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\begin{document}

\title{U-Net enhanced real-time LED-based photoacoustic imaging}



\author[1]{Avijit Paul}%
\author[1]{Srivalleesha  Mallidi }%
\affil[1]{Tufts University}%


\vspace{-1em}



  
  \date{May 22, 2024}


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\selectlanguage{english}
\begin{abstract}
Photoacoustic (PA) imaging is hybrid imaging modality with good optical
contrast and spatial resolution. Portable, cost-effective, smaller
footprint LEDs are rapidly becoming important PA optical sources.
However, the key challenge faced by the LED-based systems is the low
fluence that is generally compensated by high frame averaging;
consequently reducing acquisition frame-rate. In this study, we present
a simple deep learning U-Net framework that enhances the signal-to-noise
ratio (SNR) and contrast of the low number of frame-averaged PA images.
The SNR increased by approximately 4-fold for both in-class in vitro
phantoms (4.39 \selectlanguage{ngerman}± 2.55) and out-of-class in vivo models (4.27 ± 0.87). We
also demonstrate the noise invariancy of the network and discuss the
downsides (blurry outcome and fails to reduce the salt \& pepper noise).
Overall, the developed U-Net framework can provide a real-time image
enhancement platform for clinically translatable low-cost and low-energy
light source-based PA imaging systems.%
\end{abstract}\selectlanguage{ngerman}%



\sloppy


Research article

U-Net enhanced real-time LED-based photoacoustic imaging

Avijit Paul\textsuperscript{1} and Srivalleesha
Mallidi*,\textsuperscript{1}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textsuperscript{1}Tufts University, Medford, MA, USA

\textbf{*~Correspondence}

Srivalleesha Mallidi,

Tiampo Family Assistant Professor,

Department of Biomedical Engineering,

4 Colby Street, 02155 Medford, USA

Email: srivalleesha.mallidi@tufts.edu

Photoacoustic (PA) imaging is hybrid imaging modality with good optical
contrast and spatial resolution. Portable, cost-effective, smaller
footprint LEDs are rapidly becoming important PA optical sources.
However, the key challenge faced by the LED-based systems is the low
fluence that is generally compensated by high frame averaging;
consequently reducing acquisition frame-rate. In this study, we present
a simple deep learning U-Net framework that enhances the signal-to-noise
ratio (SNR) and contrast of the low number of frame-averaged PA images.
The SNR increased by approximately 4-fold for both in-class in vitro
phantoms (4.39 \selectlanguage{ngerman}± 2.55) and out-of-class in vivo models (4.27 ± 0.87). We
also demonstrate the noise invariancy of the network and discuss the
downsides (blurry outcome and fails to reduce the salt \& pepper noise).
Overall, the developed U-Net framework can provide a real-time image
enhancement platform for clinically translatable low-cost and low-energy
light source-based PA imaging systems.

\textbf{KEYWORDS}

Photoacoustics; LED Acoustic-X; Non-learning noise removal methods;
U-Net; Signal-to-noise ratio. Peak signal-to-noise ratio;
Contrast-to-noise ratio; Frequency spectrum analysis

1 \textbar{} INTRODUCTION

The advances in laser and ultrasound technology enabled the development
of photoacoustic (PA) imaging, a concept coined by Bell {[}1{]}, into a
full-fledged non-invasive, non-ionizing and label-free {[}2{]} hybrid
imaging modality. PA imaging, armed with good optical contrast and high
spatial resolution, has a potential for clinical use as it facilitates
functional and physiological imaging at high penetration depth {[}3{]}.
Photoacoustic signals are generated when the laser pulse width satisfies
the thermal and stress relaxation confinement condition and typical
pulse widths are in the nanosecond range. Traditionally, tissues are
irradiated with nanosecond pulse laser illumination at energies in the
range of 20-35 mJ/cm\textsuperscript{2} at pre-determined wavelengths
based on the optical absorption properties of the sample {[}4-7{]}. A
transducer captures the generated photoacoustic signal that is then
reconstructed to form an image representative of the optical absorption
properties of the object. Traditionally, Nd-YAG pumped optical
parametric oscillator (OPO) lasers are used for photoacoustic imaging.
These are often expensive and until recently, were not mobile. In the
context of cost-effectivity, portability, and high pulse repetition
frequency (PRF), recently, light emitting diode (LED)-based illumination
systems {[}8-16{]} are emerging as a compelling solution for clinical
and preclinical use. The LED arrays, even with the above-mentioned
advantages, cannot provide a high fluence output similar to that
delivered by a laser. The low energy (in the order of nanojoules)
generated by the LED arrays is generally compensated by the high frame
averaging as the arrays can be operated at a high PRF of 1 - 4 kHz
{[}17, 18{]}. Increasing the number of frames will lead to an increase
in acquisition time, sometimes on the order of several seconds to
minutes for obtaining a 2D image. Understanding the functional dynamics
of biological events \emph{in vivo} requires real time imaging
capabilities and should not be limited by the time required for the
image acquisition process. Therefore, there is a need to significantly
improve the acquisition speed in PA imaging systems without compromising
the signal-to-noise ratio (SNR).

