Improve GRAPPA with Cross-sampled ACS Lines and Nonlinear Kernel Model

Wang, Xiaoyan; Wang, Haifeng; Zhou, Jing

doi:10.3233/bme-130909

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…This method gives a broader model to define noise behavior in GRAPPA reconstruction, resulting in a considerable increase in SNR. Subsequent studies have extended this work by incorporating additional regularizations, aiming to enhance its performance for a diverse range of applications (Wang et al 2014, Wang et al 2019a, Hu et al 2021b. Specially, Lyu et al (2019) investigated the generalized kernel-based nonlinear technique to calibration-based parallel MRI reconstruction, expanding on the nonlinear theory.…”

Section: Non-commercialized Parallel Imaging Techniques and Their 3d ...mentioning

confidence: 99%

Recent advances in highly accelerated 3D MRI

et al. 2023

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Three-dimensional MRI has been increasingly incorporated into various clinical applications, as it provided better through-plane spatial resolution which potentially enhanced the ability to detect more-subtle abnormalities, and provides much more valuable information for clinical usage. However, one major drawback of 3D MRI is the long data-acquisition time and high computational cost. In this review article, we attempted to summarize the most recent development of accelerated 3D MR techniques, from MR signal excitation and encoding to the advancement of reconstruction algorithms and potential applications, by going through over 200 extraordinary researches conducted during the past 20 years. As the field is fast-growing, we hope this survey may serve as a map to understand the current state of the field.

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Section: Non-commercialized Parallel Imaging Techniques and Their 3d ...mentioning

confidence: 99%

Recent advances in highly accelerated 3D MRI

et al. 2023

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“…At the same time, someone thought of improving the effect of the reconstruction algorithm by improving the sampling method. Park et al proposed to use variable density sampling (VDS) to improve the effect of GRAPPA 26 , Zhong et al proposed to combine GRAPPA and NL-GRAPPA on the basis of VDS to improve the reconstruction effect 27 , Wang et al proposed Use Crosssampling to improve GRAPPA 28 .…”

Section: Introductionmentioning

confidence: 99%

Improved parallel magnetic resonance imaging reconstruction with multiple variable density sampling

Sheng

Shi

Zhang

2021

Sci Rep

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Generalized auto-calibrating partially parallel acquisitions (GRAPPA) and other parallel Magnetic Resonance Imaging (pMRI) methods restore the unacquired data in k-space by linearly calculating the undersampled data around the missing points. In order to obtain the weight of the linear calculation, a small number of auto-calibration signal (ACS) lines need to be sampled at the center of the k-space. Therefore, the sampling pattern used in this type of method is to full sample data in the middle area and undersample in the outer k-space with nominal reduction factors. In this paper, we propose a novel reconstruction method with a multiple variable density sampling (MVDS) that is different from traditional sampling patterns. Our method can significantly improve the image quality using multiple reduction factors with fewer ACS lines. Specifically, the traditional sampling pattern only uses a single reduction factor to uniformly undersample data in the region outside the ACS, but we use multiple reduction factors. When sampling the k-space data, we keep the ACS lines unchanged, use a smaller reduction factor for undersampling data near the ACS lines and a larger reduction factor for the outermost part of k-space. The error is lower after reconstruction of this region by undersampled data with a smaller reduction factor. The experimental results show that with the same amount of data sampled, using NL-GRAPPA to reconstruct the k-space data sampled by our method can result in lower noise and fewer artifacts than traditional methods. In particular, our method is extremely effective when the number of ACS lines is small.

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MR Brain Image Enhancement via Learning Ensemble

Wang

et al. 2018

2018 IEEE International Conference on Intelligence and Safety for Robotics (ISR)

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Improve GRAPPA with Cross-sampled ACS Lines and Nonlinear Kernel Model

Cited by 4 publications

References 15 publications

Recent advances in highly accelerated 3D MRI

Recent advances in highly accelerated 3D MRI

Improved parallel magnetic resonance imaging reconstruction with multiple variable density sampling

MR Brain Image Enhancement via Learning Ensemble

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