Multidimensionally encoded magnetic resonance imaging

Lin, Fa‐Hsuan

doi:10.1002/mrm.24443

Cited by 21 publications

(25 citation statements)

References 14 publications

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“…The encoding fields from previous PatLoc and O-space implementations are thus theoretically related to each other by using different SEMs generated from a system of surface gradient elements with a circumferential arrangement. Although we did not explore including O-ring SEM for PatLoc imaging here, some preliminary studies have suggested that different SEMs, including O-ring, M1/M2, and L1/L2 SEMs, can be used jointly to improve the spatial encoding efficiency (19). We will pursue this research topic in the near future.…”

Section: Discussionmentioning

confidence: 99%

Reconstruction of MRI data encoded by multiple nonbijective curvilinear magnetic fields

Lin

Witzel

Schultz

et al. 2012

Magnetic Resonance in Med

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Parallel imaging technique using localized gradients (PatLoc) uses the combination of surface gradient coils generating non-bijective curvilinear magnetic fields for spatial encoding. PatLoc imaging using one pair of multipolar spatial encoding fields (SEMs) has two major caveats: 1) The direct inversion of the encoding matrix requires exact determination of multiple locations, which are ambiguously encoded by the SEMs. 2) Reconstructed images have a prominent loss of spatial resolution at the center of FOV using a symmetric coil array for signal detection. This study shows that a PatLoc system actually has a higher degree of freedom in spatial encoding to mitigate the two challenges mentioned above. Specifically, a PatLoc system can generate not only multipolar but also linear SEMs, which can be used to reduce the loss of spatial resolution at the FOV center. Here we present an efficient and generalized image reconstruction method for PatLoc imaging using multiple SEMs without explicitly identifying the locations where SEM encoding is not unique. Reconstructions using simulations and empirical experimental data are compared with those using conventional linear gradients to demonstrate that the general combination of SEMs can improve image reconstructions.

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Section: Discussionmentioning

confidence: 99%

Reconstruction of MRI data encoded by multiple nonbijective curvilinear magnetic fields

Lin

Witzel

Schultz

et al. 2012

Magnetic Resonance in Med

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“…But if we simulate radial imaging with 16, 32 and 64 points per echo, the rank of each encoding matrix E is 954, 1609 and 2461, respectively. More efficient encoding in MRI is possible, since MR images typically represent rank deficient matrices [29-30], and a higher rank encoding matrix E generally denotes a more efficient encoding [19, 31-32]. Therefore, the rank of the encoding matrices suggests that the proposed strategy, parallel imaging with O-Space acquisitions, is superior to parallel imaging with radial encoding, and the difference is increasingly pronounced at higher resolution readouts.…”

Section: Theorymentioning

confidence: 99%

“…O-Space imaging [6-7] was the first sequence explicitly designed to improve highly undersampled parallel imaging with nonlinear gradients, though many other methods have since been studied. These include Patloc Imaging [1], Null Space imaging [5], O-Space TSE imaging [8-10], Single Echo MRI [11], FRONSAC imaging [12-15], 4D-RIO [16], EPI-PatLoc [17], CS O-Space [18], Multi-Dimensional Encoding [19] and others [20-23]. …”

Section: Introductionmentioning

confidence: 99%

O-space with high resolution readouts outperforms radial imaging

Wang

Tam

Kopanoğlu

et al. 2017

Magnetic Resonance Imaging

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Purpose While O-Space imaging is well known to accelerate image acquisition beyond traditional Cartesian sampling, its advantages compared to undersampled radial imaging, the linear trajectory most akin to O-Space imaging, have not been detailed. In addition, previous studies have focused on ultrafast imaging with very high acceleration factors and relatively low resolution. The purpose of this work is to directly compare O-Space and radial imaging in their potential to deliver highly undersampled images of high resolution and minimal artifacts, as needed for diagnostic applications. We report that the greatest advantages to O-Space imaging are observed with extended data acquisition readouts. Theory and Methods A sampling strategy that uses high resolution readouts is presented and applied to compare the potential of radial and O-Space sequences to generate high resolution images at high undersampling factors. Simulations and phantom studies were performed to investigate whether use of extended readout windows in O-Space imaging would increase k-space sampling and improve image quality, compared to radial imaging. Results Experimental O-Space images acquired with high resolution readouts show fewer artifacts and greater sharpness than radial imaging with equivalent scan parameters. Radial images taken with longer readouts show stronger undersampling artifacts, which can cause small or subtle image features to disappear. These features are preserved in a comparable O-Space image. Conclusions High resolution O-Space imaging yields highly undersampled images of high resolution and minimal artifacts. The additional nonlinear gradient field improves image quality beyond conventional radial imaging.

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“…For instance, linear imaging gradients create a constant frequency encoding across the image weighted by the coil sensitivity profile . Developments in the application of nonlinear SEM fields for parallel imaging have focused on reducing peripheral nerve stimulation , perfecting local k‐space coverage , signal to noise (SNR) optimization through frame analysis , and algebraic determination of arbitrary encoding fields such as null space imaging (NSI) . Local k‐space analysis, which plots the k‐space from the spatial derivative of encoded phase,

k (t) = \nabla φ (x, t)

, provides an intuitive examination of the spatially‐varying nature of nonlinear acquisitions albeit without accounting for spatial localization from the receivers .…”

Section: Introductionmentioning

confidence: 99%

Pseudo‐random center placement O‐space imaging for improved incoherence compressed sensing parallel MRI

Tam

Galiana

Stockmann

et al. 2014

Magnetic Resonance in Med

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Purpose Nonlinear spatial encoding magnetic (SEM) field strategies such as O-space imaging have previously reported dispersed artifacts during accelerated scans. Compressed sensing (CS) has shown a sparsity-promoting convex program allows image reconstruction from a reduced data set when using the appropriate sampling. The development of a pseudo-random center placement (CP) O-space CS approach optimizes incoherence through SEM field modulation to reconstruct an image with reduced error. Theory and Methods The incoherence parameter determines the sparsity levels for which CS is valid and the related transform point spread function measures the maximum interference for a single point. The O-space acquisition is optimized for CS by perturbing the Z2 strength within 30% of the nominal value and demonstrated on a human 3T scanner. Results Pseudo-random CP O-space imaging is shown to improve incoherence between the sensing and sparse domains. Images indicate pseudo-random CP O-space has reduced mean squared error compared with a typical linear SEM field acquisition method. Conclusion Pseudo-random CP O-space imaging, with a nonlinear SEM field designed for CS, is shown to reduce mean squared error of images at high acceleration over linear encoding methods for a 2D slice when using an eight channel circumferential receiver array for parallel imaging.

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Multidimensionally encoded magnetic resonance imaging

Cited by 21 publications

References 14 publications

Reconstruction of MRI data encoded by multiple nonbijective curvilinear magnetic fields

Reconstruction of MRI data encoded by multiple nonbijective curvilinear magnetic fields

O-space with high resolution readouts outperforms radial imaging

Pseudo‐random center placement O‐space imaging for improved incoherence compressed sensing parallel MRI

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