Several recent developments, including advances in array transducers,
light delivery systems and deep-learning strategies have occurred in the
field of PA imaging {[}19{]}. Specifically in the realm of machine
learning strategies, many deep-learning network models are being
investigated to improve PA image reconstruction methodologies
{[}20-23{]}. Several important studies, including recent reviews
{[}24-27{]} in deep learning with laser-based systems, addressed the
problem of under-sampled data sparsity due to the limited number of
detectors. While many studies demonstrated the results on numerically
simulated data and \emph{in vitro} phantoms {[}28-35{]}; Only a handful
of studies tested their deep-learning models on \emph{in vivo} data. In
cases where \emph{in vivo} data was used, both the training and testing
data sets were drawn from similar in-class sample data (for example,
training and testing were both done using similar phantoms or \emph{in
vivo} vasculature datasets) {[}36-40{]}. The above approach ultimately
puts a limitation on the generalizability of the deep network, and there
is also a huge burden of high training sample requirement {[}41, 42{]}.
Another mentionable fact is that all the above studies mainly
concentrated on laser illumination to alleviate the problem of limited
view under-sampled data and bandwidth correction but did not utilize the
LED-based illumination.

Currently, the main roadblock for LED-based PAI systems to make clinical
impact is its low signal-to-noise ratio due to low fluence. In this
study, we address this roadblock with deep-learning strategy -- i.e., by
implementing a U-Net architecture with data augmentation that is trained
on few datasets, can be employed on spatially invariant out-class
samples in real-time and is agnostic to various noise levels.
Specifically, we trained our U-Net architecture with input images
(averaging 128 frames, i.e., low frame average image) and labels
(averaging 25,600 frames, i.e., high frame average image) generated with
a LED-based photoacoustic system Acoustic X.

We performed \emph{in vivo} mouse subcutaneous tumor imaging with the
Acoustic X system and quantitatively compared the results on various
image metrics between our U-Net architecture and other traditional noise
reduction filters such as Median, Wiener, Total Variation (TV) and
Savitzky-Golay (SG) filters. Next, we did a comparative study of the
low, high no. of frame averaging and U-Net algorithms in frequency
domain regarding the magnitude spectrum and concluded the reason of the
blurring effects of the U-Net outcomes. Last, we also performed a study
to verify the noise distribution-type invariancy of the network with
Gaussian and Salt \& Pepper (S\&P) noise. Though U-Net is unable to
handle the S\&P noise, it would not possibly create any major hurdle in
real life clinics, as most of the electronic noise follow Gaussian noise
distribution, and our U-Net is well adapted to the Gaussian noise. In
summary, the simple U-Net architecture can provide high SNR
photoacoustic images by compensating for the low fluence in LED-based
systems and this might yield a closer step towards the clinical
translation (or from bench to bedside) of PA imaging even though the
network is susceptible to brightness contrast illusions and produces a
blurry image comparative to the ground truth.

2 \textbar{} EXPERIMENTAL METHODS

2.1 \textbar{} Imaging System

The LED-based photoacoustic system, Acoustic X, by Cyberdyne Inc was
used for capturing all the images reported in this study. The array
transducer used for imaging had a central frequency of 7 MHz (128
elements, \selectlanguage{english}-6 dB bandwidth is 80\%). The LEDs connected at around 43deg
angular position to the transducer produced 850 nm wavelength
irradiation with 30 nanosecond pulse width. A PRF of 4 kHz was utilized
in the study. Gain varied between 60 to 67 dB, depending on \emph{in
vitro} or \emph{in vivo} samples being imaged, and was kept constant for
a given experiment. The dynamic range was set to 19 dB for the \emph{in
vitro} - phantoms and 19 - 25 dB for the mouse tumor models
respectively. The low-frame rate was 30 Hz (images generated with 128
image frames being averaged) and 0.15 Hz (images generated with 25600
frames averaging) was considered as the high-frame rate respectively.
After capturing the data from the LED system, the beamformed images were
further processed with custom-written MATLAB (R2021b) code for cropping
and resizing to images to a 256 x 256-pixel image. All the image metrics
were calculated in MATLAB.

2.2 \textbar{} \emph{In vitro} tissue mimicking phantoms

Several cylindrical graphite powder phantoms in titanium oxide
(TiO\textsubscript{2}; Sigma Aldrich) scattering medium were prepared
inside a 6 well plate dish (Falcon\selectlanguage{english} 353046, Fisher Scientific).
TiO\textsubscript{2} (0.1\%) was first mixed with hot (43 -- 45 degC)
gelatine solution (8\% w/v) and then poured into the wells after the
temperature reached around 35 degC along with a cylindrical tube adjusted
at the centre to facilitate graphite inclusion in the centre. A portion
of the TiO\textsubscript{2} gelatine solution was mixed with graphite
powder at different concentrations of 0.01\%, 0.025\%, 0.05\% and 0.1\%
respectively, and poured into the hole created in the phantom.

