A research grant is available at the University of Salento, Lecce, Italy. 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.

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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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