Optimal k-space sampling for single point imaging of transient systems

Parasoglou, Prodromos; Sederman, Andrew J.; Rasburn, J.; Powell, Hugh; Johns, Michael L.

doi:10.1016/j.jmr.2008.06.005

Cited by 15 publications

(15 citation statements)

References 42 publications

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“…A time interval of 5 Â T 1 is allowed between each interleaf to allow the magnetization to fully recover. As has been demonstrated in our previous study [9] improvement in the SNR and image quality can be achieved by following this sampling scheme.…”

Section: Quantitative Single Point Imaging With Optimised Sampling Trsupporting

confidence: 59%

“…A comprehensive study of a near optimum sampling scheme has been presented in previous work [9], where the sampling trajectory is dictated and ranked by the expected magnitude of the sampled k-space points which we predict based on available prior knowledge of the sample shape. k-Space points are sorted from maximum to minimum expected modulus signal intensity and divided into interleafs consistent with the centric scan sampling practise [8,15].…”

Section: Quantitative Single Point Imaging With Optimised Sampling Trmentioning

confidence: 99%

“…With the above sampling method (as presented in [9]), once the k-space points have been ranked then a subset of them with the highest intensity can be acquired in a sparse k-space sampling scheme. In order for this method to be used for compressed sensing, it is required that under-sampling causes incoherent artefacts, or more formally the sampling operator must not be easily (sparsely) represented in the transform domain [14].…”

Section: Quantitative Single Point Imaging With Optimised Sampling Trmentioning

confidence: 99%

“…Centric scans with different geometrical functions such as spirals and square spirals [7,8] have appeared in the literature. As we have shown in a previous study [9], prior knowledge of the sample shape can enable the design of near optimum sampling trajectories, where the SNR is maximised by sampling k-space points with the highest expected value convolved with the largest excited magnetization. Note that SPI, as a pure phase-encoding imaging pulse sequence, has the desirable feature of placing no constraints on the sampling trajectory.…”

Section: Introductionmentioning

confidence: 99%

“…The sampling trajectory was optimised based on the expected amplitude of the points in k-space as outlined in our previous publication [9]. Reconstruction of the images from this acquired k-space data is achieved via a non-linear iteration (based on optimizing a convex function involving l 1 -norms) as proposed by Lustig and co-workers [11].…”

Section: Introductionmentioning

confidence: 99%

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Quantitative single point imaging with compressed sensing

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Section: Quantitative Single Point Imaging With Optimised Sampling Trsupporting

confidence: 59%

Section: Quantitative Single Point Imaging With Optimised Sampling Trmentioning

confidence: 99%

Section: Quantitative Single Point Imaging With Optimised Sampling Trmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Quantitative single point imaging with compressed sensing

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Why are pretreatment prostate‐specific antigen levels and biochemical recurrence poor predictors of prostate cancer survival?

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An ultra scale‐down (USD) device that provides insight of how industrial homogenization impacts bioprocess performance is desirable in the biopharmaceutical industry, especially at the early stage of process development where only a small quantity of material is available. In this work, we assess the effectiveness of focused acoustics as the basis of an USD cell disruption method to mimic and study high‐pressure, step‐wise homogenization of rec Escherichia coli cells for the recovery of an intracellular protein, antibody fragment (Fab′). The release of both Fab′ and of overall protein follows first‐order reaction kinetics with respect to time of exposure to focused acoustics. The rate constant is directly proportional to applied electrical power input per unit volume. For nearly total protein or Fab′ release (>99%), the key physical properties of the disruptate produced by focused acoustics, such as cell debris particle size distribution and apparent viscosity show good agreement with those for homogenates produced by high‐pressure homogenization operated to give the same fractional release. The only key difference is observed for partial disruption of cells where focused acoustics yields a disruptate of lower viscosity than homogenization, evidently due to a greater extent of polynucleic acids degradation. Verification of this USD approach to cell disruption by high‐pressure homogenization is achieved using USD centrifugation to demonstrate the same sedimentation characteristics of disruptates prepared using both the scaled‐down focused acoustic and the pilot‐scale homogenization methods for the same fraction of protein release. Biotechnol. Bioeng. 2012; 109:2059–2069. © 2012 Wiley Periodicals, Inc.

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Rapid 3D‐imaging of phosphocreatine recovery kinetics in the human lower leg muscles with compressed sensing

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The rate of phosphocreatine (PCr) resynthesis following physical exercise is an accepted index of mitochondrial oxidative metabolism and has been studied extensively with unlocalized 31P-MRS methods and small surface coils. Imaging experiments using volume coils that measure several muscles simultaneously can provide new insights into the variability of muscle function in healthy and diseased states. However, they are limited by long acquisition times relative to the dynamics of PCr recovery. This work focuses on the implementation of a compressed sensing (CS) technique to accelerate imaging of PCr resynthesis following physical exercise, using a modified 3D turbo-spin-echo sequence and principal component analysis (PCA) as sparsifying transform. The CS technique was initially validated using 2-fold retrospective undersampling of fully-sampled data from four volunteers acquired on a 7T MRI system (voxel size: 1.6 ml, temporal resolution: 24 s), which led to an accurate estimation of the mono-exponential PCr resynthesis rate constant (mean error < 6.4%). Acquisitions with prospective 2-fold acceleration (temporal resolution: 12 s) demonstrated that 3D mapping of PCr resynthesis is possible at a temporal resolution that is sufficiently high for characterizing the recovery curve of several muscles in a single measurement.

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Optimal k-space sampling for single point imaging of transient systems

Cited by 15 publications

References 42 publications

Quantitative single point imaging with compressed sensing

Quantitative single point imaging with compressed sensing

Why are pretreatment prostate‐specific antigen levels and biochemical recurrence poor predictors of prostate cancer survival?

Rapid 3D‐imaging of phosphocreatine recovery kinetics in the human lower leg muscles with compressed sensing

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