scholarly journals Quantification of liver fat in mice: comparing dual-echo Dixon imaging, chemical shift imaging, and 1H-MR spectroscopy

2011 ◽  
Vol 52 (10) ◽  
pp. 1847-1855 ◽  
Author(s):  
Xin-Gui Peng ◽  
Shenghong Ju ◽  
Yujiao Qin ◽  
Fang Fang ◽  
Xin Cui ◽  
...  
2015 ◽  
Vol 84 (8) ◽  
pp. 1452-1458 ◽  
Author(s):  
Janakan Satkunasingham ◽  
Cecilia Besa ◽  
Octavia Bane ◽  
Ami Shah ◽  
André de Oliveira ◽  
...  

2011 ◽  
Vol 43 (5) ◽  
pp. 1570-1575 ◽  
Author(s):  
P. Vyhnanovská ◽  
M. Dezortová ◽  
V. Herynek ◽  
P. Táborský ◽  
O. Viklický ◽  
...  

Radiology ◽  
2009 ◽  
Vol 250 (1) ◽  
pp. 95-102 ◽  
Author(s):  
Boris Guiu ◽  
Jean-Michel Petit ◽  
Romaric Loffroy ◽  
Douraied Ben Salem ◽  
Serge Aho ◽  
...  

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Pamela Franco ◽  
Urs Würtemberger ◽  
Karam Dacca ◽  
Irene Hübschle ◽  
Jürgen Beck ◽  
...  

Abstract Background The revised 2016 WHO-Classification of CNS-tumours now integrates molecular information of glial brain tumours for accurate diagnosis as well as for the development of targeted therapies. In this prospective study, our aim is to investigate the predictive value of MR-spectroscopy in order to establish a solid preoperative molecular stratification algorithm of these tumours. We will process a 1H MR-spectroscopy sequence within a radiomics analytics pipeline. Methods Patients treated at our institution with WHO-Grade II, III and IV gliomas will receive preoperative anatomical (T2- and T1-weighted imaging with and without contrast enhancement) and proton MR spectroscopy (MRS) by using chemical shift imaging (MRS) (5 × 5 × 15 mm3 voxel size). Tumour regions will be segmented and co-registered to corresponding spectroscopic voxels. Raw signals will be processed by a deep-learning approach for identifying patterns in metabolic data that provides information with respect to the histological diagnosis as well patient characteristics obtained and genomic data such as target sequencing and transcriptional data. Discussion By imaging the metabolic profile of a glioma using a customized chemical shift 1H MR spectroscopy sequence and by processing the metabolic profiles with a machine learning tool we intend to non-invasively uncover the genetic signature of gliomas. This work-up will support surgical and oncological decisions to improve personalized tumour treatment. Trial registration This study was initially registered under another name and was later retrospectively registered under the current name at the German Clinical Trials Register (DRKS) under DRKS00019855.


Radiology ◽  
1991 ◽  
Vol 180 (2) ◽  
pp. 341-344 ◽  
Author(s):  
I R Francis ◽  
T L Chenevert ◽  
B Gubin ◽  
L Collomb ◽  
W Ensminger ◽  
...  

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