A Tomographic Multiview-Multistatic Ultrasound System

for Biomedical Imaging Applications

S. Franceschini

, M. Ambrosanio

, F. Baselice

and V. Pascazio

Department of Engineering, University of Naples Parthenope, Naples, Italy

Keywords:

Ultrasound Systems, Tomographic Imaging, MIMO Systems, Biomedical Imaging, Object Detection.

Abstract:

Medical imaging is a paramount concern in modern society. Thus, there is an increasing interest and attention

to new imaging modalities which can support standard exams and/or replace them in diseases diagnosis. In

this framework, ultrasound tomography could have an important role for some biomedical applications, such

as for breast cancer imaging, since it would allow to overcome some limitations related to standard ultra-

sound exams which are operator-dependent and usually are not based on a coherent processing, reducing the

reconstruction performance considerably. To this aim, in this article a preliminary air-based ultrasound tomo-

graphic imaging system is described and tested. The prototype was designed, built and tested at the University

of Naples Parthenope with the aim of providing some interesting data sets for testing and comparison of imag-

ing algorithms in a laboratory-controlled environment, which represents a mandatory step before moving to

the realistic case of a water-matched device.

1 MOTIVATION

Ultrasound tomography (UT) is an interesting non-

destructive imaging modality which exploits mechan-

ical waves to provide quantitative as well as quali-

tative maps of the objects located in an investigated

domain which is usually inaccessible, such as in

some industrial and biomedical applications (Abdol-

lahi et al., 2019; Alqadah, 2016; Mojabi and LoVetri,

2017).

In clinical practice, Ultrasound (US) scanners are

widely adopted due to the low cost, the easy man-

agement and the safety for the patient. Their main

limitation is that a subjective analysis is allowed, as

produced results are operator-dependent. This is also

the reason why the examination is conducted by the

doctor himself instead by a technician, as in mag-

netic resonance imaging (MRI) or computerized to-

mography (CT) systems. UT approach has the in-

tent of overcoming this issue, being a tomographic,

operator-independent acquisition system that makes

use of acoustic waves. Moreover, its peculiarity of

implementing coherent processing allows to obtain

https://orcid.org/0000-0002-7608-6686

https://orcid.org/0000-0003-3669-8183

https://orcid.org/0000-0002-5964-8667

https://orcid.org/0000-0002-5403-5482

data characterized by much higher quality with re-

spect to classical US systems.

The data collected by means of an active sys-

tem, which usually consists of transmitters and/or re-

ceivers, can be processed via several algorithms to

yield ultrasonic images of the objects of interest (OI).

The problem of recovering the features of the objects

in the imaging domain is an inverse scattering (IS)

problem (Colton and Kress, 2012; Pastorino, 2010).

Solving the acoustic problem in a fast, accurate

and robust way is still challenging mainly due to the

ill-posedness and non-linearity issues. The ﬁrst one

is responsible for the instability of the solution and is

related to the fact that the problem at hand is under-

determined; therefore a small of amount of noise in

the data may drive into completely unreliable solu-

tions. Thus, the need of proper regularisation strate-

gies becomes mandatory in order to obtain good re-

coveries.

On the other hand, the non-linearity of the IS

problem increases its difﬁculty and forces the use of

proper minimisation strategies in order to avoid false

solutions. Generally, the commonest approach to han-

dle the non-linearity of the IS problem is to employ

weak scattering approximations (Pierri et al., 1999;

Salucci et al., 2013; Cui et al., 2004; Ambrosanio

et al., 2014), which does not handle the ill-posedness

issue. However, weak scattering approximations do

274

Franceschini, S., Ambrosanio, M., Baselice, F. and Pascazio, V.

A Tomographic Multiview-Multistatic Ultrasound System for Biomedical Imaging Applications.

DOI: 10.5220/0009173102740279

In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 1: BIODEVICES, pages 274-279

ISBN: 978-989-758-398-8; ISSN: 2184-4305

not consider for important non-linear waves, intro-

ducing artifacts and distortions in the solution. More-

over, several applications require the knowledge of

the mechanical features of the objects under test, es-

pecially in the biomedical framework. For instance,

this data might be fundamental as a priori informa-

tion for hybrid strategies which exploit also electro-

magnetic features (i.e., microwave imaging) as well

as other techniques, which can take advantage of

the synergy with ultrasound approaches (Omer et al.,

2018).

