paMELA - Photoacoustic Melanoma Detector Design for Real-Time

Imaging of Melanin with 18 db SNR and 10 μm Precision

Elia Arturo Vallicelli

, Giberto Chirico

, Oliver Cosmi

, Lorenzo Stevenazzi

and Mattia Tambaro

University of Milano, Bicocca, Milano, Italy

University of Padova, Padova, Italy

Keywords: Photoacoustics, Analog Front-end, Digital Signal Processing.

Abstract: This article presents the complete photon-to-bit cross-domain model of a photoacoustic melanoma detector

(paMELA), consisting of a pulsed laser, a multichannel acoustic sensor, an analog front-end and a DSP stage

for the implementation of an acoustic imaging algorithm. The photoacoustic effect can be exploited to obtain

complementary information on a suspected melanoma with respect to classical dermatoscopic techniques. By

modelling the physical phenomena (generation and propagation of the acoustic signal), electromechanical

process (pressure-voltage transduction by the acoustic sensor), the analog and digital signal processing, it is

possible to evaluate the impact of each stage on the quality of the final image. Finally, the simulation results

of paMELA allow to evaluate the performance of the detector in terms of localization precision and signal-

to-noise ratio, using both a single point-like source and a complete biological tissue phantom with different

sources sizes and features, obtaining 18 dB of SNR and 10 μm of precision in 1s acquisition.

1 INTRODUCTION

Melanoma is an aggressive malignant tumor that

initially develops on the epidermis and subsequently

expands deep into the tissues until it generates

metastases. Although it represents a small percentage

of skin cancers (<10%), it is responsible for 75% of

deaths in the entire category (Stewart, 2003). The

average onset is also young: it is the third most

frequent cancer under the age of 50 and the highest

cost in terms of years not lived. Between 2008 and

2016, melanoma represented the cancer with the

greatest annual average increase in Italy, with + 8.8%

in total in men and + 7.1% in women. If diagnosed in

the early stages, treatment involves simple surgical

excision with a 5-year survival rate of 98.4%. In the

more advanced stages, however, when the melanoma

has grown in depth to reach the dermis and lymphatic

vessels, the risk of metastasis is very high. The 5-year

survival rate drops drastically, reaching 63% if

metastases are present in the regional lymph nodes

and 22% if distant metastases are present making

https://orcid.org/0000-0003-0905-151X

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https://orcid.org/0000-0002-7593-5084

necessary treatments that are invasive for the patient

(chemotherapy, radiotherapy) and with high costs for

health systems. An early diagnosis of melanoma is

therefore of fundamental importance in order to be

able to recognize it, have a favorable prognosis,

minimally invasive treatment and a reduction in the

associated socio-economic impact. Currently, the

screening of melanomas is carried out by a

specialized dermatologist who performs a visual

inspection of the skin nevi using the dermatoscope

(magnifying glass with polarized light, Figure (1),

evaluating their morphological aspects, that is, shape,

size and color.

In the event of a suspicious situation, surgical

removal and histological examination of the tissue

sample is carried out to assess the possible presence

of melanoma and its thickness (Figure 1). Staging is

in fact defined according to thickness since

melanomas in the initial stages are found only in the

epidermis (superficial layer), while in the more

advanced stages they begin to penetrate the dermis

(vascularized underlying layer), with the possibility

102

Vallicelli, E., Chirico, G., Cosmi, O., Stevenazzi, L. and Tambaro, M.

paMELA - Photoacoustic Melanoma Detector Design for Real-Time Imaging of Melanin with 18 db SNR and 10 m Precision.

DOI: 10.5220/0011013000003123

In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 1: BIODEVICES, pages 102-108

ISBN: 978-989-758-552-4; ISSN: 2184-4305

Figure 1: Comparison between standard dermatoscopic technique and photoacoustic technique.

Figure 2: Photoacoustic imaging process.

of generating metastases. It is therefore known that

measuring the thickness of melanoma is of

fundamental importance for an early diagnosis and an

effective clinical approach. However, this is not

possible with standard dermatoscopy which is limited

to observing the surface morphological

characteristics to identify suspected cases to be

surgically removed, postponing the staging to the

next histological examination and strongly depending

on the experience of the dermatologist.

