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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
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Outline
Motivation Purpose Related Work Methodology Results Post-Processing Analysis Conclusion
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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?
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Purpose Extension of previous work: Semantic
Mapping What CT parameters influence predicting of
Semantic Characteristics?
Raicu, Medical Imaging Projects at Depaul CDM, 2008
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Project Goals
Study the effects of CT parameters on semantic mapping.
Identify most important parameters. Normalize differences of these important
parameters.
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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
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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]
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Diagram of Methodology
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Methods: Data Collection Extracted DICOM header information Previous Work: Automatic feature extraction Merged header information with image
features.
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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
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Methods: Z Nodule Location
Lung Base: 5
Lung Apex: 1
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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
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Results: Texture DTConvolution Kernel
Reconstruction Diameter
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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)
- - -
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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
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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
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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)
- - -
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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?)
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Convolution Kernel
Reconstruction Diameter
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Results: Box PlotConvolution Kernel influencing intensity features for Texture DT
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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
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Box Plots: Normalized vs. Un-Normalized
Minimum Intensity BEFORE normalization
AFTER normalization
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Normalizing: No effectConvolution Kernel still appears
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Post-Processing: Binned Values
14 variables 10 Variables Equal-size binning (2-3 bins) Convolution Kernel:
Smoothing vs. Edge vs. Neither
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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
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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
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Future Work
Logistic Regression Perform similar experiment on a larger
dataset Normalize parameters so they no longer are
influential
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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.