Comparison among Three Different Reprocessing Technologies for Quantitation and Influence of STEAM and PRESS Sequences on Metabolic Concentration Xiu-Qing.

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Presentation transcript:

Comparison among Three Different Reprocessing Technologies for Quantitation and Influence of STEAM and PRESS Sequences on Metabolic Concentration Xiu-Qing Guo, Xiao-Fang Cheng, Ren-Hua Wu Department of Medical Imaging Shantou University China

 Background and purpose  Methods  Results  Conclusion

 In vivo localized magnetic resonance spectroscopy (MRS) is the best method for non- invasive assessment of cell metabolism.  A number of models and algorithms have been published during the past. Background and purpose

 To study the influence of 3 different kinds of post-processing technologies: LCModel, Functool and SAGE on quantificaiton.  To analyze metabolic concentration difference in anterior central gyrus acquired with PRESS or STEAM sequence.

Methods  Fourteen health volunteers were recruited for this study. male 10, female 4

Methods  MRS data were acquired on a 1.5T GE signa HDx MR scanner with a standard head coil.  Voxels were placed in left anterior central gyrus.  Using both PRESS and STEAM sequences.

Methods

LCModel SAGE Functool

Methods PRESS STEAM

Methods  Metabolite ratios to Cr were acquired LCModel, SAGE and Functool with data gained from STEAM sequence.  Absolute quantification of All main metabolite was done using LCModel.

Methods  Multiple Comparison were performed to compare metabolite ratios obtained from LCModel, SAGE and Functool.

 Sample paired t-test was employed to compare data obtained from PRESS and STEAM sequences. Methods

Results LCModel SAGE Functool

Results  MI/Cr, Cho/Cr were significantly different using the three post-processing technologies. Cho/Cr obtained from LCModel was obviously deviated from the reference. NAA/Cr and MI/Cr analyzed by Functool were also deviated from the reference.

NAA/Cr （ n=14 ） LCModelSAGEFunctool SAGE0.551 Functool0.897 LCModel0.710 Cho/Cr （ n=14 ） LCModelSAGEFunctool SAGE0.936 Functool0.000* LCModel0.000* mI/Cr （ n=14 ） LCModelSAGEFunctool SAGE0.470 Functool0.000* LCModel0.470 Results: p value

Results LCModelSAGEFunctoolliterature NAA/Cr1.612± ± ± ±0.2 Cho/Cr0.254± ± ± ±0.2 mI/Cr0.771± ± ± ±0.2

Results The peak areas through a known formula, Cho/Cr and mI/Cr were still deviated from the reference when we analyzed the spectrum by SAGE software.

Results PRESS （ n=14 ） (mmol/kgww) STEAM （ n=14 ） (mmol/kgww) p Cho1.161± ± Cr5.347± ± mI3.823± ± Glu+Gln9.023± ± NAA7.868± ±

Results PRESS (mmol/kgww) STEAM (mmol/kgww) Varanavasi Govindaraju et al. (mmol/kgww) [18] NAA7.868±1.493*8.358± Cho1.161± ± Cr5.347± ± mI3.823±2.535*4.105± Glx9.023±3.392*7.740±2.622*14-16

 No significantly difference of metabolic concentration was observed between PRESS and STEAM sequences method. But compared with literature, result from STEAM is more close to it. Results

Conclusion  Compared with Functool and SAGE, LCModel shows an advantage at the quantitation because of simulating the base-line spectrum and handling the systematic error.  Compared with PRESS sequence, STEAM is better in simulating line shape and handling baseline which makes the spectrum more stable.

Acknowledgements National Natural Science Foundation of China (NSFC) Li Ka Shing Foundation

Acknowledgements