Images may be corrupted by salt and pepper impulse noise during image acquisitions or transmissions. Although promising denoising performances have been recently obtained with sparse representations, how to restore high-quality images remains challenging and open. In this work, image sparsity is enhanced with a fast multiclass dictionary learning, and then both the sparsity regularization and robust data fidelity are formulated as minimizations of L0-L0 norms for salt and pepper impulse noise removal. Additionally, a numerical algorithm of modified alternating direction minimization is derived to solve the proposed denoising model. Experimental results demonstrate that the proposed method outperforms the compared state-of-the-art ones on preserving image details and achieving higher objective evaluation criteria.

Multidimensional nuclear magnetic resonance (NMR) spectroscopy is one of the most crucial detection tools for molecular structure analysis and has been widely used in biomedicine and chemistry. However, the development of NMR spectroscopy is hampered by long data collection time. Non-uniform sampling empowers rapid signal acquisition by collecting a small subset of data. Since the sampling rate is lower than that of the Nyquist sampling ratio, undersampling artifacts arise in reconstructed spectra. To obtain a high-quality spectrum, it is necessary to apply reasonable prior constraints in spectrum reconstruction models. The self-learning subspace method has been shown to possess superior advantages than that of the state-of-the-art low-rank Hankel matrix method when adopting high acceleration in data sampling. However, the self-learning subspace method is time-consuming due to the singular value decomposition in iterations. In this paper, we propose a fast self-learning subspace method to enable fast and high-quality reconstructions. Aided by parallel computing, the experiment results show that the proposed method can reconstruct high-fidelity spectra but spend less than 10% of the time required by the non-parallel self-learning subspace method.

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Background: Lung cancer brain metastases are very common and one of the common causes of treatment failure. We aimed to examine the clinical use of chemical exchange saturation transfer (CEST) technology in the evaluation of brain metastases for lung cancer diagnosis and prognosis. Methods: We included26 cases of lung cancer brain metastases, 15 cases of gliomas, and 20 cases with normal tests. The magnetization transfer ratio (MTR;3.5 ppm) image from the GRE-EPI-CEST sequence was analyzed using the ASSET technique and APT technology. The MTR values were measured in the lesion-parenchymal, edema, and non-focus regions, and the MTR image was compared with the conventional MRI. ANOVA and t-test were used for statistical analysis. Results: The lesion-parenchymal, edema, and non-focus areas in the metastatic-tumor-group were red-yellow, yellow-green, and green-blue, and the MTR values were 3.29 ± 1.14%,1.28 ± 0.36%,and 1.26 ± 0.31%, respectively. However, in the glioma-group, the corresponding areas were red, red-yellow, and green-blue, and the MTR values were 6.29 ± 1.58%, 2.87 ± 0.65%, and 1.03 ± 0.30%, respectively. The MTR values of the corresponding areas in the normal-group were 1.07 ± 0.22%,1.04 ± 0.23%, and 1.06 ± 0.24%, respectively. Traditional MR images are in blackwhite contrast and no metabolic information is displayed. The MTRvalues of the three regions were significantly different among the three groups. The values were also significantly different between the parenchymal and edema areas in the metastatic-tumor-group. There were significant differences in the MTR values between the non-lesion and edema regions, but there was no significant difference between the edema and non-focus areas. In the glioma-group, there were significant differences in the MTR values between the parenchymal and edema areas, between the parenchymal and non-focus areas, and between the edema and non-focus areas. Conclusions: CEST reflects the protein metabolism; therefore, early diagnosis of brain metastases and assessment of the prognosis can be achieved using molecular imaging.

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