Photoacoustic Imaging Paradigm Shift: Towards Using
Vendor-Independent Ultrasound Scanners
Haichong K. Zhang, Xiaoyu Guo, Behnoosh Tavakoli, and Emad M. Boctor
The Johns Hopkins University, Baltimore, MD 21218
{hzhang61, xguo9}@jhu.edu,
behnoosh.tavakoli@gmail.com,
eboctor1@jhmi.edu
Abstract. Photoacoustic (PA) imaging requires channel data acquisition
synchronized with a laser firing system. Unfortunately, the access to these
channel data is only available on specialized research systems, and most clinical
ultrasound scanners do not offer an interface to obtain this data. To broaden the
impact of clinical PA imaging, we propose a vendor-independent PA imaging
system utilizing ultrasound post-beamformed radio frequency (RF) data, which
is readily accessible in some clinical scanners. In this paper, two PA
beamforming algorithms that use the post-beamformed RF data as the input are
introduced: inverse beamforming, and synthetic aperture (SA) based
re-beamforming. Inverse beamforming recovers the channel data by taking into
account the ultrasound beamforming delay function. The recovered channel data
can then be used to reconstruct a PA image. SA based re-beamforming
algorithm regards the defocused RF data as a set of pre-beamformed RF data
received by virtual elements; an adaptive synthetic aperture beamforming
algorithm is applied to refocus it. We demonstrated the concepts in simulation,
and experimentally validated their applicability on a clinical ultrasound scanner
using a pseudo-PA point source and in vivo data. Results indicate the full width
at the half maximum (FWHM) of the point target using the proposed inverse
beamforming and SA re-beamforming were 1.33 mm, and 1.08 mm,
respectively. This is comparable to conventional delay-and-sum PA
beamforming, for which the measured FWHM was 1.49 mm.
1
Introduction
Photoacoustic (PA) imaging is an emerging image modality that visualizes optical
absorption property with acoustic penetration depth. PA imaging has a wide range of
applications from small animals to human patients [1-3]. However, there are several
factors that prevent PA imaging from being more widely used in clinical research and
applications. The first limitation is the laser. Most of the laser systems used for PA
imaging have high power and with low pulse repetition frequency (PRF). Those laser
systems are expensive, bulky, and non-portable. Thus, a portable low cost laser system
with sufficient power output such as a pulsed laser diode (PLD) is desired for easier
access to PA data acquisition [4]. The second limitation is in PA signals receiving. PA
signals contain broad-band spectral information, while conventional ultrasound (US)
has a frequency window for reception [5]. Although a narrow-band probe cannot
utilize the full potential of PA imaging, it still can receive signals, and is sometimes
useful for collecting signals from deeper regions. The previous two limitations have
been addressed or studied in the past research. The third limitation is the necessity of
using channel data, which has not been well studied. PA reconstruction requires a
delay function calculated based on the time-of-flight (TOF) from the PA source to the
receiving probe element [6-7], while US beamforming takes into account the round
trip initiated from the transmitting and receiving probe element.
Thus, the reconstructed PA image with US beamformer would be defocused due to
the incorrect delay function (Fig. 1a). Real-time channel data acquisition systems are
only accessible from limited research platforms. Most of them are not FDA approved,
which hinders the development of PA imaging in the clinical setting. Therefore, there
is a strong demand to implement PA imaging on more widely used clinical machines.
This third limitation motivated our research, whose goal is to investigate a vendor
independent PA imaging system.
Conventional photoacoustic
Imaging system
Clinical ultrasound system
Ultrasound
beamforming
PA Channel data
Inverse
beamforming
SA based
Re-beamforming
PA Channel data
Ultrasound beamforming
US beamformed PA data
Inverse
beamforming
PA Beamforming
PA Channel data
Defocused
image
Synthetic aperture
beamforming
PA beamforming
PA image
PA image
(a)
(b)
Fig. 1. Conventional PA imaging system (a) and proposed PA imaging system using
clinical US scanners (b). Channel data is necessary for PA beamforming because US
beamformed PA data is defocused with the incorrect delay function. The proposed two
approaches could overcome the problem.
To use clinical US systems for PA image formation, Harrison, et al. [8] proposed
to change the speed of sound parameter. However, the access to the speed of sound
parameter is uncommon, and the changeable range of this parameter is bounded.
Zhang et al. [9] proposed to use US post-beamformed RF data with a fixed focal
point. Our paper considers more general US beamformed data applied with
delay-and-sum dynamic receive focusing. Two PA beamforming algorithms are
introduced: inverse beamforming, and synthetic aperture (SA) based re-beamforming
(Fig. 1). US beamforming is a sequential process scanning line by line. Using those
sequentially beamformed data as input, inverse beamforming recovers channel data by
taking into account the delay function used to construct US post-beamformed RF data.
The recovered channel data can then be used to reconstruct a PA image. SA based
re-beamforming algorithm regards the defocused RF data as a set of pre-beamformed
RF data from virtual elements; an adaptive synthetic aperture beamforming algorithm
is applied on the RF data to refocus it.
