Detecting Muons with a Siemens Biograph Vision Quadra
Lorenzo Mercolli, William M. Steinberger
Abstract
Atmospheric muons are routinely detected with a variety of instruments, from cloud chambers to scintillation detectors. In this study, we demonstrate that atmospheric muons can also be detected with a clinical long axial field-of-view positron emission tomograph (PET). We performed a blank scan without any radioactive source, using a prototype feature that records all single crystal interactions. The energy a muon deposits in the PET crystals far exceeds the upper limit of the recorded energy range, which means muons cannot be identified through energy discrimination. Instead, we used the scanner's time-of-flight capabilities to select crossing events, i.e. pairs of high-energy single crystal events across the PET gantry whose spatial and temporal separation is consistent with a downward-going ultrarelativistic particle. Random pairings, however, cannot be rejected on an event-by-event basis. We therefore established the muon origin of the crossing events through their characteristic angular distribution. In order to account for geometry and event selection bias, we compare the measured crossing events to a Monte Carlo simulation of muons following the expected angular distribution. This confirms that the measured crossing events in the PET scanner are consistent with atmospheric muons.
Create a lesson
Related papers
Dielectric characterisation of human adrenal glands and adrenal tumours for the development of microwave ablation technologies for hypertension treatment
Bilal Amin, Adnan Elahi, Grazia Cappiello et al.
Dielectric Models of Normal and Diseased Human Trabecular Bones at Microwave Frequencies
Bilal Amin, Amra Mehboob, Eoghan Dunne et al.
PET Attenuation Map Generation for Brain Imaging from Virtual Non-Contrast and Virtual Non-Iodine Images Obtained with Photon Counting CT Scanners
Heba Alrakh, Lutz Tellmann, Omid Nikoubashman et al.
Implementation Science Driven Identification of Barriers to AI Diagnostic Decision Support: A Stakeholder-Informed Concept Mapping Study
Ali Fathi Jouzdani, Arman Gorji, Mohammad Zahedi et al.
Replayable Monte Carlo MRI: From Persistent Tissue Microphysics to Queryable Virtual Phantoms
Rutger H. J. Fick
From Radiotherapy Dataset to Cherenkov Dose Map: A Digital Linac Monte Carlo Simulation Pipeline
Xianghuan He, Changyu Sun, Andy N. Wiles et al.