Candidate:
Saleh
Ba
Raean
Title:
Microwave
Medical
Imaging
Using
Inverse
Radon
Transform
and
Quasi-Static
Simulator
Date:
September
12,
2022
Time:
1:00pm
Place:
online
Supervisor:
Ramahi,
Omar
Abstract:
The
features
of
microwave
imaging
(MI)
drive
its
potential
to
be
widely
implemented
in
different
fields.
Its
high
penetration
depth,
non-hazardous,
non-ionizing
nature,
and
low-cost
operation
have
attracted
researchers
in
engineering
and
medical
areas
to
use
MI
as
an
effective
tool
for
medical
imaging,
remote
sensing,
and
industrial
imaging
application.
One
of
its
most
relevant
and
highly
required
application
is
to
use
it
as
an
early-stage
medical
diagnostic
tool
for
tumor
detection
as
an
alternative
to
the
conventional
medical
imaging
approaches.
MI
can
overcome
the
drawbacks
of
conventional
medical
imaging
techniques
like
high
operation
frequency,
high
energy
intensity,
and
relatively
expensive
operation
(e.g.,
X-Rays
and
MRI).
Therefore,
in
this
work,
we
propose
a
novel
technique
for
implementing
MI
as
an
effective
imaging
tool
and
implement
that
in
the
tumor
detection
process.
In this study, numerical simulation in a Quasi-static environment is developed to model the imaging process of breast tissue containing either one or more tumors. This model uses a movable and wavelength-independent electrostatic dipole point as a ray-like source to detect the hypothetically formed tumor. The variation of the power (electric field) of the waves generated from the vertically moving localized source that passes through a circular-like rotating object around its center is measured. Accordingly, the projection profile for each point at an imaginary detection line is registered. After that, Filtered Back Projection (FBP) and Inverse Radon Transform algorithms are applied to reconstruct a 2D image of the scanned body. In order to reconstruct the body image, the power profile at different source elevations and different body angles are aggregated and processed using inverse Radon transform. Due to different dielectric properties (e.g., permittivity) of the object and the tumor, different measured power profiles are generated and therefore tumors can be detected. Simulations are performed for inhomogeneous and asymmetric structures. Results show the ability of the proposed method to successfully reconstruct good resolution images and the capability of distinguishing between abnormal and normal tissues. The proposed method can be used as an early-stage and a cheap approach for sensing tumors in human tissues as well as different upcoming applications.