Compressively sampled magnetic resonance imaging reconstruction based on split Bregman iteration with general non-uniform threshold shrinkage

Wang, Wei; Cao, Da; Li, Xiuhan; Cao, Ning

doi:10.1016/j.mri.2021.10.015

Cited by 3 publications

(1 citation statement)

References 22 publications

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“…Combined with artificial intelligence image enhancement technology, small ulcers or polyps of colon cancer in ulcerative colorectal lesions can be clearly observed [ 13 ]. Bergman dictionary learning algorithm has remarkable effect in improving the quality of MRI images, and has been widely studied [ 14 ]. However, there are relatively few studies on its application in the treatment of colon cancer.…”

Section: Introductionmentioning

confidence: 99%

Dark-Lumen Magnetic Resonance Image Based on Artificial Intelligence Algorithm in Differential Diagnosis of Colon Cancer

Fang¹,

Kang

Yang

et al. 2022

Computational Intelligence and Neuroscience

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This research was aimed o investigate the application value and diagnostic effect of dark-lumen magnetic resonance imaging (dark-lumen MRI) based on artificial intelligence algorithm on colon cancer. A total of 98 patients with ulcerated colon cancer were selected as the study subjects. All patients underwent colonic endoscopy. The patients were divided into algorithm group (artificial intelligence algorithm processing image group) and control group (conventional method processing image group) according to different dark-lumen MRI processing methods. The detection efficiency of colon cancer was compared between the two groups. It showed that the diagnostic effect of dark-lumen MRI based on artificial intelligence algorithm was significant. The apparent diffusion coefficient (ADC) in the control group was 0.92 ± 0.14 mm2/s (minimum: 0.74, maximum: 1.30), ADC in the algorithm group was 1.55 ± 0.31 mm2/s (minimum: 1.22, maximum: 2.42). The ADC of patients in algorithm group was significantly higher than that of patients in control group, with statistical difference (t = 7.827, P < 0.001). The correct number of cases was 46 and the diagnostic error number was 3 in algorithm group, with accuracy of 93%. The correct number of cases was 41 and the diagnostic error number was 8 in control group, with accuracy of 83%. In comparison, the correct rate was 10% higher in algorithm group, indicating that the diagnostic effect was better in algorithm group. The mean value of invasion depth was 10.42 in the algorithm group and 5.27 in the control group, indicating that the algorithm group was more accurate in the judgment of invasion depth, had a good prospect of clinical application, and had guiding significance for the diagnosis of colon cancer.

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Section: Introductionmentioning

confidence: 99%

Dark-Lumen Magnetic Resonance Image Based on Artificial Intelligence Algorithm in Differential Diagnosis of Colon Cancer

Fang¹,

Kang

Yang

et al. 2022

Computational Intelligence and Neuroscience

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Multifrequency Magnetic Induction Tomography for Hemorrhagic Stroke Detection Using an Adaptive Threshold Split Bregman Algorithm

Zhang

Liu

et al. 2022

IEEE Trans. Instrum. Meas.

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A new difference of anisotropic and isotropic total variation regularization method for image restoration

Zhang

Wang

et al. 2023

MBE

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<abstract><p>Total variation (TV) regularizer has diffusely emerged in image processing. In this paper, we propose a new nonconvex total variation regularization method based on the generalized Fischer-Burmeister function for image restoration. Since our model is nonconvex and nonsmooth, the specific difference of convex algorithms (DCA) are presented, in which the subproblem can be minimized by the alternating direction method of multipliers (ADMM). The algorithms have a low computational complexity in each iteration. Experiment results including image denoising and magnetic resonance imaging demonstrate that the proposed models produce more preferable results compared with state-of-the-art methods.</p></abstract>

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Compressively sampled magnetic resonance imaging reconstruction based on split Bregman iteration with general non-uniform threshold shrinkage

Cited by 3 publications

References 22 publications

Dark-Lumen Magnetic Resonance Image Based on Artificial Intelligence Algorithm in Differential Diagnosis of Colon Cancer

Dark-Lumen Magnetic Resonance Image Based on Artificial Intelligence Algorithm in Differential Diagnosis of Colon Cancer

Multifrequency Magnetic Induction Tomography for Hemorrhagic Stroke Detection Using an Adaptive Threshold Split Bregman Algorithm

A new difference of anisotropic and isotropic total variation regularization method for image restoration

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