2.3 \textbar{} \emph{In vivo} murine tumor models

Mice with subcutaneous xenograft tumors were used in this study to
evaluate our algorithm. Human pancreatic adenocarcinoma cells (AsPC-1)
were suspended in a mixture of Matrigel (BD Bioscience) and phosphate
buffered saline (1:1 v/v) at a density of 5 million cells per 100 \selectlanguage{greek}μ\selectlanguage{english}L and
injected subcutaneously in nude mice. At around 55-60 days post
inoculation, the tumors grew to approximately 300 - 400
mm\textsuperscript{3} volume with a heterogenous tumor microenvironment
containing both vascular and avascular regions. At the time of imaging,
the mice were anesthetized with 2\% isoflurane and rested on a
custom-made platform in a water bath with the head raised above the
water level. During imaging, the isoflurane was reduced to 1-1.5\% to
maintain general anaesthesia. A total of 8 mice were imaged in this
study. Three frames \textasciitilde{} 4-5 mm apart were acquired on each
mouse. All procedures were done in compliance with the Institutional
Animal Care and Use Committee of Tufts University.

2.4 \textbar{} Traditional denoising algorithms

Our network's performance was compared to several established
non-machine learning de-noising filters. We input our low number of
frame averaged images into the Wiener {[}46{]}, Median {[}47{]}, TV
{[}48{]}, and SG {[}49{]} filters. We used pixel-wise adaptive Wiener
filtering with a neighborhood of 5 x 5 window for the local mean and
standard deviation estimation. We estimated the additive noise and then
passed that noise information into the Wiener filtering using the MATLAB
``wiener2'' command. The median filter was achieved via the MATLAB
in-built ``medfilt2'' command. The primal-dual algorithm was utilized
for the TV-L1 denoising where the regularization parameter (\selectlanguage{greek}λ\selectlanguage{english}) was fixed
at 5 and the number of iterations was set to 200. The parameter values
are generally set by trial-and-error method and the lower value of \selectlanguage{greek}λ
\selectlanguage{english}denotes more aggressive de-noising which might produce a more smoothed
image. The SG filtering was implemented in MATLAB with the in-built
cubic ``sgolayfilt'' command where the polynomial order was set to 3 and
the frame length was set to 15.

2.5 \textbar{} Deep learning architecture

Following the work on biomedical image segmentation {[}50{]}, we built a
U-Net deep learning network to predict high-frame averaging spatial
images from the low number of frame-averaged ones. The U-Net code (main
model, training, data augmentation and testing) was written in a deep
learning API, Keras that runs on top of an end-to-end open-source
platform, TensorFlow. Google Collab platform with Python3 Google compute
engine backend GPU (Tesla K80, CUDA Version: 11.2, Intel(R) Xeon(R) CPU
@ 2.00GHz) has been utilized for this end. The fully convolutional
network U-Net comes with an encoding and a decoding part while the lost
part during the down-conversion of the image flows through the skip
connection (yellow arrow) to the corresponding up-converted layer as
shown in Fig. 1 and this concatenation of feature maps give localization
information. The input image was set to be 256 x 256 pixels. Each layer
of the network has two multichannel feature maps (deep blue box in Fig.
1) with varying numbers of feature spaces (starting from 64). The
convolution operation was applied with kernel sizes of 3 x 3 (green
arrow) along with the `same' padding so that we do not miss the corners
of the image. It was then passed through a non-linear (piecewise linear)
rectified linear unit (ReLU) activation function. After
the convolution and the non-linearization steps, the max-pooling was
done in a 2 x 2 stride (orange down arrow) which reduces the pixel
dimension to half of the original size and again the same feature space
convolution and pooling operation are repeated three times. From the
bottommost layer, sometimes known as the bottleneck layer, up-sampling
(black up arrow) is executed with the same stride as that of the
max-pooling and the feature space convolution operations are implemented
exactly similar to the contracting pathway at the corresponding
hierarchy. The only extra operation is the concatenation as denoted by
the light blue box in Fig. 1. At the end of the contracting path, there
are two dropout operations denoted by the brown arrows. The last
convolution layer is passed through the sigmoid activation
function which provides the predicted denoised outputs. Mean squared
error (MSE) was used as the loss function and the
replacement optimization algorithm --Adam with 1e-4 as the
initial learning rate was applied.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{FIGURE 1} ~The U-Net architecture used for the low-frame rate to
high frame rate beam-formed image prediction. This is a 4-layer deep net
architecture that takes 256 x 256 pixels as the inputs. Dilution or
dropout layers are added for regularization to avoid overfitting. Metal
plates and graphite powder phantoms imaged with Acoustic X system at
4kHz PRF and 128 frame averages are considered for the training images,
and the corresponding images with 25,600 frame averages at 4kHz PRF
(obtained without disturbing any equipment setup or phantoms) are the
training labels. The bottom panel showcases out-of-class testing data,
i.e., subcutaneous mouse tumor images used for testing our network.

