Deep-silicon photon-counting x-ray projection denoising through reinforcement learning.

Area of Science:

• Medical Imaging
• Artificial Intelligence

Background:

• Deep reinforcement learning (RL) shows promise in medical applications.
• Photon-counting CT (PCCT) offers superior resolution but requires advanced noise reduction.

Purpose of the Study:

• To demonstrate the feasibility of deep RL for denoising simulated PCCT data.
• To improve image quality in both full and interior scan modes of PCCT.

Main Methods:

• Applied a dueling double deep Q network (DDDQN) for denoising.
• Utilized a multi-agent approach to address data non-stationarity.
• Optimized for maximum contrast-to-noise ratio (CNR).

Main Results:

• Significant image quality improvement in single-channel scans (PSNR increase from 33.4 to 37.4 dB, SSIM from 0.916 to 0.979).
• Consistent improvement across all three channels in multichannel scans.
• Demonstrated effective noise suppression and enhanced image fidelity.

Conclusions:

• RL approach effectively denoises PCCT data, enhancing image quality.
• The method is efficient and consistent across spectral channels.
• Shows significant potential for clinical translation in medical imaging.