3D Image Quality Metrics for Porosity in Tissue Scaffolds

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Presentation transcript:

3D Image Quality Metrics for Porosity in Tissue Scaffolds Craig Schroeder Advisors: Ana Ivelisse Aviles - Statistical Eng. Div., ITL Marcus Cicerone - Polymers Div., MSEL August 11, 2005

Preliminaries Scaffold, tissue scaffold Image (three-dimensional) Porosity Volume of pore / total volume 1 - density

Porosity Measurement Measure porosity Scan in 3D scaffold MRI, CT scan Compute porosity

Problem Inaccuracy in computed porosity Scans contain noise Noise affects computation How accurate is the porosity?

Proposed Solution Develop an “Image Quality Metric” Number computed for image Higher quality indicates better accuracy 7 5 2 1 High Quality Low Quality

Metric Candidate One Find statistical relationships between Computed porosity Porosity after applying a filter Actual porosity Estimate difference between “Image Quality Metric” Original Noisy Stats Filtered IQM

Statistical Relationships

Theory of Observed Slopes Clear trends in least squares angle Explain trend with simple theory (left) Improve the theory for a better fit (right)

Power Spectrum Signal processing Amount of power broken down by frequency Computed from Fourier transform

Metric Candidate Two Compute “power spectrum” Find relationship between Computed porosity Actual porosity Power spectrum Use power spectrum to estimate error Original Noisy Stats Power Spectrum IQM

Noise Level vs Power Spectrum Need strong relationship between Noise level (x axes) Some aspect of the power spectrum (y axes)

Future Work Three weeks left! Write this up as part of a publication Consider permeability Improve second metric

Comments? Acknowledgments Fredrick Phelan Martin Chiang NSF NIST/ITL