1. A study on the effect of imaging acquisition parameters on lung nodule image interpretation Presenters: Shirley Yu (University of Southern California) Joe Wantroba (DePaul University) Mentors: Daniela Raicu Jacob Furst

2. Outline Motivation Purpose Related Work Methodology Results Post-Processing Analysis Conclusion

3. Motivation: Why are CT image acquisition parameters important? Studies develop CAD systems using images from one CT scanner Different CT scanners use different parameters. Do varying parameters affect the image features read by CAD systems? How do we know if these CAD systems apply to other CT scanners?

4. Purpose Extension of previous work: Semantic Mapping What CT parameters influence predicting of Semantic Characteristics? Raicu, Medical Imaging Projects at Depaul CDM, 2008

5. Project Goals Study the effects of CT parameters on semantic mapping. Identify most important parameters. Normalize differences of these important parameters.

6. Related Work Effect on image quality 1 Slice Thickness, Manufacturer, kVp, Convolution kernel Effect on volumetric measurement 2 Threshold, Section Thickness Manufacturer, Collimation, Section Thickness Effect on nodule detection algorithm 3 Convolution Kernel 1 Zerhouni et.al, 1982, Birnbaum et al, 2007; 2 Goo et. Al, 2005, Das et al, 2007, Way et al, 2008; 3 Armato et al, 2003

7. Methods: LIDC Dataset All cases from the LIDC Dataset: 85 cases 60 cases with 149 nodules Multiple slices per nodule Up to 4 radiologist ratings per nodule per slice [1]

8. Diagram of Methodology

9. Methods: Data Collection Extracted DICOM header information Previous Work: Automatic feature extraction Merged header information with image features.

10. Methods: Data Pre-Processing 103 variables  14 variables Eliminated if Unique identifiers Missing values Confounding variables 1.Slice Thickness2. Pixel Spacing 1 3. kVp4. Pixel Spacing 2 5. Reconstruction Diameter6. Bits Stored 7. Distance SourceToPatient8. High Bit 9. Exposure 10. Pixel Representation 11. Bit Depth 12. Rescale Intercept 13. Convolution Kernel 14. Z Nodule Location

11. Methods: Z Nodule Location Lung Base: 5 Lung Apex: 1

12. Results: Decision Tree Target Variables: Texture, Subtlety, Sphericity, Spiculation, Margin, Malignancy, Lobulation Specifications Cross-validation: 10 folds Growth Method: C &RT Max Tree Depth: 50 Parent Node: 5 Child Node: 2

13. Results: Texture DT Convolution Kernel Reconstruction Diameter

14. Results: CT parameters and semantic characteristics they predict for Convolution Kernel Reconstruction Diameter ExposureDistance Source to Patient Z Nodule Location kVpSlice Thickness Texture(0.032, 3)(0.018, 8)----- Subtlety(0.032, 3) (0.014, 8) -(0.022, 6)-(0.017, 10) -- Spiculation--(0.043, 2)(0.016, 6) --(0.016, 9) Sphericity----(0.019, 6)(0.036, 3) - Margin(0.020, 9)(0.019, 10)----- Malignancy--(0.015, 3)-(0.019, 6)-- Lobulation--(0.052, 2)(0.021, 6) ---

15. Outline Motivation Purpose Related Work Methodology Results Post-Processing Analysis Box plots: Analyze influence of CT parameters on image features Binning values: Minimize influence of wide- ranging values Conclusion

16. Results: Box Plots of Image Features CT ParametersImage Features Convolution Kernel (B30f, B31f, B31s, Bone, C, D, FC01, Stan) Gabor, Inverse Variance, Major Axis Length, Elongation, Compactness Reconstruction Diameter (260-390 mm)Markov Exposure (25-2108 mAs) Gabor, Minimum Intensity, Circularity, Homogeneity, Compactness kVp(120, 130, 135, 140)Elongation, Perimeter Z Nodule Location (1-5; 1= lung apex, 5 = lung base)Radial Distance, Minimum Intensity Distance Source to Patient (535, 541, and 570 mm)Contrast, Gabor

