Partial Fourier reconstruction of complex MR images using complex‐valued convolutional neural networks

Xiao, Linfang; Liu, Yilong; Yi, Zheyuan; Zhao, Yujiao; Xie, Linshan; Cao, Peng; Leong, Alex T. L.; Wu, Ed X.

doi:10.1002/mrm.29033

Cited by 14 publications

(8 citation statements)

References 43 publications

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“…Several recent works have also demonstrated the advantages and significance of building complex-valued networks for DL MR reconstruction. 35,[37][38][39] It is desirable for DL networks to be able to operate on complex-valued data. And it is worth noting that the nonlinear activation function can be complex-valued as well.…”

Section: Complex-valued Mr Imagesmentioning

confidence: 99%

Knowledge‐driven deep learning for fast MR imaging: Undersampled MR image reconstruction from supervised to un‐supervised learning

Wang,

Wu,

Jia

et al. 2024

Magnetic Resonance in Med

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Deep learning (DL) has emerged as a leading approach in accelerating MRI. It employs deep neural networks to extract knowledge from available datasets and then applies the trained networks to reconstruct accurate images from limited measurements. Unlike natural image restoration problems, MRI involves physics‐based imaging processes, unique data properties, and diverse imaging tasks. This domain knowledge needs to be integrated with data‐driven approaches. Our review will introduce the significant challenges faced by such knowledge‐driven DL approaches in the context of fast MRI along with several notable solutions, which include learning neural networks and addressing different imaging application scenarios. The traits and trends of these techniques have also been given which have shifted from supervised learning to semi‐supervised learning, and finally, to unsupervised learning methods. In addition, MR vendors' choices of DL reconstruction have been provided along with some discussions on open questions and future directions, which are critical for the reliable imaging systems.

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Section: Complex-valued Mr Imagesmentioning

confidence: 99%

Knowledge‐driven deep learning for fast MR imaging: Undersampled MR image reconstruction from supervised to un‐supervised learning

Wang,

Wu,

Jia

et al. 2024

Magnetic Resonance in Med

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“…With such artifact patterns embedded in training input data but not output/target data, deep learning is highly effective in learning and removing the semiglobal, physics-dictated artifacts for high-resolution MRI, as already demonstrated for high-field MRI by relatively simple convolution neural networks. 46,47 It is possible to expand our proposed model to remove other important MRI artifacts, such as partial Fourier image reconstruction artifacts, 48,49 to accelerate ULF MRI data acquisition or improve its image quality for a given scan time.…”

Section: Three-dimensional Superresolution With Noise and Artifact Su...mentioning

confidence: 99%

Pushing the limits of low‐cost ultra‐low‐field MRI by dual‐acquisition deep learning 3D superresolution

Lau

Xiao

Zhao

et al. 2023

Magnetic Resonance in Med

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Recent development of ultra-low-field (ULF) MRI presents opportunities for low-power, shielding-free, and portable clinical applications at a fraction of the cost. However, its performance remains limited by poor image quality. Here, a computational approach is formulated to advance ULF MR brain imaging through deep learning of large-scale publicly available 3T brain data. Methods: A dual-acquisition 3D superresolution model is developed for ULF brain MRI at 0.055 T. It consists of deep cross-scale feature extraction, attentional fusion of two acquisitions, and reconstruction. Models for T 1 -weighted and T 2 -weighted imaging were trained with 3D ULF image data sets synthesized from the high-resolution 3T brain data from the Human Connectome Project.They were applied to 0.055T brain MRI with two repetitions and isotropic 3-mm acquisition resolution in healthy volunteers, young and old, as well as patients. Results:The proposed approach significantly enhanced image spatial resolution and suppressed noise/artifacts. It yielded high 3D image quality at 0.055 T for the two most common neuroimaging protocols with isotropic 1.5-mm synthetic resolution and total scan time under 20 min. Fine anatomical details were restored with intrasubject reproducibility, intercontrast consistency, and confirmed by 3T MRI. Conclusion:The proposed dual-acquisition 3D superresolution approach advances ULF MRI for quality brain imaging through deep learning of high-field brain data. Such strategy can empower ULF MRI for low-cost brain imaging, especially in point-of-care scenarios or/and in low-income and mid-income countries.

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“…DL powers a paradigm shift and has shown promise in various high-field MR image reconstruction tasks, including artifact reduction, denoising, and reconstruction from undersampled k-space data (25)(26)(27)(28)(29)(30). This derives from the exceptional feature extraction capability of deep neural network from historical data.…”

Section: Introductionmentioning

confidence: 99%

Deep learning enabled fast 3D brain MRI at 0.055 tesla

Man,

Lau,

et al. 2023

Sci. Adv.

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In recent years, there has been an intensive development of portable ultralow-field magnetic resonance imaging (MRI) for low-cost, shielding-free, and point-of-care applications. However, its quality is poor and scan time is long. We propose a fast acquisition and deep learning reconstruction framework to accelerate brain MRI at 0.055 tesla. The acquisition consists of a single average three-dimensional (3D) encoding with 2D partial Fourier sampling, reducing the scan time of T1- and T2-weighted imaging protocols to 2.5 and 3.2 minutes, respectively. The 3D deep learning leverages the homogeneous brain anatomy available in high-field human brain data to enhance image quality, reduce artifacts and noise, and improve spatial resolution to synthetic 1.5-mm isotropic resolution. Our method successfully overcomes low-signal barrier, reconstructing fine anatomical structures that are reproducible within subjects and consistent across two protocols. It enables fast and quality whole-brain MRI at 0.055 tesla, with potential for widespread biomedical applications.

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Partial Fourier reconstruction of complex MR images using complex‐valued convolutional neural networks

Cited by 14 publications

References 43 publications

Knowledge‐driven deep learning for fast MR imaging: Undersampled MR image reconstruction from supervised to un‐supervised learning

Knowledge‐driven deep learning for fast MR imaging: Undersampled MR image reconstruction from supervised to un‐supervised learning

Pushing the limits of low‐cost ultra‐low‐field MRI by dual‐acquisition deep learning 3D superresolution

Deep learning enabled fast 3D brain MRI at 0.055 tesla

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