Reduced spatial side lobes in chemical-shift imaging

Adalsteinsson, Elfar; Star‐Lack, Josh; Meyer, Craig H.; Spielman, Daniel

doi:10.1002/(sici)1522-2594(199908)42:2<314::aid-mrm14>3.0.co;2-x

Cited by 56 publications

(55 citation statements)

References 22 publications

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“…At

B_{0} < 7

T some SSE approaches are even better suited for DW k‐space acquisition than acquisition weighting. For instance, spiral trajectories can be tailored to shape a Hanning filter without the need for averaging 27. Yet, this prolongs the spiral trajectory, and therefore requires more temporal or angular interleaves to maintain the same spectral bandwidth.…”

Section: Discussionmentioning

confidence: 99%

“…Ideally, the target filter can be not only approximated by the natural density of the trajectory, but exactly matched. So far, Hanning weighting has been achieved via gradient slew‐rate demanding variable‐density spirals at 3T 27, which are unsuitable for

B_{0} \geq 7

T. EPSI can be hardly adapted to low‐pass filtering. Rosettes have also unfavorable contributions of high‐pass filters 20, 21, although this is subject to optimization.…”

Section: Introductionmentioning

confidence: 99%

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Density‐weighted concentric circle trajectories for high resolution brain magnetic resonance spectroscopic imaging at 7T

Hingerl

Bogner

Moser

et al. 2017

Magnetic Resonance in Med

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PurposeFull‐slice magnetic resonance spectroscopic imaging at ≥7 T is especially vulnerable to lipid contaminations arising from regions close to the skull. This contamination can be mitigated by improving the point spread function via higher spatial resolution sampling and k‐space filtering, but this prolongs scan times and reduces the signal‐to‐noise ratio (SNR) efficiency. Currently applied parallel imaging methods accelerate magnetic resonance spectroscopic imaging scans at 7T, but increase lipid artifacts and lower SNR‐efficiency further. In this study, we propose an SNR‐efficient spatial‐spectral sampling scheme using concentric circle echo planar trajectories (CONCEPT), which was adapted to intrinsically acquire a Hamming‐weighted k‐space, thus termed density‐weighted‐CONCEPT. This minimizes voxel bleeding, while preserving an optimal SNR.Theory and MethodsTrajectories were theoretically derived and verified in phantoms as well as in the human brain via measurements of five volunteers (single‐slice, field‐of‐view 220 × 220 mm2, matrix 64 × 64, scan time 6 min) with free induction decay magnetic resonance spectroscopic imaging. Density‐weighted‐CONCEPT was compared to (a) the originally proposed CONCEPT with equidistant circles (here termed e‐CONCEPT), (b) elliptical phase‐encoding, and (c) 5‐fold Controlled Aliasing In Parallel Imaging Results IN Higher Acceleration accelerated elliptical phase‐encoding.ResultsBy intrinsically sampling a Hamming‐weighted k‐space, density‐weighted‐CONCEPT removed Gibbs‐ringing artifacts and had in vivo +9.5%, +24.4%, and +39.7% higher SNR than e‐CONCEPT, elliptical phase‐encoding, and the Controlled Aliasing In Parallel Imaging Results IN Higher Acceleration accelerated elliptical phase‐encoding (all P < 0.05), respectively, which lead to improved metabolic maps.ConclusionDensity‐weighted‐CONCEPT provides clinically attractive full‐slice high‐resolution magnetic resonance spectroscopic imaging with optimal SNR at 7T. Magn Reson Med 79:2874–2885, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…At

B_{0} < 7

Section: Discussionmentioning

confidence: 99%

B_{0} \geq 7

T. EPSI can be hardly adapted to low‐pass filtering. Rosettes have also unfavorable contributions of high‐pass filters 20, 21, although this is subject to optimization.…”

Section: Introductionmentioning

confidence: 99%

Density‐weighted concentric circle trajectories for high resolution brain magnetic resonance spectroscopic imaging at 7T

Hingerl

Bogner

Moser

et al. 2017

Magnetic Resonance in Med

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“…The continuous weighting function defined in Eq. [4] is discretized in a different manner in AW and DW to derive the PE sampling scheme. Both approaches are described in the following subsections.…”

Section: Acquisition-weighted Phase-encodingmentioning

confidence: 99%

“…DW has been used successfully in PE and frequency-encoding by readout trajectories to reduce scan time (13) or to improve the shape of the SRF in MR experiments (4,14). Here the concept is applied to PE to exploit the modified FOV properties of DW while maintaining the high SNR and reduced contamination of acquisition-weighting.…”

