Brain tumor segmentation using 3D Mask R-CNN for dynamic susceptibility contrast enhanced perfusion imaging

2020 ◽  
Vol 65 (18) ◽  
pp. 185009
Author(s):  
Jiwoong Jeong ◽  
Yang Lei ◽  
Shannon Kahn ◽  
Tian Liu ◽  
Walter J Curran ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Perfusion Imaging ◽  
Tumor Segmentation ◽  
Dynamic Susceptibility ◽  
Dynamic Susceptibility Contrast ◽  
Brain Tumor Segmentation ◽  
Contrast Enhanced ◽  
Susceptibility Contrast

scholarly journals Classification of Primary Cerebral Lymphoma and Glioblastoma Featuring Dynamic Susceptibility Contrast and Apparent Diffusion Coefficient

2020 ◽  
Vol 10 (11) ◽  
pp. 886
Author(s):  
Felix Eisenhut ◽  
Manuel A. Schmidt ◽  
Florian Putz ◽  
Sebastian Lettmaier ◽  
Kilian Fröhlich ◽  
...  
Keyword(s):  
Diffusion Coefficient ◽  
Apparent Diffusion Coefficient ◽  
Perfusion Imaging ◽  
Hot Spot ◽  
Central Nervous System Lymphoma ◽  
Dynamic Susceptibility ◽  
Dynamic Susceptibility Contrast ◽  
Contrast Enhanced ◽  
Apparent Diffusion ◽  
Susceptibility Contrast

This study aimed to differentiate primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM) via multimodal MRI featuring radiomic analysis. MRI data sets of patients with histological proven PCNSL and GBM were analyzed retrospectively. Diffusion-weighted imaging (DWI) and dynamic susceptibility contrast (DSC) perfusion imaging were evaluated to differentiate contrast enhancing intracerebral lesions. Selective (contrast enhanced tumor area with the highest mean cerebral blood volume (CBV) value) and unselective (contouring whole contrast enhanced lesion) Apparent diffusion coefficient (ADC) measurement was performed. By multivariate logistic regression, a multiparametric model was compiled and tested for its diagnostic strength. A total of 74 patients were included in our study. Selective and unselective mean and maximum ADC values, mean and maximum CBV and ratioCBV as quotient of tumor CBV and CBV in contralateral healthy white matter were significantly larger in patients with GBM than PCNSL; minimum CBV was significantly lower in GBM than in PCNSL. The highest AUC for discrimination of PCNSL and GBM was obtained for selective mean and maximum ADC, mean and maximum CBV and ratioCBV. By integrating these five in a multiparametric model 100% of the patients were classified correctly. The combination of perfusion imaging (CBV) and tumor hot-spot selective ADC measurement yields reliable radiological discrimination of PCNSL from GBM with highest accuracy and is readily available in clinical routine.

scholarly journals Assessment of the Agreement between Cerebral Hemodynamic Indices Quantified Using Dynamic Susceptibility Contrast and Dynamic Contrast-enhanced Perfusion Magnetic Resonance Imagings

2018 ◽  
Vol 8 ◽  
pp. 2 ◽  
Author(s):  
Seyed Salman Zakariaee ◽  
Mohammad Ali Oghabian ◽  
Kavous Firouznia ◽  
Guive Sharifi ◽  
Farshid Arbabi ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Intraclass Correlation ◽  
Dynamic Susceptibility ◽  
Dynamic Susceptibility Contrast ◽  
Dce Mri ◽  
Cerebral Hemodynamic ◽  
Dynamic Contrast Enhanced ◽  
Dsc Mri ◽  
Contrast Enhanced ◽  
Susceptibility Contrast

Background: Brain tumor is one of the most common tumors. A successful treatment might be achieved with an early identification. Pathological investigation as the gold standard method for tumor identification has some limitations. Noninvasive assessment of tumor specifications may be possible using perfusion-weighted magnetic resonance imaging (MRI). Cerebral blood volume (CBV) and cerebral blood flow (CBF) could be calculated based on dynamic contrast-enhanced MRI (DCE-MRI) in addition to dynamic susceptibility contrast MRI (DSC-MRI) modality. Each category of the cerebral hemodynamic and permeability indices revealed the specific tumor characteristics and their collection could help for better identification of the tumor. Some mathematical methods were developed to determine both cerebral hemodynamic and permeability indices based on a single-dose DCE perfusion MRI. There are only a few studies available on the comparison of DSC- and DCE-derived cerebral hemodynamic indices such as CBF and CBV. Aim: The objective of the study was to validate first-pass perfusion parameters derived from T1-based DCE method in comparison to the routine T2*-based DSC protocol. Materials and Methods: Twenty-nine patients with brain tumor underwent DCE- and DSC-MRIs to evaluate the agreement between DSC- and DCE-derived cerebral hemodynamic parameters. Agreement between DSC- and DCE-derived cerebral hemodynamic indices was determined using the statistical method described by Bland and Altman. The reliability between DSC- and DCE-derived cerebral hemodynamic indices was measured using the intraclass correlation analysis. Results: The achieved magnitudes for DCE-derived CBV (gray matter [GM]: 5.01 ± 1.40 mL/100 g vs. white matter [WM]: 1.84 ± 0.74 mL/100 g) and DCE-derived CBF (GM: 60.53 ± 12.70 mL/100 g/min vs. WM: 32.00 ± 6.00 mL/100 g/min) were in good agreement with other studies. The intraclass correlation coefficients showed that the cerebral hemodynamic indices could accurately be estimated based on the DCE-MRI using a single-compartment model (>0.87), and DCE-derived cerebral hemodynamic indices are significantly similar to the magnitudes achieved based on the DSC-MRI (P < 0.001). Furthermore, an acceptable agreement was observed between DSC- and DCE-derived cerebral hemodynamic indices. Conclusion: Based on the measurement of the cerebral hemodynamic and blood–brain barrier permeability using DCE-MRI, a more comprehensive collection of the physiological parameters cloud be achieved for tumor evaluations.

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