Three-dimensional recoil-electron reconstruction using combined optical imaging and waveform readout for electron-tracking Compton cameras

TL;DR

This paper introduces a practical method combining optical imaging, waveform data, and deep learning to reconstruct 3D recoil-electron directions in Compton cameras, improving imaging performance without full 3D readout.

Contribution

It presents a novel approach that infers 3D electron tracks using combined imaging and waveform data, enhancing resolution over previous methods.

Findings

Achieved ~44° angular resolution for recoil-electrons in 40-50 keV range.

Improved starting-point resolution across 5-50 keV electron energies.

Demonstrated effectiveness of combining transverse images and waveforms for 3D reconstruction.

Abstract

Accurate reconstruction of recoil-electron directions is critical for enhancing the point-spread function of electron-tracking Compton cameras (ETCCs) in gamma-ray imaging. Although full three-dimensional (3D) readout systems achieve high-precision reconstruction, they are impractical for large-area detectors because of the enormous data volume. This study proposes and demonstrates a practical alternative for inferring the 3D recoil-electron direction in Compton scattering. This method combines a high-resolution two-dimensional optical image, a one-dimensional waveform signal, and a deep-learning-based method through simulations. The proposed method achieved an angular resolution of approximately $44^\circ$ for the recoil-electron direction in the 40-50 keV range, corresponding to an improvement of a factor of about 1.3 compared with our previous strip-readout approach using pseudo-experimental data generated by Geant4 and MAGBOLTZ simulations for an argon-based gas time projection chamber. In addition, the starting-point resolution of the electron track was improved over the previous method across the 5-50 keV electron energy range. These results demonstrate that complementary information from the transverse image and longitudinal waveform can effectively recover the 3D track topology without requiring full 3D readout. The proposed approach provides a realistic pathway for improving ETCC imaging performance.