.

·        Electronically Stored and AccessedDatasets: The MAS can be compiled into an accessible dataset by population fordiagnosis.

Published Literature

1.       P.K.Saha, et al., “Active index model: a unique approach for regional quantitativemorphometry in longitudinal and cross-sectional studies”, in Proceedings ofSPIE: Medical Imaging, San Diego, CA 6512, 65121B1-12, 2006.

2.       PKSaha, G Liang, JM Elkins, A Coimbra, LT Duong, DS Williams, M Sonka, “A newosteophyte segmentation algorithm using partial shape model and itsapplications to rabbit femur anterior cruciate ligament transection viamicro-CT imaging” IEEE Transactions on Biomedical Engineering, 58(8),2212-2227, 2011.

3.       CLi, D Jin, C Chen, EM Letuchy, KF Janz, TL Burns JC Torner, SM Levy, PK Saha,“Automated cortical bone segmentation for multirow-detector CT imaging withvalidation and application to human studies”, Medical Physics, 42(8),4553-4565, 2015.

4.       PKSaha, Y Liu, C Chen, D Jin, EM Letuchy, Z Xu, RE Amelon, TL Burns, JC Torner,SM Levy, CA Calarge, “Characterization of trabecular bone plate-rodmicro-architecture using multi-row detector CT and the tensor scale:algorithms, validation, and applications to pilot human studies”, MedicalPhysics,42(9), 5410-5

*Licensing

Kenneth KaranjaLicensingAssociateUniversityof Iowa Research FoundationPhone(319) 335-4607kenneth-karanja@uiowa.edu

Country/Region

USA