An emerging alternative technique for the study

of melanomas is based on photoacoustics. When a

light pulse radiates an optical absorber, the rapid

deposition of energy generates an increase in

temperature and pressure that propagates in the

medium like an acoustic wave (Figure 2).

This acoustic wave can be acquired by a dedicated

acoustic detector to obtain information on the

absorber that generated it. By exploiting the sound

generated by a suspect melanoma it is possible to

obtain information on its morphology and in

particular on its thickness (Zhou 2014, Sinnamon

2019). With this technique, during a simple

paMELA - Photoacoustic Melanoma Detector Design for Real-Time Imaging of Melanin with 18 db SNR and 10 m Precision

103

Figure 3: Block scheme of paMELA.

dermatological examination, it is possible to perform

a quick and painless in-vivo biopsy, which allows to

evaluate the actual presence and stage of melanoma

(Wang 2016, Park 2021). The photoacoustic effect

can be used to observe a wide range of biological

systems and the photoacoustic instruments currently

on the market are general-purpose to adapt to

different research needs, therefore not being

optimized for melanomas.

For these reasons, this work presents the design

and simulation characterization of paMELA

(photoacoustic melanoma detector), a compact real-

time photoacoustic detector optimized for

characterizing melanoma. This work is organized as

follows. Section II describes the design of paMELA,

Section III presents the simulation results from a

complete cross-domain model of the system and its

performance characterization. Finally, in Section IV

conclusions will be drawn.

2 PHOTOACOUSTIC

MELANOMA DETECTOR

DESIGN

Photoacoustic imaging can be obtained by several

techniques that can be summarized in two main

categories: Photoacoustic Microscopy (PAM)

exploits a single channel acoustic sensor and a

focused laser beam to perform a raster scan above the

sample and obtain a pixel-by-pixel image. Although

the instrumentation is simple, the mechanical

scanning over thousands of pixels is time consuming

(several minutes) and requires the tissue under

observation to be perfectly still for long periods, to

evitare misalignment in the picture. For this reason,

PAM is particularly suited for pre-clinical

application, but it has practical limitation as a clinical

tool. paMELA exploits a different technique, called

Photoacoustic Tomography (PAT), that by using a

multichannel acoustic sensor and dedicated acoustic

imaging algorithms can acquire a 2/3D image of the

sample without any movement of the sensor, relying

on acoustic beam steering in digital domain.

The laser beam spot is wide enough to illuminate

all the sample and for each beam pulse an acoustic

image of the whole area under observation can be

taken. The acquisition time are therefore very quick

and multiple images can be taken and averaged at a

frame rate up to thousands/sec (limited by the laser

pulse repetition rate and electronic throughput).

A block scheme of the hereby presented

photoacoustic imaging setup is shown in Figure 3. It

is composed by a pulsed laser, a multichannel

acoustic sensor (AS), an analog front-end (AFE) and

DSP stages on FPGA.

2.1 Signal Generation (Laser)

The signal is generated through the thermoacoustic

effect, where a rapid deposition of energy in a certain

volume generates an increase in localized

temperature and a consequent increase in pressure,

which propagates in space like an acoustic wave. The

deposition of energy occurs when the light pulse

produced by the laser encounters a tissue with a high

absorption coefficient (such as melanin). The melanin

inside the tissue acts as sources of an acoustic wave,

and by acquiring this signal with special multichannel

acoustic sensors (MAS) it is possible to locate the

acoustic sources and produce a 2D image of the

vascularization of the biological sample. To generate

an appreciable pressure signal, the pressure

deposition must be fast enough to comply with the

stress confinement condition, which is 20 ns for

BIODEVICES 2022 - 15th International Conference on Biomedical Electronics and Devices

104

samples of 30 μm in size. A pulse length of 15 ns has

been chosen as a tradeoff between preserving the

signal linearity by respecting stress confinement and

improving the acoustic signal amplitude which is

proportional to the energy dose deposition as in (1),

where D is the dose deposition (defined as the ratio

between energy deposition and mass of the absorber

volume) and Γ is the Grüneisen parameter, equal to

around 100 Pa/Gy for water and tissues.

