Magnetic Resonance Imaging And Computer NLS-Graphy And In Evaluation Of Surgical Intervention Extent For Brain Tumors Treatment.

10.5580/2c91 ◽  
2012 ◽  
Vol 7 (1) ◽  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Surgical Intervention ◽  
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.

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

Magnetic resonance imaging of brain tumors: measurement of T1. Work in progress.

Radiology ◽  
1984 ◽  
Vol 150 (1) ◽  
pp. 95-98 ◽  
Author(s):  
T Araki ◽  
T Inouye ◽  
H Suzuki ◽  
T Machida ◽  
M Iio
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Resonance Imaging ◽  
Work In Progress

Diffusion-Weighted Magnetic Resonance Imaging Procedure as a Cancer Diagnosis Application Tool

2017 ◽  
pp. 660-667
Author(s):  
I. Shirazu ◽  
Y. B Mensah ◽  
T. A Sackey ◽  
M. Boadu ◽  
E K Eduful ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Tissue Characterization ◽  
Measured Signal ◽  
Resonance Imaging ◽  
Imaging Procedure ◽  
Tissue Organization ◽  
Diffusion Weighted ◽  
Weighted Magnetic Resonance Imaging

Physical imaging technique described as Diffusion Weighted-Magnetic Resonance Imaging (DW-MRI) is based on classically principle of Brownian motion, where the molecules are thermal agitated and is highly influenced by the cellular availability of water. The aim of this study is to discuss the use of DW-MRI as a cancer diagnostic application tool using the basic physics principles as versus other available procedures and modalities in terms of accuracy and acceptability. Based on extravascular diffusion measurements where the measured signal is related to tissue cellularity, tissue organization and extracellular space tortuosity and on the intactness of cellular membranes that are intrinsically hydrophobic. The methodology involve the application of DW-MRI procedure, to qualitatively and quantitatively access DW-MR images to diagnose brain tumors, prostate and other organ cancers compared to other imaging modalities including other MRI procedures. It also include safety assessment and other consideration before, during and after imaging with MRI as compare to other radiological modalities. The results of the data of ten (10) MRI centers and 112 DW-MRI images and 99 other procedure and modalities were analysed, 34% were prostate cases, 27% were brain cases and 39% formed all other cases. In addition, DW-MRI compare to other single imaging procedure formed 53% of all diagnostic procedure that had 87% accurate predictability of prostate and brain cases. It can therefore concluded that DW-MRI is the best single imaging procedure that can be used to diagnose prostate cancers and brain tumors. It has a major advantage of non-ionizing radiation technique, with multiple planes image acquisitions, together with superior soft tissue contrast. In addition its perfusion allow for precise tissue characterization rather than merely 'macroscopic' imaging and superior visualization of both active parts of the brain during certain activities and understanding of the underlying networks. However, there are two outstanding challenges of DW-MRI scans in Ghana: it is expensive as compared to other modalities and not safe for patients with some metal implants. Despite these challenges, its advantages override its disadvantages and therefore it is recommended to clinicians as the first diagnostic tool to use in prostate cancer and brain tumor diagnoses.

Comparison of Diagnostic Accuracy of Magnetic Resonance Spectroscopy with Conventional Magnetic Resonance Imaging for Brain Tumors and Correlation with Histopathology as Gold Standard

2021 ◽  
Vol 15 (9) ◽  
pp. 4009-4011
Author(s):  
Saulat Sarfraz ◽  
Mahwish Farzana
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Magnetic Resonance Spectroscopy ◽  
Brain Tumors ◽  
Diagnostic Accuracy ◽  
Gold Standard ◽  
Age Groups ◽  
Resonance Spectroscopy ◽  
Resonance Imaging ◽  
Conventional Mri

Background: In spite of recent advances in the use of diagnostic imaging modalities none of them has a hundred percent accuracy. So, misdiagnosis still occurs. Many trials are being done to evaluate the accuracy of these tools individually or in combination. The most useful investigation is MRI which broadly gives information of lesion as well its relationship with surrounding structures. While magnetic resonance spectroscopy further characterizes the lesion into benign or malignant. So this study is bit superior giving more details. By enlarge histopathology is gold standard for ultimate diagnosis. However these radiological investigations are extremely important for preoperative planning as well management of the lesion. In this study we compare the diagnostic accuracy of Magnetic Resonance Spectroscopy (MRS) with conventional MRI (Magnetic Resonance Imaging) sequences for diagnosis of brain tumors keeping histopathology as gold standard. Methods: The study was performed in 150 clinically suspected cases which were referred to Radiology Department from OPD, Indoor, Emergency and private sources from outside the hospital. Results: Majority 85(56.7%) were adult males and 65(43.3%) were adult females. The study was divided into two major age groups. There were 33cases (22%) with average age 20-35 years. The other age group 36-50 years had 40(26.7%) Majority of the cases 77(51.3%) were of average >50 years of age. The higher age groups showed a female dominance. Histopathology of 100(66.7%) cases confirmed positive and 50(33.3%) negative for MR Spectroscopy. On comparison of conventional MRI with contrast, and Histopathology it was observed that the sensitivity of MRI was 74.0% and the specificity 82.0%.The positive and negative predictive values gave a lower accuracy rate of 76.6%. Conclusion: The conclusion of our study is that MRS is a rigorous, non-invasive, safe and convenient imaging modality for the evaluation of brain tumors as compared to MRI. Keywords: Brain tumors, MRI, MRS, Histopathology

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