2000 Fiscal Year Final Research Report Summary
Development of diffusion-weighted ultra-fast magnetic resonance spectroscopic metabolite imaging
| Project/Area Number |
10557088
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| Research Category |
Grant-in-Aid for Scientific Research (B).
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| Allocation Type | Single-year Grants |
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| Section | 展開研究 |
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| Research Field |
Radiation science
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| Research Institution | Kyoto Prefectural University of Medicine |
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Principal Investigator |
FURUYA Seiichi Kyoto Prefectural University of Medicine, Faculty of Medicine, Assistant, 医学部, 助手 (10271173)
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| Co-Investigator(Kenkyū-buntansha) |
KOSE Katsumi Tsukuba University, Institute of Physics, Associate Professor, 物理工学系, 助教授 (60186690)
KIZU Osamu Kyoto Prefectural University of Medicine, Faculty of Medicine, Assistant, 医学部, 助手 (70305598)
NARUSE Shoji Kyoto Prefectural University of Medicine, Faculty of Medicine, Associate Professor, 医学部, 助教授 (50106407)
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| Project Period (FY) |
1998 – 2000
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| Keywords | MR Spectroscopy / MR Spectroscopic Imaging / Ultra-fast MRSI Method / Relaxation time / Brain metabolism / Diffusion Weighted MRS / Echo Planar imaging |
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| Research Abstract |
The aim of this research was to develop a diffusion-weighted imaging of brain metabolite by using ultra-fast magnetic resonance imaging (MRSI) methods. We used three different MRI devices ; a 7T high-resolution NMR spectrometer for animal use, a microimaging deice worked in a clinical MRI magnet (MR mics) and a 1.5T clinical MRI scanner. The MR mics was used to transfer the software designed on the high-resolution NMR spectrometer to the clinical scanner. First, we have developed the echo planar imaging (EPI) sequence to get the ultra-fast MRSI (EPI-MRSI). Secondary, we tried to apply this technique to a diffusion-weighted imaging of brain metabolites. Thirdly, we developed the diffusion tensor imaging to get the information of metabolite diffusion.
We successfully developed the EPI-MRSI for human brain on the clinical MRI scanner. Comparing data of EPI-MRSI with conventional MRSI (SE-MRSI), the measurement time was slightly shorter in EPI-MRSI than in SE-MRSI but the spectral resolution was worse in EPI-MRSI.We realized that the more increased signal intensity was necessary to get a marked improvement of measuring time with EPI-MRSI.Also, it is necessary to have the improvement of the signal-intensity to apply this method to the diffusion weighted metabolite imaging.
We could succeed to develop the diffusion tensor imaging by obtaining the Eigenvector mapping, Eigenvalue mapping and fractional anisotropy mapping. These images clearly demonstrate the nerve fiber direction and anisotropy. We will try to apply these methods to get the information of diffusion metabolite in the brain as a next step.
Although there existed many technical difficulty in completing diffusion-weighted EPI-MRSI, it is indispensable to develop this method, because it has a high potentiality to investigate dynamic aspect of brain metabolites and is promising for both experimentally and clinically. In this sense, further research should be continued.
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