Maria Giuseppina Bisogni, Andrea Attili, Giuseppe Battistoni, Nicola Belcari, Niccolo’ Camarlinghi, Piergiorgio Cerello, Silvia Coli, Alberto Del Guerra, Alfredo Ferrari, Veronica Ferrero, Elisa Fiorina, Giuseppe Giraudo, Eleftheria Kostara, Matteo Morrocchi, Francesco Pennazio, Cristiana Peroni, Maria Antonietta Piliero, Giovanni Pirrone, Angelo Rivetti, Manuel Rolo, Valeria Rosso, Paola Sala, Giancarlo Sportelli, Richard Wheadon
The quality assurance of particle therapy treatment is a fundamental issue that can be addressed by developing reliable monitoring techniques and indicators of the treatment plan correctness. Among the available imaging techniques, positron emission tomography (PET) has long been investigated and then clinically applied to proton and carbon beams. In 2013, the Innovative Solutions for Dosimetry in Hadrontherapy (INSIDE) collaboration proposed an innovative bimodal imaging concept that combines an in-beam PET scanner with a tracking system for charged particle imaging. This paper presents the general architecture of the INSIDE project but focuses on the in-beam PET scanner that has been designed to reconstruct the particles range with millimetric resolution within a fraction of the dose delivered in a treatment of head and neck tumors. The in-beam PET scanner has been recently installed at the Italian National Center of Oncologic Hadrontherapy (CNAO) in Pavia, Italy, and the commissioning phase has just started. The results of the first beam test with clinical proton beams on phantoms clearly show the capability of the in-beam PET to operate during the irradiation delivery and to reconstruct on-line the beam-induced activity map. The accuracy in the activity distal fall-off determination is millimetric for therapeutic doses.
N. Marino, S. Saponara, G. Ambrosi, F. Baronti, M. G. Bisogni, P, Cerello, F. Ciciriello, F. Corsi, L. Fanucci, M. Ionica, F. Licciulli, C. Marzocca, M. Morrocchi, F. Pennazio, R. Roncella, C. Santoni, R. Wheadon, A. Del Guerra
Positron emission tomography (PET) is a clinical and research tool for in vivo metabolic imaging. The demand for better
image quality entails continuous research to improve PET instrumentation. In clinical applications, PET image quality
benefits from the time of flight (TOF) feature. Indeed, by measuring the photons arrival time on the detectors with a
resolution less than 100 ps, the annihilation point can be estimated with centimeter resolution. This leads to better noise
level, contrast and clarity of detail in the images either using analytical or iterative reconstruction algorithms. This work
discusses a silicon photomultiplier (SiPM)-based magnetic-field compatible TOF-PET module with depth of interaction
(DOI) correction. The detector features a 3D architecture with two tiles of SiPMs coupled to a single LYSO scintillator
on both its faces. The real-time front-end electronics is based on a current-mode ASIC where a low input impedance, fast
current buffer allows achieving the required time resolution. A pipelined time to digital converter (TDC) measures and
digitizes the arrival time and the energy of the events with a timestamp of 100 ps and 400 ps, respectively. An FPGA
clusters the data and evaluates the DOI, with a simulated z resolution of the PET image of 1.4 mm FWHM.
The Channeler Ant Model (CAM) is an algorithm based on virtual ant colonies, conceived for the segmentation
of complex structures with different shapes and intensity in a 3D environment. It exploits the natural capabilities
of virtual ant colonies to modify the environment and communicate with each other by pheromone deposition.
When applied to lung CTs, the CAM can be turned into a Computer Aided Detection (CAD) method for the
identification of pulmonary nodules and the support to radiologists in the identification of early-stage pathological
objects. The CAM has been validated with the segmentation of 3D artificial objects and it has already been
successfully applied to the lung nodules detection in Computed Tomography images within the ANODE09
challenge. The model improvements for the segmentation of nodules attached to the pleura and to the vessel
tree are discussed, as well as a method to enhance the detection of low-intensity nodules. The results on five
datasets annotated with different criteria show that the analytical modules (i.e. up to the filtering stage) provide
a sensitivity in the 80 - 90% range with a number of FP/scan of the order of 20. The classification module,
although not yet optimised, keeps the sensitivity in the 70 - 85% range at about 10 FP/scan, in spite of the
fact that the annotation criteria for the training and the validation samples are different.
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