𝑑𝑃 = 𝛤𝐷 (1)

A 650 nm laser diode with 10 W peak power was

used to maximize the melanin absorption. The

irradiated volume can be approximated as a cylinder

with 3 mm diameter and 1 mm thickness. This leads

to an average dose deposition of 20 mGy and 2 Pa

acoustic signal amplitude. The pressure wave then

propagates in space until it reaches the acoustic

sensor, undergoing spherical attenuation which is

proportional to the distance between the source and

the sensor (5 mm), and equal to 20 dB, thus leading

to 200 mPa signal at the sensor surface. Finally, the

frequency of the acoustic wave is linked to the

thickness of the source measured in the AS-source

direction according to equation (2).

𝑓

𝑐



2 𝐵𝑃



𝑐



2 𝑇𝐻



(2)

Thus, to achieve 30 μm resolution paMELA has

to acquire signals in the 25 MHz range.

2.2 Acoustic Signal Sensing

(Multichannel Acoustic Sensor)

The acoustic signal is acquired through a

piezoelectric sensor array that acts as a pressure-

voltage transducer through a parameter called

Sensitivity, defined in equation (3).

𝑉=𝑃·𝑆 (3)

The choice of AS depends primarily on the

frequency characteristics of the acoustic signal under

examination. In fact it is necessary to use an AS such

that its resonance frequency corresponds with the

band of the signal to be observed. The signal band in

turn depends on the size of the sources, as shown in

the previous section. Furthermore, in order to recreate

an image of the acoustic source, it is necessary to use

an array of sensors. In this way the difference in the

arrival times of the acoustic wave to the different

sensor channels can be used to locate the source in

space in 2D and to obtain an image of the same. For

this reason, a 64-channel linear array with a central

frequency of 25 MHz and 2 μV/Pa sensitivity.

Knowing the sensitivity and the electrical capacity of

each channel (calculated around 50 pF), it is possible

to calculate the output noise power (kT/C) and the

equivalent input noise of each channel, equal to 9

uVRMS and 4.5 PaRMS respectively. Considering a

64 channel array, when acoustic imaging algorithms

are applied the signals from all channels are re-phased

and added, reducing random noise fluctuation while

preserving the (deterministic) signal amplitude, thus

lowering the total noise floor by 18 dB following

equation (4), for a total of 570 mPa

RMS

𝑁



=𝑁



/𝑠𝑞𝑟𝑡



𝑁





(4)

This value indicates the background noise of the

sensor and is to be compared with the amplitude of

the pressure signal to obtain the SNR. It is however

possible to further lower the background noise by

acquiring and averaging multiple shots of the laser

beam.

2.3 Analog Signal Processing (Analog

Front-end)

The output signal from the acoustic sensor is typically

in the range of a fraction of uV amplitude for 200 mPa

pressure at the sensor surface and 2 uV/Pa sensitivity.

Therefore it must be amplified by about 60-80 dB

before being converted into the digital domain.

Piezoelectric acoustic sensors are typically

characterized by a very low spectral density of noise

output power, therefore they require dedicated

electronics that allow to obtain an acceptable noise

figure (NF) while preserving the signal quality. In

particular, the spectral density of noise power for the

sensor used is about 2 nV/√Hz. Commercial

operational amplifiers with BJT or JFET inputs allow

to achieve in-band noise PSD around 1 nV/√Hz, thus

achieving 1 dB NF (Vallicelli, 2020). A dedicated

Low-Pass Filter is then used to reject out-of-band

noise and interferers. Finally, ADC converts the

signal into digital domain for signal processing.

2.4 Digital Signal Processing

(Beamforming)

The DSP stages have two main goal, that are to

further improve the SNR by averaging signals from

multiple laser pulses and to implement acoustic

imaging algorithms to obtain information about the

suspect melanoma. A Delay&Sum beamforming

exploits the different acoustic wave time of arrival to

obtain an acoustic image of the source.

paMELA - Photoacoustic Melanoma Detector Design for Real-Time Imaging of Melanin with 18 db SNR and 10 m Precision

105

Figure 4: Single spherical source testbench.

Figure 5: Localization precision performances vs. single

channel SNR.

For every pixel of the acoustic image, the signals

from each channel are re-phased according to the

relative pixel-channel distance to highlight acoustic

sources located inside the pixel and reject other

sources by destructive interference. By repeating the

process for each pixel (changing the re-phasing

delays) an acoustic image is obtained.