In this paper, we first introduce the theory behind the proposed PA reconstruction
method. Afterwards, we present the evaluation of our method through simulation and
experiments that validate its feasibility for practical implementation.
2
Methods
2.1
Approach I: Inverse Beamforming
The idea of inverse beamforming is based on three hypotheses; 1. Localizing an
US point source does not require measuring its whole wavefront. 2. According to
Huygens-Fresnel principle, a non-point source can be considered as a cloud of
multiple point sub-sources. 3. Given the distribution, intensity and phase of the
sub-sources, the pre-beamforming data can be derived using the previous two
hypotheses.
According to Huygens-Fresnel principle, given any wavefront, each point on this
wavefront is a sub-signal source. Thus, it is possible to reverse the beam propagation
process, and reconstruct a map of the original signal source from beamformed RF
data. In this specific case, each pixel on this image is considered as a sub-signal
source. The value of the pixel represents the signal amplitude. Once the signal source
map is derived, we can “fire” an US pulse from each pixel. By summing up all the
time reversal wavefronts, and correcting the known distortion caused by the incorrect
beamforming, the original channel data can be derived.
Figure 2 shows the three steps of the inverse beamforming method. The first step
is to reconstruct the signal source map. Suppose at the beginning of the image frame
t0, a laser pulse is sent to the field of view (FOV) and stimulates PA waves. The PA
wave amplitude distribution at t0 is I(x, y) under the continuous geometry (x, y). The
US system receives the signal, performed the conventional pulse-echo beam forming,
and output an incorrectly constructed image A(xm, yn) under the discrete geometry (xm,
yn). If the FOV is quantized as an M by N grid, each cell can be viewed as a PA point
sub-source, and the distribution I(xm, yn) is the signal source map. The value of each
cell I(xm, yn) on the map indicates the sub-source intensity at that particular position.
For each sub-source (xm, yn), the intensity I(xm, yn) is derived by integrating along the
curve C1:
y
1
2
yn2   x  xm  ,
2
(1)
𝐼(𝑥𝑚 , 𝑦𝑛 ) = ∮𝐶1 𝐴(𝑥, 𝑦) × 𝑆(𝑥, 𝑦),
(2)
where S(x, y) is an amplitude correction factor to correct the wave intensity change
caused by the distance. The signal source map can be formed by repeating the
integration for all pixels. The second step is to mimic the PA data acquisition, and find
the signal value of each sampling point on a pre-beamformed image. As shown in Fig.
2, at t0, the signal source map in FOV is I(xm, yn), the PA waves from each sub-source
propagate through the media, and reach the US probe array at the top of the image. At
a given time t1, a particular array element at xm receives signal from a circle with a
radius y, where y = C * (t1 - t0). For each pixel of the recovered channel data geometry
P(xm, yn), we integrate along the circle C2:
𝑃(𝑥𝑚 , 𝑦𝑛 ) = ∮𝐶2 𝐼(𝑥, 𝑦) ×
1
𝑦2
,
(3)
where P(xm, yn) is the pixel amplitude received by the xm sample at time t1. The last
step is to repeat step two for all pre-beamforming image sampling points, so a
pre-beamformed image is reconstructed.
Beamformed ultrasound data
and real signal source
Integration
Creating signal source map
for each pixel
Recovered channel data
Integration
P(xm, yn)
A(xm, yn)
I(xm, yn)
Pre-beamformed
data
Fig. 2. Illustration of inverse beamforming processes.
2.2
Approach II: Synthetic Aperture Based Re-Beamforming
The difference between US beamforming and PA beamforming is the TOF and
accompanied delay function. US beamforming takes into account the TOF of the
round trip of acoustic signals transmitted and received by the US probe elements (that
is sent to and reflected from targets), while PA beamforming only counts a one way
trip from the PA source to the US probe. Therefore, the PA signals under US
beamforming is defocused due to an incorrect delay function.
The delay function in dynamically focused US beamforming takes into account the
round trip between the transmitter and the reflecting point, thus the focus point at each
depth becomes the half distance for that in PA beamforming. Thereby, it is possible to
consider that there is a virtual point source, of which depth is dynamically varied in
the axial dimension by a maximum value equal to half distance of the true focal point.
The TOF from the virtual element to the receiving elements can be formulated as
r
(4)
t ,
c
where
2
y 
r   n   xm2 .
 2 
Beamformed ultrasound data
with dynamic focusing
(5)
PA beamformed data
Re-beamforming
yN /2
Virtual
element
Defocused
wavefront
yN
Sequential beamforming
Fig. 3. Illustration of synthetic aperture based re-beamforming processes.
2.3
Simulation and Experiment Setup
For the simulation, five point targets were placed at the depth of 10 mm to 50 mm
with 10 mm intervals. A 0.3 mm pitch linear array transducer with 128 elements was
designed to receive the PA signals. Delay-and-sum with dynamic receive focusing and
an aperture size of 4.8 mm was used to beamform the simulated channel data
assuming US delays.
