Current deep learning reconstruction for accelerated cardiac cine MRI suffers
from spatial and temporal blurring. We aim to improve image sharpness and
motion delineation for cine MRI under high undersampling rates. A
spatiotemporal diffusion enhancement model conditional on an existing deep
learning reconstruction along with a novel paired sampling strategy was
developed. The diffusion model provided sharper tissue boundaries and clearer
motion than the original reconstruction in experts evaluation on clinical data.
The innovative paired sampling strategy substantially reduced artificial noises
in the generative results.

改进深度学习重建方法，以提高高欠采样率条件下心脏动态磁共振成像的图像清晰度和运动描绘，通过创新的配对采样策略和时空扩散增强模型，实现了比原始重建方法更清晰的组织边界和运动细节，且生成结果中的人工噪声显著减少。

空间时间扩散模型与成对采样的加速心脏电影式磁共振成像

Spatiotemporal Diffusion Model with Paired Sampling for Accelerated  Cardiac Cine MRI

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。