2.6 \textbar{} Frequency spectrum analysis

We converted the low, high no. of frame averaging and U-Net generated
images from spatial domain to the frequency domain by \emph{fft2}command
in MATLAB. We also used \emph{fftshift} to rearrange the Fourier
transformed frequencies by shifting the zero-frequency component towards
the central region. For visualization purpose, we converted the
frequency array into the log scale adding 1 with the shifted values just
to avoid the zero-division error.

The training images consist of various metal plates, graphite powder
inclusion and gelatin-based graphite inclusion phantoms which were
captured with the low-frame averaging and the corresponding high-frame
averaged images were considered as labels (Fig. S1). The graphite rod
diameter was 0.5 mm. The gelatin phantoms with inclusion were made with
three different graphite powder concentrations -- 0.01\%, 0.025\%, and
0.05\% respectively that acted as our training samples. A snapshot of
some of the training images and their corresponding labels have been
provided in the supplementary document (Fig. S1). The paucity of proper
training samples necessitated implementing multiple ways of data
augmentation with which the network learned the spatial invariance and
robustness properties of the images. Rotation, pixel shifting,
horizontal flip, zooming, and shear have been performed in this respect.
The in vitro graphite powder phantoms were used as the validation set
for the hyper-parameter tuning like the batch size, optimized learning
rate determination and dropout probability fixation. Minimal to
negligible overfitting occurred in this network which can be confirmed
from Fig. S2 where the training and the validation loss did not diverge
significantly. 800 training datasets were used with 20 epochs and 300
steps per epoch and 80 datasets were used for the validation steps. For
the testing purpose, 8 mice tumor image data at different cross-sections
totalling more than 50 individual samples were utilized.

Several image quality metrics were utilized in this study. SNR was
determined as a ratio of actual signal from the object to the background
noise as per the formula {[}52{]}:

\(\text{SNR}=\ \frac{m_{\text{target}}}{\sigma_{\text{background}}}\),

where \(m_{\text{target}}\)is the mean intensity data of the region of
interest (ROI) and \(\sigma_{\text{background}}\) denotes the standard deviation of
the selected background region. Similar to SNR, another image metric
Peak SNR (PSNR) has been used {[}53{]}:

\(\text{PSNR}=\ \frac{max(\text{Target})}{\sigma_{\text{background}}}\),

where \(max(\text{Target})\) is the maximum intensity data of the ROI. CNR
determines how well the target object can be distinguished from the
background. The following formula has been used for calculating CNR
{[}52{]}:

\(\text{CNR}=\ \frac{m_{\text{target}}{-\ m}_{\text{background}}}{\sigma_{\text{background}}}\ \),

where \(m_{\text{background}}\) is the mean intensity data of the noisy
background.

3 \textbar{} RESULTS AND DISCUSSION

\textbf{3.1 \textbar{} Evaluation of U-Net architecture on
in-class\emph{in vitro} tissue mimicking phantoms}

The top row of Fig. 2 (a-e) shows the ultrasound images of two groups of
in vitro phantoms: one of them is the cross-section of thin wire
phantom, and the other is made with gelatin and TiO2 as acoustic and
optical scatterers. The low frame number averaged photoacoustic images
displayed in the second row of Fig. 2 have high background noise due to
the low pulse energy of the LED system. Upon tuning the hyperparameters
of our network, we fed the U-Net with the low number of frame averaged
LED images (Fig. 2 (f-j)) of in vitro phantoms. The images in the third
row (Fig. 2 (k-o)) are the corresponding high-frame averaged images of
Fig. 2 (f-j). The outcome of our network depicted in Fig. 2 (p-t) are
visually i.e., qualitatively similar to the high number of frames
averaged photoacoustic images. Furthermore, we also observe significant
reduction in the noise in the U-Net outcome compared to the low frame
number averaged images. Another polyvinyl acetate-based text logo with
gelatin embedded 3D phantom (See Fig. 3) was raster scanned with 29
cross-sectional frames where one sample cross-section plane is
demarcated in yellow dotted line. The left column shows a 2D
representation of a plane and the right column shows the 3D view. The
imaging planes are spaced 1 mm apart from each other. We compare the
imaging speed for capturing the 3D view with three different modes - low
no. of frame averaging, high no. of frame averaging, and U-Net outcomes.
Low no. of frame averaging took approximately 15.5 sec (frame rate: 30
Hz, and 0.5 sec for the motor movement at each imaging cross-section)
whereas high no. of frame averaging took 274.5 sec (frame rate: 0.15 Hz,
and 0.5 sec for the motor movement at each imaging cross-section). Our
U-Net took almost same time as low no. of frame averaging process
because testing the deep learning networks with GPUs consume only 0.25
millisecond per frame (We just have to capture the low no. frame
averaging images and run our U-Net as a downstream module). We compared
the noise level distribution in the low number of frame averaged inputs
and the U-Net output and representative photoacoustic images are shown
in Fig. S3(a). The ROI (blue square) of 8 x 8 pixels represented the
background. The noise level in the ROI for the Low frame number averaged
image input and U-Net output for all the in vitro samples are shown in
Fig. S3(b). Clearly the noise levels in the U-Net outcome image were
significantly lower than the input image. Specifically, an approximately
8-fold (8.57 \selectlanguage{ngerman}± 3.5) reduction in the noise levels was observed in the
U-Net output images.

