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

Autor: Masih Saboori, Mohamad Bagher Tavakoli, Amir Khorasani, Milad Jalilian
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Grade
R895-920
Medical physics. Medical radiology. Nuclear medicine
ADC
apparent diffusion coefficient
T1Gd
T1 post enhancement
OD
overlapping domain
Fusion
LP
Laplacian Pyramid
medicine.diagnostic_test
food and beverages
Glioma
MLD
Maximum Local Difference
TR
repetition time
Glioma grading
MRS
Magnetic resonance spectroscopy
PACS
PACS picture archiving and communication system
GBM
glioblastomas
ROC
receiver operating characteristic curve
TI
time of inversion
Algorithm
Brain tumor
TE
time of echo
Article
Magnetic resonance imaging
DCE
Dynamic contrast enhancement
ROI
regions of interest
medicine
Medical imaging
RSC
Relative Signal Contrast
Radiology
Nuclear Medicine and imaging
CBV
Cerebral Blood Volume
DWI
Diffusion-weighted imaging
neoplasms
LRD
Laplacian Re-decomposition
Grading (tumors)
SCE
Susceptibility contrast enhancement
DGR
Decision Graph Re-decomposition
Receiver operating characteristic
business.industry
fungi
MST
Multi-scale transform
medicine.disease
nervous system diseases
FA
flip angle
LEM
Local Energy Maximum
Laplacian Re-decomposition
Diffusion-weighted images
IRS
Inverse Re-decomposition Scheme
Fusion algorithm
AUC
Aera Under Curve
GDIE
Gradient Domain Image Enhancement
BOLD
blood oxygen level dependent imaging
business
MRI
magnetic resonance imaging
NOD
Non-overlapping domain
Zdroj: European Journal of Radiology Open, Vol 8, Iss, Pp 100378-(2021)
European Journal of Radiology Open
ISSN: 2352-0477
Popis: Highlights • LRD medical image fusion algorithm can be used for glioma grading. • We can use the LRD fusion algorithm with MRI image for glioma grading. • Fusing of DWI (b50) and T1 enhancement (T1Gd) by LRD, have highest diagnostic value for glioma grading.
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.
Databáze: OpenAIRE
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