  The latest X-ray photon-counting computed tomography (PCCT) for extremity
allows multi-energy high-resolution (HR) imaging for tissue characterization
and material decomposition. However, both radiation dose and imaging speed need
improvement for contrast-enhanced and other studies. Despite the success of
deep learning methods for 2D few-view reconstruction, applying them to HR
volumetric reconstruction of extremity scans for clinical diagnosis has been
limited due to GPU memory constraints, 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 in a New Zealand clinical
trial. Particularly, we present 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
real-world data. The simulation and phantom experiments demonstrate
consistently improved results under different acquisition conditions on both
in- and off-domain structures using a fixed network. The image quality of 8
patients from the clinical trial are evaluated by three radiologists in
comparison with the standard image reconstruction with a full-view dataset. It
is shown that our proposed approach is essentially identical to or better than
the clinical benchmark in terms of diagnostic image quality scores. Our
approach has a great potential to improve the safety and efficiency of PCCT
without compromising image quality.
