Modified block uniform resampling (BURS) algorithm using truncated singular value decomposition: Fast accurate gridding with noise and artifact reduction

Moriguchi, Hisamoto; Duerk, Jeffrey L.

doi:10.1002/mrm.1316

Cited by 26 publications

(24 citation statements)

References 25 publications

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“…(3). Another interesting limit is when the number of coils L in the second summation series ¥ lЈ ϭ 1 L becomes one (as in nonparallel imaging), in which case PARS converges to a variant of the block uniform resampling (BURS) reconstruction (12).…”

Section: Theorymentioning

confidence: 99%

“…PARS reconstructions with a range of k R were performed, and the total error power was calculated as described by Eq. [12]. Similarly, SMASH and SENSE reconstructions were performed.…”

Section: Data Reconstruction and Analysismentioning

confidence: 99%

“…PARS, in its application of k-space locality and choice of kernel size, bears a close analogy with the BURS algorithm, (12,22,23) a "regridding" algorithm for nonparallel imaging. As shown in Eq.…”

Section: Pars and Bursmentioning

confidence: 99%

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3Parallel magnetic resonance imaging with adaptive radius in k‐space (PARS): Constrained image reconstruction using k‐space locality in radiofrequency coil encoded data

Yeh

McKenzie

Ohliger

et al. 2005

Magnetic Resonance in Med

115

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A parallel image reconstruction algorithm is presented that exploits the k-space locality in radiofrequency (RF) coil encoded data. In RF coil encoding, information relevant to reconstructing an omitted datum rapidly diminishes as a function of k-space separation between the omitted datum and the acquired signal data. The proposed method, parallel magnetic resonance imaging with adaptive radius in k-space (PARS), harnesses this physical property of RF coil encoding via a sliding-kernel approach. Unlike generalized parallel imaging approaches that might typically involve inverting a prohibitively large matrix for arbitrary sampling trajectories, the PARS sliding-kernel approach creates manageable and distributable independent matrices to be inverted, achieving both computational efficiency and numerical stability. An empirical method designed to measure total error power is described, and the total error power of PARS reconstructions is studied over a range of k-space radii and accelerations, revealing "minimalerror" conditions at comparatively modest k-space radii.

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Section: Theorymentioning

confidence: 99%

“…PARS reconstructions with a range of k R were performed, and the total error power was calculated as described by Eq. [12]. Similarly, SMASH and SENSE reconstructions were performed.…”

Section: Data Reconstruction and Analysismentioning

confidence: 99%

See 1 more Smart Citation

3Parallel magnetic resonance imaging with adaptive radius in k‐space (PARS): Constrained image reconstruction using k‐space locality in radiofrequency coil encoded data

Yeh

McKenzie

Ohliger

et al. 2005

Magnetic Resonance in Med

115

View full text Add to dashboard Cite

show abstract

“…Research focusing on gridding methods is extensive (22)(23)(24)(25)(26). The basic concept of gridding is to transform nonCartesian data (such as radial trajectories) to Cartesian data.…”

Section: Accuracy Of Implicit Griddingmentioning

confidence: 99%

“…One may argue that if we could implement a perfect gridding method, k-t FOCUSS for Cartesian trajectory (11,12) might be simply used for radial trajectory after gridding. However, conventional gridding methods are imperfect and error prone (22)(23)(24)(25)(26). More specifically, they first precompensate sampled data for spatially varying densities of sampled measurements on k-space, using the inverse of the sampling density.…”