\textbf{3.2 \textbar{} Validating U-Net architecture with out-of-class
data (photoacoustic images of \emph{in vivo} mouse tumors)}

The subcutaneous mouse tumors were imaged with Acoustic X system under
LED illumination. A representative schematic of the parameters used for
low frame averaged image for a certain cross-section of the tumor has
been provided. Clearly the blood vessels in the tumor that absorb the
LED illumination generate photoacoustic response that is shown with
yellow arrow in Fig. 4(a). The rightmost image denotes the outcome from
our U-Net whereas the high frame averaged image is shown at the bottom
of Fig. 4(a). Similar subcutaneous tumors have been imaged for several
mice (n=8) out of which three representative images are shown in
different columns (Fig. 4 (b-m)). The first row (Fig. 4 (b-d)) shows the
respective ultrasound images of the tumor, second row (Fig. 4 (e-g))
denotes low number of frame averaged images of the tumors, and the last
row (Fig. 4 (k-m)) has the corresponding high-number of frames averaged
photoacoustic images. Our U-Net outcomes are given in the middle row
(Fig. 4 (h-j)). The deep network model can remove the noise
significantly enough to qualitatively be similar to the high-frame
averaged image where the PA signals from the blood vessels are retained.
Given the importance of enhancing SNR in our images, we compared the
background noise distribution for the low number of frame averaged input
images with the corresponding U-Net outcome image. In Fig. S3(c), we
showed a low number of frame averaged image and the blue ROI that is
considered to calculate the background noise level. The noise level of
the same region in the corresponding U-Net provided output image was
also calculated and plotted in Fig. S3(d). Both the high-frame averaged
images and our U-Net model outcomes are visually congruent to each other
with respect to noise levels where U-Net images had 7-fold lower noise
(7.72 ± 3.07) than the low number of frame averaged image.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{FIGURE 2} (a-e): Ultrasound images of the cross-section of thin
wire phantoms in longitudinal (a), and transverse mode (b, c), graphite
inclusion with 0.025\% graphite and 0.01\% TiO2. The base of the
phantoms has 8\% gelatin. (f-j) Corresponding photoacoustic images of
obtained with the Acoustic X system with LED illumination. The PRF was 4
kHz, and 128 frames were averaged. (k-o): These PA images were captured
at the PRF of 4kHz with 25,600 frames averaged at the same cross-section
as that of (a-e). (p-t): Images predicted by the U-Net deep learning
technique where the SNR is improved compared to the corresponding low
number of frame averaged image.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{FIGURE 3} (a) Photograph of the 3D phantom and the 2D
cross-section view in yellow dotted line whose scanning plane is imaged
with LED-based PA system. (b) The corresponding US image. (c, d) Low
number of frame averaging images in 2D and 3D view. (e, f) Corresponding
high number of frame averaging images in 2D and 3D view. (g, h) The
U-Net outcomes in 2D and 3D view.

We calculated various quantitative metrics to measure the enhancement of
image quality in the U-Net images over the low number of frame averaged
image inputs. In Fig. 5 (a) and 5 (e), a representative US image and a
low-averaged photoacoustic image of in vitro phantom (graphite rod
embedded into gelatin base) and \emph{in vivo} subcutaneous tumor are
shown where the orange ROI box denotes the background, and the green ROI
box signifies the target signal ROI.

We considered three image quality metrices namely SNR, PSNR and CNR for
the analysis. There was a statistically significant difference for all
the metrics between the U-Net and low frame average images (Student's
t-test with 99\% confidence level (\selectlanguage{greek}α \selectlanguage{english}= 0.01)). The SNR improvement
achieved with U-Net architecture as compared to the low number of frame
averaging is approximately 4.39 \selectlanguage{ngerman}± 2.55-fold. Similar to Fig. 5 (a-d),
the data calculated from 24 mouse tumor images (8 mice x 3
cross-sections/mouse) is shown in Fig. 5 (e-h). All the metrices were
calculated considering various tumor ROIs and background zones. There is
approximately a 5-fold increase (4.27 ± 0.87) in SNR between low number
of frames averaging and the U-Net outcomes. Clearly, we also notice no
significant difference in the image quality metrics observed between the
U-Net and the high number of frame averaged images.

