Abstract
In computed tomography (CT), artifacts due to patient rigid motion often significantly degrade image quality. This paper suggests a method based on iterative blind deconvolution to eliminate motion artifacts. The proposed method alternately reconstructs the image and reduces motion artifacts in an iterative scheme until the difference measure between two successive iterations is smaller than a threshold. In this iterative process, Richardson–Lucy (RL) deconvolution with spatially adaptive total variation (SATV) regularization is inserted into the iterative process of the ordered subsets expectation maximization (OSEM) reconstruction algorithm. The proposed method is evaluated on a numerical phantom, a head phantom, and patient scan. The reconstructed images indicate that the proposed method can reduce motion artifacts and provide high-quality images. Quantitative evaluations also show the proposed method yielded an appreciable improvement on all metrics, reducing root-mean-square error (RMSE) by about 30% and increasing Pearson correlation coefficient (CC) and mean structural similarity (MSSIM) by about 15% and 20%, respectively, compared to the RL-OSEM method. Furthermore, the proposed method only needs measured raw data and no additional measurements are needed. Compared with the previous work, it can be applied to any scanning mode and can realize six degrees of freedom motion artifact reduction, so the artifact reduction effect is better in clinical experiments.
Highlights
As one of the important technologies in medical diagnosis, a computed tomography (CT) image can achieve a high performance in detecting and measuring small lesions [1,2]
The root-mean-square error (RMSE) of the proposed method can reduce by about 30%, compared to the RL-ordered subset expectation maximization (OSEM) method; the CC and mean structural similarity (MSSIM) of the proposed method can increase by about 15% and 21%, respectively, compared to the RL-OSEM
A method based on iterative blind deconvolution is developed to reduce the rigid motion artifacts for CT, which only requires the measured raw data
Summary
As one of the important technologies in medical diagnosis, a CT image can achieve a high performance in detecting and measuring small lesions [1,2]. Since the third category of methods has the advantages of neither increasing hardware cost or design difficulty nor requiring additional devices, it has been widely studied Among these methods, some were intended for 2D parallel-beam or fan-beam geometries and needed to assume a motion model that is an approximation of the real motion [9,10,11]. This method has the disadvantages of large computational complexity and poor estimation accuracy To solve this problem, a new method based on frequency domain analysis was proposed [10]. Motion parameters can be determined by the magnitude correlation of projections in frequency domain This method was more accurate and faster on the performance of Algorithms 2019, 12, 155; doi:10.3390/a12080155 www.mdpi.com/journal/algorithms
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