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Related Experiment Video

Updated: Jan 18, 2026

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Edge computing-based FPGA real-time material decomposition system for photon counting CT.

Mengqing Su1, Xiaopeng Yu2, Qianyu Wu1

  • 1Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, 210096, China; School of Biomedical Engineering, ShanghaiTech University, Shanghai, 201210, China; Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing, Southeast University, Nanjing, 210096, China.

Computer Methods and Programs in Biomedicine
|September 10, 2025
PubMed
Summary
This summary is machine-generated.

Photon counting computed tomography (PCCT) faces data challenges. This study integrates material decomposition into field-programmable gate arrays (FPGAs) within the CT gantry, enabling faster, high-precision imaging.

Keywords:
Edge-computingMaterial decompositionPhoton counting CTReal-time

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Area of Science:

  • Medical Imaging
  • Computed Tomography
  • Data Processing

Background:

  • Photon counting computed tomography (PCCT) offers superior spatial resolution using photon counting detectors (PCDs).
  • PCCT generates significantly larger raw data (20-100x traditional CT) due to multiple energy bins and smaller pixels.
  • Existing slip ring bandwidth limits the transfer of large PCCT datasets for offline processing.

Purpose of the Study:

  • To develop an efficient edge computing solution for PCCT data processing.
  • To implement real-time material decomposition directly on an FPGA within the CT gantry.
  • To overcome data transfer bottlenecks associated with high-resolution PCCT.

Main Methods:

  • An edge computing framework was designed for PCCT data processing.
  • A fast material decomposition algorithm was developed and implemented on an FPGA.
  • The processing workflow was shifted from offline analysis to the CT gantry, utilizing integrated FPGA resources.

Main Results:

  • The proposed system successfully generated ring-artifact-free material decomposition results.
  • Accurate material decomposition was achieved with reduced data volume.
  • Real phantom datasets demonstrated the system's efficiency.

Conclusions:

  • Offline material decomposition processing was migrated to the detector blade using FPGA resources.
  • The system significantly enhances processing speed and throughput compared to traditional methods.
  • The proposed approach maintains the precision of offline processing while enabling rapid results.