Review of Ultrasonic Imaging
Clinical Values of Ultrasound
• Visualization of anatomical structures in real time.
Clinical Values of Ultrasound
• Detection of blood flows, including
direction, velocity distribution, variance and
energy.
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.
Characteristics of Ultrasonic
Imaging
• Real-time.
• Reflection mode.
• Non-invasive.
• Access limited.
• Body type dependent.
Factors of Image Quality
•
•
•
•
•
•
Spatial resolution.
Contrast resolution.
Temporal resolution.
Uniformity.
Sensitivity.
Penetration.
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.
Y
X
3D sample volume
Z
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?
Elevational Resolution (Y)
• Fixed lens (geometric focus).
• Determined by frequency, aperture size and
depth.
• 2D array and alternative 1D array designs.
Geometric focus
Axial Resolution (Z)
•
•
•
•
•
Pulse width (absolute bandwidth).
System and transducer bandwidth.
Transmit power.
Attenuation consideration.
Coded waveform – long pulse + large
bandwidth.
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.
Contrast Resolution
• Contrast-to-Noise Ratio (CNR):
CNR 
I1  I 2
 IA

I
I
N
• On a log display
 I1 
10 log  
I2 

CNR 
N
D
I2
I1
A
Contrast Resolution
• Contrast resolution is primarily limited by
speckle noise.
• Speckle is a multiplicative noise.
• On a logarithmic display,
 D  4.34dB.
Spatial vs. Contrast
I
10 log  1
 I2
CNR 
4.34



N
• 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.
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.
Frequency Compounding
• In-coherently adding images acquired at
different frequencies.
• Loss in axial resolution.
• Maximal reduction is N1/2.
f
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.
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.
Increasing Frame Rate
• Smaller field of view.
• Reduced transmit line number:
– Spatial Nyquist criterion.
– Parallel beamformation.
Parallel Beamformation
• Simultaneously transmit multiple beams.
• Interference between beams, spatial
ambiguity.
t1/r1
t2/r2
t1/r1
t2/r2
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).
t
r1
t r2
r1
r2
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.
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.
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.
Performance Issues in Doppler
Ultrasound
Fundamental Tradeoffs
• In pulsed modes (PW and color), maximum
velocity without aliasing is vmax   / 4PRI .
• In pulsed modes, maximum depth of the Doppler
gate is Rmax  c  PRI / 2.
• Combining the above two equations, we have
vmax  Rmax  c / 8.
• In Doppler, the lowest acceptable frequency is
usually used.
Fundamental Tradeoffs
Scatterer motion
Sample volume
Scatterer motion
Sample volume
• 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).
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.
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.
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.
Doppler Ambiguity Function
• Typical examples:
–
–
–
–
CW.
Single pulse.
PW waveform.
Color Doppler waveform.
• Matched filtering may not be implemented
in practical systems.
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.
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.
Design Problems
• Simultaneous B and Doppler imaging:
–
–
–
–
–
Duplex.
Triplex.
B and Doppler interleave.
Recovery of missing samples.
Effects on spectral and audio quality.