Aliasing‐free reduced field‐of‐view parallel imaging

Jia, Sen; Qiu, Zhilang; Zhang, Lei; Wang, Haifeng; Yang, Gang; Liu, Xin; Liang, Dong; Zheng, Hairong

doi:10.1002/mrm.29046

Cited by 3 publications

(1 citation statement)

References 34 publications

(81 reference statements)

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“…Furthermore, the combination of wave encoding and the sparse prior or low‐rank features has been demonstrated to enable highly accelerated imaging 18,19 . The wave encoding further exploits the better performance and achieves faster acceleration imaging by the optimized gradients or virtual conjugate coils (VCCs) in our previous works 20–24 . Either way, the acquisition for estimating multichannel CSM or calculating linear kernels from ACS can bring some inaccuracy reconstructions and time‐consuming calculations.…”

Section: Introductionmentioning

confidence: 99%

Accelerated submillimeter wave‐encoded magnetic resonance imaging via deep untrained neural network

et al. 2023

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BackgroundWave gradient encoding can adequately utilize coil sensitivity profiles to facilitate higher accelerations in parallel magnetic resonance imaging (pMRI). However, there are limitations in mainstream pMRI and a few deep learning (DL) methods for recovering missing data under wave encoding framework: the former is prone to introduce errors from the auto‐calibration signals (ACS) signal acquisition and is time‐consuming, while the latter requires a large amount of training data.PurposeTo tackle the above issues, an untrained neural network (UNN) model incorporating wave‐encoded physical properties and deep generative model, named WDGM, was proposed with additional ACS‐ and training data‐free.MethodsGenerally, the proposed method can provide powerful missing data interpolation capability using the wave physical encoding framework and designed UNN to characterize the MR image (k‐space data) priors. Specifically, the MRI reconstruction combining physical wave encoding and elaborate UNN is modeled as a generalized minimization problem. The designation of UNN is driven by the coil sensitivity maps (CSM) smoothness and k‐space linear predictability. And then, the iterative paradigm to recover the full k‐space signal is determined by the projected gradient descent, and the complex computation is unrolled to the network with optimized parameters by the optimizer. Simulated wave encoding and in vivo experiments are exploited to demonstrate the feasibility of the proposed method. The best quantitative metrics RMSE/SSIM/PSNR of 0.0413, 0.9514, and 37.4862 gave competitive results in all experiments with at least six‐fold acceleration, respectively.ResultsIn vivo experiments of human brains and knees showed that the proposed method can achieve comparable reconstruction quality and even has superiority relative to the comparison, especially at a high resolution of 0.67 mm and fewer ACS. In addition, the proposed method has a higher computational efficiency achieving a computation time of 9.6 s/per slice.ConclusionsThe model proposed in this work addresses two limitations of MRI reconstruction in the wave encoding framework. The first is to eliminate the need for ACS signal acquisition to perform the time‐consuming calibration process and to avoid errors such as motion during the acquisition procedure. Furthermore, the proposed method has clinical application friendly without the need to prepare large training datasets, which is difficult in the clinical. All results of the proposed method demonstrate more confidence in both quantitative and qualitative metrics. In addition, the proposed method can achieve higher computational efficiency.

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Section: Introductionmentioning

confidence: 99%

Accelerated submillimeter wave‐encoded magnetic resonance imaging via deep untrained neural network

et al. 2023

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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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Application of Deep Learning in Signal Modulation Recognition System for Wireless Communication

Zhou,

Gao,

et al. 2023

2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques (EASCT)

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Aliasing‐free reduced field‐of‐view parallel imaging

Cited by 3 publications

References 34 publications

Accelerated submillimeter wave‐encoded magnetic resonance imaging via deep untrained neural network

Accelerated submillimeter wave‐encoded magnetic resonance imaging via deep untrained neural network

Recent advances in highly accelerated 3D MRI

Application of Deep Learning in Signal Modulation Recognition System for Wireless Communication

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