Research Articles

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TOMOGRAPHY, March 2016, Volume 2, Issue 1: 17-25
DOI: 10.18383/j.tom.2016.00100

Gradient-Based Algorithm for Determining Tumor Volumes in Small Animals Using Planar Fluorescence Imaging Platform

Jessica P. Miller1,2, Christopher Egbulefu1, Julie L. Prior1, Mingzhou Zhou1, and Samuel Achilefu1,2

1Department of Radiology, Washington University School of Medicine, St. Louis, Missouri; and 2Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri.

Abstract

Planar fluorescence imaging is widely used in biological research because of its simplicity, use of nonionizing radiation, and high-throughput data acquisition. In cancer research, where small animal models are used to study the in vivo effects of cancer therapeutics, the output of interest is often the tumor volume. Unfortunately, inaccuracies in determining tumor volume from surface-weighted projection fluorescence images undermine the data, and alternative physical or conventional tomographic approaches are prone to error or are tedious for most laboratories. Here, we report a method that uses a priori knowledge of a tumor xenograft model, a tumor-targeting near infrared probe, and a custom-developed image analysis planar view tumor volume algorithm (PV-TVA) to estimate tumor volume from planar fluorescence images. Our algorithm processes images obtained using near infrared light for improving imaging depth in tissue in comparison with light in the visible spectrum. We benchmarked our results against the actual tumor volume obtained from a standard water volume displacement method. Compared with a caliper-based method that has an average deviation from an actual volume of 18% (204.34 ± 115.35 mm3), our PV-TVA average deviation from the actual volume was 9% (97.24 ± 70.45 mm3; < .001). Using a normalization-based analysis, we found that bioluminescence imaging and PV-TVA average deviations from actual volume were 36% and 10%, respectively. The improved accuracy of tumor volume assessment from planar fluorescence images, rapid data analysis, and the ease of archiving images for subsequent retrieval and analysis potentially lend our PV-TVA method to diverse cancer imaging applications.

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