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Updated: Mar 20, 2026
Computed Tomography-guided Time-domain Diffuse Fluorescence Tomography in Small Animals for Localization of Cancer Biomarkers
Published on: July 17, 2012
Gradient-Based Algorithm for Determining Tumor Volumes in Small Animals Using Planar Fluorescence Imaging Platform
Jessica P Miller1, Christopher Egbulefu2, Julie L Prior2
1Department of Radiology, Washington University School of Medicine, St. Louis, Missouri; Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri.
A new planar view tumor volume algorithm (PV-TVA) accurately estimates tumor volume from fluorescence images in cancer research. This method improves upon caliper-based measurements, offering a more precise tool for analyzing therapeutic effects in vivo.
Area of Science:
- Biomedical Imaging
- Cancer Research
- Optical Imaging
Background:
- Planar fluorescence imaging is common in biological research for its simplicity and high-throughput capabilities.
- Accurate tumor volume determination is crucial in cancer research using small animal models, but current methods using planar fluorescence images are often inaccurate.
- Existing alternatives like physical or tomographic methods are either error-prone or too time-consuming for routine laboratory use.
Purpose of the Study:
- To develop and validate a novel algorithm for accurate tumor volume estimation from planar fluorescence images.
- To improve the accuracy of in vivo tumor volume assessment in cancer research compared to existing methods.
- To provide a rapid, user-friendly, and archive-friendly tool for diverse cancer imaging applications.
Main Methods:
- A custom-developed planar view tumor volume algorithm (PV-TVA) was created utilizing a priori knowledge of tumor xenograft models and a tumor-targeting near-infrared probe.
- The algorithm processes near-infrared fluorescence images to enhance imaging depth within tissues.
- Results were benchmarked against actual tumor volumes determined by water volume displacement and compared with caliper-based measurements and bioluminescence imaging.
Main Results:
- The PV-TVA method demonstrated a significantly lower average deviation from actual tumor volume (9%) compared to the caliper-based method (18%).
- PV-TVA showed a 10% average deviation from actual volume, outperforming bioluminescence imaging (36% deviation).
- The algorithm offers improved accuracy, rapid data analysis, and ease of image archiving for subsequent analysis.
Conclusions:
- The developed PV-TVA offers a more accurate and efficient method for estimating tumor volume from planar fluorescence images in cancer research.
- This approach overcomes limitations of traditional methods, providing reliable data for evaluating cancer therapeutics in vivo.
- The PV-TVA method holds potential for broad application in various cancer imaging studies due to its accuracy and ease of use.
