PRIN 2022 PROGRAMME - PRIN2022 CALL FOR PROPOSALS D.D. No.104 OF 2 FEBRUARY 2022
PROJECT TITLE: Quantitative Imaging of Vascular Dysregulation as a Functional Basis for Autoimmune Disorders and Tumors (AI-VASCUES)
CUP CODE: D53D23003480006
Budget: € 55.045
P.I. or U.R. Manager: Vitoantonio Bevilacqua
Other Research Units or any Sub-Units
Polytechnic University of Turin
University of Turin
University of Bari
Brief project description
The AI-VASCUES project investigated vascular dysregulation as a shared functional biomarker across autoimmune/inflammatory conditions and tumors, leveraging quantitative photoacoustic (PA) imaging to visualize and analyze vascular networks non-invasively. The project’s main technological direction was to enable volumetric and functional PA imaging using a conventional linear ultrasound probe and multi-angle illumination, supported by artificial intelligence methods for reconstruction, artifact reduction, and automated analysis.
Purpose
The project aimed to develop an imaging and analysis pipeline capable of extracting quantitative vascular parameters (and, where possible, metabolic parameters) correlatable with clinically relevant endpoints. In the original plan, clinical translation focused on two reference use cases, Achilles tendinopathy and cutaneous squamous cell carcinoma, using vascular information derived from PA imaging as the main indicator, with the long-term goal of supporting clinically usable biomarkers of dysregulation.
Expected results
The expected results included: (i) a robust PA/US acquisition system, with optimized multi-angle illumination and compatibility with linear probes; (ii) artificial intelligence-based methods for PA image reconstruction and enhancement/analysis (including strategies specifically designed for limited-view geometries); and (iii) progressive validation, from simulations and phantom studies to preclinical/clinical studies, aimed at enabling the quantitative assessment of vascular signatures associated with inflammatory and tumour processes.
Achieved results
AI-VASCUES produced enabling results to support subsequent clinical translation: an experimental 3D PA/US imaging system based on a linear probe; extensive safety engineering and system characterization activities; two ethical approvals; preliminary in vivo feasibility studies in healthy volunteers; artificial intelligence-based methods for image enhancement and automated analysis; as well as benchmarking activities, supported by simulations and phantoms, of volumetric reconstruction strategies for limited-view acquisitions. In parallel, clinical progress was maintained through complementary developments in tendon analysis (3D imaging/quantitative ultrasound) and dermatological studies using biomedical optical imaging techniques, such as LC-OCT.
