1. Mutual Information as a Measure for Image Quality of Temporally Subtracted Chest Radiographs Samantha Passen Samuel G. Armato III, Ph.D.

2. Introduction Commonly, radiologists compare multiple chest radiographs side-by-side Current Previous

3. Introduction Kano et al. (1994)- Temporal Subtraction Detect ribcage edges and denote “lung mask” Current

4. Introduction ROIs involved in nonlinear geometric warping to align previous image to current Current Warped Previous

5. Introduction Temporal Subtraction Image

6. Related Work Difazio et. Al (1997) demonstrated improved radiologist diagnostic accuracy with temporal subtraction images Ishida et. Al. (1999) used local cross-correlation method to maximize alignment Armato et al. (2006) – automated identification of registration accuracy Feature-based linear discriminant analysis Based on radiologist ratings of images

7. Motivation While temporal subtraction images effectively enhance areas of pathologic change, misregistration of the images can mislead radiologists in diagnosis by obscuring or creating interval change Mutual information as a metric to quantify misregistered cases

8. Related Work Mutual Information confirmed to: Coselmon et al. (2004)- register volumetric image data Sanjay-Gopal et al. (1999) – register mammograms Pluim et al. (2003) – transformation technique to align images

9. Mutual Information (MI) Joint Histogram: Lower Entropy------------------Higher Entropy (misregistration) Mutual Information:

10. Materials Radiologists rated 138 temporal subtraction images from 1.0-5.9 Rating= 1.0Rating= 5.8

11. Previous Methods Calculate the correlation of the two radiologists’ ratings= 0.785 Calculated correlation coefficient of NMI values and radiologist’s ratings Correlation = 0.649

12. Good Rating: 5, Bad MI: 1.135 Clear difference between the two images, not due to misregistration but to interval change

13. Good Rating: 5, Bad MI: 1.135 Clear difference between the two images, not due to misregistration but to interval change

14. Motivation for New Data C alculate the NMI on portions of the bottom removed Pathologic change Positioning of the body affects diaphragm Inaccurately defining inferior bottom ribcages

15. Previous Key Results Maximum correlation = 0.785

16. New Methods Same radiologist rated left and right lungs on subtraction image separately Calculate NMI on right and left lung

17. Results- Correlation Coefficient Right max: 0.746 Left max: 0.752 Average max: 0.782

18. New Methods Randomly divide 69 patients into 2 sets Training Set: 34 patients, ~66.5 pairs of images Testing Set 35 patients, ~71.5 pairs of images 20 different trials Calculated NMI on all 35 combinations of parameters of testing set Determine correlation coefficient Choose 3 trials with maximum correlation coefficient

19. New Method Apply these parameters to testing sets and calculate: Correlation coefficient Calculate predicted rating Use regression line from training set and substitute MI value from testing set ROC analysis

20. Results 0%10%20%30%40%50%60% Full Resolution------- 256 Gray Levels------- 128 Gray Levels-----23 64 Gray Levels-1--210 32 Gray Levels-11-41115

21. Results Sensitivity = TP/(TP + FN) Specificity = TN/(TN+FP) TP = Calculated Rating < 3, True Rating < 3 TN = Calculated Rating ≥ 3, True Rating ≥ 3

22. Results SpecificitySensitivity Correlation Coefficient Max0.9360.8500.864 Min0.6960.4400.632 Average0.8510.6670.785

23. Results

24. New Method Calculate normalized cross-correlation to compare usefulness of MI technique Only compute 1 cross-correlation value for each pair of image when directly aligned

25. Results All cross-correlation values range from 0.999-1.0 Correlation with Radiologist’s ratings = 0.035 – 0.180 No Information Gained

26. Conclusion Successfully demonstrated a correlation between MI and radiologist evaluation Calculating the NMI on the top 50% of the lung mask and scaling to 128 bins has a correlation of 0.785, comparable to that of the two radiologists

27. Conclusion Maximum A z value for 60 testing sets = 0.915 For training set, cropping 50% of the lung mask and scaling to 32 gray levels maximum correlation and A z Mutual information gives complimentary information to that of cross-correlation

28. Future Work Mutual information can be incorporated into existing temporal subtraction algorithm Calculate NMI on warped previous and current images Determine if predicted rating < 3 Re-warp or inform radiologists

29. Questions?