muPPET: Investigating the Muon Puzzle with J-PET Detectors
A. Porcelli, K. Valsan Eliyan, G. Moskal, N. Nasrin Protiti, D. L. Sirghi, E. Yitayew Beyene, N. Chug, C. Curceanu, E. Czerwiński, M. Das, M. Gorgol, J. Hajduga, S. Jalali, B. Jasińska, K. Kacprzak, T. Kaplanoglu, Ł. Kapłon, K. Kasperska, A. Khreptak, G. Korcyl, T. Kozik, D. Kumar, K. Kubat, E. Lisowski, F. Lisowski, J. Mędrala-Sowa, W. Mryka, S. Moyo, S. Niedźwiecki, S. Parzych, P. Pandey, E. Perez del Rio, B. Rachwał, M. Rädler, S. Sharma, M. Skurzok, E. Ł. Stępień, T. Szumlak, P. Tanty, K. Tayefi Ardebili, S. Tiwari, and P. Moskal
abstract
The muPPET [muon Probe with J-PET] project aims to investigate the Muon
Puzzle seen in cosmic ray air showers. This puzzle arises from the observation of a significantly
larger number of muons on Earth's surface than that predicted by the current
theoretical models. The investigated hypothesis is based on recently observed asymmetries
in the parameters for the strong interaction cross-section and trajectory of an outgoing particle
due to projectile-target polarization. The measurements require detailed information
about muons at the ground level, including their track and charge distributions. To achieve
this, the two PET scanners developed at the Jagiellonian University in Krakow (Poland),
the J-PET detectors, will be employed, taking advantage of their well-known resolution
and convenient location for detecting muons that reach long depths in the atmosphere.
One station will be used as a muon tracker, while the second will reconstruct the core of
the air shower. In parallel, the existing hadronic interaction models will be modified and
fine-tuned based on the experimental results. In this work, we present the conceptualization
and preliminary designs of muPPET.
Medical imaging data analysis using 3D deep learning models towards improving the individual treatment plans
K. Kalecińska, T. Fiutowski, P. Jurgielewicz, D. Kabat, B. Rachwał, Ł. Kapłon, M. Kopeć, S. Koperny, D. Kulig, J. Moroń, G. Moskal, A. Ruciński, P. Wiącek, B. Mindur, T. Szumlak
abstract
This work is a part of a research project aiming at delivering the next generation active medical phantom, Dose-3D, with high spatial granulation for quasi-real time measurement of the volumetric radiotherapeutic dose deposited during photon therapy. The preliminary results, discussed here, pertain to the intelligent medical data augmentation using Generative Adversarial Networks (GANs) technique implemented inside MONAI framework. However, in the scope of the project, we perform a broad search for the most efficient and advanced Deep Learning (DL) models to create tools for 3D Computed Tomography (CT) images segmentation and cancer diagnosis improvement that will be an integral part of the custom designed software platform for processing data collected with Dose-3D phantom. Apart from the innovative detection system the software itself may prove to be disruptive in the context of the currently available tools by offering open-source high quality toolkit for wide use in everyday clinical applications.