\textbf{3.3 \textbar{} Comparison of U-Net architecture outcome with
traditional noise removal algorithms}

Our U-Net architecture is compared to the outcomes generated by the
traditional algorithms like Wiener, Median, TV and SG filtering that
were not reported in previous studies involving deep learning
technologies and LED based photoacoustic images {[}54, 55{]}. The result
of the subcutaneous tumor images of three different mice applying all
those algorithms along with the low number of frame averaged inputs is
shown in Fig. 6. It is to be noted that the varying amount of noise for
each of the input images was intentionally chosen to evaluate noise
invariancy of our U-Net algorithm with respect to the noise levels. The
noise level for mouse 2 is the lowest among the three data sets shown
and that of mouse 3 is highest (achieved by varying the gain range). The
images for mice \#1, \#2, and \#3 were acquired with 22-, 19-, and 25-dB
gain, respectively. We did a quantitative comparison of all the
different noise removal algorithms along with our U-Net and the box
plots for PSNR, SNR and CNR are shown in Fig. 6 (h), 6 (p), \& 6 (x),
respectively. When we compared the performance of our model network with
the traditional noise removal algorithms like Wiener, Median, TV, and SG
filter, the U-Net images produced a better-enhanced version of outputs
than the other ones which can visually be confirmed from Fig. 6 (b-g,
j-o, and r-w). The different noise levels in Fig. 6 (b), (j), \& (r) did
not affect our network outcomes, which makes the U-Net framework a
noise-level invariant model, while other established algorithms are not
robust enough to deal with different kinds of noise profiles. In
comparison with the other algorithms, U-Net emerges not only as a
noise-robust process, but it maintains the original phantom structure
that other methods are incapable of achieving. Wiener and SG filters
blur the images along with SG showing an artifact similar to hazy motion
artifact during acquisition. There is not much improvement in the signal
and contrast concerning the background noise for the Median filter, a
traditionally used noise filter. The TV-L1 filter reduced the noise
homogeneously, however only the brightest parts of the images remained
intact and a significant enhancement in background occurred in the
entire image.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

.

\textbf{FIGURE 4 (} a) LED-based photoacoustic images of subcutaneous
tumors in mouse are taken and the several cross sections were captures
in direction from tail towards head. Low number of frame averages:
Images captured with LED illumination by the Acoustic X system, where
the LEDs had PRF of 4 kHz and 128 frames were averaged. High number of
frame averages: The PA image was captured at the PRF of 4kHz with 25,600
averaging at the same cross-section as that of low-frame average image.
U-Net outcomes: Image predicted by the U-Net deep learning technique
where it improves the SNR of the corresponding low number of frame
averaged image. Photoacoustics images across different subcutaneous
tumor cross-sections for three mice. (b-d): The ultrasound images of
three different tumors are shown where each tumor is demarcated with
yellow-dotted circle. The tumors are placed at different distances from
the transducer and LED array to achieve different light fluence
deposited on the tumor. (e-g): Images obtained with low number of frame
averages. (h-j): Images predicted by the U-Net deep learning technique
where it improves the SNR of the above corresponding (Fig. 4 (a-c)) low
number of frame averaged images. (k-m): PA images of the corresponding
cross-sections as that in (Fig. 3 (e-g)) with high number of frame
averages.

Illumination sources such as the LED arrays are becoming popular sources
for PA imaging compared to the lasers due to their light weight, small
footprint, low cost, longer lifetime, easier portability, etc. The low
fluence generated by the LEDs is a main concern that is currently
circumvented by acquiring high number of frames, i.e.., averaging
\textasciitilde{}25,600 frames that corresponds to 0.15 Hz. Reducing the
number of frames to be averaged, though increases the acquisition speed,
significantly decreases the SNR of the images making noise-free
real-time imaging arduous. Here we developed a deep learning-based
simple U-Net framework capable of providing a denoised image in
real-time. Many deep-learning models have already been devised with the
laser (higher energy output than LEDs) to handle the problem of
under-sampling due to the limited-view dataset {[}32, 41{]}, but most of
the studies tested their models on in vitro phantoms. Furthermore, there
are no \emph{in vivo} studies reported where training samples were drawn
from the different in-class distributions or from out-class training
data. A mentionable fact is that our training and testing set are not
drawn from a similar dataset as implemented in other studies
{[}54-56{]}, but rather metal plates and graphite powder-based phantoms
were used in the training phase while the \emph{in vivo} mouse tumor
images were utilized as the testing dataset.

