Clinical Full Field Digital Mammography system performance evaluation

1. Evaluation of clinical Full Field Digital
Mammography with the Task-Specific-
System-Model-Based Fourier (TSMF)
SNR
Haimo Liu
Fischell Department of Bioengineering
University of Maryland-College Park
FDA CDRH OSEL/DIAM
Haimo Liu: haimo.liu@fda.hhs.gov

2. Outline
Task specific evaluation methodology
Development
Benefits of using phantom for FFDM image
quality assessment
Application on clinical systems
GE Senographe DS
Comparison between two image acquisition modes
Validation of the method
Hologic Selenia
Conclusion
2 Haimo Liu: haimo.liu@fda.hhs.gov

3. Background: current FFDM evaluation
FFDM system
performance
Clinical trials Mean pixel value, Fourier based Image based
standard deviation method method
Expensive,
time consuming
Detector based:
Diff. signal
MTF, NPS
Human observers: Pixel SNR Cov. matrix
radiologists
Limitations: No system
Suffer from model
background DQE
reader’s variability
variability, signal Hotelling SNR
shape and size Does not account
for scatter, focal Large number
spot unsharpness of images
3 Haimo Liu: haimo.liu@fda.hhs.gov

4. Motivation
Develop Fourier based
X-ray tube Compression paddle
evaluation methodology
for clinical FFDM systems
Entire FFDM image
acquisition
With phantom that models
breast attenuation
Objective, task based
Practical, collection of
limited number of images
Empirical model of the
system, results not limited
to specific settings
Detector Computer console
4 Haimo Liu: haimo.liu@fda.hhs.gov

5. Assumptions
Linear system
No non-linear/adaptive image processing
Cyclostationary system
Stationary noise
Shift-invariant system transfer function
Infinite detector area
5 Haimo Liu: haimo.liu@fda.hhs.gov

6. Phantom assembly
Inspired by the CDMAM
phantom
Same uniform background
Same HVL
Simulated signals
Uniform PMMA plates
Four 1 cm plates
Aluminum plate
0.5 mm thick
Model the Aluminum base
(where signals are
attached to) of the
CDMAM phantom
6 Haimo Liu: haimo.liu@fda.hhs.gov

7. Clinical systems
System GE Senograhe Hologic Selenia
DS
Detector Indirect Direct
Pixel size 0.1mm ×0.1mm 0.07mm ×0.07mm
Kvp 30 30
Target/Filt Mo/Mo Mo/Mo
er
Image A:Fine View Mode Phantom Mode
acquisition B:Standard Mode
mode
Grid Linear Cross-hatch grid
7 Haimo Liu: haimo.liu@fda.hhs.gov

8. System schematic
8 Haimo Liu: haimo.liu@fda.hhs.gov

9. Generalized Modulation Transfer Function (GMTF)
GMTF
The modulus of the Fourier transformation of the line
response function measured within the breast phantom
Five images of a copper edge placed between the PMMA
plates
Three detector entrance exposures
Convert to mean glandular doses
2D GMTF
Spline interpolation between 1D profiles along the two
axes
X direction: parallel to chest wall
Y direction: perpendicular to chest wall
9 Haimo Liu: haimo.liu@fda.hhs.gov

10. Evaluation of scatter removal method (GE)
MTF (x direction)
100 mAs, x-direction
Without phantom
System without grid gives
better performance
GMTF(x direction)
100 mAs, x-direction
With phantom placed in
the FOV
System with grid gives
better performance
10 Haimo Liu: haimo.liu@fda.hhs.gov

11. GMTF (x direction) for two clinical systems
GE
11 Haimo Liu: haimo.liu@fda.hhs.gov

12. GMTF along the two axes
12 Haimo Liu: haimo.liu@fda.hhs.gov

13. Generalized Normalized Noise Power Spectrum (GNNPS)
GNNPS
The square of the Fourier transformation of the system
noise measured at the center of the breast phantom
Five images of the background phantom
Three detector entrance exposures
Convert to mean glandular doses
13 Haimo Liu: haimo.liu@fda.hhs.gov

14. GNNPS (x direction) for two clinical systems
14 Haimo Liu: haimo.liu@fda.hhs.gov

15. Hotelling observer SNR
GMTF2 Difference signal2
2 2 Simulated gold
disc signals
inspired by the
CDMAM phantom
SNR² =
GNNPS
15 Haimo Liu: haimo.liu@fda.hhs.gov

16. Methodology validation – comparison between methods
Fourier based method with GMTF 2 FT[ΔSs ]2
SNR =
simulated signals (Ss): GNNPS
Phantom Image based method
(image based signal: SI):
SNR = ΔS I T K -1ΔS I
Fourier based method with FT[ΔSI ]2
phantom image based signals, SNR =
GNNPS ROI size 256×256: GNNPS256 256
Fourier based method with
FT[ΔSI ]2
phantom image based signals, SNR =
GNNPS ROI size 19×19: GNNPS19 19
16 Haimo Liu: haimo.liu@fda.hhs.gov

17. Methodology validation
17 Haimo Liu: haimo.liu@fda.hhs.gov

18. Contrast-Detail curve
Contrast-Detail
(CD) curve
Four alternative
forced choice (4
AFC) task
Link SNR to detection
probability
62.5% detection
probability as the
threshold
Threshold thickness
Generate CD curve
18 Haimo Liu: haimo.liu@fda.hhs.gov

19. Contrast-Detail curve: system performance prediction
CD curve at unit dose
SNR2 is linearly proportional
to dose
Normalize SNR by dose
Normalize CD curve by dose
Can be used to predict
system performance
Two clinical systems
Different noise and
deterministic properties
Identical CD curves
19 Haimo Liu: haimo.liu@fda.hhs.gov

20. Contrast-Detail curve: human performance prediction
Human performance
prediction (GE Senographe
DS)
SNR 2
Human
FHuman 2
30% 5%
SNR TSMF
TSMF CD curve at 51 μGy
TSMF CD curve (adjusted by
human efficiency) at 51 μGy
Human CD curve at 70 μGy (GE
Senographe 2000D)
Human CD curve at 140 μGy (GE
Senographe 2000D)
20 Haimo Liu: haimo.liu@fda.hhs.gov

21. Conclusions
Fourier based evaluation methodology for clinical FFDM
Provide more information of the system using phantom
Scatter from the phantom
Focal spot unsharpness
Magnification
Create a model of the system
Not limited to specific system settings
Predict system performance
Get closer to link image quality to diagnostic performance of the
system
Clinical application
GE Senographe
Hologic Selenia
21 Haimo Liu: haimo.liu@fda.hhs.gov