Ultrafast Microscopy of Microfluidics: Compressed Sensing and Remote Detection

Paciok, Eva; Blümich, Bernhard

doi:10.1002/anie.201100965

Cited by 10 publications

(6 citation statements)

References 32 publications

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“…14 Hyperpolarisation methods can be combined with micro-NMR detectors and microfluidic systems. [15][16][17][18][19][20][21][22] One such method involves the chemical reaction of the singlet spin isomer of molecular hydrogen, and is called parahydrogen-induced hyperpolarisation (PHIP). [23][24][25][26] While most studies have so far brought the reaction liquid in direct contact with hydrogen gas either through bubbling or by atomisation of the liquid in a hydrogen-filled chamber, [27][28][29][30][31][32][33] liquid-gas interfaces (and bubbles in particular) pose difficulties in the context of microfluidic devices, since they tend to alter the flow properties, and can block fluid transport altogether.…”

Section: Introductionmentioning

confidence: 99%

High-Resolution Nuclear Magnetic Resonance Spectroscopy with Picomole Sensitivity by Hyperpolarization on a Chip

et al. 2019

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We show that high-resolution NMR can reach picomole sensitivity for micromolar concentrations of analyte by combining parahydrogen induced hyperpolarisation (PHIP) with a high-sensitivity transmission line micro-detector. The para-enriched hydrogen gas is introduced into solution by diffusion through a membrane integrated into a microfluidic chip.NMR microdetectors, operating with sample volumes of a few µL or less, benefit from a favourable scaling of mass sensitivity. However, the small volumes make it very difficult to detect species present at less than millimolar concentrations in microfluidic NMR systems.In view of overcoming this limitation, we implement parahydrogen-induced polarisation (PHIP) on a microfluidic device with 2.5 µL detection volume. Integrating the hydrogenation reaction into the chip minimises polarisation losses to spin-lattice relaxation, allowing the detection of picomoles of substance. This corresponds to a concentration limit of detection of better than 1 µM √ s, unprecedented at this sample volume. The stability and sensitivity of the system allows quantitative characterisation of the signal dependence on flow rates and other reaction parameters and permits homo-( 1 H-1 H) and heteronuclear( 1 H-13 C) 2D NMR experiments at natural 13 C abundance.

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Section: Introductionmentioning

confidence: 99%

High-Resolution Nuclear Magnetic Resonance Spectroscopy with Picomole Sensitivity by Hyperpolarization on a Chip

et al. 2019

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“…The main idea is to exploit redundancy in some specific domain of the measured data. This approach is strongly related to the theory of compressed sensing (CS) [17, 18,19] and many image reconstruction techniques have been proposed [11,14,20,21,22,23]. Depending on whether γξ is sampled on a uniform or non-uniform grid, SF can be realised via the Fast Fourier Transform (FFT) [24] or via a non-uniform Fourier Transform such as NUFFT [25].…”

Section: Samplingmentioning

confidence: 99%

“…for a subgradient q ∈ ∂ J(v). Note that algorithm (21) has update rules for the subgradients, as J u , J v and J φ are allowed to be non-smooth, which makes the selection of particular subgradients necessary.…”

Section: Optimisationmentioning

confidence: 99%

“…For both algorithms global convergence results, motivated by [38,39], have been established. Since we deal with imperfect data potentially corrupted by measurement noise and numerical errors, we will, however, use (21) in combination with an early-stopping criterion in order not to converge to a minimiser of (20) but to approximate the solution of ( 16) via iterative regularisation.…”

Section: Optimisationmentioning

confidence: 99%

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Joint phase reconstruction and magnitude segmentation from velocity-encoded MRI data

Corona¹,

Benning²,

Gladden³

et al. 2019

Preprint

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Velocity-encoded MRI is an imaging technique used in different areas to assess flow motion. Some applications include medical imaging such as cardiovascular blood flow studies, and industrial settings in the areas of rheology, pipe flows, and reactor hydrodynamics, where the goal is to characterise dynamic components of some quantity of interest. The problem of estimating velocities from such measurements is a nonlinear dynamic inverse problem. To retrieve time-dependent velocity information, careful mathematical modelling and appropriate regularisation is required. In this work, we propose an optimisation algorithm based on non-convex Bregman iteration to jointly estimate velocity-, magnitude-and segmentation-information for the application of bubbly flow imaging. Furthermore, we demonstrate through numerical experiments on synthetic and real data that the joint model improves velocity, magnitude and segmentation over a classical sequential approach.

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“…Many NMR spectra for example are ideally suited to CS reconstruction, as they consist of relatively few isolated peaks in the Fourier basis, and therefore are inherently sparse. The combinations of CS with reconstruction techniques that exploit prior knowledge of the signal allow sampling of down to $1/6 of k-space [9][10][11][12][13]. While these developments are very significant for rapid MRI, it is also of interest to minimize the sampling of k-space further and avoid Fourier transforming k-space all together to obtain essential signal density statistics.…”

Section: Introductionmentioning

confidence: 98%