Purpose: Acquiring fully-sampled MRI $k$-space data is time-consuming, and collecting accelerated data can reduce the acquisition time. Employing 2D Cartesian-rectilinear subsampling schemes is a conventional approach for accelerated acquisitions; however, this often results in imprecise reconstructions, even with the use of Deep Learning (DL), especially at high acceleration factors. Non-rectilinear or non-Cartesian trajectories can be implemented in MRI scanners as alternative subsampling options. This work investigates the impact of the $k$-space subsampling scheme on the quality of reconstructed accelerated MRI measurements produced by trained DL models. Methods: The Recurrent Variational Network (RecurrentVarNet) was used as the DL-based MRI-reconstruction architecture. Cartesian, fully-sampled multi-coil $k$-space measurements from three datasets were retrospectively subsampled with different accelerations using eight distinct subsampling schemes: four Cartesian-rectilinear, two Cartesian non-rectilinear, and two non-Cartesian. Experiments were conducted in two frameworks: scheme-specific, where a distinct model was trained and evaluated for each dataset-subsampling scheme pair, and multi-scheme, where for each dataset a single model was trained on data randomly subsampled by any of the eight schemes and evaluated on data subsampled by all schemes. Results: In both frameworks, RecurrentVarNets trained and evaluated on non-rectilinearly subsampled data demonstrated superior performance, particularly for high accelerations. In the multi-scheme setting, reconstruction performance on rectilinearly subsampled data improved when compared to the scheme-specific experiments. Conclusion: Our findings demonstrate the potential for using DL-based methods, trained on non-rectilinearly subsampled measurements, to optimize scan time and image quality.

翻译：目的：采集完全采样的MRI k空间数据耗时较长，而加速数据采集可缩短采集时间。采用2D笛卡尔直线子采样方案是加速采集的传统方法；然而，即使在深度学习（DL）的辅助下，尤其是在高加速因子下，这种方法往往导致不精确的重建。非直线或非笛卡尔轨迹可作为替代子采样选项在MRI扫描仪中实现。本研究旨在探究k空间子采样方案对训练好的DL模型生成的重建加速MRI测量数据质量的影响。方法：采用递归变分网络（RecurrentVarNet）作为基于DL的MRI重建架构。对来自三个数据集的笛卡尔全采样多线圈k空间测量数据，使用八种不同的子采样方案进行回顾性子采样（不同加速因子）：四种笛卡尔直线子采样方案、两种笛卡尔非直线子采样方案和两种非笛卡尔子采样方案。实验在两个框架下进行：方案特定框架（针对每个数据集-子采样方案对训练并评估一个独立模型）和多方案框架（针对每个数据集，使用八种方案中任意一种随机子采样的数据训练单个模型，并用所有方案子采样的数据评估）。结果：在两个框架中，基于非直线子采样数据训练和评估的RecurrentVarNet均表现出优越性能，尤其是在高加速因子下。在多方案框架中，与方案特定实验相比，直线子采样数据的重建性能有所提升。结论：我们的研究结果表明，基于非直线子采样测量数据训练的DL方法在优化扫描时间和图像质量方面具有潜力。