To this aim, in this paper an ultrasound (US)

multiview-multistatic (MV-MS) tomographic imag-

ing system is proposed. The system is a cheap,

in-house device which has been designed, built and

tested at University of Naples Parthenope. Some

qualitative imaging results for an air-based setup are

presented as preliminary case study with the perspec-

tive of providing a test-bed for biomedical imaging

of tissues, e.g. for breast cancer imaging applica-

tions, in order to promote pure ultrasound or hybrid

microwave and acoustic strategies for achieving clin-

ically effective imaging systems.

This system might support standard medical ex-

ams as complementary strategy for early breast cancer

diagnosis. This would allow a more frequent screen-

ing by overcoming the safety issues related to classi-

cal mammography as well as the high costs related to

MRI. Last, but not least, the proposed system might

overcome some important limitations of standard ul-

trasound imaging, such as the reliance on the human

operator who performs the exam and the poor quality

of the images.

In order to evaluate the performance of differ-

ent inversion approaches, experimental data sets are

mandatory. This is the main motivation of this work,

where we present an UT system and share the pro-

duced data.

2 PRINCIPLES OF ULTRASOUND

TOMOGRAPHY

In the ﬁeld of UT, the main goal is to create quantita-

tive as well as qualitative maps of the morphological

and mechanical features of unknown objects located

in inaccessible domains. Due to the non-destructive

feature of this analysis, it can be performed starting

from data collected outside of the imaging domain,

which can be of high interest mainly for biomedical

applications due to the non-invasive feature of this

methodology.

Regarding the collection of data, the object of in-

terest (OI) is surrounded by several US sensors (both

𝜖

𝑠

𝒓

Figure 1: Two-dimensional geometry of the imaging prob-

lem. Red circle: active sensor (i.e., transmitter), gray cir-

cles: receivers. The unknown object is located in the imag-

ing domain Ω and the sensors are on a measurement curve

Γ.

transmitters and receivers) which operate in a MV-MS

fashion, i.e. one transducer per time is active while all

the others record the scattered signal. This procedure

continues until all the transducers have acted as trans-

mitters per each frequency in the selected bandwidth.

The background medium is usually homogeneous and

its mechanical features are chosen in order to max-

imise the matching with the targets and allow the pen-

etration. A sketch of the simpliﬁed two-dimensional

(2D) geometry at hand is shown in Fig. 1.

In this framework, two different geometries can be

deﬁned. The ﬁrst one containing the objects of inter-

est, which is known as imaging domain and is denoted

as Ω, while the second one contains all the transmit-

ters and receivers and is known as data domain and

is denoted as Γ. For practical applications, the data

which are acquired in order to provide an estimate of

the unknown objects are the scattered ﬁeld pressure

data. In order to obtain them, some measurements

without the objects in the imaging domain are per-

formed, also known as incident ﬁeld measurements.

Then, the difference between the pressure ﬁeld with

the objects and the incident ﬁeld provides the scat-

tered ﬁeld, which represents the input of the imaging

chain.

2.1 Mathematical Formulation

For the considered simpliﬁed 2D scenario, the inte-

gral equation which governs the scattered ﬁeld pres-

sure as a function of targets’ compressibility, attenu-

ation and density proﬁles can be written as (Haynes

and Moghaddam, 2010; Mojabi and LoVetri, 2015):

A Tomographic Multiview-Multistatic Ultrasound System for Biomedical Imaging Applications

275

sct

, r

, ω) =

= k

Ω

g(r

, r

, ω)· χ

, ω)· u

tot

, r

, ω)dr

Ω

g(r

, r

, ω)∇·



, ω)∇u

tot

, r

, ω)



, r

∈ Γ, (1)

where g(r

, r

, ω) is the Green’s function of the

background medium, k

is the complex background

wavenumber and u

sct

and u

tot

are the scattered and

total pressure ﬁelds, and a time-harmonic exponen-

tial factor e

jωt

is omitted. Finally, χ

and χ

are the

contrasts of compressibility and of inverse density re-

spectively, deﬁned as:

r, ω) =

κ(r

r, ω) − κ

(ω)

− j

2α

r, ω)

, (2)

r, ω) =

−1

r, ω) − ρ

−1

(ω)

−1

(ω)

, (3)

with κ(r

r, ω) compressibility ρ(r

r, ω) density and

r, ω) attenuation at position r

r and frequency ω,

respectively, while the subscript b stands for the back-

ground case.

