Clinical relevance of diffusion and perfusion magnetic resonance imaging in assessing intra-axial brain tumors

2006 ◽  
Vol 48 (3) ◽  
pp. 150-159 ◽  
Author(s):  
N. Rollin ◽  
J. Guyotat ◽  
N. Streichenberger ◽  
J. Honnorat ◽  
V.-A. Tran Minh ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Clinical Relevance ◽  
Perfusion Magnetic Resonance Imaging ◽  
Resonance Imaging

Role of Dynamic Contrast-Enhanced Perfusion Magnetic Resonance Imaging in Grading of Pediatric Brain Tumors on 3T

2017 ◽  
Vol 52 (5) ◽  
pp. 298-305 ◽  
Author(s):  
Pradeep Kumar Gupta ◽  
Jitender Saini ◽  
Prativa Sahoo ◽  
Rana Patir ◽  
Sunita Ahlawat ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Pediatric Brain Tumors ◽  
Perfusion Magnetic Resonance Imaging ◽  
Resonance Imaging ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced ◽  
Pediatric Brain

Perfusion magnetic resonance imaging: Comparison of semiologic characteristics in first-pass perfusion of brain tumors at 1.5 and 3 Tesla

2012 ◽  
Vol 39 (5) ◽  
pp. 308-316 ◽  
Author(s):  
Natacha Mauz ◽  
Alexandre Krainik ◽  
Irène Tropres ◽  
Laurent Lamalle ◽  
Elodie Sellier ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
3 Tesla ◽  
Perfusion Magnetic Resonance Imaging ◽  
Resonance Imaging ◽  
Pass Perfusion ◽  
First Pass ◽  
First Pass Perfusion

Perfusion magnetic resonance imaging in pediatric brain tumors

2017 ◽  
Vol 59 (11) ◽  
pp. 1143-1153 ◽  
Author(s):  
F. Dallery ◽  
R. Bouzerar ◽  
D. Michel ◽  
C. Attencourt ◽  
V. Promelle ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Pediatric Brain Tumors ◽  
Perfusion Magnetic Resonance Imaging ◽  
Resonance Imaging ◽  
Pediatric Brain

Spectroscopic and perfusion magnetic resonance imaging predictors of progression in pediatric brain tumors

Cancer ◽  
2004 ◽  
Vol 100 (6) ◽  
pp. 1246-1256 ◽  
Author(s):  
A. Aria Tzika ◽  
Loukas G. Astrakas ◽  
Maria K. Zarifi ◽  
David Zurakowski ◽  
Tina Young Poussaint ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Pediatric Brain Tumors ◽  
Perfusion Magnetic Resonance Imaging ◽  
Resonance Imaging ◽  
Pediatric Brain

Diffusion and perfusion magnetic resonance imaging in brain tumors

1993 ◽  
Vol 5 (2) ◽  
pp. 25???31 ◽  
Author(s):  
Denis Le Bihan ◽  
Philippe Douek ◽  
Maria Argyropoulou ◽  
Robert Turner ◽  
Nicholas Patronas ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Perfusion Magnetic Resonance Imaging ◽  
Resonance Imaging

scholarly journals Differential Deep Convolutional Neural Network Model for Brain Tumor Classification

2021 ◽  
Vol 11 (3) ◽  
pp. 352
Author(s):  
Isselmou Abd El Kader ◽  
Guizhi Xu ◽  
Zhang Shuai ◽  
Sani Saminu ◽  
Imran Javaid ◽  
...  
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Medical Image ◽  
Resonance Imaging ◽  
Feature Maps ◽  
Deep Cnn

The classification of brain tumors is a difficult task in the field of medical image analysis. Improving algorithms and machine learning technology helps radiologists to easily diagnose the tumor without surgical intervention. In recent years, deep learning techniques have made excellent progress in the field of medical image processing and analysis. However, there are many difficulties in classifying brain tumors using magnetic resonance imaging; first, the difficulty of brain structure and the intertwining of tissues in it; and secondly, the difficulty of classifying brain tumors due to the high density nature of the brain. We propose a differential deep convolutional neural network model (differential deep-CNN) to classify different types of brain tumor, including abnormal and normal magnetic resonance (MR) images. Using differential operators in the differential deep-CNN architecture, we derived the additional differential feature maps in the original CNN feature maps. The derivation process led to an improvement in the performance of the proposed approach in accordance with the results of the evaluation parameters used. The advantage of the differential deep-CNN model is an analysis of a pixel directional pattern of images using contrast calculations and its high ability to classify a large database of images with high accuracy and without technical problems. Therefore, the proposed approach gives an excellent overall performance. To test and train the performance of this model, we used a dataset consisting of 25,000 brain magnetic resonance imaging (MRI) images, which includes abnormal and normal images. The experimental results showed that the proposed model achieved an accuracy of 99.25%. This study demonstrates that the proposed differential deep-CNN model can be used to facilitate the automatic classification of brain tumors.

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