Identification of sampling patterns for high‐resolution compressed sensing MRI of porous materials: ‘learning’ from X‐ray microcomputed tomography data

Summary 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. Lay Description Magnetic resonance imaging (MRI) is widely used to study the microstructure of, and fluid transport phenom