ArticleContrast media & molecular imaging2022
Magnetic Resonance Imaging Evaluation of Hemangioma Resection for Encephalofacial Angiomatosis (Sturge-Weber Syndrome) in Children under Intelligent Algorithm.
Article in Contrast media & molecular imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed, 3 citations in OpenAlex.
- New Trigger for Stroke-like Episode in Sturge-Weber Syndrome: A Case Report.Children (Basel, Switzerland) · 2025Article
- Intelligent Algorithm-Based MRI Image Features for Evaluating the Effect of Nursing on Recovery of the Neurological Function of Patients with Acute Stroke.Contrast media & molecular imaging · 2022Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study was aimed to evaluate the clinical efficacy of hemangioma resection in the treatment of infantile encephalofacial angiomatosis (Sturge-Weber syndrome, SWS) through magnetic resonance imaging (MRI) images, and intelligent algorithms were employed to process MRI images. A retrospective study of 45 children diagnosed with facial hemangioma admitted to hospital was conducted. Then, MRS images were acquired, and a mathematical model for MRI image denoising and reconstruction was constructed based on nonlocal similar block low-rank prior algorithms. The processing effect was assessed regarding the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). Finally, MRI images were collected to analyze the difference between the metabolites of N-acetylaspartic acid (NAA), creatine (Cr), choline (Cho), and their ratios in the lesions of the children before and after treatment. The improvement rate was analyzed through a twelve-month follow-up. The algorithm test results showed that compared with the classic K-singular value decomposition (K-SVD) denoising algorithm and the Sparse MRI reconstruction algorithm, the proposed algorithm processed MRI images more clearly and had more detailed information. The quantitative results showed that the PSNR and SSIM in the image processed by the algorithm proposed were remarkably large. The clinical treatment results showed that compared with those before treatment, the nCho level after treatment, the ratio of Cho/Cr and Cho/NAA were remarkably reduced, and the difference was remarkable (
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Registered trials
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