Recently, few research studies demonstrated the concept of compensating
the low-fluence problem of the LED-based systems with deep learning
{[}54-57{]}. In the study by Hariri et al {[}57{]} and Anas et. al.
{[}54{]}, the complexity of the network necessitated the use of huge
training data to converge. The U-Net architecture presented in this
study required a very low amount of training dataset (it can have as low
as 800 training data sets even though we performed various data
augmentations like rotation, pixel shifting, horizontal flip, zooming,
shear etc.). Capturing the training data can also be easily achieved as
we have used only one LED system for both the training images and labels
without even modifying any setup parameters because the low and high
frame averaging is done by the Acoustic X software and its internal
hardware. Compared to Anas et al {[}54{]}, our U-Net frame gain rate
(which is 25,600 / 128 = 200) is on the higher side and the training
data volume is comparatively low. In other studies, by Manwar et. al.
{[}56{]} and Singh et. al. {[}55{]} the testing was done with only the
in-class samples which might limit the generalizability of the network
when applied in different and unknown \emph{in vivo} samples. Our model,
on the other hand, is simple and requires less amount of easily
replicable training data and was tested with both the in-class and
out-of-class samples at different noise levels to achieve a more
generalized framework. When we compared the published result of Vu et.
al. {[}38{]} with our model, we found that our model provides
significantly high SNR and contrast to noise ratio (CNR). The data
augmentation in the form of various spatial deformation of the training
images namely rotation, sheer, zoom, flip, height and width shift
generated more different data with which the network was trained. The
invariance to multiple geometric transformations might make the U-Net
robust and less training data hungry {[}50{]}. The contracting path of
the U-Net architecture is designed for achieving higher level features
compromising locality while the up-sampling path integrates those poorly
localized high-level features into the original image and regains the
lost information in each of the max-pooling steps by the skip
connections of corresponding hierarchical layers.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

\textbf{FIGURE 5} (a) The panel shows US image and low no. of frame
average PA images of a sample in-vitro graphite rod phantom (left
panel), and a subcutaneous mouse tumor (right panel), respectively. The
background is indicated by the orange square and the signal ROI is
denoted by the green square. (b-d) The box plot shows the SNR, PSNR and
CNR values over the specified ROI in the image (a) for the low number of
frame averaged LED acquired images and the images that are predicted by
the U-Net deep learning technique. There is significant improvement
(p\textless{}0.01) in the SNR, PSNR and CNR compared to the
corresponding low number of frame averaged images. (e) A representative
low number of frames averaged LED image of a mouse subcutaneous tumor.
(f-h) The corresponding SNR, PSNR and CNR plots, respectively for the
images acquired on subcutaneous tumors. Data points beyond the
interquartile range are displayed using red plus. Student's t-test has
been used for the statistical analysis of all the data.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

\textbf{FIGURE 6} (a) US image of a subcutaneous tumor in a mouse. (b)
Low number of frames averaged (LA) beamformed LED-based photoacoustic
image of the tumor in a mouse. (c-g) The post-processed image with our
devised U-Net (UN) deep network model (c), Wiener filter (WN) (d),
Median filter (MD) (e), TV denoising approach (f) and SG filter (g)
respectively. (i-o) \& (q-w) The image panels arrangement is similar to
the left column of Fig. 5 (a-g); The only difference is that they are
obtained from different cross-sections on two other mice. (h, p \& x)
The corresponding PSNR, SNR and CNR plots for various algorithms
calculated from 24 images (8 mice x 3 cross-sections per mouse). One-way
ANOVA multiple comparison test yielded that U-Net had significantly
higher SNR, PSNR and CNR compared to other noise reduction techniques.

\textbf{3.4 \textbar{} Fourier analysis to decipher effects of blurring
in the images}

In general, we observed that the outcomes of U-Net are blurry compared
to the ground truth despite significant removal of background noise. To
understand the effects of blurring for U-Net outcomes in more details,
we analyzed the scenario in frequency domain. The 2D frequency domain
images with magnitude spectrum are shown in 2nd, and 4th rows
corresponding to the spatial images depicted in 1st, and 3rd rows,
respectively of Fig. 7. The 1\textsuperscript{st} row represents
graphite lead phantom, and 3\textsuperscript{rd} row depicts
subcutaneous mouse tumor image. The inner dotted circular cores mainly
cover the low frequencies while the outside of the inner cores indicate
high frequencies. According to O' Hare et al {[}58{]}, the amplitude
reduction at high spatial frequency zone indicates a degree of perceived
blurriness of an image.