Imaging approaches aims at ﬁnding a stable so-

lution of the inverse scattering problem illustrated in

Eq. (1), providing quantitative maps of compressibil-

ity, density and attenuation of the objects located in

the investigation domain, usually in a nonlinear fash-

ion via iterative methods in order to determine both

morphological and mechanical contrast of the targets.

However, due to the ill-posedness of the problem at

hand, some regularization strategies based on some a

priori information are also mandatory to obtain stable

solutions.

In addition to these classical approaches, the so-

called qualitative methods can be of interest, since

they only aims at the reconstruction of the morpho-

logical features of the scatterers, and usually exploit a

linear framework, which avoids the issue of false so-

lutions meantime keeping the computational burden

low (Belkebir et al., 1997).

2.2 Imaging Via the Linear Sampling

Method

Among the most employed qualitative approaches,

the linear sampling method (LSM) seems to be a good

candidate to carry out the inversion. It provides an es-

timate of targets’ support via an auxiliary linear prob-

lem based on the far-ﬁeld equation:

sct

, r

)ξ



, r



= g (r

, r

) , (4)

with r

∈ Ω denoting an arbitrary point that samples

the region under test, r

∈ Γ the curve on which trans-

mitters and receivers are located, and ξ is the un-

known function related to target support to be sought

(Crocco et al., 2012; Bevacqua and Palmeri, 2019).

The problem shown in Eq. (4) is ill-posed (Colton

and Kress, 2012) and thus requires a proper regular-

isation strategy in order to obtain a stable solution.

A well-known, simple and efﬁcient strategy is rep-

resented by the Tikhonov regularisation (Tikhonov

et al., 2013), which can be applied for the solution

of Eq. (4). Then, an estimate of targets’ support is

obtained by evaluating the L

-norm of the Herglotz

density ξ for every sampling point r

∈ Ω, since this

function assumes low values in the targets’ support

location and diverges in the other points. Thus, it is

possible to deﬁne the LSM indicator:

I (r

) =





, r





, (5)

which can be easily evaluated via singular value de-

composition.

3 PROTOTYPE OVERVIEW AND

IMAGING RESULTS

In this section, the cheap in-house MV-MS UT sys-

tem designed, built and tested at University of Naples

Parthenope is presented. A picture of the prototype

is shown in Fig. 2. In order to obtain a spatial di-

versity of the data, which represents an important re-

quirement for the imaging via MV-MS systems, a ro-

tating platform was located in the centre of the imag-

ing domain. This platform is controlled remotely via

a micro-stepper engine, which has an angular preci-

sion of one thousandth degree, which allows a good

accuracy in the position of the sensors circular array.

The region of interest (ROI), i.e. the imaging area in

which the objects are located, coincides with the ro-

tating platform, i.e. a circle with a diameter of 28 cm.

The transmitter is connected to a waveform gener-

ator (model 33220A manufactured by Agilent Tech-

nologies) that produces a cosine signal at 40 kHz. At

this operating frequency, the wavelength is approxi-

mately 8.5 mm in air background. The transducer ra-

diating pattern is approximately ±30

◦

at -6 dB and

it is the same both in the vertical as well as hori-

zontal planes. All the sensors, one transmitter and

twenty-one receivers, are located on a wooden ring

of 17.5-cm radius at a height of 44 cm, and they are

equally-spaced on the circle (with an error in their lo-

cation lower than one fourth of wavelength). A sec-

ond wooden ring is mounted on the top of the pro-

totype in order to ensure the stiffness of structure.