Section: Accuracy Of Implicit Griddingmentioning

confidence: 99%

Radial k‐t FOCUSS for high‐resolution cardiac cine MRI

Jung

Park

Yoo

et al. 2009

Magnetic Resonance in Med

111

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A compressed sensing dynamic MR technique called k-t FOCUSS (k-t FOCal UnderdeterminedSystem Solver) has been recently proposed. It outperforms the conventional k-t BLAST/SENSE (Broad-use Linear Acquisition Speed-up Technique/SENSitivity Encoding) technique by exploiting the sparsity of x-f signals. This paper applies this idea to radial trajectories for high-resolution cardiac cine imaging. Radial trajectories are more suitable for high-resolution dynamic MRI than Cartesian trajectories since there is smaller tradeoff between spatial resolution and number of views if streaking artifacts due to limited views can be resolved. As shown for Cartesian trajectories, k-t FOCUSS algorithm efficiently removes artifacts while preserving high temporal resolution. k-t FOCUSS algorithm applied to radial trajectories is expected to enhance dynamic MRI quality. Rather than using an explicit gridding method, which transforms radial k-space sampling data to Cartesian grid prior to applying k-t FOCUSS algorithms, we use implicit gridding during FOCUSS iterations to prevent k-space sampling errors from being propagated. In addition, motion estimation and motion compensation after the first FOCUSS iteration were used to further sparsify the residual image. By applying an additional k-t FOCUSS step to the residual image, Cardiac cine MRI requires high-speed data acquisition to maximize spatiotemporal resolution while reducing breath-hold times for patients. For studies of left ventricular functions, an in-plane spatial resolution of 2-2.5 mm is usually used. Higher resolutions up to 1-2 mm are often required to study finer structures, such as cardiac valves and patent foramen ovale (1). Typical imaging field of view (FOV) for cardiac MR studies is about 250×250 mm 2 . Therefore, to cover the entire volume of a heart, standard matrix sizes of 100×256 and higher resolution matrix sizes of 192×256 are used to meet resolution requirements (1).

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Spiral imaging: A critical appraisal

Block

Frahm

2005

Magnetic Resonance Imaging

106

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In view of recent applications in cardiovascular and functional brain imaging, this work revisits the basic performance characteristics of spiral imaging in direct comparison to echo-planar imaging (EPI) and conventional rapid gradient-echo imaging. Using both computer simulations and experiments on phantoms and human subjects at 2.9 T, the study emphasizes single-shot applications and addresses the design of a suitable trajectory, the choice of a gridding algorithm, and the sensitivity to experimental inadequacies. As a general result, the combination of a spiral trajectory with regridding of the k-space data poses no principle obstacle for high-quality imaging. On the other hand, experimental difficulties such as gradient deviations, resonance offset contributions, and concomitant field effects cause more pronounced and even less acceptable image artifacts than usually obtained for EPI. Moreover, when ignoring parallel imaging strategies that are also applicable to EPI, improvements of image quality via reduced acquisition periods are only achievable by interleaved multishot spirals because partial Fourier sampling and rectangular fields of view (FOVs) cannot be exploited for nonCartesian trajectories. Taken together, while spiral imaging may find its niche applications, most high-speed imaging needs are more easily served by EPI. ALTHOUGH SPIRAL SCANNING TRAJECTORIES have been known since the early days of rapid scan magnetic resonance imaging (MRI), spiral imaging never became a widely accepted and clinically used method. More recently, however, based on significant hardware improvements in the development of flexible gradient systems, spiral scanning has regained interest with applications in cardiovascular and functional brain imaging. Due to its ability to acquire the k-space data of a twodimensional image after only a single radio frequency (RF) excitation, single-shot spiral imaging emerges as a competing technique to echo-planar imaging (EPI).In principle, spiral trajectories offer some inherent refocusing of motion-and flow-induced phase errors (1-3) not delivered by conventional phase-and frequency-encoding gradients, that is Cartesian trajectories that scan the k-space along straight lines. Furthermore, spiral trajectories allow for an efficient and temporally flexible sampling of k-space as shorter pathways are required to cover a desired region and the data acquisition may start in the center of k-space. These advantages are counterbalanced by practical difficulties that-at least in the eyes of a typical MRI userrender spiral imaging a technical challenge that seems to be better suited for a few expert users. It is therefore the primary aim of this article to provide a comprehensive review of the basic performance characteristics of (single-shot) spiral imaging in direct comparison with EPI. Furthermore, the analysis attempts to sort out whether spiral imaging suffers problems of a principle nature other than EPI or whether existing complications represent mere difficulties that are to be overcome...

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Modified block uniform resampling (BURS) algorithm using truncated singular value decomposition: Fast accurate gridding with noise and artifact reduction

Cited by 26 publications

References 25 publications

3Parallel magnetic resonance imaging with adaptive radius in k‐space (PARS): Constrained image reconstruction using k‐space locality in radiofrequency coil encoded data

3Parallel magnetic resonance imaging with adaptive radius in k‐space (PARS): Constrained image reconstruction using k‐space locality in radiofrequency coil encoded data

Radial k‐t FOCUSS for high‐resolution cardiac cine MRI

Spiral imaging: A critical appraisal

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