3 SIMULATION RESULTS

To evaluate performance, a cross-domain model of

the whole system was made using k-Wave and

Matlab. The model includes the deposition of energy

in space due to the laser, the generation and

propagation of the acoustic wave from the source to

the sensor (including attenuation and absorption

effects), the noise power and frequency response of

the sensor and AFE and finally the A / D conversion

and DSP. This complete model allows to evaluate the

performance of the system before physically creating

it, in order to have a benchmark to validate the

performance of the setup in the future.

3.1 Point-like Source and Performance

Evaluation

To evaluate the performance of the system in terms of

resolution and SNR, a single spherical source was

considered placed in the centre of the observation area

(Figure 4). The performance of the system was

assessed in a low, medium and high SNR case to

estimate the ability of the system to locate the position

of the source (in two dimensions) and reconstruct its

dimensions (in the axial and lateral direction) (Fig. 5).

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Figure 6: Time domain simulation of biological phantom.

Figure 7: Final acoustic image with linear (left) and C-shaped (right) sensor.

Figure 8: Time-domain output signals without noise (10

channels).

In Figure 4 the acoustic signal time track

(including the noise of the sensor and electronics)

acquired by the central channel of the sensor is

shown. The signal-to-noise ratio in the three cases is

-6 dB, 0 dB and 7 dB respectively. By combining the

signals of the 64 channels, an 18 dB increase in SNR

is obtained, bringing the final value in the acoustic

image to 12 dB, 18 dB and 25 dB respectively.

Looking at the acoustic images, it can be seen that in

all cases it is possible to clearly locate the source,

distinguishing it from the background noise, although

in the case of low SNR the random fluctuations due

to noise are clearly visible. It is interesting to note that

the size in the image of these random fluctuations

have dimensions comparable to the acoustic source

(and not less) due to the fact that the lowpass filter

limits the high frequency noise components, which

would in turn generate random fluctuations in the

image with a scale smaller than that of the source.

The precision in the localization of the centre of

the source was assessed by varying the SNR by

repeating 100 times the position measurement in the

presence of different noise realizations (always with

the same SNR) and calculating the variance of the

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107

result. Figure 5 shows how the accuracy in axial (X)

and lateral localization (Y) varies with the SNR

(Vallicelli, 2021). With 1kpulse/sec, 1 second

acquisition allows averaging 1000 samples, leading to

a single channel SNR of 0 dB (18 dB final detector

SNR) and <10 μm precision.

3.2 Biological Phantom Simulation

Finally, paMELA has been validated using a

biological phantom simulation composed of several

pint-like and cylindrical sources in different locations

of the imaging area. The time domain simulation of

such testbench is shown in Figure 6 where a linear

array sensor is placed in the left. Figure 7 (left) shows

the resulting acoustic image where the D&S

algorithm highlights the edges of the pressure

sources. It is possible to observe that the sources

parallel to the sensor are clearly visible, while the

sources located at an angle are fainter. This happens

because linear sources irradiate acoustic energy

mainly perpendicular to their direction and thus most

of the acoustic wave might not be acquired by a linear

sensor. To overcome this issue, Figure 7 (right) shows

the results of a curved C-shaped sensor that improves

the angle of observation, making all the sources

clearly visible. Finally, Figure 8 shows the time-

domain signals from 10 channels (one every 6) that

have been used to generate the acoustic images.

4 CONCLUSIONS

This paper presents the preliminary design and

complete cross-domain simulation validation of

paMELA, a compact photoacoustic detector

optimized for fast melanoma imaging. Through the

complete characterization and design of dedicated

detectors it is possible to increase the performance of

these instruments to provide dermatologists with an

additional tool for the early diagnosis of melanoma.

paMELA manages to obtain a clear image of an area

of 7 mm

in one second, obtaining 18 dB SNR and a

precision of 10 μm using a simple laser diode of 10

W of peak power.

ACKNOWLEDGEMENTS

This work has been supported by the Proton Sound

Detector (ProSD) Project (founded by the Italian

Institute for Nuclear Physics, INFN) and the

paMELA – Photoacoustic Melanoma Detector

project (co-founded by University of Milano –

Bicocca, BiUniCrowd crowdfunding campaign and

Carolina Zani Melanoma Foundation).

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