BIODEVICES 2020 - 13th International Conference on Biomedical Electronics and Devices

276

Figure 2: A picture of the laboratory-developed US imaging

system. Sensors (1 Tx, 21 Rx) are located on a wooden ring.

The multiview-multistatic conﬁguration is realised via a ro-

tating table the targets are located on, in order to virtually

simulate the movement of the sensors ring.

Figure 3: A simple test case with a single metallic cylinder

located on the rotating table.

The sensors used as transmitter and receiver are, re-

spectively, 40LT16 and 40LR16 both manufactured

by SensComp.

The scattered US waves received by 40LR16 sen-

sors are acquired by PCI-6251 analog-to-digital con-

verters boards manufactured by National Instruments.

These boards have a sixteen-bit resolution and a maxi-

mum sampling rate of 1.25 MS/s. In the measurement

campaign, the sampling frequency was ﬁxed at 100

kHz with sixteen-bit precision. A LabVIEW code was

employed to manage the acquisition steps, including

the control of ROI rotation. The acquisition proto-

col consists of a stepped movement and in each posi-

tion the system stops and acquires for 0.5 second, and

then goes on to the next angular position. Once ac-

quired, the signals were pre-processed in Matlab en-

vironment. At this step, the signals were ﬁltered in the

frequency domain in order to extract only the compo-

nent at 40 kHz. Subsequently, the the beat signals

(amplitude and phase) are measured.

In order to make available several heterogeneous

dataset, different scenarios have been implemented.

More in detail, objects of different materials (metal,

wood, polystyrene) and shapes (circular, rectangular

and irregular sections) have been acquired. Within

this manuscript, results related to metallic cylinders

(4-mm diameter) are reported. A picture of the con-

sidered acquisition is shown in Fig 3.

In Fig. 4, the reconstructed sections in case of one

(left image) and two (right image) cylinders are re-

ported. It is worth to note that, despite of the high con-

trast between the mechanical properties of the metal

and the air, the shape and the location of the targets

are correctly identiﬁed, appearing in a ring shape (Fig.

4a). This behaviour in the case of impenetrable ob-

jects is in accordance with the literature (Bevacqua

and Isernia, 2017), as the mechanical vibrations in-

duced by an external active source in the targets only

exist on the boundary of targets support. However,

even though the non-linearity of the scenario under

test becomes more stressed in the case of two cylin-

ders (Fig. 4b), it is still possible to identify the tar-

gets correctly, both in terms of shape and position.

future acquisitions related to all the considered sce-

narios will we uploaded on-line and made available

on request.

4 CONCLUSION

In this manuscript, a tomographic in-house

ultrasound-based multiview-multistatic imaging

system has been designed, built and tested. The

performance assessment on cylindrical metallic

objects via a classic inversion approach (i.e., the

linear sampling method) is reported.

It is worth to underline that the relationship be-

tween the scattered ﬁeld and the object to be retrieved

deﬁnes a problem that is non-linear and ill-posed.

In order to cure this instability, some regularisation

strategies are required to avoid unreliable recoveries.

From this point of view, the use of multiview data can

partially overcome this limitation, especially for noise

reduction. Moreover, the use of multi-frequency data

could improve considerably the available independent

information, allowing better reconstructions, which

A Tomographic Multiview-Multistatic Ultrasound System for Biomedical Imaging Applications

277

(a) (b)

Figure 4: Imaging results of the data acquired by the proposed prototype via the linear sampling method. (a) single cylinder

and (b) two-cylinder reconstructions. The references are the light-green circles.

can be easily implemented by employing proper hard-

ware solutions.

Even though the proposed system is air-coupled

and provides a qualitative imaging of targets support,

it can be easily generalised to the underwater acoustic

imaging case, which represents a convenient, prelim-

inary test before moving to the imaging of human tis-

sues. For instance, in the framework of breast cancer

imaging, the use of this kind of systems could be ben-

eﬁcial for early diagnosis, since it is safe and allows

a frequent screening which may be a complementary

exam to support cancer diagnosis and advancing clas-

sic ultrasound.

Future work will focus on the design and build-

ing of a water-matched prototype for the performance

assessment with breast tissue mimicking phantoms.

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