Diffusion MRI is commonly performed using echo-planar imaging (EPI) due to
its rapid acquisition time. However, the resolution of diffusion-weighted
images is often limited by magnetic field inhomogeneity-related artifacts and
blurring induced by T2- and T2*-relaxation effects. To address these
limitations, multi-shot EPI (msEPI) combined with parallel imaging techniques
is frequently employed. Nevertheless, reconstructing msEPI can be challenging
due to phase variation between multiple shots. In this study, we introduce a
novel msEPI reconstruction approach called zero-MIRID (zero-shot
self-supervised learning of Multi-shot Image Reconstruction for Improved
Diffusion MRI). This method jointly reconstructs msEPI data by incorporating
deep learning-based image regularization techniques. The network incorporates
CNN denoisers in both k- and image-spaces, while leveraging virtual coils to
enhance image reconstruction conditioning. By employing a self-supervised
learning technique and dividing sampled data into three groups, the proposed
approach achieves superior results compared to the state-of-the-art parallel
imaging method, as demonstrated in an in-vivo experiment.

使用 zero-MIRID 的深度学习多段 EPI 重建方法，在体内实验中表现出卓越的成果，并实现了对扩散 MRI 图像的改进。

零样本自监督学习重建的改进多次扫描弥散加权 MRI

Improved Multi-Shot Diffusion-Weighted MRI with Zero-Shot  Self-Supervised Learning Reconstruction

At a time when information seekers first turn to digital sources for news and
opinion, it is critical that we understand the role that social media plays in
human behavior. This is especially true when information consumers also act as
information producers and editors by their online activity. In order to better
understand the effects that editorial ratings have on online human behavior, we
report the results of a large-scale in-vivo experiment in social media. We find
that small, random rating manipulations on social media submissions created
significant changes in downstream ratings resulting in significantly different
final outcomes. Positive treatment resulted in a positive effect that increased
the final rating by 11.02% on average. Compared to the control group, positive
treatment also increased the probability of reaching a high rating (>=2000) by
24.6%. Contrary to the results of related work we also find that negative
treatment resulted in a negative effect that decreased the final rating by
5.15% on average.

通过社交媒体上的大规模实验，研究设定好坏评分对信息流和行为影响的结果，发现社交媒体的个人和编辑们的活动至关重要，小幅度的随机技术可以在信息的下行评级上产生显著的结果，进而影响最终的评分和结果。