Under-sampling and compressed sensing of 3D spatially-resolved displacement propagators in porous media using APGSTE-RARE MRI

Kort, Daan W. de; Hertel, Stefan; Appel, Matthias; Jong, H. de; Mantle, Michael D.; Sederman, Andrew J.; Gladden, Lynn F.

doi:10.1016/j.mri.2018.08.014

Cited by 9 publications

(3 citation statements)

References 21 publications

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“…oil and water), which can potentially alter the properties of the system. Although the routinely used spatial resolution of MRI is lower (a few hundred µm), it offers a range of different noninvasive contrast mechanisms, based on, for example, fluid type via the MR chemical shift (Ramskill et al ., ), wetting properties via MR relaxation time constants (Reci et al ., ), and fluid mobility via MR measurements of molecular self‐diffusivity, flow dispersion and velocity (Mitchell et al ., ; Colbourne et al ., ; de Kort et al ., , b). Thus, the high spatial resolution MRI acquisitions enabled by the present work are of particular relevance in studying structure‐flow correlations, imaging displacement processes and mapping spatial variation in fluid‐surface interactions in rocks.…”

Section: Introductionmentioning

confidence: 99%

“…Rapid 3D MRI acquisition methods, such as Rapid Acquisition with Relaxation Enhancement (RARE) (Hennig et al ., ), can alleviate this problem by significantly reducing the acquisition time (from weeks to days). The acquisitions of 3D MRI can be further sped up by combining these rapid imaging techniques with compressed sensing (CS) techniques (Lustig et al ., ; Ramskill et al ., , ; de Kort et al ., ) which can reduce the total image acquisition time to a few hours, which is comparable to scan times of 3D X‐ray microcomputed tomography (µCT). This is achieved by acquiring only a subset of the data that would be acquired in conventional MRI.…”

Section: Introductionmentioning

confidence: 99%

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

Karlsons

Kort

Sederman

et al. 2019

Journal of Microscopy

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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...

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Karlsons

Kort

Sederman

et al. 2019

Journal of Microscopy

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“…An alternative method is to measure fluid propagators using (pulsed-field gradient) magnetic resonance imaging (Gladden and Sederman, 2013). While having several advantages, including not requiring tracers, this method has only recently started to reach the required micron-scale spatial resolutions (de Kort et al, 2019). Several hours are required to measure a single flow field at this resolution, restricting its applicability to static flow fields.…”

Section: Introductionmentioning

confidence: 99%

X-ray tomographic micro-particle velocimetry in porous media

et al. 2022

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Fluid flow through intricate confining geometries often exhibits complex behaviors, certainly in porous materials, e.g., in groundwater flows or the operation of filtration devices and porous catalysts. However, it has remained extremely challenging to measure 3D flow fields in such micrometer-scale geometries. Here, we introduce a new 3D velocimetry approach for optically opaque porous materials, based on time-resolved x-ray micro-computed tomography (CT). We imaged the movement of x-ray tracing micro-particles in creeping flows through the pores of a sandpack and a porous filter, using laboratory-based CT at frame rates of tens of seconds and voxel sizes of 12 μm. For both experiments, fully three-dimensional velocity fields were determined based on thousands of individual particle trajectories, showing a good match to computational fluid dynamics simulations. Error analysis was performed by investigating a realistic simulation of the experiments. The method has the potential to measure complex, unsteady 3D flows in porous media and other intricate microscopic geometries. This could cause a breakthrough in the study of fluid dynamics in a range of scientific and industrial application fields.

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