There exists a strong motivation to increase the spatial resolution of magnetic resonance imaging (MRI) acquisitions so that MRI can be used as a microscopy technique in the study of porous materials. This work introduces a method for identifying novel data sampling patterns to achieve undersampling schemes for compressed sensing MRI (CS‐MRI) acquisitions, enabling 3D spatial resolutions of 17.6 µm to be achieved. A data‐driven learning approach is used to derive k‐space undersampling schemes for 3D MRI acquisitions from 3D X‐ray microcomputed tomography (µCT) datasets acquired at a higher spatial resolution than can be acquired using MRI. The performance of the new sampling approach was compared to other, well‐established sampling strategies using simulated MRI data obtained from high‐resolution µCT images of rock core plugs. These simulations were performed for a range of different k‐space sampling fractions (0.125–0.375) using images of Ketton limestone. The method was then extended to consideration of imaging Estaillades limestone and Fontainebleau sandstone. The results show that the new sampling approach performs as well as or better than conventional variable density sampling and without need for time‐consuming parameter optimisation. Further, a bespoke sampling pattern is produced for each rock type. The novel undersampling strategy was employed to acquire 3D magnetic resonance images of a Ketton limestone rock at spatial resolutions of 35 and 17.6 µm. The ability of the k‐space sampling scheme produced using the new approach in enabling reconstruction of the pore space characteristics of the rock was then demonstrated by benchmarking against the pore space statistics obtained from high‐resolution µCT data. The MRI data acquired at 17.6 µm resolution gave excellent agreement with the pore size distribution obtained from the X‐ray microcomputed tomography dataset, while the pore coordination number distribution obtained from the MRI data was slightly skewed to lower coordination numbers. This approach provides a method of producing a k‐space undersampling pattern for MRI acquisition at a spatial resolution for which a fully sampled acquisition at that spatial resolution would be impractically long. The approach can be easily extended to other CS‐MRI techniques, such as spatially resolved flow and relaxation time mapping.

中文翻译：

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多孔材料高分辨率压缩传感 MRI 采样模式的识别：从 X 射线显微计算机断层扫描数据中“学习”

存在增加磁共振成像 (MRI) 采集的空间分辨率的强烈动机，以便 MRI 可用作多孔材料研究中的显微技术。这项工作介绍了一种识别新数据采样模式的方法，以实现压缩传感 MRI (CS-MRI) 采集的欠采样方案，从而实现 17.6 µm 的 3D 空间分辨率。数据驱动的学习方法用于从以比使用 MRI 获得的空间分辨率更高的空间分辨率获得的 3D X 射线显微计算机断层扫描 (μCT) 数据集导出用于 3D MRI 采集的 k 空间欠采样方案。使用从岩芯塞的高分辨率 μCT 图像中获得的模拟 MRI 数据，将新采样方法的性能与其他完善的采样策略进行了比较。这些模拟是使用 Ketton 石灰岩图像对一系列不同的 k 空间采样分数 (0.125-0.375) 进行的。然后将该方法扩展到考虑对 Estaillades 石灰岩和枫丹白露砂岩进行成像。结果表明，新的采样方法与传统的可变密度采样一样好或更好，并且不需要耗时的参数优化。此外，为每种岩石类型生成定制的采样模式。采用新的欠采样策略以 35 和 17.6 µm 的空间分辨率获取 Ketton 石灰岩的 3D 磁共振图像。然后，通过对从高分辨率 μCT 数据获得的孔隙空间统计数据进行基准测试，证明了使用新方法产生的 k 空间采样方案能够重建岩石孔隙空间特征的能力。以 17.6 µm 分辨率获得的 MRI 数据与从 X 射线显微计算机断层扫描数据集获得的孔径分布非常吻合，而从 MRI 数据获得的孔配位数分布略微偏向较低的配位数。这种方法提供了一种在空间分辨率下为 MRI 采集生成 ak 空间欠采样模式的方法，对于该空间分辨率下的完全采样采集将是不切实际的长。该方法可以很容易地扩展到其他 CS-MRI 技术，