Comparison of CT and MRI, with and without Contrast Enhancement, in the Detection of Brain Tumors

Author(s):  
B. Schulz ◽  
A. Kern ◽  
M. Laniado ◽  
W. Schörner ◽  
J. Iglesias-Rozas ◽  
...  
1987 ◽  
Vol 66 (6) ◽  
pp. 865-874 ◽  
Author(s):  
Patrick J. Kelly ◽  
Catherine Daumas-Duport ◽  
David B. Kispert ◽  
Bruce A. Kall ◽  
Bernd W. Scheithauer ◽  
...  

✓ Forty patients with previously untreated intracranial glial neoplasms underwent stereotaxic serial biopsies assisted by computerized tomography (CT) and magnetic resonance imaging (MRI). Tumor volumes defined by computer reconstruction of contrast enhancement and low-attenuation boundaries on CT and T1 and T2 prolongation on MRI revealed that tumor volumes defined by T2-weighted MRI scans were larger than those defined by low-attenuation or contrast enhancement on CT scans. Histological analysis of 195 biopsy specimens obtained from various locations within the volumes defined by CT and MRI revealed that: 1) contrast enhancement most often corresponded to tumor tissue without intervening parenchyma; 2) hypodensity corresponded to parenchyma infiltrated by isolated tumor cells or in some instances to tumor tissue in low-grade gliomas or to simple edema; and 3) isolated tumor cell infiltration extended at least as far as T2 prolongation on magnetic resonance images. This information may be useful in planning surgical procedures and radiation therapy in patients with intracranial glial neoplasms.


1991 ◽  
Vol 19 (2) ◽  
pp. 293-298 ◽  
Author(s):  
P. R. Bullock ◽  
P. Mansfield ◽  
P. Gowland ◽  
B. S. Worthington ◽  
J. L. Firth

2021 ◽  
Author(s):  
Batuhan Sözer ◽  
Alperen Sözer ◽  
Mustafa Çağlar Şahin ◽  
Kerem Nernekli ◽  
Şule Eylem Erdoğan ◽  
...  

<p>Early diagnosis of brain tumors is extremely important, and shortening the interval between the acquisition of MRI images and reporting of the results is critical for patients. In the diagnosis of brain tumors, CT and MRI are some of the core diagnostic techniques used today. Our main goal is to reduce the workload of radiologists by developing a neural network that segments MRI images of the brain so we propose a multi-path segmentation algorithm based on U-Net architecture that uses residual extended skip blocks. Our proposed model is trained and tested with Gazi Brains 2020 Dataset. We evaluated the results using the dice similarity coefficient and compared the results with other segmentation algorithms and saw that our proposed model has comparatively better results. Our proposed model is using T1-Weighted, T2-Weighted, and Flair MRI images together as inputs, whereas other segmentation models, are using T2-Weighted or Flair MRI images as input. Implementation of the model and trained models are available at </p> <p><b>https://github.com/batuhansozer/brain-segmentation-with-novel-multi-path-model</b></p>


Neurosonology ◽  
2008 ◽  
Vol 21 (1) ◽  
pp. 6-11
Author(s):  
Tetsuhiro HIGASHIDA ◽  
Hiroshi KANNO ◽  
Katsumi SAKATA ◽  
Yutaka TANABE ◽  
Hidetoshi MURATA ◽  
...  

1996 ◽  
Vol 55 (5) ◽  
pp. 639
Author(s):  
Gregory N. Fuller ◽  
Lawrence E. Ginsberg ◽  
Donald F. Schomer ◽  
Masood Hashmi ◽  
Ashok J. Kumar ◽  
...  

2012 ◽  
Vol 67 (5) ◽  
pp. 387
Author(s):  
Eun Kyung Park ◽  
Deuk Jae Sung ◽  
Beom Jin Park ◽  
Min Ju Kim ◽  
Na Yeon Han ◽  
...  

Pancreatology ◽  
2008 ◽  
Vol 8 (2) ◽  
pp. 199-203 ◽  
Author(s):  
Moritz Palmowski ◽  
Nicola Hacke ◽  
Stefanie Satzl ◽  
Miriam Klauss ◽  
Moritz N. Wente ◽  
...  

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