Motion blur in fluoroscopy: effects, identification, and restoration

In continuous X-ray fluoroscopy images are sometimes blurred uniformly due to motion of the operating table. Additionally, low-dose fluoroscopy images are degraded by relatively strong quantum noise, which is not affected by the blur. We quantify the degradation due to motion blur by assessing the blur's effect on the Detective Quantum Efficiency (DQE), which captures the signal- and noise transfer properties of an imaging system. The estimation of the motion blur parameters, viz. direction and extent, is carried out one after the other. The central idea for direction detection is to apply an inertia-like matrix to the global spectrum of the degraded image, which assesses the anisotropy caused by the blur. Once the blur direction is obtained by this tensor approach, its extent is identified from an estimated power spectrum or bispectrum slice along this direction. The decision for either method is based on the eigenvalues of the inertia matrix. The blur parameters are used as input for a nonlinear Maximum-a-posteriori restoration technique based on a Generalized Gauss-Markov Random field for which several efficient optimization strategies are presented. This approach includes a thresholdless edge model. The DQE is generalized as a quality measure to assess the signal- and noise transfer properties of the restoration method.