Sinusoidal hemangioma with secondary intravascular papillary endothelial hyperplasia

Gurumurthy, Radhika Yajaman; Shankar, Nadig Siddharth; Raj, C S Mohan; Sriram, Nadig

doi:10.4103/ijpm.ijpm_230_19

Cited by 4 publications

(1 citation statement)

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“…Generally, it grows rapidly and spreads six months after birth, seriously damaging the normal tissues and making the eyelid, lip, and ear covered by hemangioma tissues [ 4 , 5 ]. Hemangiomas are irregular in shape, prone to bleeding, necrosis, infection, and other problems, and can cause respiratory, eating, and hearing dysfunction, or even death or disability in serious cases [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Magnetic Resonance Imaging Evaluation of Hemangioma Resection for Encephalofacial Angiomatosis (Sturge–Weber Syndrome) in Children under Intelligent Algorithm

Liang

Fan

et al. 2022

Contrast Media & Molecular Imaging

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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 (P < 0.05). The follow-up results showed that the considerable improvement rate was 88.89%, the postoperative organ remodeling rate was 17.78%, and the probability of reoperation was only 6.67%. In summary, the introduction of intelligent algorithms for denoising and reconstruction of MRI images can remarkably improve image quality and help doctors use image information to diagnose diseases and evaluate treatment effects. The hemangioma resection for the treatment of pediatric SWS had a high treatment improvement rate and was worthy of clinical adoption.

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Section: Introductionmentioning

confidence: 99%