A study on the effect of

daughterduckΠολεοδομικά Έργα

15 Νοε 2013 (πριν από 3 χρόνια και 11 μήνες)

116 εμφανίσεις

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 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

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 Depth

12. 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 DT

Convolution
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

Convolutio
n

Kernel

Reconstructi
on

Diameter

Exposur
e

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 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?)


Convolution
Kernel

Reconstruction
Diameter

Results: Box Plot

Convolution 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 effect

Convolution 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 Values

Z Nodule

Location

Distance

Source to

Patient

KVP

Rescale

Intercept

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.