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Exploring charge sharing compensation using inter-pixel coincidence counters for photon counting detectors by

Shengzi Zhao1, Le Shen2, Katsuyuki Taguchi3

  • 1Department of engineering physics, Tsinghua University, Shuangqing department, Beijing, 100084, CHINA.

Physics in Medicine and Biology
|October 7, 2024
PubMed
Summary

This study introduces a deep-learning method to compensate for charge sharing in photon counting detectors (PCDs) using multi-energy inter-pixel coincidence counters (MEICC). The approach significantly improves virtual monochromatic attenuation integral (VMAI) estimation accuracy, outperforming conventional PCDs in computed tomography (CT) imaging.

Keywords:
MEICC PCDSpectral CTcharge sharingneural networkphoton counting detector

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Area of Science:

  • Medical Imaging
  • Detector Physics
  • Machine Learning

Background:

  • Photon counting detectors (PCDs) offer advantages in computed tomography (CT) but face challenges like charge sharing, limiting their diagnostic potential.
  • Multi-energy inter-pixel coincidence counters (MEICC) provide spatial information to address charge sharing, potentially lowering the Cramér-Rao Lower Bound (CRLB).

Purpose of the Study:

  • To explore charge sharing compensation in MEICC detectors using a deep-learning method that leverages local spatial coincidence counter information.
  • To evaluate the effectiveness of the deep-learning approach in improving virtual monochromatic attenuation integral (VMAI) estimation compared to conventional PCDs.

Main Methods:

  • A deep-learning network was designed to focus on individual pixels, using MEICC data patches as input for charge sharing compensation.
  • A fast, online data generation method and a novel loss function for high-noise data were developed.
  • Validation was performed using Monte Carlo (MC) simulation data, comparing MEICC detectors with conventional PCDs.

Main Results:

  • The deep-learning method achieved minimal bias (0.6-1.3%) and reduced standard deviation/NRMSE in VMAI estimation, outperforming polynomial fitting (>3% bias).
  • MEICC detectors showed superior performance across all metrics, with approximately 10% lower noise compared to conventional PCDs.
  • An ablation study confirmed the benefit of an additional loss function for high-noise data training.

Conclusions:

  • Network-based methods can effectively utilize local information from PCDs for charge sharing compensation via patch-based learning.
  • MEICC detectors offer valuable local spatial information, enabling more accurate VMAI estimation than conventional PCDs.
  • The proposed deep-learning approach enhances diagnostic CT imaging by mitigating charge sharing artifacts.