1. 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

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

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

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

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

6. 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

7. Statistical Relationships

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

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

10. 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

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

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

13. Comments?
Acknowledgments
Fredrick Phelan
Martin Chiang
NSF
NIST/ITL