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

Abstract

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 phenomena in porous media relevant for engineering applications. A major application is the study of water and hydrocarbon transport in porous sedimentary rocks, which typically have pore sizes smaller than 100 µm. The spatial resolution of routine MRI acquisitions, however, is limited to several hundred µm due to the relatively low sensitivity of the magnetic resonance method. Therefore, there exists a strong motivation to increase the spatial resolution of MRI by one to two orders of magnitude to be able to study these rocks at a pore scale. This work reports the in...

MRI Applications and Research in Materials Science

“…Investigating pore size, shape, and interconnections is important in academic research. As highlighted in the same study, novel data sampling patterns have been developed to achieve 3D spatial resolutions as precise as 17.6 µm, demonstrating MRI's potential as a microscopy technique in studying porous materials [2]. The utilization of MRI is viable for monitoring the dynamics of fluid flow within porous materials due to its susceptibility to moisture.…”

“…This is primarily attributed to the capability of MRI to furnish comprehensive insights into the internal composition of the material while ensuring the absence of any detrimental effects on the sample. This aspect is crucial, as noted in a study on identifying sampling patterns for high-resolution MRI, where the motivation to increase MRI's spatial resolution for studying porous materials emphasizes its non-destructive analysis capabilities [2]. One significant benefit of MRI is its notable spatial resolution.…”

“…This capability is especially valuable in studying porous materials. MRI's noninvasive nature reveals intricate microstructures-such as pore size, shape, and distribution-essential for understanding material permeability and transport properties [2]. Despite these developments, many obstacles remain to overcome before MRI may be used in material science.…”

Latest advances in imaging techniques for characterizing soft, multiphasic food materials

“…However, due to the relative low intensity of MRI radiation, acquisition times are typically lengthy, and the spatial resolution is limited to hundreds of microns. There is renewed interest in improving both acquisition times and spatial resolution, as reported in Reci et al (2018) and Karlsons et al (2019). Due to the quantitative nature of the measurement (signal is proportional to proton density), MRI has found application in food science for the study of transport phenomena such as moisture diffusion (Paluri et al, 2015) and oil migration in confectionery products (Wang & Maleky, 2018) (Cikrikci & Oztop, 2018).…”