Compressed‐sensing accelerated magnetic resonance imaging of inner ear

Jiang, Yuan; Wang, Xiaoying; Zhu, Lina; Liu, Jing; Zhang, Xiaodong; Hu, Xiaoyu; Lin, Zhiyong; Wang, Ke; Qin, Naishan

doi:10.1002/acm2.13383

Cited by 2 publications

(2 citation statements)

References 29 publications

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“…This combination of the SENSE technique and iterating L1-regularized de-noising filters has been applied to various sequences, and several groups have reported that it achieves improved image quality and shortened acquisition time. [9][10][11] In contrast, it has been reported that iterating L1-regularized de-noising filters can be applied to EPI (EPI with L1-regularized iterative SENSE-based DWI [L1-DWI]) to improve the image quality of EPI-based DWI. [12][13][14][15][16] Although there are several problematic issues behind iterative approaches, including the long computation time, variation of the performance due to the details of fine-tuning, and the influence of the spatial signal distribution in target images, the latest hardware advancements have markedly reduced the computation time and implemented well-optimized fine-tuning.…”

Section: Introductionmentioning

confidence: 99%

Clinical utility of single-shot echo-planar diffusion-weighted imaging using L1-regularized iterative sensitivity encoding in prostate MRI

et al. 2023

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We investigated the ability of echo-planar imaging with L1-regularized iterative sensitivity encoding-based diffusion-weighted imaging (DWI) to improve the image quality and reduce the scanning time in prostate magnetic resonance imaging. We retrospectively analyzed 109 cases of prostate magnetic resonance imaging. We compared variables in the quantitative and qualitative assessments among 3 imaging groups: conventional parallel imaging-based DWI (PI-DWI) with an acquisition time of 3 minutes 15 seconds; echo-planar imaging with L1-regularized iterative sensitivity encoding-based DWI (L1-DWI) with a normal acquisition time (L1-DWI NEX12 ) of 3 minutes 15 seconds; and L1-DWI with a half acquisition time (L1-DWI NEX6 ) of 1 minute 45 seconds. As a quantitative assessment, the signal-to-noise ratio (SNR) of DWI (SNR-DWI), the contrast-to-noise ratio (CNR) of DWI (CNR-DWI), and the CNR of apparent diffusion coefficient were measured. As a qualitative assessment, the image quality and visual detectability of prostate carcinoma were evaluated. In the quantitative analysis, L1-DWI NEX12 showed significantly higher SNR-DWI than PI-DWI (P = .0058) and L1-DWI NEX6 (P < .0001). In the qualitative analysis, the image quality score for L1-DWI NEX12 was significantly higher than those of PI-DWI and L1-DWI NEX6 . A non-inferiority assessment demonstrated that L1-DWI NEX6 was non-inferior to PI-DWI in terms of both quantitative CNR-DWI and qualitative grading of image quality with a <20% inferior margin. L1-DWI successfully demonstrated a reduced scanning time while maintaining good image quality. Abbreviations: ADC = apparent diffusion coefficient, CI = confidence intervals, CNR = contrast-to-noise ratio, DWI = diffusionweighted imaging, EPI = echo-planar imaging, FOV = field of view, L1-DWI = L1-regularized iterative SENSE-based DWI, L1-DWI NEX12 = L1-DWI with normal acquisition time, L1-DWI NEX6 = L1-DWI with half acquisition time, mp-MRI = multiparametric magnetic resonance imaging, PCa = prostate carcinoma, PI = parallel imaging, PI-DWI = PI-based DWI, PI-RADS = prostate imaging reporting and data system, ROI = region of interest, SENSE = sensitivity encoding, SNR = signal-to-noise ratio, ssEPI = single-shot spin-echo echo-planar imaging sequence, T2WI = T2-weighted image.

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Section: Introductionmentioning

confidence: 99%

Clinical utility of single-shot echo-planar diffusion-weighted imaging using L1-regularized iterative sensitivity encoding in prostate MRI

et al. 2023

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“…[5] Compared with the traditional full sampling and recompressing process, it can carry out a low-power and high-efficiency data process. Accordingly, various CS algorithms have been proposed, [5][6][7] and well applied in many fields, for instance, wireless sensor networks and loT, [8][9][10] electrocardiogram data compression, [11][12][13] and magnetic resonance imaging, [14][15][16] to name a few. In recent years, there has been an increasing interest in the hardware implementation of CS, [17][18][19][20] which originally suffered from several limitations, including limited scalability and separate sensing and sub-sampling.…”

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confidence: 99%

Bio‐Inspired In‐Sensor Compression and Computing Based on Phototransistors

Wang

2022

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The biological nervous system possesses a powerful information processing capability, and only needs a partial signal stimulation to perceive the entire signal. Likewise, the hardware implementation of an information processing system with similar capabilities is of great significance, for reducing the dimensions of data from sensors and improving the processing efficiency. Here, it is reported that indium‐gallium‐zinc‐oxide thin film phototransistors exhibit the optoelectronic switching and light‐tunable synaptic characteristics for in‐sensor compression and computing. Phototransistor arrays can compress the signal while sensing, to realize in‐sensor compression. Additionally, a reservoir computing network can also be implemented via phototransistors for in‐sensor computing. By integrating these two systems, a neuromorphic system for high‐efficiency in‐sensor compression and computing is demonstrated. The results reveal that even for cases where the signal is compressed by 50%, the recognition accuracy of reconstructed signal still reaches ≈96%. The work paves the way for efficient information processing of human–computer interactions and the Internet of Things.

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Compressed‐sensing accelerated magnetic resonance imaging of inner ear

Abstract: This is an open access article under the terms of the Creat ive Commo ns Attri bution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 2 publications

References 29 publications

Clinical utility of single-shot echo-planar diffusion-weighted imaging using L1-regularized iterative sensitivity encoding in prostate MRI

Clinical utility of single-shot echo-planar diffusion-weighted imaging using L1-regularized iterative sensitivity encoding in prostate MRI

Bio‐Inspired In‐Sensor Compression and Computing Based on Phototransistors

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