Ring artifacts correction based on the projection-field in neutron CT

Ring artifacts will happen mostly when the detector has inconsistent response among the detector channels,and the characteristic produced rings centered in the iso-center in the reconstructed slices inevitably affect the recognition and analysis of the corresponding sample structures in neutron computed tomography(CT).In this work,a ring correction method based on the projection-field(RCP)is proposed,it is a pre-processing method and provides the corrected projection data directly,which is also conducive to efficient data storage and other algorithmic researches.Simulation and physical experiments are performed for verifying the effect of the method,and one of the correction methods based on the image-field is used for comparison.The results demonstrate that the RCP can correct the ring artifacts well without reducing the image resolution or over-correction.

Ring artifacts removal in X-ray-induced acoustic computed tomography

X-ray-induced acoustic computed tomography(XACT)is a hybrid imaging modality for detecting X-ray absorption distribution via ultrasound emission.It facilitates imaging from a single projection X-ray illumination,thus reducing the radiation exposure and improving imaging speed.Nonuniform detector response caused by the interference between multichannel data acquisition for ring array transducers and amplifier systems yields ring artifacts in the reconstructed XACT images,which compromises the image quality.We propose model-based algorithms for ring artifacts corrected XACT imaging and demonstrate their effcacy on numerical and experimental measurements.The corrected reconstructions indicate significantly reduced ring artifacts as compared to their conventional counterparts.

Multiscale anisotropic diffusion for ringing artifact suppression in geophysical deconvolution data

Ringing artifact degradations always appear in the deconvolution of geophysical data. To address this problem, we propose a postprocessing approach to suppress ringing artifacts that uses a novel anisotropic diffusion based on a stationary wavelet transform （SWT） algorithm. In this paper, we discuss the ringing artifact suppression problem and analyze the characteristics of the deconvolu- tion ringing artifact. The deconvolution data containing ringing artifacts are decomposed into different SWT sub- bands for analysis, and a new multiscale adaptive aniso- tropic filter is developed to suppress these degradations. Finally, we demonstrate the performance of the proposed method and describe the experiments in detail.

A deep learning-based ring artifact correctionmethod for X-ray CT

Purpose In X-ray CT systems,ring artifacts caused by the nonuniform response of detector elements degrades the reconstruction quality and affects the subsequent processing and quantitative analysis of the image.Method In this paper,a novel method is proposed to remove the ring artifacts in CT image by applying deep learning algorithm based on convolutional neural network(CNN)and recurrent neural network(RNN).First,the reconstructed CT images is transformed into polar coordinate system to make rings appear as stripes.Then,a CNN is constructed to detect the stripes,and a RNN is utilized to process the line artifact correction.After that,by retransforming the corrected image from polar coordinate system to Cartesian coordinate system,a ring artifact removal image can be achieved.Results The presented method can successfully reduce the CT ring artifact on simulated and real data.Specifically,in the experiment with real water phantom,the center and peripheral standard deviations reduced 46%and 24%,respectively.Conclusions The proposed method is potential to be widely deployed in industrial and medical CT systems,due to the excellent results on correction and the real-time performance without adjusting parameters manually.