1. Review of Ultrasonic Imaging

2. Clinical Values of Ultrasound
Visualization of anatomical structures in real time.

3. Clinical Values of Ultrasound
Detection of blood flows, including direction, velocity distribution, variance and energy.

4. Clinical Values of Ultrasound
Estimation of mechanical properties such as strain, elasticity, attenuation, acoustic backscattering, …etc.
Treatment of diseased tissue by hyperthermia.
Treatment of stones by extracorporeal lithotripsy.

5. Characteristics of Ultrasonic Imaging
Real-time.
Reflection mode.
Non-invasive.
Access limited.
Body type dependent.

6. Factors of Image Quality
Spatial resolution.
Contrast resolution.
Temporal resolution.
Uniformity.
Sensitivity.
Penetration.

7. Spatial Resolution Lateral and elevational : diffraction limited.
Axial resolution : the width of the pulse.
Given limited total bandwidth, there exists a tradeoff between axial and lateral/elevational resolutions. Z Y X
3D sample volume

8. Lateral Resolution (X)
Diffraction limited.
Determined by frequency, active aperture size and depth.
Fixed transmit and dynamic receive focusing.
Is dynamic transmit focusing possible?
Is a bigger aperture always better?

9. Elevational Resolution (Y)
Fixed lens (geometric focus).
Determined by frequency, aperture size and depth.
2D array and alternative 1D array designs.
Geometric focus

10. Axial Resolution (Z) Pulse width (absolute bandwidth).
System and transducer bandwidth.
Transmit power.
Attenuation consideration.
Coded waveform – long pulse + large bandwidth.

11. Contrast Resolution
Contrast resolution is determined by both spatial resolution and speckle noise variations.
Speckle comes from coherent interference of diffuse scatterers. In-coherent processing must be used to reduce speckle noise.
There exists a tradeoff between contrast and spatial resolutions.

12. Contrast Resolution Contrast-to-Noise Ratio (CNR): On a log display I2

13. Contrast Resolution
Contrast resolution is primarily limited by speckle noise.
Speckle is a multiplicative noise.
On a logarithmic display,

14. Spatial vs. Contrast
Speckle noise is 4.34dB for true speckle, a figure of merit for detectability.
CNR increases as speckle noise decreases, generally resulting in loss in spatial resolution.
Both CNR and spatial resolution can be improved by reducing sample volume.

15. Speckle Reduction Techniques
Must be done in-coherently.
Spatial filtering, loss in spatial resolution.
Compounding:
Compound image of the same object with different speckle appearance.
Better edge definition.
Sub-optimal spatial resolution.

16. Frequency Compounding
In-coherently adding images acquired at different frequencies.
Loss in axial resolution.
Maximal reduction is N1/2. f

17. Spatial Compounding In-coherently adding images from different angles.
Loss in lateral resolution.
Improved edge definition.
Laterally or elevationally (with a 2D array).
Maximal reduction is N1/2.

18. Temporal Resolution
Temporal resolution is determined by acoustic frame rate. It is also related to spatial Nyquist criterion.
Temporal resolution is fundamentally limited by sound velocity but can be improved by signal processing in some cases.
There exists a tradeoff between temporal and spatial resolutions.

19. Increasing Frame Rate Smaller field of view.
Reduced transmit line number:
Spatial Nyquist criterion.
Parallel beamformation.

20. Parallel Beamformation
Simultaneously transmit multiple beams.
Interference between beams, spatial ambiguity.
t1/r1
t2/r2

21. Parallel Beamformation
Simultaneously receive multiple beams.
Correlation between beams, spatial ambiguity.
Require duplicate hardware (higher cost) or time sharing (reduced processing time and axial resolution). r1 r2 t

22. Image Uniformity
Image uniformity is usually referred to as the variations of the system’s point spread function throughout the entire image.
Factors of image uniformity include depth of field, pulse shapes and variations due to lateral displacements.
To achieve image uniformity, a sophisticated imaging system is required.
Image brightness uniformity is also desired.

23. Sensitivity
Sensitivity is defined in the context of the detection of weak signals.
Sensitivity is determined by transducer design and system’s dynamic range.
Sensitivity is particularly important in Doppler imaging and can be improved by signal processing.

24. Penetration
Penetration is determined by acoustic power delivered to the body on transmit and the dynamic range of the system on receive.
The transmit power is regulated for safety reasons. Hence, penetration must be improved without exceeding regulations.

25. Performance Issues in Doppler Ultrasound

26. Fundamental Tradeoffs
In pulsed modes (PW and color), maximum velocity without aliasing is
In pulsed modes, maximum depth of the Doppler gate is
Combining the above two equations, we have
In Doppler, the lowest acceptable frequency is usually used.

27. Fundamental Tradeoffs
Sample volume
Scatterer motion
The velocity (frequency) resolution is determined by the inverse of the smaller of transit time and observation time.
It may be preferable to increase the sample volume (i.e., degrade spatial resolution) in order to reduce spectral broadening (i.e., increase velocity resolution).

28. Fundamental Tradeoffs
Longer pulses (Doppler gates) are often used for increased SNR. Thus, axial resolution is degraded.
Higher frame rates require larger beam spacing. Thus, lateral resolution is not optimal.

29. Matched Filtering
A matched filter is a time-delayed version of the time reversed input signal.
A matched filter on the receiver maximizes the SNR given a transmit waveform.
By maximizing the SNR, both the frequency and the time errors can be reduced.
Gaussian signal gives the poorest estimation from this point of view.

30. Doppler Ambiguity Function
The Doppler ambiguity function is designed to evaluate the amount of ambiguity in both time and frequency given a transmit waveform. Matched filtering is typically assumed at the receiver end.
The total potential ambiguity is the same for all signals that possess the same energy. Therefore, the goal of choosing an “optimal”waveform is to distribute the ambiguity in an optimal way based on specific imaging requirements.

31. Doppler Ambiguity Function
Typical examples:
CW.
Single pulse.
PW waveform.
Color Doppler waveform.
Matched filtering may not be implemented in practical systems.

32. Design Problems Adaptive wall filter:
Design a wall filter that adaptively change the cut-off frequency based on characteristics of the Doppler signal.
Particularly effective for reducing “flash” artifact.

33. Design Problems High PRF (measuring high velocity at a large depth).
Strong signals from secondary gates at shallower depths.
The receiver needs to have very large dynamic range.
Digital system implementation.

34. Design Problems Simultaneous B and Doppler imaging: Duplex. Triplex.
B and Doppler interleave.
Recovery of missing samples.
Effects on spectral and audio quality.