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

Purpose: To evaluate the quality of deep learning reconstruction for
prospectively accelerated intraoperative magnetic resonance imaging (iMRI)
during resective brain tumor surgery.
Materials and Methods: Accelerated iMRI was performed during brain surgery
using dual surface coils positioned around the area of resection. A deep
learning (DL) model was trained on the fastMRI neuro dataset to mimic the data
from the iMRI protocol. Evaluation was performed on imaging material from 40
patients imaged between 01.11.2021 - 01.06.2023 that underwent iMRI during
tumor resection surgery. A comparative analysis was conducted between the
conventional compressed sense (CS) method and the trained DL reconstruction
method. Blinded evaluation of multiple image quality metrics was performed by
two working neuro-radiologists and a working neurosurgeon on a 1 to 5 Likert
scale (1=non diagnostic, 2=poor, 3=acceptable, 4=good, 5=excellent), and the
favored reconstruction variant.
Results: The DL reconstruction was strongly favored or favored over the CS
reconstruction for 33/40, 39/40, and 8/40 of cases for reader 1, 2, and 3,
respectively. Two of three readers consistently assigned higher ratings for the
DL reconstructions, and the DL reconstructions had a higher score than their
respective CS counterparts for 72%, 72%, and 14% of the cases for reader 1, 2,
and 3, respectively. Still, the DL reconstructions exhibited shortcomings such
as a striping artifact and reduced signal.
Conclusion: DL shows promise to allow for high-quality reconstructions of
intraoperative MRI with equal to or improved perceived spatial resolution,
signal-to-noise ratio, diagnostic confidence, diagnostic conspicuity, and
spatial resolution compared to compressed sense.

通过对深度学习重建的质量进行评估，本研究旨在评估前瞻性加速的术中磁共振成像（iMRI）在切除性脑肿瘤手术中的质量。