The Computed Tomography (CT) for diagnosis of lesions in human internal
organs is one of the most fundamental topics in medical imaging. Low-dose CT,
which offers reduced radiation exposure, is preferred over standard-dose CT,
and therefore its reconstruction approaches have been extensively studied.
However, current low-dose CT reconstruction techniques mainly rely on
model-based methods or deep-learning-based techniques, which often ignore the
coherence and smoothness for sequential CT slices. To address this issue, we
propose a novel approach using generative adversarial networks (GANs) with
enhanced local coherence. The proposed method can capture the local coherence
of adjacent images by optical flow, which yields significant improvements in
the precision and stability of the constructed images. We evaluate our proposed
method on real datasets and the experimental results suggest that it can
outperform existing state-of-the-art reconstruction approaches significantly.

通过使用改进的生成对抗网络（GANs）和局部一致性技术，该研究提出了一种新的低剂量 CT 重建方法，能够在以往的技术基础上显著提高图像构建的准确度和稳定性。

基于 GAN 与局部一致性的低剂量 CT 重建解决方案

Solving Low-Dose CT Reconstruction via GAN with Local Coherence

Cardiac cine MRI is the gold standard for cardiac functional assessment, but
the inherently slow acquisition process creates the necessity of reconstruction
approaches for accelerated undersampled acquisitions. Several regularization
approaches that exploit spatial-temporal redundancy have been proposed to
reconstruct undersampled cardiac cine MRI. More recently, methods based on
supervised deep learning have been also proposed to further accelerate
acquisition and reconstruction. However, these techniques rely on usually large
dataset for training, which are not always available. In this work, we propose
an unsupervised approach based on implicit neural field representations for
cardiac cine MRI (so called NF-cMRI). The proposed method was evaluated in
in-vivo undersampled golden-angle radial multi-coil acquisitions for
undersampling factors of 26x and 52x, achieving good image quality, and
comparable spatial and improved temporal depiction than a state-of-the-art
reconstruction technique.

提出了一种基于隐式神经场表征的非监督方法用于心脏心电影 MRI（所谓 NF-cMRI），该方法在体内进行了评估，对于 26 倍和 52 倍的欠采样黄金角度径向多线圈采集，实现了良好的图像质量和可比较的空间和改进的时间描述，优于先进的重建技术。

使用神经场进行加速心脏影像 MRI 的无监督重建

Unsupervised reconstruction of accelerated cardiac cine MRI using Neural  Fields

Event cameras are novel sensors that report brightness changes in the form of
a stream of asynchronous "events" instead of intensity frames. They offer
significant advantages with respect to conventional cameras: high temporal
resolution, high dynamic range, and no motion blur. While the stream of events
encodes in principle the complete visual signal, the reconstruction of an
intensity image from a stream of events is an ill-posed problem in practice.
Existing reconstruction approaches are based on hand-crafted priors and strong
assumptions about the imaging process as well as the statistics of natural
images. In this work we propose to learn to reconstruct intensity images from
event streams directly from data instead of relying on any hand-crafted priors.
We propose a novel recurrent network to reconstruct videos from a stream of
events, and train it on a large amount of simulated event data. During training
we propose to use a perceptual loss to encourage reconstructions to follow
natural image statistics. We further extend our approach to synthesize color
images from color event streams. Our network surpasses state-of-the-art
reconstruction methods by a large margin in terms of image quality (> 20%),
while comfortably running in real-time. We show that the network is able to
synthesize high framerate videos (> 5,000 frames per second) of high-speed
phenomena (e.g. a bullet hitting an object) and is able to provide high dynamic
range reconstructions in challenging lighting conditions. We also demonstrate
the effectiveness of our reconstructions as an intermediate representation for
event data. We show that off-the-shelf computer vision algorithms can be
applied to our reconstructions for tasks such as object classification and
visual-inertial odometry and that this strategy consistently outperforms
algorithms that were specifically designed for event data.

本文提出了一种使用循环网络从事件流中直接学习重建图像的方法，并在此基础上扩展到从事件流合成彩色图像的方法，实验证明该网络在处理高速现象和挑战性照明条件下具有较好的图像质量和高动态范围重建，并作为事件数据的中间表示进行了分类和视觉惯性积分导航等应用。