Deep Few-view High-resolution Photon-counting CT at Halved Dose for Extremity Imaging
This work addresses radiation dose reduction in clinical extremity imaging, representing an incremental improvement over existing methods.
The paper tackled the challenge of reducing radiation dose in high-resolution photon-counting CT for extremity imaging by proposing a deep learning-based reconstruction method, achieving halved dose and doubled speed while maintaining image quality and diagnostic value in a clinical trial with 8 patients.
X-ray photon-counting computed tomography (PCCT) for extremity allows multi-energy high-resolution (HR) imaging but its radiation dose can be further improved. Despite the great potential of deep learning techniques, their application in HR volumetric PCCT reconstruction has been challenged by the large memory burden, training data scarcity, and domain gap issues. In this paper, we propose a deep learning-based approach for PCCT image reconstruction at halved dose and doubled speed validated in a New Zealand clinical trial. Specifically, we design a patch-based volumetric refinement network to alleviate the GPU memory limitation, train network with synthetic data, and use model-based iterative refinement to bridge the gap between synthetic and clinical data. Our results in a reader study of 8 patients from the clinical trial demonstrate a great potential to cut the radiation dose to half that of the clinical PCCT standard without compromising image quality and diagnostic value.