A statistical model in detecting small blood vessels with Power Doppler Imaging

1. A statistical model in detecting small blood vessels with Power Doppler Imaging Department of Medical Biophysics 07/04/10

2. Outline • Introduction • Objective • Methods • Power Doppler Imaging • Example • Methods • Mathematical Model • Results • Discussion • Conclusion • Acknowledgements

3. Introduction • Angiogenesis • Cancer research • Imaging these small blood vessels can provide valuable information to their spatial distribution in the vasculature

4. Objective • To improve the statistical model in determining the blood flow in a small vessel • Develop another Gaussian distribution to account for the region that lies between the background and vessel

5. Methods and Apparatus

6. Power Doppler Imaging

9. Example

11. Methods • Flow phantoms were developed with the following properties; • vessel sizes: • 160, 200, 250, 300, 360 µm • flow velocity • 4, 3, 2, 1, 0.5 mm/s • transducer frequency • 30 and 40 MHz

12. Mathematical Model Single Vessel Multiple Vessel

13. Results

14. Results Consideration of the extra region lead to the statistical model, more closely reflecting the actual data

15. Discussion • Consideration of an extra region lead to the increase in accuracy between the statistical model and empirical data • Changes made are reflected by the considering a greater range of data • The standard statistical model for a specific vessel size can act to determine the actual vessel as opposed to the background

16. Further Research and Implications • Working with multiple layer tissue • Developing a standard model but taking into consideration the vessel sizes • Differentiation between vessels in tortuous vessels

17. Conclusion • Addition of a new region to the statistical model led to results which reflected the empirical data much closer

18. Acknowledgements • Dr. James Lacefield PhD • Mai Elfarnawany Masters Candidate

19. Questions and/or Comments?