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Micro‐CT based quantification of non‐mineralized tissue on cultured hydroxyapatite scaffolds
Author(s) -
Hilldore Amanda,
Wojtowicz Abigail,
Johnson Amy Wagoner
Publication year - 2007
Publication title -
journal of biomedical materials research part a
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.849
H-Index - 150
eISSN - 1552-4965
pISSN - 1549-3296
DOI - 10.1002/jbm.a.31264
Subject(s) - materials science , intersection (aeronautics) , thresholding , biological system , attenuation , scaffold , biomedical engineering , histogram , gaussian , volume fraction , computer science , composite material , artificial intelligence , optics , image (mathematics) , chemistry , physics , medicine , computational chemistry , engineering , biology , aerospace engineering
An improved method to determine material volumes from microcomputed tomography (micro‐CT) data is presented. In particular, the method can account for materials with significantly overlapping peaks and small volumes. The example case is a hydroxyapatite scaffold cultured with osteoprogenitor cells. The histogram obtained from the micro‐CT data is decomposed into a Gaussian attenuation distribution for each material in the sample, including scaffold, pore and surface tissue, and background. This is done by creating a training set of attenuation data to find initial parameters and then using a nonlinear curve fit, which produced R 2 values greater than 0.998. To determine the material volumes, the curves that simulated each material are integrated, allowing small volume fractions to be accurately quantified. Thresholds for visualizing the samples are chosen based on volume fractions of the Gaussian curves. Additionally, the use of dual‐material regions helps accurately visualize tissue on the scaffold, which is otherwise difficult because of the large volume fraction of scaffold. Finally, the curve integration method is compared with Bayesian estimation and intersection thresholding methods. The pore tissue is not represented at all by the Bayesian estimation, and the intersection thresholding method is less accurate than the curve integration method. © 2007 Wiley Periodicals, Inc. J Biomed Mater Res 2007