Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea.
Artificial Intelligence and Robotics Institute, Korea Institute of Science and Technology, Seoul, Republic of Korea.
Magn Reson Med. 2024 Jun;91(6):2483-2497. doi: 10.1002/mrm.30021. Epub 2024 Feb 11.
We introduced a novel reconstruction network, jointly unrolled cross-domain optimization-based spatio-temporal reconstruction network (JUST-Net), aimed at accelerating 3D multi-echo gradient-echo (mGRE) data acquisition and improving the quality of resulting myelin water imaging (MWI) maps.
An unrolled cross-domain spatio-temporal reconstruction network was designed. The main idea is to combine frequency and spatio-temporal image feature representations and to sequentially implement convolution layers in both domains. The k-space subnetwork utilizes shared information from adjacent frames, whereas the image subnetwork applies separate convolutions in both spatial and temporal dimensions. The proposed reconstruction network was evaluated for both retrospectively and prospectively accelerated acquisition. Furthermore, it was assessed in simulation studies and real-world cases with k-space corruptions to evaluate its potential for motion artifact reduction.
The proposed JUST-Net enabled highly reproducible and accelerated 3D mGRE acquisition for whole-brain MWI, reducing the acquisition time from fully sampled 15:23 to 2:22 min within a 3-min reconstruction time. The normalized root mean squared error of the reconstructed mGRE images increased by less than 4.0%, and the correlation coefficients for MWI showed a value of over 0.68 when compared to the fully sampled reference. Additionally, the proposed method demonstrated a mitigating effect on both simulated and clinical motion-corrupted cases.
The proposed JUST-Net has demonstrated the capability to achieve high acceleration factors for 3D mGRE-based MWI, which is expected to facilitate widespread clinical applications of MWI.
我们引入了一种新颖的重建网络，联合解卷的跨域优化时空重建网络（JUST-Net），旨在加速 3D 多回波梯度回波（mGRE）数据采集并提高由此产生的髓鞘水成像（MWI）图的质量。
设计了一个解卷的跨域时空重建网络。其主要思想是结合频率和时空图像特征表示，并在两个域中依次执行卷积层。k 空间子网利用来自相邻帧的共享信息，而图像子网在空间和时间维度上分别应用单独的卷积。评估了所提出的重建网络用于回顾性和前瞻性加速采集的性能。此外，还在存在 k 空间污染的模拟研究和实际案例中评估了其减少运动伪影的潜力。
所提出的 JUST-Net 实现了全脑 MWI 的高度可重复和加速 3D mGRE 采集，将采集时间从完全采样的 15:23 减少到 3 分钟重建时间内的 2:22 分钟。重建 mGRE 图像的归一化均方根误差增加小于 4.0%，与完全采样参考相比，MWI 的相关系数超过 0.68。此外，该方法还对模拟和临床运动污染病例显示出了缓解作用。
所提出的 JUST-Net 已经证明了在基于 3D mGRE 的 MWI 中实现高加速因子的能力，这有望促进 MWI 的广泛临床应用。
