Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
University of Chinese Academy of Sciences, Beijing, China.
Med Phys. 2023 Dec;50(12):7684-7699. doi: 10.1002/mp.16425. Epub 2023 Apr 19.
Wave gradient encoding can adequately utilize coil sensitivity profiles to facilitate higher accelerations in parallel magnetic resonance imaging (pMRI). However, there are limitations in mainstream pMRI and a few deep learning (DL) methods for recovering missing data under wave encoding framework: the former is prone to introduce errors from the auto-calibration signals (ACS) signal acquisition and is time-consuming, while the latter requires a large amount of training data.
To tackle the above issues, an untrained neural network (UNN) model incorporating wave-encoded physical properties and deep generative model, named WDGM, was proposed with additional ACS- and training data-free.
Generally, the proposed method can provide powerful missing data interpolation capability using the wave physical encoding framework and designed UNN to characterize the MR image (k-space data) priors. Specifically, the MRI reconstruction combining physical wave encoding and elaborate UNN is modeled as a generalized minimization problem. The designation of UNN is driven by the coil sensitivity maps (CSM) smoothness and k-space linear predictability. And then, the iterative paradigm to recover the full k-space signal is determined by the projected gradient descent, and the complex computation is unrolled to the network with optimized parameters by the optimizer. Simulated wave encoding and in vivo experiments are exploited to demonstrate the feasibility of the proposed method. The best quantitative metrics RMSE/SSIM/PSNR of 0.0413, 0.9514, and 37.4862 gave competitive results in all experiments with at least six-fold acceleration, respectively.
In vivo experiments of human brains and knees showed that the proposed method can achieve comparable reconstruction quality and even has superiority relative to the comparison, especially at a high resolution of 0.67 mm and fewer ACS. In addition, the proposed method has a higher computational efficiency achieving a computation time of 9.6 s/per slice.
The model proposed in this work addresses two limitations of MRI reconstruction in the wave encoding framework. The first is to eliminate the need for ACS signal acquisition to perform the time-consuming calibration process and to avoid errors such as motion during the acquisition procedure. Furthermore, the proposed method has clinical application friendly without the need to prepare large training datasets, which is difficult in the clinical. All results of the proposed method demonstrate more confidence in both quantitative and qualitative metrics. In addition, the proposed method can achieve higher computational efficiency.
波梯度编码可以充分利用线圈灵敏度分布，以促进并行磁共振成像（pMRI）中的更高加速。然而，在主流的 pMRI 和一些用于在波编码框架下恢复缺失数据的深度学习（DL）方法中存在一些限制：前者容易从自动校准信号（ACS）信号采集引入误差，并且耗时，而后者需要大量的训练数据。
为了解决上述问题，提出了一种未训练的神经网络（UNN）模型，该模型结合了波编码物理特性和深度生成模型，称为 WDGM，并且不需要额外的 ACS 和训练数据。
通常，该方法可以使用波物理编码框架和设计的 UNN 来提供强大的缺失数据插值能力，以对磁共振图像（k 空间数据）先验进行特征化。具体来说，将结合物理波编码和精细 UNN 的 MRI 重建建模为广义最小化问题。UNN 的设计由线圈灵敏度图（CSM）的平滑度和 k 空间线性可预测性驱动。然后，通过投影梯度下降确定恢复完整 k 空间信号的迭代范例，并且通过优化器将复杂的计算展开到具有优化参数的网络中。利用模拟波编码和体内实验来证明该方法的可行性。在所有至少六倍加速的实验中，该方法的最佳定量指标 RMSE/SSIM/PSNR 分别为 0.0413、0.9514 和 37.4862，给出了具有竞争力的结果。
人体大脑和膝盖的体内实验表明，该方法可以实现可比较的重建质量，甚至相对于比较方法具有优势，特别是在高分辨率为 0.67mm 和更少 ACS 的情况下。此外，该方法具有更高的计算效率，每片计算时间为 9.6s。
本工作中提出的模型解决了波编码框架下 MRI 重建的两个限制。第一个是消除了对 ACS 信号采集的需求，从而避免了耗时的校准过程，并避免了采集过程中运动等误差。此外，该方法具有临床应用友好性，无需准备大型训练数据集，这在临床中是困难的。该方法的所有结果都在定量和定性指标上都表现出更高的置信度。此外，该方法可以实现更高的计算效率。
