Accelerating flow propagator measurements for the investigation of reactive transport in porous media

Colbourne, AA; Sederman, Andrew J.; Mantle, Michael D.; Gladden, Lynn F.

doi:10.1016/j.jmr.2016.08.018

Cited by 14 publications

(9 citation statements)

References 17 publications

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“…The asymmetric component of the propagator has been used to measure slow flows in plants [80]. There are methods of reducing the data requirements of propagator measurements which are applicable to slow flows [81,82]. A propagator provides more information about flow than D app and v in instances where it cannot be simply described by a shifted Gaussian.…”

Section: Other Pgse Methodsmentioning

confidence: 99%

Limits to flow detection in phase contrast MRI

Williamson

Komlosh

Benjamini

et al. 2020

Preprint

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Velocity detection with single pulsed gradient spin echo (PGSE) NMR is known to be limited by diffusion smearing the phase coherence and obscuring dispersive attenuation. We present an equation for the lowest detectable velocity with phase contrast velocimetry which incorporates the signal-to-noise ratio. We show how to best sample q-space when approaching this limit. We confirm that the apparent diffusion/dispersion coefficient can be used to infer fluid velocity for Péclet number Pé > 1. When measuring flow in biological systems we determined that with realistic experimental protocols the limits are roughly 1 µm/s for velocimetry and 300 µm/s for diffusometry.There is a strong interest to measure slow flows in vivo, e.g., measuring microcirculation of blood in capillary networks [6] and advective transport in the glymphatic system [7,8]. Knowledge of the lower limit of velocity resolution, as well as how to best measure slow velocities with PGSE MRI may open the door for these and other new applications. General TheoryOne beautiful aspect of magnetic resonance is the multitude of information which can be encoded into its complex signal. Pulsed gradient spin echo (PGSE) NMR exemplifies this in the solution to the Bloch-Torrey equations for the normalized complex signal E(q) arising from a fluid undergoing self-diffusion and coherent flow [9, 10, 11, 1],in which q sensitizes the signal to displacement during an observation time ∆. With rectangular gradient pulse shapes of amplitude g and duration δ, in the case that δ << ∆, q = γgδ where γ is the gyromagnetic ratio of the nuclei. Equation (1) shows that the phase shift between the real and imaginary channels and the attenuation of the signal intensity provide the ability to measure both mean coherent velocity, v, and random diffusion,

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Section: Other Pgse Methodsmentioning

confidence: 99%

Limits to flow detection in phase contrast MRI

Williamson

Komlosh

Benjamini

et al. 2020

Preprint

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“…To accelerate the acquisitions of multi-dimensional data such as spatially-resolved propagators, we have previously demonstrated the use of interpolation of under-sampled qspace data to reconstruct a 1D spatially-resolved displacement propagator [13], and the use of k-space under-sampling and compressed sensing (CS) to acquire velocity maps in porous media [14] and 3D images of porous media using Rapid Acquisition with Relaxation Enhancement (RARE) MRI [15,16]. Further, Paulsen et al have demonstrated the use of compressed sensing to accelerate the acquisition of a multi-dimensional diffusive propagator in an anisotropic porous medium [17].…”

Section: Displacement Propagators Are Probability Distributions Of Momentioning

confidence: 99%

Acquisition of spatially-resolved displacement propagators using compressed sensing APGSTE-RARE MRI

Kort

Reci

Ramskill

et al. 2018

Journal of Magnetic Resonance

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A method is presented for accelerating the acquisition of spatially-resolved displacement propagators via under-sampling of an Alternating Pulsed Gradient Stimulated Echo - Rapid Acquisition with Relaxation Enhancement (APGSTE-RARE) data acquisition with compressed sensing image reconstruction. The method was demonstrated with respect to the acquisition of 2D spatially-resolved displacement propagators of water flowing through a packed bed of hollow cylinders. The q,k-space was under-sampled according to variable-density pseudo-random sampling patterns. The quality of compressed sensing reconstructions of spatially-resolved propagators at a range of sampling fractions was assessed using the peak signal-to-noise ratio (PSNR) as a quality metric. Propagators of good quality (PSNR 33.2 dB) were reconstructed from only 6.25% of all data points in q,k-space, resulting in a reduction in the data acquisition time from 4 h to 14 min. The spatially-resolved propagators were reconstructed using both the total variation and nuclear norm sparsifying transforms; use of total variation resulted in a slightly higher quality of the reconstructed image in most cases. To illustrate the power of this method to characterise heterogeneous flow in porous media, the method is applied to the characterisation of flow in a vuggy carbonate rock.

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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%

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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Accelerating flow propagator measurements for the investigation of reactive transport in porous media

Cited by 14 publications

References 17 publications

Limits to flow detection in phase contrast MRI

Limits to flow detection in phase contrast MRI

Acquisition of spatially-resolved displacement propagators using compressed sensing APGSTE-RARE MRI

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

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