17. Convolutio n Kernel Reconstructi on Diameter Exposur e Distance Source to Patient Z Nodule Location kVpSlice Thickness Texture(0.032, 3)(0.018, 8)----- Subtlety(0.032, 3) (0.014, 8) -(0.022, 6) -(0.017, 10) -- Spiculation--(0.043, 2) (0.016, 6) --(0.016, 9) Sphericity----(0.019, 6) (0.036, 3) - Margin(0.020, 9)(0.019, 10)----- Malignancy--(0.015, 3) -(0.019, 6) -- Lobulation--(0.052, 2) (0.021, 6) ---

18. Post-Processing: Box Plots -Box plots on image features above and below the CT parameter split -Two graphs with no overlapping values: Radial Diameter for Exposure and 3 rd Order for Z Nodule Location -Number of cases in child node too small (2 or 3 cases) -Run box plot on all image features for leaf nodes < 2 cases and remaining cases (Are they outliers?)

19. Convolution Kernel Reconstruction Diameter

20. Results: Box Plot Convolution Kernel influencing intensity features for Texture DT

21. Post-Processing: Normalization Image feature values normalized between 0-1 Convolution kernel influences 6 intensity features Z-transformation to normalize curves: (X- avg)/ σ Distribution Curve for Min Intensity values before Normalizing After Normalizing

22. Box Plots: Normalized vs. Un- Normalized Minimum Intensity BEFORE normalization AFTER normalization

23. Normalizing: No effect Convolution Kernel still appears

24. Post-Processing: Binned Values 14 variables  10 Variables Equal-size binning (2-3 bins) Convolution Kernel: Smoothing vs. Edge vs. Neither

25. Results: Binned Values Z Nodule Location Distance Source to Patient KVPRescale Intercept Texture ---- SubtletyX -- X SpiculationXX -- Sphericty -- X - Margin ---- Malignancy ---- Lobulation - X -- -Eliminated ! Convolution Kernel, Reconstruction Diameter, Exposure -New parameter: Rescale Intercept

26. Conclusion Influential CT parameters Convolution Kernel Reconstruction Diameter Exposure Distance Source to Patient Slice Thickness kVp Z Nodule Location Influential CT parameters post-binning Z Nodule Location Distance Source to Patient kVp Rescale Intercept

27. Future Work Logistic Regression Perform similar experiment on a larger dataset Normalize parameters so they no longer are influential

28. References Horsthemke, William H., D. S. Raicu, J. D. Furst, "Evaluation Challenges for Bridging Semantic Gap: Shape Disagreements on Pulmonary Nodules in the Lung Image Database Consortium", International Journal of Healthcare Information Systems and Informatics (IJHISI) Special Edition on Content-based Medical Image Retrieval., 2008 Goo et al. “Volumetric Measurement of Synthetic Lung Nodules with Multi–Detector Row CT: Effect of Various Image Reconstruction Parameters and Segmentation Thresholds on Measurement Accuracy”, Radiology 2005 235: 850-856. Zerhouni et al. Factors influencing quantitative CT measurements of solitary pulmonary nodules. J Comput Assist Tomogr 1982; 6:1075-1087 Way, TW; Chan, HP; Goodsitt, MM, et al. “Effect of CT scanning parameters on volumetric measurements of pulmonary nodules by 3D active contour segmentation: a phantom study.” Physic in Medicine and Biology, 2008. 53: 1295-1312 Birnbaum, B; Hindman, N; Lee, J; Babb, J. “Multi-detector row CT attentuation measurements: assessment of intra- and interscanner variability with an anthropomorphic body CT phantom.” Radiology, 2007. 242: 110-119. Das, M; Ley-Zaporozhan, J; Gietema, H.A., et al. “Accuracy of automated volumetry of pulmonary nodules across different multislice CT scanners.” European Radiology, 2007. 17: 1979-1984. Armato, S G., M B. Altman, and P J. La Riviere. "Automated Detection of Lung Nodules in CT Scans: Effect of Image Reconstruction Algorithm." Medical Physics 30 (2003): 461-472.