Reconstruction of hit-time and hit-position of annihilation quanta in the J-PET detector using the Mahalanobis distance
N. G. Sharma, M. Silarski, T. Bednarski, P. Bia{\l}as, E., Czerwi\'nski, A. Gajos, M. Gorgol, B. Jasi\'nska, D. Kami\'nska, {\L}., Kap{\l}on, G. Korcyl, P. Kowalski, T. Kozik, W. Krzemie\'n, E. Kubicz, Sz., Nied\'zwiecki, M. Pa{\l}ka, L. Raczy\'nski, Z. Rudy, O. Rundel, A.

TL;DR
This paper presents a novel method for reconstructing hit-time and hit-position of gamma quanta in the J-PET detector using Mahalanobis distance, enhancing resolution with a library-based signal comparison approach.
Contribution
It introduces a new reconstruction technique utilizing Mahalanobis distance and dedicated electronics for improved timing and positional accuracy in positron emission tomography.
Findings
Preliminary results demonstrate effective hit-time and hit-position reconstruction.
The method achieves high temporal resolution of about 20 ps.
Signal sampling thresholds influence the accuracy of reconstruction.
Abstract
The J-PET detector being developed at Jagiellonian University, is a Positron Emission Tomograph composed of the long strips of polymer scintillators. At the same time it is a detector system which will be used for studies of the decays of positronium atoms. The shape of photomultiplier signals depends on the hit-time and hit-position of the gamma quantum. In order to take advantage of this fact a dedicated sampling front-end electronics which enables to sample signals in voltage domain with the time precision of about 20 ps and novel reconstruction method based on the comparison of examined signal with the model signals stored in the library has been developed. As a measure of the similarity we use the Mahalanobis distance. The achievable position and time-resolution depends on number and values of the threshold levels at which the signal is sampled. A reconstruction method, as well as…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
