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

Outline

Motivation Purpose Related Work Methodology Results Post-Processing Analysis Conclusion

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?

Purpose Extension of previous work: Semantic

Mapping What CT parameters influence predicting of

Semantic Characteristics?

Raicu, Medical Imaging Projects at Depaul CDM, 2008

Project Goals

Study the effects of CT parameters on semantic mapping.

Identify most important parameters. Normalize differences of these important

parameters.

Related Work

Effect on image quality1

Slice Thickness, Manufacturer, kVp, Convolution kernel

Effect on volumetric measurement2

Threshold, Section Thickness Manufacturer, Collimation, Section Thickness

Effect on nodule detection algorithm3

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

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]

Diagram of Methodology

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

features.

Methods: Data Pre-Processing

103 variables 14 variables

Eliminated if Unique identifiers Missing values Confounding

variables

1. Slice Thickness 2. Pixel Spacing 1

3. kVp 4. Pixel Spacing 2

5. Reconstruction Diameter 6. Bits Stored

7. Distance SourceToPatient 8. High Bit

9. Exposure 10. Pixel

Representation

11. Bit Depth12. Rescale Intercept

13. Convolution Kernel 14. Z Nodule Location

Methods: Z Nodule Location

Lung Base: 5

Lung Apex: 1

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

Results: Texture DTConvolution Kernel

Reconstruction Diameter

Results: CT parameters and semantic characteristics they predict for

Convolution Kernel

Reconstruction Diameter

Exposure Distance Source to Patient

Z Nodule Location

kVp Slice 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)

- - -

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

Results: Box Plots of Image Features

CT Parameters Image 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

Convolution Kernel

Reconstruction Diameter

Exposure

Distance Source to Patient

Z Nodule Location

kVp Slice 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)

- - -

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 3rd 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?)

Convolution Kernel

Reconstruction Diameter

Results: Box PlotConvolution Kernel influencing intensity features for Texture DT

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

Box Plots: Normalized vs. Un-Normalized

Minimum Intensity BEFORE normalization

AFTER normalization

Normalizing: No effectConvolution Kernel still appears

Post-Processing: Binned Values

14 variables 10 Variables Equal-size binning (2-3 bins) Convolution Kernel:

Smoothing vs. Edge vs. Neither

Results: Binned ValuesZ NoduleLocation

DistanceSource toPatient

KVP RescaleIntercept

Texture - - - -

Subtlety X - - X

Spiculation X X - -

Sphericty - - X -

Margin - - - -

Malignancy - - - -

Lobulation - X - -

-Eliminated! Convolution Kernel, Reconstruction Diameter, Exposure

-New parameter: Rescale Intercept

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

Future Work

Logistic Regression Perform similar experiment on a larger

dataset Normalize parameters so they no longer are

influential

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.