Motion-adaptive 3D nonlocal means filter based on stochastic distance for low-dose X-ray fluoroscopy

Low-dose X-ray fluoroscopy avoids radiation risks, but X-ray fluoroscopic image sequences are contaminated by quantum noise. In this paper, a 3D nonlocal means (NLM) filter based on stochastic distance that incorporates motion information is proposed, which can be applied to the denoising of X-ray fluoroscopic image sequences. First, the stochastic distance is obtained for use as the NLM filter similarity measure. This facilitates the removal of Poisson noise between the patches to be denoised within motion-compensated 3D volumes for spatio-temporal filtering. Second, motion state detection and motion-adaptive weights are proposed, which preserve the details of the motion that can occur during medical procedures. Experimental results obtained by applying the proposed method to real X-ray fluoroscopic image sequence images are shown in visual and numerical comparison with state-of-the-art denoising methods for spatio-temporal filtering techniques. The quantitative and qualitative results confirm that the proposed novel frameworks outperform other techniques in terms of objective criteria, as well as the subjective visual perception of the real X-ray fluoroscopic image sequences.