Arterial spin labeling MR image denoising and reconstruction using unsupervised deep learning

Arterial spin labeling (ASL) imaging is a powerful magnetic resonance imaging technique that allows to quantitatively measure blood perfusion non-invasively, which has great potential for assessing tissue viability in various clinical settings. However, the clinical applications of ASL are currently limited by its low signal-to-noise ratio (SNR), limited spatial resolution, and long imaging time. In this work, we propose an unsupervised deep learning-based image denoising and reconstruction framework to improve the SNR and accelerate the imaging speed of high resolution ASL imaging. The unique feature of the proposed framework is that it does not require any prior training pairs but only the subject's own anatomical prior, such as T1-weighted images, as network input. The neural network was trained from scratch in the denoising or reconstruction process, with noisy images or sparely sampled k-space data as training labels. Performance of the proposed method was evaluated using in vivo experiment data obtained from 3 healthy subjects on a 3T MR scanner, using ASL images acquired with 44-min acquisition time as the ground truth. Both qualitative and quantitative analyses demonstrate the superior performance of the proposed txtc framework over the reference methods. In summary, our proposed unsupervised deep learning-based denoising and reconstruction framework can improve the image quality and accelerate the imaging speed of ASL imaging.     

Cognitive Impairments in Patients with Bipolar Affective Disorder in Remission

Neuroscience and Behavioral Physiology - Objective. To determine the frequency, structure, and severity of cognitive impairments in patients with bipolar affective disorder (BAD) in remission....     

Analysis of the correlation between P27 expression and Helicobacter pylori infection in gastric cancer

We aimed to explore the correlation between P27 expression and Helicobacter pylori (H. pylori) infection in gastric cancer, so as to provide evidence for understanding the pathogenesis of gastric cancer caused by H. pylori infection. A total of 82 samples of gastric cancer tissues and 56 samples of tumor-adjacent normal tissues collected from the gastrectomy were enrolled in this study. Then, 14C-urease breathing test was carried out to evaluate the infection of H. pylori in gastric cancer tissues, the expression of P27 in the tissue samples was detected by the immunohistochemistry staining, and the correlation between the H. pylori infection and P27 expression in gastric cancer was analyzed. Of 82 gastric cancer patients, there were 53 patients with H. pylori infection (64.63%). Among the patients with highly or moderately differentiated gastric cancer, the expression of P27 was much higher than that of patients with poorly differentiated gastric cancer (p < 0.01). Besides, comparison of the P27 expression between males and females, among different age groups, tumor sizes, TNM stages, tumor infiltration degrees, or lymph node metastasis, showed no significant differences (p > 0.05). Analysis of the correlation revealed that P27 expression was negatively correlated with the infection of H. pylori (p < 0.01). Multifactorial logistics regression analysis indicated that tumor differentiation was a risk factor of P27-positive expression in gastric cancer tissues (p < 0.01). In addition, P27 expression in the gastric cancer tissues was lower than that in the tumor-adjacent normal tissues (p < 0.01). In gastric cancer patients, expression of P27 is correlated with H. pylori infection which, via downregulating P27, can cause the cancerization of gastric mucosa, and P27, for its role in the development and progression of gastric cancer, is a potential auxiliary indicator for clinical diagnosis whether gastric cancer is complicated with H. pylori infection. So, P27 is a key indicator for diagnosis, treatment, and prognostic evaluation of disease in the advanced stage.