We believe that noise is generally distributed within the outer dotted
circular region which is reduced (substantiated by lower spectrum
magnitude than low no. of frame averaging) by both high no. of frame
averaging and U-Net. As the magnitude spectrum of the high frequency
region between two dotted circles (indicated by red arrows in Fig. 7) is
attenuated more by both U-Net algorithm than the high no. of frame
averaging, the U-Net outcomes are comparatively blurrier than high no.
of frame averaging images. The inset zoomed-in spatial domain images in
white boxes in 3\textsuperscript{rd} row pictorially shows the blurring
effects. Lower frequencies for both the cases are somewhat preserved.
The U-Net outcomes also show small edge effects which the high no. of
frame averaging images does not possess. Another important fact to note
is the high diagonal frequencies of the high no. of frame averaging and
U-Net outcomes generate less magnitude than low no. of frame averaging.
It suggests that the averaging process degrades the high diagonal
frequencies. We tried to quantify the degree of blurring by relative
decay of sharpness in the U-Net outcomes compared to the high no. of
frame averaging ones as mentioned in {[}59{]}, and found a 25.5 \selectlanguage{ngerman}± 4.99
\% sharpness decrease in U-Net with respect to the high no. of frame
averaging. The representative tumor images are provided in supplementary
figure S4.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image8/image8}

\end{center}
\end{figure}

\textbf{FIGURE 7} Spatial and frequency domain analysis of low, high
number of frames averaging and U-Net outcomes.

The issue of blurring or degeneration of sharpness can be mitigated by
an additional downstream sharpening filter {[}60{]}, or modifying the
architecture with a generative model {[}61{]} approximating the
probability distribution of the training data.

\textbf{3.5 \textbar{} Evaluation of the deep learning network
invariance with various noise distributions}

Lastly, we evaluated the network's invariancy with respect to different
noise types such as Gaussian, S\&P, etc. As observed in Fig. 8, the
images in the 2\textsuperscript{nd} row are corrupted with Gaussian
white noise (variance = 0.01), and the 4\textsuperscript{th} row
displays S\&P noise corrupted images of subcutaneous tumors. The
3\textsuperscript{rd} and 5\textsuperscript{th} rows are the
corresponding denoised U-Net outcomes. Our denoising U-Net efficiently
removed the Gaussian white noise whereas it is unable to reduce S\&P
noise types. The reason for the good performance of the U-Net in
removing the Gaussian noise is mainly because the noise distribution of
the training images captured from the LED Acoustic-X system somewhat
looks similar to the Gaussian noise type which essentially holds the
i.i.d (independent and identically distributed) assumption. However, the
network is not effective at removing the S\&P noise which needs further
investigation. Generally, the electronic noise comprises of a variety of
thermal and shot noise profiles that can be approximated to Gaussian
distribution, and thus our denoising algorithm will not have any
difficulty in handling such types of noise. Due to this capability, the
U-Net algorithm developed here has a potential of being an electronic
platform independent denoiser and further studies are under way to
evaluate this feature.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image9/image9}

\end{center}
\end{figure}

\textbf{FIGURE 8} Top row: US image of subcutaneous tumors of three
different mice. (Row starting with d) Gaussian white noise corrupted
images being fed to denoising U-Net to generate corresponding outcomes
in (Row starting with g). (Row starting with j) S\&P noise corrupted
images being fed to denoising U-Net to generate corresponding outcomes
in (Row starting with m).

4 \textbar{} CONCLUSION

In this study we developed a simple, easily trainable and generalized
deep learning U-Net model to denoise LED-based photoacoustic images
obtained with low number of frame averages. The portable and
cost-effective LED-based systems have already shown tremendous promise
in the preclinical and clinical arena despite many challenges like low
fluence, wider pulse width, and non-tunability of wavelengths.
Particularly, the low energy condition is compensated by high number of
frames averaging at the expense of low temporal resolution. Our U-Net
architecture achieves high SNR in LED-based photoacoustic imaging with
low number of frames enabling real-time implementation. The present
study discussed two prominent downsides to the U-Net framework, namely,
that it can produce blurry outcomes while de-noising the images and it
falls prey to S\&P noise while being invariant to Gaussian white noise.
In our current study we acquired the images with only one type of
transducer, i.e., a transducer operating at 7 MHz. Our future studies
will involve testing the architecture on data acquired with different
frequency transducers and LED array configurations. Furthermore our
future work will also incorporate transfer learning and network
downsizing along with building a proper training database, that can
eventually promote more generalized version of our network for different
types of tumors and \emph{in vivo} applications.

ACKNOWLEDGMENTS

The authors would like to acknowledge support from Tufts School of
Engineering, Tufts Data Intensive Science Center Pilot grant, NIH grant
and subcontract funds on NIH grant 5R01CA231606. The authors would also
like to acknowledge Mr. Marvin Xavierselvan for help with tumor
implantation, Ms. Allison Sweeney for handling animal care, Mr.
Christopher Nguyen for proof-reading the manuscript and Ms. Sahanvi
Pothamsetty for sketches used in Fig. 1.

AUTHOR CONTRIBUTIONS

A.P. and S.M. were involved in conceptualization, investigation,
writing---original draft, writing---review and editing. S.M. secured the
funds for the project.

CONFLICT OF INTEREST

The authors declare no conflicts of interest.

DATA AVAILABILITY STATEMENT

Data and respective codes will be available upon reasonable request.

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SUPPORTING INFORMATION

Additional Supporting Information may be found online in the supporting
information tab for this article.\selectlanguage{english}
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