The research shall be developed in close collaboration with Echolight
(https://www.echolightmedical.com, 12 months out of 18 in total), and is
related to the following topic:
The main goal of the project is to improve the tuning and calibration
process of noninvasive diagnostic imaging devices used for imaging. One
of the most critical steps during the implementation of a diagnostic
imaging device is its calibration. In fact, poor calibration can lead to
unreliable instrument performance with noisy images and the presence of
unwanted artefacts that could mislead the diagnosis made by the
physician. The calibration phase involves a repeated try-and-check
procedure during which the instrument parameters are repeatedly changed
in order to obtain images that are sharp and as closely matched as
possible to the target reference. This phase often requires considerable
time expenditure and expert supervision; moreover, if one considers that
calibration is carried out both following the production of the
diagnostic instrument but also after several months of its use in the
operational context, it is easy to deduce that automating this process
on the one hand would improve the diagnostic yield, and on the other
hand would reduce downtime and recalibration. The project aims to
improve and automate the calibration process by introducing machine
learning techniques for image classification. The results of the project
find application on all instruments used for imaging, whether they are
based on MRI, computed tomography, X-ray or ultrasound techniques. In
fact, the goal is to relate the configuration parameters of the
instrument to the images it produces in order to eliminate noise and
artefacts produced by misconfiguration. Despite this, in the project we
will consider as a case study the images produced by an ultrasound-based
device produced by Echolight S.p.A. Medical devices produced by
Echolight S.p.A. exploit images derived from ultrasound scans (B-Mode)
to automatically identify anatomical reference targets (lumbar vertebrae
bone interfaces of the L1-L4 tract and proximal femur bone interface).
Once the regions of interest (ROIs) are identified, a proprietary
algorithm evaluates the spectral characteristics of selected portions of
the raw ultrasound signal related to the analyzed bone tissues. From the
analysis of the raw signal characteristics, a measure of the bone
mineral density (BMD) of the analyzed anatomical sites is determined. In
order to provide reliable, repeatable, and accurate BMD measurements,
special calibration and testing procedures have been developed, however,
which require several manual measurements and checks, resulting in a
high human-time commitment and, consequently, introducing a risk of
human error on the collection and interpretation of the collected
measurements and results. Leveraging the image processing and image
classification techniques developed within the project, the algorithm
will provide output indicative of the presence of artefacts or other
alterations in the performance of the ultrasound system in production in
order to possibly intervene with further modifications and calibrations.
As part of the project, standard conditions for conducting tests will
also be defined through the use of specific ultrasound phantoms provided
by the company.
Prof. Italo Epicoco (italo.epicoco@unisalento.it) is the scientific
responsible for this research grant.
DEADLINE: June 24, 2022
ALL INCLUSIVE GROSS AMOUNT (for 18 months): 29050,50 euro (i.e., 19367
euro annual gross amount)
NOTE: Foreign candidates are strongly encouraged to contact me by email
if they need help/support in order to prepare their application: I will
be glad to assist.
Here are, attached, an unofficial English translation of the call and
the corresponding application and self declaration forms, translated in
English.
NOTE: Foreign candidates are strongly encouraged to contact Prof.
Epicoco by email if they need help/support in order to prepare their
application: hewill be glad to assist.
*******************************************************************************************
Prof. Massimo Cafaro, Ph.D.
Associate Professor of Parallel Algorithms and Data Mining/Machine Learning
Head of HPC Lab https://hpc-lab.unisalento.it
Director of Master in Applied Data Science
Department of Engineering for Innovation
University of Salento, Lecce, Italy
Via per Monteroni
73100 Lecce, Italy
Voice/Fax +39 0832 297371
Web https://www.massimocafaro.it
Web https://www.unisalento.it/people/massimo.cafaro
E-mail massimo.cafaro@unisalento.it
E-mail cafaro@ieee.org
E-mail cafaro@acm.org
INGV
National Institute of Geophysics and Volcanology
Via di Vigna Murata 605
Roma
CMCC Foundation
Euro-Mediterranean Center on Climate Change
Via Augusto Imperatore, 16 - 73100 Lecce
massimo.cafaro@cmcc.it
*******************************************************************************************
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