scholarly journals Re: The Benefits of High Relaxivity for Brain Tumor Imaging: Results of a Multicenter Intraindividual Crossover Comparison of Gadobenate Dimeglumine with Gadoterate Meglumine (The BENEFIT Study)

2016 ◽  
Vol 37 (3) ◽  
pp. E20-E21
Author(s):  
E. Lancelot ◽  
B. Piednoir ◽  
P. Desché
Keyword(s):  
Brain Tumor ◽  
Tumor Imaging ◽  
Gadobenate Dimeglumine ◽  
High Relaxivity ◽  
Imaging Results ◽  
Gadoterate Meglumine ◽  
Crossover Comparison

scholarly journals Deep Learning-Based Studies on Pediatric Brain Tumors Imaging: Narrative Review of Techniques and Challenges

2021 ◽  
Vol 11 (6) ◽  
pp. 716
Author(s):  
Hala Shaari ◽  
Jasmin Kevrić ◽  
Samed Jukić ◽  
Larisa Bešić ◽  
Dejan Jokić ◽  
...  
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Tumor Imaging ◽  
Pediatric Brain Tumor ◽  
Pediatric Brain Tumors ◽  
Research Issues ◽  
Functional Changes ◽  
Imaging Results ◽  
Pediatric Brain

Brain tumors diagnosis in children is a scientific concern due to rapid anatomical, metabolic, and functional changes arising in the brain and non-specific or conflicting imaging results. Pediatric brain tumors diagnosis is typically centralized in clinical practice on the basis of diagnostic clues such as, child age, tumor location and incidence, clinical history, and imaging (Magnetic resonance imaging MRI / computed tomography CT) findings. The implementation of deep learning has rapidly propagated in almost every field in recent years, particularly in the medical images’ evaluation. This review would only address critical deep learning issues specific to pediatric brain tumor imaging research in view of the vast spectrum of other applications of deep learning. The purpose of this review paper is to include a detailed summary by first providing a succinct guide to the types of pediatric brain tumors and pediatric brain tumor imaging techniques. Then, we will present the research carried out by summarizing the scientific contributions to the field of pediatric brain tumor imaging processing and analysis. Finally, to establish open research issues and guidance for potential study in this emerging area, the medical and technical limitations of the deep learning-based approach were included.

Vasovist® in Brain Tumor Imaging

2008 ◽  
pp. 169-179
Author(s):  
Marco Essig ◽  
Clemens Fitzek
Keyword(s):  
Brain Tumor ◽  
Tumor Imaging

Fluorination Enhances NIR‐II Fluorescence of Polymer Dots for Quantitative Brain Tumor Imaging

2020 ◽  
Vol 132 (47) ◽  
pp. 21235-21243
Author(s):  
Ye Liu ◽  
Jinfeng Liu ◽  
Dandan Chen ◽  
Xiaosha Wang ◽  
Zhe Zhang ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Tumor Imaging ◽  
Polymer Dots
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