Subjects: Medicine, Pharmacy >> Preclinical Medicine submitted time 2018-06-15 Cooperative journals: 《南方医科大学学报》
Abstract： Objective In the clinic, myocardial perfusion computed tomography (MPCT) imaging is commonly used to detect and assess myocardial ischemia quantitatively. However, repeated scanning on the myocardial region in the cine mode will increase the radiation dose for patients. With lowering radiation dose, the quality of images are degraded by noise induced artifact, which hampers the diagnostic accuracy. Therefore, in this paper, we propose a redundancy information induced iterative reconstruction framework for high quality MPCT images at the case of low dose. Methods MPCT images have redundant structural information within frames and highly similarity between adjacent frames. Inspired by the two properties, in this work we propose a penalized weighted least-squares (PWLS) model incorporating NLM and TV based hybrid constraints, which is referred to as PWLS-aviNLM-TV for simplicity. The proposed algorithm can effectively eliminate noise and artifacts by taking into account the similarity between adjacent frames and redundancy information within frames, which also can improve spatial resolution within frames and maintain temporal resolution. Results The experimental results on the 4D extended cardiac-torso (XCAT) phantom and preclinical porcine dataset demonstrates that the PWLS-aviNLM-TV algorithm obtains better performance in terms of noise reduction and artifacts suppression than the PWLS-TV and PWLSaviNLM algorithm. Moreover, the proposed algorithm can preserve the edges and detail information thereby efficiently differentiate ischemia from myocardium. Conclusion The present redundancy information induced reconstruction algorithm can reconstruct high-quality images from low-dose MPCT for better clinical imaging diagnosis.
Subjects: Medicine, Pharmacy >> Preclinical Medicine submitted time 2017-12-07 Cooperative journals: 《南方医科大学学报》
Abstract： Objective Ring artifacts often appear in flat-panel detector-based CT images due to the malfunction or mis-calibration of the detector elements that result in stripe artifacts in the line integral projection (sinogram) data. The ring artifacts lower the image quality and affect image-based diagnoses. Here we propose a ring artifacts removal approach based on wavelet filtering in the sinogram domain. The line integral projection (sinogram) dataset were divided into 4 sub-sinogram dataset, and for each of them the wavelet decomposition operation was employed to produce the associated wavelet dataset, followed by filtering operation on the vertical detail band and the low-pass detail band. Wavelet reconstruction operation was then performed, and the weighted moving average filter was used to yield the filtered sinogram, which was processed using filtered back-projection (FBP) for image reconstruction. The results showed that the proposed approach could effectively remove the ring artifacts while preserving the structural information of the image.
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