Micro-CT artifacts reduction based on detector random shifting and fast data inpainting.

Medical Physics

PubMedID: 23464294

Zhu Y, Zhao M, Li H, Zhang P. Micro-CT artifacts reduction based on detector random shifting and fast data inpainting. Med Phys. 2013;40(3):031114.
PURPOSE
In Micro-CT systems based on optical coupling detectors, the defects of scintillator or CCD-camera would lead to heavy artifacts in reconstructed CT images. Meanwhile, different detector units usually suffer from inhomogeneous response, which also leads to artifacts in the CT images. Detector shifting is a simple and efficient method to remove the artifacts due to inhomogeneous responses of detector units. However, it does not work well for heavy artifacts due to defects in scintillator or CCD. The purpose of this paper is to develop a data preprocessing method to reduce both kinds of artifacts.

METHODS
A hybrid method which involves detector random shifting and data inpainting is proposed to correct the projection data, so as to suppress the artifacts in the reconstructed CT images. The defects in scintillator or CCD-camera lead to data lost in some areas of the projection data. The Criminisi algorithm is employed to recover the lost data. By detector random shifting, the location of the lost data in one view might be shifted away in adjacent views. This feature is utilized to design the search window, such that the best match patch shall be searched across adjacent views. By this way, the best match patches should really enjoy high similarity. As a result, the heavy artifacts due to defects of scintillator or CCD-camera should be suppressed. Furthermore, a multiscale tessellation method is proposed to locate the defects and similarity patches, which makes the Criminisi algorithm very fast.

RESULTS
The authors tested the proposed method on both simulated projection data and real projection data. Experiments show that the proposed method could correct the bad data in the projections quite well. Compared to other popular methods, such as linear interpolation, wavelet combining Fourier transform, and TV-inpainting, experimental results suggest that the CT images reconstructed from the preprocessed data sets by our method is significantly better in quality.

CONCLUSIONS
They have proposed a hybrid method for projection data preprocessing which fits well to typical Micro-CT systems. The hybrid method could suppress the ring artifacts in the reconstructed CT images efficiently, while the spatial resolution is not reduced even with a critical eye.