scholarly journals Neural-network classification of cardiac disease from 31P cardiovascular magnetic resonance spectroscopy measures of creatine kinase energy metabolism

2019 ◽  
Vol 21 (1) ◽  
Author(s):  
Meiyappan Solaiyappan ◽  
Robert G. Weiss ◽  
Paul A. Bottomley
Keyword(s):  
Neural Network ◽  
Creatine Kinase ◽  
Cardiovascular Magnetic Resonance ◽  
Energy Metabolism ◽  
Magnetic Resonance ◽  
Magnetic Resonance Spectroscopy ◽  
Cardiac Disease ◽  
Resonance Spectroscopy ◽  
Neural Network Classification

scholarly journals Evaluation of energy metabolism in masseter muscles of TMD patients using 31P-MRS(magnetic resonance spectroscopy).

1997 ◽  
Vol 4 (1) ◽  
pp. 9-15 ◽  
Author(s):  
Taihiko Yamaguchi ◽  
Kaori Satoh ◽  
Kosetsu Komatsu ◽  
Tomoyoshi Kimura ◽  
Yoichi Uchiyama ◽  
...  
Keyword(s):  
Energy Metabolism ◽  
Magnetic Resonance ◽  
Magnetic Resonance Spectroscopy ◽  
Resonance Spectroscopy ◽  
31P Mrs ◽  
Masseter Muscles

4101 ORAL Multicentre Prospective Classification of Childhood Brain Tumours Using Magnetic Resonance Spectroscopy

2011 ◽  
Vol 47 ◽  
pp. S284
Author(s):  
A.C. Peet ◽  
T.N. Arvanitis ◽  
D. Auer ◽  
R.G. Grundy ◽  
T. Jaspan ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Magnetic Resonance Spectroscopy ◽  
Brain Tumours ◽  
Resonance Spectroscopy ◽  
Childhood Brain Tumours

Application of CT Energy Spectrum Combined with MR Spectroscopy in the Classification of Meningiomas

2020 ◽  
Vol 10 (8) ◽  
pp. 1949-1954
Author(s):  
Ruiping Chai ◽  
Hong Zhang ◽  
Hongcan Ma ◽  
Ximi Xu ◽  
Hamris Andrew
Keyword(s):  
Energy Spectrum ◽  
Magnetic Resonance ◽  
Magnetic Resonance Spectroscopy ◽  
Mr Spectroscopy ◽  
Low Energy ◽  
Resonance Spectroscopy ◽  
Who Grade ◽  
Quantitative Parameters ◽  
Cr Concentration

Objective: To study the application value of CT spectroscopy combined with MR spectroscopy in the classification and classification of meningioma, in order to provide more information for the classification and classification of meningioma from the perspective of imaging, and to improve the intraoperative tumor resection. Provide reference for selection and evaluation of prognosis, judgment of efficacy, and prediction of recurrence. Methods: A total of 102 patients who underwent preoperative energy spectroscopy and 3.0T magnetic resonance spectroscopy were enrolled in our hospital from March 2017 to March 2018. There were 48 patients with spectral CT scan, 43 patients with MR spectroscopy, and 11 patients with energy spectrum CT and MR spectroscopy. Correlation between grading and typing of meningiomas was performed using quantitative parameters of energy spectrum CT and magnetic resonance spectroscopy. Study and evaluate the diagnostic efficacy of quantitative parameters. Results: The results of CT spectra combined with MR spectroscopy in the pathological grading of meningiomas showed significant differences in CT values at low energy (40–70 KeV) (P < 0.05), which showed that the CT values of WHOI-level single energy were less than WHO II grade; WHO grade I meningioma curve slope was significantly smaller than WHO II grade (P < 0.05), statistically significant; also found that WHO grade I meningioma Cho concentration, Cr concentration, Cho/Cr ratio and WHO II grade There was a significant difference (P < 0.05). Conclusion: CT spectroscopy combined with MR spectroscopy imaging in the diagnosis of meningioma, the single-energy CT value and slope of the low-energy (40–70 KeV) level of WHO grade I meningioma, the Cho concentration is significantly lower than the WHO II-level meninges. Tumor, and the Cr concentration is significantly higher than the WHO I level, the combination of the two can more accurately diagnose the meningioma.

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