Section: Density-weighted Phase-encoding (Dw)mentioning

confidence: 99%

“…Although fast imaging methods with readout encoding offer comparably high sensitivity (1)(2)(3)(4), pure phase-encoding (PE) in all spatially resolved dimensions is still the gold standard in sensitivity-limited MR experiments (5). The low achievable spatial resolution results in severe signal contamination from neighboring regions due to the sidelobes of the spatial response function (SRF).…”

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Efficient k‐space sampling by density‐weighted phase‐encoding

Greiser

Kienlin

2003

Magnetic Resonance in Med

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Acquisition-weighting improves the localization of MRI experiments. An approach to acquisition-weighting in a purely phaseencoded experiment is presented that is based on a variation of the sampling density in k-space. In contrast to conventional imaging or to accumulation-weighting, where k-space is sampled with uniform increments, density-weighting varies the distance between neighboring sampling points ⌬k to approximate a given radial weighting function. A fast, noniterative algorithm has been developed to calculate the sampling matrix in one, two, and three dimensions from a radial weighting function w(k), the desired number of scans NA tot and the nominal spatial resolution ⌬x nom . Density-weighted phase-encoding combines the improved shape of the spatial response function and the high SNR of acquisition-weighting with an extended field of view. The artifact energy that results from aliasing due to a small field of view is substantially reduced. The properties of density-weighting are compared to uniform and to accumulation-weighted phase-encoding in simulations and experiments. Density-weighted 31 In metabolic MRI in vivo, low concentrations commonly lead to low signal-to-noise ratios (SNRs). Although fast imaging methods with readout encoding offer comparably high sensitivity (1-4), pure phase-encoding (PE) in all spatially resolved dimensions is still the gold standard in sensitivity-limited MR experiments (5). The low achievable spatial resolution results in severe signal contamination from neighboring regions due to the sidelobes of the spatial response function (SRF). The SRF characterizes the spatial origin of the signals contributing to a distinct voxel (6). If averaging is necessary, acquisition-weighting by variable accumulation numbers for different k-space positions can strongly suppress the sidelobes of the SRF, resulting in a significant reduction of the contamination (7,8). This kind of acquisition-weighting, which is based on variable accumulation of the different PE steps, is in the following referred to as "accumulation-weighting" or "AW." The term "conventional" is used in this context for a uniformly sampled (UW) experiment with equal accumulation numbers. Compared to conventional uniform (UW) weighting, AW improves the SRF without any loss of SNR at identical resolution and experiment duration (6 -8). However, due to the higher accumulation numbers in central k-space and the need for an increased number of different PE steps to preserve spatial resolution, ⌬x nom , AW results in a smaller achievable field of view (FOV) compared to a UW experiment with identical total number of accumulations and spatial resolution. An improved form of acquisition-weighting is presented, "densityweighting" (DW).DW is based on a variation of the density of sampling points in k-space. Compared to AW, DW preserves the favorable shape of the SRF, but extends the accessible FOV. In DW the overall number of different sampling positions is maximized to the total number of scans. Furthermore, the sampling poin...

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A multiscale approach for analyzing in vivo spectroscopic imaging data

Zhang¹,

Heberlein²,

Sarkar³

et al. 2000

Magn. Reson. Med.

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A multiscale approach for analyzing in vivo magnetic resonance spectroscopic imaging (SI) data is described in this paper. With this method, fitting is performed at multiple spatial scales in a coarse-to-fine order. Results obtained at one scale are used as prior knowledge in fitting spectra at the next scale. The multiscale approach was validated with simulated data and demonstrated with proton SI datasets of the human brains. The results showed that this method improved the robustness and efficiency of the fitting and facilitated the automatic analysis of in vivo SI data. Magn Reson Med 43:331-334, 2000.

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Reduced spatial side lobes in chemical-shift imaging

Cited by 56 publications

References 22 publications

Density‐weighted concentric circle trajectories for high resolution brain magnetic resonance spectroscopic imaging at 7T

Density‐weighted concentric circle trajectories for high resolution brain magnetic resonance spectroscopic imaging at 7T

Efficient k‐space sampling by density‐weighted phase‐encoding

A multiscale approach for analyzing in vivo spectroscopic imaging data

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