Background: Grade 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.
Findings: We 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.
Conclusions: This 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.
Keywords: ADC, apparent diffusion coefficient; AUC, Aera Under Curve; BOLD, blood oxygen level dependent imaging; CBV, Cerebral Blood Volume; DCE, Dynamic contrast enhancement; DGR, Decision Graph Re-decomposition; DWI, Diffusion-weighted imaging; Diffusion-weighted images; FA, flip angle; Fusion algorithm; GBM, glioblastomas; GDIE, Gradient Domain Image Enhancement; Glioma; Grade; IRS, Inverse Re-decomposition Scheme; LEM, Local Energy Maximum; LP, Laplacian Pyramid; LRD, Laplacian Re-decomposition; Laplacian Re-decomposition; MLD, Maximum Local Difference; MRI, magnetic resonance imaging; MRS, Magnetic resonance spectroscopy; MST, Multi-scale transform; Magnetic resonance imaging; NOD, Non-overlapping domain; OD, overlapping domain; PACS, PACS picture archiving and communication system; ROC, receiver operating characteristic curve; ROI, regions of interest; RSC, Relative Signal Contrast; SCE, Susceptibility contrast enhancement; T1Gd, T1 post enhancement; TE, time of echo; TI, time of inversion; TR, repetition time.
© 2021 The Authors. Published by Elsevier Ltd.