Edge computing-based FPGA real-time material decomposition system for photon counting CT
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Published on: November 16, 2019
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.
Background:
Photon counting computed tomography (PCCT) has emerged as a potential technology that is revolutionizing clinical CT imaging. Using photon counting detectors (PCDs), the PCCT counts each X-ray event and measures the corresponding energy above the noise floor with significantly higher spatial resolution. However, the multiple-energy-bin setting and much smaller pixels increase the raw data size of PCCT by 20-100 times compared to traditional CT. The current bandwidth of the slip ring struggles to handle such a large volume of data.
Methods:
To address the challenge above, we propose an innovative edge computing-based PCCT data processing flow and implement a fast material decomposition program directly on an FPGA system. Our approach eliminates the need to transfer raw data from the CT gantry for later offline processing. Instead, we develop real-time material decomposition and embed the algorithm into an FPGA, which is typically integrated in current PCDs.
Results:
Visual and quantitative results on the real phantom dataset have shown that the proposed system can efficiently generate ring-artifact-free and accurate material decomposition results, while also reducing the data volume.
Conclusion:
Our proposed system effectively migrates the offline material decomposition processing to the detector blade, thereby fully leveraging the FPGA resources available within the CT gantry to rapidly obtain material decomposition results. Compared to the traditional process flow, our approach offers significantly faster speed and higher throughput, while maintaining the same level of precision as offline processing.
