RESPONSABILE DELLA RICERCA
Team di ricerca
G. Mettivier, P. Russo. A. Sarno
Partenariati e/o collaborazioni in essere (se esistenti)
Policlinico Universitario, Imaging Scientific ltd (USA), Dipartimento di Fisica Università Ferrara, CERN Ginevra
Tipo di attività prevalente
accademica con ricadute industriali
Ricadute applicative
Descrizione dell'attività di ricerca
The project aims at improving the image quality of the conventional cone-beam computed tomography scanner (CBCT) by means of the use of collimated monochromatic x-ray beam, single photon counting detector and deep learning algorithms.
Breast cancer is the most common malignancy in women. Despite the introduction of many screening programs, both false negative and positive rates remain high, leading to missed tumors and unnecessary stress, respectively. This can be tracked back to the limitations of current breast imaging technologies. Currently, there is no method yielding a precise volumetric (3D) localization of a malignancy with sufficiently high spatial resolution and soft-tissue contrast, making the detection of (early) signs of breast cancer extremely challenging.
In recent times, researchers have proposed X-ray phase contrast imaging (PCI) as a new tool. PCI can boost contrast in X-ray imaging by exploiting refraction and scattering, in addition to conventional absorption. PBI might have a large impact on the radiological approach to medical X-ray imaging as it will intrinsically enable the detection of subtle differences in the electron density of a material, like a lesion delineation, and the measurement of structural fluctuations in the specimen, such as microcalcifications in a breast tissue.
Our team has been pioneering the usage of PBI for advanced breast imaging and has deployed a PBI breast CT device to UNINA for a first pilot study. In this project, the group would develop the next level system, with the goal of achieving fast, precise and sensitive 3D breast tissue visualization, addressing most of the present limitations in the field.
The overarching goal of the team efforts is to operate the PBI-BCT prototype in clinical settings, i.e. with an obvious care on dose issues. Consequently, generated data will be extremely huge to evaluate for medical doctor and the main goal of the present grant is to explore deep learning methods to design a reliable Computed Aided Diagnosis procedure.
This project brings together researchers in a joint, interdisciplinary effort. The proposed research method will involve advanced Xray measurements on a first-of-its-kind equipment and the development of equally advanced and novel data processing tools for the extraction of valuable, clinically relevant contents.
With this collaboration it is expected to provide a unique medical device for early breast cancer detection.
Stato dell'attività
idea progettuale
Numero di pubblicazioni strettamente pertinenti, partecipazioni a convegni/ conferenze:
- Sarno et al., “Dedicated breast computed tomography: basic aspect”, Medical Physics 42, 2786-2804, 2015
- Longo et al., “Advancements towards the implementation of clinical phase-contrast breast computed tomography at Elettra”, J. Synghrothron Rad. 26, 1343-1353, 2019
- Sarno et al. “Imaging performance of phase-contrast breast computed tomography with synchrotron radiation and a CdTe photon-counting detector”, Physica Medica 32, 681-690, 2016
Luogo ove viene svolta principalmente l'attività