1. A novel functional ultrasound image based on generalized Rayleigh scattering distribution for tissue characterization
以廣義雷利散射分佈為基礎之新世代功能性超音波影像
Po-Hsiang Tsui (崔博翔) and Chien-Cheng Chang (張建成)
Division of Mechanics, Research Center for Applied Sciences, Academia Sinica
中央研究院應用科學研究中心 力學與工程科學專題中心

2. Ultrasonic imaging Noninvasive Soft tissues Real time Portable
Non-ionizing
Resolution: < 1 mm

3. Fundamental of imaging
Scatterers
Ultrasound
transducer
Reflection
Scattering
Backscattered echoes
speckle
B-mode image
Reflected echoes

4. Ultrasonic imaging system

5. Shortcomings of ultrasound image
TGC
Image process
Operator-dependent
Qualitative information
Morphology analysis
Hard to characterize
scatterers
Gain
Low gain
High gain
Imaging display settings

6. Shortcomings of ultrasound image
B-scan (the same gain)
B-scan (the same gain)
Low scatterer
concentration
High scatterer
concentration
(but weak reflection
coefficient)
High scatterer
concentration
Low scatterer
concentration
(but the same reflection coefficient)

7. How to characterize scatterers by
B-scan data?

8. Backscattering distribution
If the resolution cell has a large number of scatterers (N scatterers), the complex ultrasonic echoes can be modeled as
According to central limit theorem, Ar and Ai are Gaussian distributed
random variables, the joint distribution of Ar and Ai is
Change from rectilinear to polar coordinate, eq. (2) can be
Rayleigh distribution A
p(A)
So the pdf of envelope A is the marginal density

9. Different backscattering conditions
Ultrasound
transducer
Scatterers
Pre-Rayleigh
Rayleigh
Post-Rayleigh

10. Generalized Rayleigh scattering model
Nakagami distribution
Γ(.): Gamma function
U(.): Step function
m < m = m > 1
R: Ultrasonic envelope
m : Nakagami parameter
Ω : Scaling parameter
E : Mean

11. Ultrasonic Nakagami imaging
- to visualize scatterer properties m mw
Envelope signal
Envelope image
Nakagami image
The appropriate size is determined when
(Local mean = global mean)
(sidelength = 3 times pulselength)

12. Simulation, animal model, and
clinical experiment

13. Nakagami imaging Low scatterer concentration (4/mm2)
High scatterer concentration (32/mm2)

14. Lens cataract PC Porcine lens Formalin solution to induce cataract
In vitro scan by a 35 MHz probe
saline
lens
capsule
40 mins
120 mins
Pulser/
Receiver
Diplexer
Transducer AD
converter
Data storage
Timer/
Counter
Motor
controller
Motor driver
Ultrasonic
motor
Encoder PC
Sync. trigger
Move transducer

15. Liver fibrosis Rat liver IMN injection to induce fibrosis
In vitro scan by a 5 MHz probe
Fibrosis scoring by doctors
Normal case
Fibrosis (score<1)

16. Tissue ablation Sample: pork tenderloin
Microwave ablation (2.45GHz, 60 W)
Imaging by portable system (7.5 MHz)
(Terason 2000)
B-scan
Nakagami image
Before before (antenna) heating heating heating and stop stop (antenna) stop
t t= sec sec sec sec sec

17. Breast tumors Patients come from Taiwan University Hospital
In vivo scan by Terason 2000
 Nakagami image
Pathology
Total
Malignant Benign
0.64
31 (TP)
9 (FP) 40
4 (FN)
26 (TN) 30 35 70
5 mm
At threshold = 0.64,
Sensitivity: 88.6%
Specificity: 74.3%
Accuracy: 81.4%
Fibroadenomas
Invasive ductal carcinoma

18. 3-D Nakagami image of rat liver
Potential:
Resolution improvement
Multi-directional information
Pathological model (e.g., fibrosis growth model)

19. Comparison between B-scan and Nakagami images
B-mode image
Nakagami image
Image pixel
Grayscale
Nakagami parameter
Image physical meaning
Echo intensity
Envelope statistics
Image type
Qualitative
Quantitative
Resolution
Relatively better
Relatively poor
Medical applications
Morphology analysis
Scatterer analysis

20. Summary and future works
Nakagami imaging (2-D and 3-D modes) reflects scatterer properties, having ability to characterize tissues and discriminate benign and malignant tumors.
Nakagami image can be complementary to the B-scan for morphology analysis and scatterers characterization
Potential for monitoring tissue treatment process
Developing very high frequency system for small scale analysis (e.g., cell)

21. Acknowledgements 中央大學數據分析方法研究中心: 黃鍔院士、張建中博士
台灣大學醫學院: 張金堅教授、 陳文翔醫師、郭文宏醫師、何明志醫師
南加州大學醫學工程系: 熊克平教授
台灣大學電機系: 李百祺教授
清華大學生醫工程與環境科學系: 葉秩光助理教授
輔仁大學電子工程系: 黃執中助理教授

22. Thank you for your attention