Efficient off-resonance correction for spiral imaging

Nayak, Krishna S.; Tsai, Chi‐Ming; Meyer, Craig H.; Nishimura, Dwight G.

doi:10.1002/1522-2594(200103)45:3<521::aid-mrm1069>3.0.co;2-6

Cited by 33 publications

(49 citation statements)

References 11 publications

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“…When field-corrected image reconstruction algorithms are used, these dynamic changes can lead to further distortions in the images for a time series. Nayak et al (9) and Nayak and Nishimura (10) presented a method to form standard, dynamic estimates of a low-resolution field map by delaying TEs of subsequent shots in a multishot experiment. However, these estimates are sensitive to the differences in reference images discussed in the previous paragraph, whereas the method we will propose here estimates the field map within a single acquisition.…”

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confidence: 99%

Dynamic field map estimation using a spiral‐in/spiral‐out acquisition

Sutton

Noll

Fessler

2004

Magnetic Resonance in Med

101

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The long readout times of single-shot acquisitions and the high field strengths desired for functional MRI (fMRI) using blood oxygenation level-dependent (BOLD) contrast make functional scans sensitive to magnetic field inhomogeneity. If it is not corrected during image reconstruction, field inhomogeneity can cause geometric distortions in the images when Cartesian k-space trajectories are used or blurring with spiral acquisitions. Many traditional methods to correct for field inhomogeneity distortions rely on a static field map measured with the use of images that are themselves distorted. In this work, we employ a regularized least-squares approach to jointly estimate both the undistorted image and field map at each acquisition using a spiral-in/spiral-out pulse sequence. Simulation and phantom studies show that this method is accurate and stable over a time series. Human functional studies show that the jointly estimated field map may be more accurate than standard field map estimates in the presence of respiration-induced phase oscillations, leading to better detection of functional activation. The proposed method measures a dynamic field map that accurately tracks magnetic field drift and respiration-induced phase oscillations during the course of a functional study.

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confidence: 99%

Dynamic field map estimation using a spiral‐in/spiral‐out acquisition

Sutton

Noll

Fessler

2004

Magnetic Resonance in Med

101

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“…ORA corrections can be performed either simultaneously when the image reconstruction is being done (10)(11)(12)(13)(14)16,18) or postreconstruction on the uncorrected images (19). Obviously, the former approach would be more attractive than the latter because good image quality can be obtained at no cost of additional time.…”

Section: Discussionmentioning

confidence: 99%

“…The images were reconstructed with the conventional gridding algorithm (5) and interpolated to 128 Â 128 before being used to generate the field map for off-resonance corrections (12). Figure 2 shows amplitude distributions of W(j) calculated with the field inhomogeneities represented by the field map shown in Fig.…”

Section: In Vivo Experimentsmentioning

confidence: 99%

“…Correction of ORA with the conjugate phase reconstruction methods, which are often used in with the convolution-based gridding algorithms (10)(11)(12)(13), requires that the off-resonance frequencies vary smoothly across the image space. Otherwise, the effectiveness of ORA correction would be reduced.…”

Section: Direct Estimationmentioning

confidence: 99%

“…Gridding is commonly done by convoluting the acquired k-space data with a kernel function, followed by resampling in the Cartesian k-space (5). With this approach, correction of ORA can be carried out while gridding with the conjugate phase reconstruction methods (10)(11)(12)(13). However, in the convolution-based gridding methods, the choices of the kernel size and the pre-and postcompensation functions may affect the outcome of reconstruction (5).…”

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An efficient gridding reconstruction method for multishot non‐Cartesian imaging with correction of off‐resonance artifacts

Meng

Lei

2010

Magnetic Resonance in Med

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An efficient iterative gridding reconstruction method with correction of off-resonance artifacts was developed, which is especially tailored for multiple-shot non-Cartesian imaging. The novelty of the method lies in that the transformation matrix for gridding (T) was constructed as the convolution of two sparse matrices, among which the former is determined by the sampling interval and the spatial distribution of the offresonance frequencies and the latter by the sampling trajectory and the target grid in the Cartesian space. The resulting T matrix is also sparse and can be solved efficiently with the iterative conjugate gradient algorithm. It was shown that, with the proposed method, the reconstruction speed in multipleshot non-Cartesian imaging can be improved significantly while retaining high reconstruction fidelity. More important, the method proposed allows tradeoff between the accuracy and the computation time of reconstruction, making customization of the use of such a method in different applications possible. The performance of the proposed method was demonstrated by numerical simulation and multiple-shot spiral imaging on rat brain at 4. Key words: non-Cartesian imaging; spiral imaging; gridding; reconstruction; off-resonance artifacts Ultrafast MRI methods employing non-Cartesian sampling schemes, such as spiral (1,2) and rosette scans (3,4), have been used in various applications. Image reconstruction from non-Cartesian k-space data, however, can be problematic in terms of speed and accuracy. The former problem arises from the fact that fast Fourier transformation (FFT) is not directly applicable (5-8), and the latter is because ultrafast imaging methods employing lengthy sampling durations are much more susceptible to the so-called off-resonance artifacts (ORA), relative to the conventional imaging methods (9).If FFT is to be used for image reconstruction, the nonCartesian k-space data need first be mapped onto a uniformly sampled k-space (i.e., gridding or resampling) (5-8). Gridding is commonly done by convoluting the acquired k-space data with a kernel function, followed by resampling in the Cartesian k-space (5). With this approach, correction of ORA can be carried out while gridding with the conjugate phase reconstruction methods (10-13). However, in the convolution-based gridding methods, the choices of the kernel size and the pre-and postcompensation functions may affect the outcome of reconstruction (5). In addition, the conjugate phase reconstruction methods for correcting ORA require field inhomogeneities varying smoothly across the image space (14) but this does not always hold in practical applications.Alternatively, gridding can be done by formulating a transformation matrix between the acquired k-space data and the target k-space data in the Cartesian space and solving it with single value decomposition (6) or iterative algorithms (7,8). With such approaches, subjective selection of the kernel and the pre-and postcompensation functions is no longer required. In practice, however, thes...

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AWSOS Pulse Sequence and High-resolution UTE Imaging

Qian

Boada

2012

Encyclopedia of Magnetic Resonance

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Efficient off-resonance correction for spiral imaging

Cited by 33 publications

References 11 publications

Dynamic field map estimation using a spiral‐in/spiral‐out acquisition

Dynamic field map estimation using a spiral‐in/spiral‐out acquisition

An efficient gridding reconstruction method for multishot non‐Cartesian imaging with correction of off‐resonance artifacts

AWSOS Pulse Sequence and High-resolution UTE Imaging

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