The experiment was performed with a clinical US machine (Sonix Touch,
Ultrasonix), which was used to display and save the received data. A 1 mm
piezoelectric element was used to imitate a PA point source target. The element was
attached to the tip of a needle and wired to an electric board controlling the voltage
and transmission. The acoustic signal transmission is triggered by the line trigger sent
from the clinical US machine. The US post-beamformed RF data with dynamic
receive focusing was then saved. To validate the channel data recovery through
inverse beamforming, the raw channel data was collected using a data acquisition
device (DAQ). For in vivo mouse experiment, a tumor mimicking prostate cancer
targeted by ICG was imaged using a 2 MHz center frequency convex probe.
3
Results
3.1
Simulation Analysis
The simulation results are shown in Fig. 4. The US beamformed RF data was
defocused due to an incorrect delay function (Fig. 4b). The reconstructed PA images
are shown in Figs. 4c-e. The proposed two approaches were compared to the ground
truth conventional PA beamforming using channel data. The measured full width at
the half maximum (FWHM) was shown in Table 1. The reconstructed point size was
comparable to the point reconstructed using a 9.6 mm aperture on the conventional PA
beamforming.
(a)
(b)
(c)
(d)
(e)
Fig. 4. Simulation results. (a) Channel data. (b) US post-beamformed RF data. (c)
Reconstructed PA image from channel data with an aperture size of 9.6 mm. (d)
Reconstructed PA image through inverse beamforming. (e) Reconstructed PA image
through SA re-beamforming.
Table 1. FWHM of the simulated point targets for corresponding beamforming methods.
FWHM (mm)
10 mm depth
20 mm depth
30 mm depth
40 mm depth
50 mm depth
3.2
Conventional
using Channel data
0.60
1.02
1.53
1.94
2.45
Inverse
Beamforming
0.62
1.06
1.39
1.76
2.11
SA
Re-beamfoming
0.63
0.99
1.43
1.91
2.42
Validation Using Pseudo-Photoacoustic Signal Source
US beamformed data was distorted due to incorrect delay (Fig. 5c), but both
algorithms were applied on the RF data. Comparing the channel data recovered
through inverse beamforming and the channel data from DAQ (Figs. 5a-b), identical
wavefront was confirmed, while there were intensity differences due to different noise
realization and recovery artifacts. However, this effect was negligible in the final PA
image (Fig 5e). The measured FWHM was also similar for both inverse beamforming
and SA re-beamforming compared to the ground truth result using channel data (Table
2). This indicates the proposed methods could replace conventional PA beamforming
using raw channel data.
(a)
(b)
(c)
(d)
(e)
(f)
Fig. 5. Experiment results with Pseudo-PA data. (a-b) Comparison of channel data. (a)
Reference channel data collected using DAQ. (b) Recovered channel data through
inverse beamforming from US post-beamformed RF data. (c) US post-beamformed RF
data collected from clinical US scanner. Reconstructed PA image using DAQ channel
data (d), inverse beamforming (e), and SA re-beamforming (f).
Table 2. FWHM of the point targets for corresponding beamforming methods.
FWHM (mm)
3.3
Conventional
1.49
Inverse Beamforming
1.33
SA Re-beamfoming
1.08
In Vivo Prostate Cancer Visualization
The tumor mimicking prostate cancer could be visualized in both approaches (Fig.
6). The main contrast features were well captured in both methods, while the
surrounding contrast varies due to different noise realization.
Tumor region
-13 dB
(a)
(b)
-10 dB
(c)
Fig. 6. In vivo evaluation results. (a) Experiment setup. Contrast agents (ICG) targeting
tumor are visualized. (b) PA image using channel data. (c) PA image through SA
re-beamforming.
4
Discussion and Conclusion
Although demonstration of PA image formation was done based on point targets,
the proposed algorithms would work for any structures that have high optical
absorption such as a blood vessel that shows strong contrast for near-infrared
wavelength light excitation. The algorithms could be also integrated into a real-time
imaging system using clinical US machines [10].
A high PRF laser system can be considered as a system requirement, as it is
necessary to synchronize the laser transmission to the US line transmission trigger. To
keep the frame rate similar to that of conventional US B-mode imaging, the PRF of
the laser transmission should be the same as the transmission rate, in the range of at
least several kHz. Therefore, a high PRF laser system such as a laser diode is
desirable. US transmission should be off or use low energy to eliminate the artifacts
from US signals.
In this paper, we proposed a new paradigm on PA imaging using US
post-beamformed RF data from clinical US systems. Two algorithms, inverse
beamforming and SA based re-beamforming, were introduced and their performance
was demonstrated in the simulation. In addition, experimental study using the
pseudo-PA signal source and in vivo targets reveals the validity and clinical
significance of these methods, in that a similar resolution was achieved compared to
conventional PA imaging using channel data. Future work includes implementing the
algorithm in a real-time environment.
Acknowledgement
Authors acknowledge Howard Huang for proofreading, and Dr. Ying Chen for assisting in vivo
experiment.
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