ArticleEuropean journal of radiology open2021
Preliminary study of multiple b-value diffusion-weighted images and T1 post enhancement magnetic resonance imaging images fusion with Laplacian Re-decomposition (LRD) medical fusion algorithm for glioma grading.
Article in European journal of radiology open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- A dual-discriminator conditional generative adversarial network (DDcGAN) approach to glioma grade classification with structural MRI images fusion.Physical and engineering sciences in medicine · 2026Article
- Glioma Grade Classification Using Machine Learning and MRI Radiomics: A Single-Center Prospective Study Comparing Original and Wavelet-Transformed Features From Anatomical, Diffusion-Weighted, and Post-Contrast Imaging.Health science reports · 2026Article
- Glioma Grading by Integrating Radiomic Features from Peritumoral Edema in Fused MRI Images and Automated Machine Learning.Journal of imaging · 2025Article
- Quantitative susceptibility mapping study of deep gray matter iron content in glioma patients.Quantitative imaging in medicine and surgery · 2025Article
- The Role of b-value Diffusion-weighted Imaging in Differentiating High-grade and Low-grade Brain Tumors: A Comprehensive Study with Standard b-value DWI.Advanced biomedical research · 2025Article
- MBRARN: multibranch residual attention reconstruction network for medical image fusion.Medical & biological engineering & computing · 2023Article
- Performance comparison of different medical image fusion algorithms for clinical glioma grade classification with advanced magnetic resonance imaging (MRI).Scientific reports · 2023Article
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4 authors.
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Abstract
backgroundGrade of brain tumor is thought to be the most significant and crucial component in treatment management. Recent development in medical imaging techniques have led to the introduce non-invasive methods for brain tumor grading such as different magnetic resonance imaging (MRI) protocols. Combination of different MRI protocols with fusion algorithms for tumor grading is used to increase diagnostic improvement. This paper investigated the efficiency of the Laplacian Re-decomposition (LRD) fusion algorithms for glioma grading. PROCEDURES: In this study, 69 patients were examined with MRI. The T1 post enhancement (T1Gd) and diffusion-weighted images (DWI) were obtained. To evaluated LRD performance for glioma grading, we compared the parameters of the receiver operating characteristic (ROC) curves.
findingsWe found that the average Relative Signal Contrast (RSC) for high-grade gliomas is greater than RSCs for low-grade gliomas in T1Gd images and all fused images. No significant difference in RSCs of DWI images was observed between low-grade and high-grade gliomas. However, a significant RSCs difference was detected between grade III and IV in the T1Gd, b50, and all fussed images.
conclusionsThis research suggests that T1Gd images are an appropriate imaging protocol for separating low-grade and high-grade gliomas. According to the findings of this study, we may use the LRD fusion algorithm to increase the diagnostic value of T1Gd and DWI picture for grades III and IV glioma distinction. In conclusion, this article has emphasized the significance of the LRD fusion algorithm as a tool for differentiating grade III and IV gliomas.
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