  X-ray photon-counting detector (PCD) offers low noise, high resolution, and
spectral characterization, representing a next generation of CT and enabling
new biomedical applications. It is well known that involuntary patient motion
may induce image artifacts with conventional CT scanning, and this problem
becomes more serious with PCD due to its high detector pitch and extended scan
time. Furthermore, PCD often comes with a substantial number of bad pixels,
making analytic image reconstruction challenging and ruling out
state-of-the-art motion correction methods that are based on analytical
reconstruction. In this paper, we extend our previous locally linear embedding
(LLE) cone-beam motion correction method to the helical scanning geometry,
which is especially desirable given the high cost of large-area PCD. In
addition to our adaption of LLE-based parametric searching to helical cone-beam
photon-counting CT geometry, we introduce an unreliable-volume mask to improve
the motion estimation accuracy and perform incremental updating on gradually
refined sampling grids for optimization of both accuracy and efficiency. Our
numerical results demonstrate that our method reduces the estimation errors
near the two longitudinal ends of the reconstructed volume and overall image
quality. The experimental results on clinical photon-counting scans of the
patient extremities show significant resolution improvement after motion
correction using our method, which reveals subtle fine structures previously
hidden under motion blurring and artifacts.
