Multi-Layer Basis Pursuit for Compressed Sensing MR Image Reconstruction

Wahid, Abdul; Shah, Jawad Ali; Khan, Adnan Umar; Ahmed, Manzoor; Razali, Hanif

doi:10.1109/access.2020.3028877

Cited by 14 publications

(6 citation statements)

References 42 publications

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“…Therefore, this technique can be used to acquire artifact-free, high-resolution images using a rather low-sensitivity MR system in a short time. This is also expected to be useful when acquiring high-resolution images in patients with claustrophobia and tremor [27].…”

Section: Discussionmentioning

confidence: 99%

Artifact Reduction in Compressed Sensing Averaging Techniques for High-Resolution Magnetic Resonance Images

2021

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This study aims to introduce a new compressed sensing averaging (CSA) technique for the reduction of blurring and/or ringing artifacts, depending on the k-space sampling ratio. A full k-space dataset and three randomly undersampled datasets were obtained for CSA images in a brain phantom and a healthy subject. An additional simulation was performed to assess the effect of the undersampling ratio on the images and the signal-to-noise ratios (SNRs). The image sharpness, spatial resolution, and contrast between tissues were analyzed and compared with other CSA techniques. Compared to CSA with multiple acquisition (CSAM) at 25%, 35%, and 45% undersampling, the reduction rates of the k-space lines of CSA with keyhole (CSAK) were 10%, 15%, and 22%, respectively, and the acquisition time was reduced by 16%, 23%, and 32%, respectively. In the simulation performed with a full sampling k-space dataset, the SNR decreased to 10.41, 9.80, and 8.86 in the white matter and 9.69, 9.35, and 8.46 in the gray matter, respectively. In addition, the ringing artifacts became substantially more predominant as the number of sampling lines decreased. The 50% modulation transfer functions were 0.38, 0.43, and 0.54 line pairs per millimeter for CSAM, CSAK with high-frequency sharing (CSAKS), and CSAK with high-frequency copying (CSAKC), respectively. In this study, we demonstrated that the smaller the sampling line, the more severe the ringing artifact, and that the CSAKC technique proposed to overcome the artifacts that occur when using CSA techniques did not generate artifacts, while it increased spatiotemporal resolution.

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Section: Discussionmentioning

confidence: 99%

Artifact Reduction in Compressed Sensing Averaging Techniques for High-Resolution Magnetic Resonance Images

2021

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“…52 DL-based image reconstruction is currently one of the most active topics in medical imaging research. New studies have combined CS iterative methods with DL models, such as in the literature 53 where the authors proposed a multilayer basis pursuit framework to improve knee images, showing a better peak signal-to-noise ratio than previous approaches. CS and DL reconstructions were compared for T 1ρ mapping using mono-and bi-exponential models, where spatial and spatiotemporal priors were compared, as shown in Fig.…”

Section: Emerging Methods For Quantitative Mri For Cartilagementioning

confidence: 99%

Updates on Compositional MRI Mapping of the Cartilage: Emerging Techniques and Applications

Zibetti

Menon

Moura

et al. 2023

Magnetic Resonance Imaging

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Osteoarthritis (OA) is a widely occurring degenerative joint disease that is severely debilitating and causes significant socioeconomic burdens to society. Magnetic resonance imaging (MRI) is the preferred imaging modality for the morphological evaluation of cartilage due to its excellent soft tissue contrast and high spatial resolution. However, its utilization typically involves subjective qualitative assessment of cartilage. Compositional MRI, which refers to the quantitative characterization of cartilage using a variety of MRI methods, can provide important information regarding underlying compositional and ultrastructural changes that occur during early OA. Cartilage compositional MRI could serve as early imaging biomarkers for the objective evaluation of cartilage and help drive diagnostics, disease characterization, and response to novel therapies. This review will summarize current and ongoing state‐of‐the‐art cartilage compositional MRI techniques and highlight emerging methods for cartilage compositional MRI including MR fingerprinting, compressed sensing, multiexponential relaxometry, improved and robust radio‐frequency pulse sequences, and deep learning‐based acquisition, reconstruction, and segmentation. The review will also briefly discuss the current challenges and future directions for adopting these emerging cartilage compositional MRI techniques for use in clinical practice and translational OA research studies. Evidence Level 2 Technical Efficacy Stage 2.

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“…Signal reconstruction algorithms are mainly divided into convex relaxation algorithms and greedy algorithms. Convex relaxation algorithms transform the nonconvex problem computational problem into a convex problem computational problem with a paradigm-based compression-aware framework, such as iterative hard thresholding (IHT), gradient projection (GP) algorithm, and basis pursuit (BP) algorithm [13]. The main greedy algorithms currently used for the signal reconstruction part of the CS framework are subspace matching pursuit (SMP) [14], orthogonal matching pursuit (OMP), regularized orthogonal matching pursuit (ROMP), matching pursuit (MP), compressed sampling matching pursuit (CSMP) [15], sparse adaptive matching pursuit [16], sparse segmented orthogonal matching pursuit (StOMP), and generalized orthogonal matching pursuit (gOMP) [17], etc.…”

Section: Introductionmentioning

confidence: 99%

Compressed Sensing of Traction Motor Bearing Vibration Signals based on Discrete Fourier Transform and Generalized Orthogonal Matching Pursuit Algorithm

Liu

Shen³

et al. 2023

J. Phys.: Conf. Ser.

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For the bogie traction motor bearings of rail vehicles under road transportation conditions, its vibration signal full-time domain data acquisition data volume, storage pressure, subsequent data analysis difficulties, and other problems. This study proposes a data compression acquisition and data reconstruction method for bearing vibration signals based on compressed sensing (CS), which can achieve compressed sampling, transmission, and storage of the original signal with fewer observations, and restore the original signal by combining reconstruction algorithms to achieve low error reconstruction. We study the effects of different sparse transform methods on signal sparsity, as well as the signal sparse performance based on different sparse transforms and the signal reconstruction effect of different reconstruction algorithms, and propose a sparse representation method based on the discrete Fourier transform (DFT) and the generalized orthogonal matching pursuit (gOMP) algorithm for CS. Through simulation and experimental comparison, it is concluded that the method can restore and reconstruct the original high-dimensional signal with small reconstruction error, and solve the problems of massive data, inconvenient transmission, and difficult data analysis in engineering.

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Multi-Layer Basis Pursuit for Compressed Sensing MR Image Reconstruction

Cited by 14 publications

References 42 publications

Artifact Reduction in Compressed Sensing Averaging Techniques for High-Resolution Magnetic Resonance Images

Artifact Reduction in Compressed Sensing Averaging Techniques for High-Resolution Magnetic Resonance Images

Updates on Compositional MRI Mapping of the Cartilage: Emerging Techniques and Applications

Compressed Sensing of Traction Motor Bearing Vibration Signals based on Discrete Fourier Transform and Generalized Orthogonal Matching Pursuit Algorithm

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