No abstract

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.

Microcomputed tomography (micro-CT) is becoming a more common imaging technique in tissue engineering and has been used to characterize scaffold pore size, pore fraction, and bone ingrowth, among other characteristics. Despite the increasingly widespread use, no standards exist for segmenting images. Manual segmentation, a common segmentation method, is subjective, time consuming, and has been shown to be inaccurate and unreliable. The curve integration method was previously introduced as a method to accurately calculate the volume fraction of constituents in bone scaffolds from micro-CT data. In this article, the curve integration method is compared to manual image segmentation in order to validate the former method. Three cases are presented from two in vivo bone regeneration studies that include cross-sections from a rabbit calvarial defect used to study drug delivery, and cross-sections and small volumes of hydroxyapatite scaffold-bone composites from a porcine intramuscular study. The analysis shows that the curve integration method models the data accurately and can be used to calculate volume fractions of the materials in the sample. Furthermore, the curve integration method is faster and less labor intensive than manual image segmentation.