Rashmi Reddy1, Imam Shaik1, Arush Honnedevasthana1, Pavan Poojar1, and Sairam Geethanath1
1Dayananda Sagar Institutions, Bangalore, India
Synopsis
Magnetic
Resonance Spectroscopic Imaging (MRSI) is used as a tool to quantify
metabolites in human brain and other organs. It is extensively used in clinical
imaging and clinical research applications1. However,
the major drawback of this technology is its long acquisition time which is the primary reason for not being used in the clinics. A novel approach of accelerating
MRSI using Magnetic Resonance Fingerprinting (MRF) has been proposed in the
current work. Purpose
The
proposed approach aims at reducing the acquisition time of Magnetic Resonance
Spectroscopic Imaging (MRSI) through application of Magnetic Resonance Fingerprinting (MRF)
2 based on dictionary generation of metabolites.
Methods
A retrospective analysis was performed on the Ethanol (CH3CH2OH)
phantom data sets. These datasets were acquired on Siemens 1.5T scanner with
dimensions of 16 X 16 X 1024 with 1024 being the time points.
A spectra was obtained with TE/TR combination of 30ms/1590ms respectively and Averages=1.
4 bottles having pure distilled water (for reference) in 1 of the bottles and 3
other bottles with different concentrations of ethanol (25%, 50% and 100%) were
scanned with total acquisition time of 4 minutes and 30 seconds. The phantom set
up is as shown in figure 1. Spectra of ethanol obtained from scanner was noisy
due to low TR/TE and less averages.
MRF-Dictionary
generation: Free Induction Decay (FID) closely approximating the response of metabolites for the molecular groups of ethanol i.e., CH3, CH2 and OH was generated
based on equation as in3 given below,
$$S(k)=\sum_{q=1}^NA_{q}exp(-t_{k}/T_{2}^{q})exp(j(2\pi f_{q}t_{k} + \phi_{q}))$$
Where, S is the simulated FID having ‘q’ number of
metabolite peaks each with amplitude Aq having an apparent
relaxation time T2q, a frequency fq and a
phase φq and tk is the kth time point in the
FID. FIDs for above mentioned metabolites were
generated for given TE/TR and for different T2 values between the
range 50-720ms, 50-750ms and 20-90ms for CH3, CH2 and OH
respectively. Spectra obtained by taking Inverse Fast Fourier Transform (IFFT) were
added up and stored in dictionary.
Pattern matching: Pattern
matching between the original spectra and dictionary was performed using vector
dot product. To find the ppm shift of the spectra, cross correlation and
circular shift operations were performed on the matched spectra from
dictionary. The original and matched spectra were subjected
to the following processing steps in jMRUI4: (a) Apodization (b) Phase Correction (c)
Frequency Shift.
Results
The spectra obtained from scanner was matched with one of the
spectra in dictionary. Signal evolution curves for original spectra obtained
from scanner and dictionary matched spectra are shown in figure 2. From figure 2, we can observe that the patterns
of scanner spectra and spectra from dictionary were matching.
Ratio of the 3 peaks in the spectrum obtained from dictionary was observed to
be 0.57, 0.327 and 0.097 for CH
3, CH
2 and OH respectively as expected.
Conclusion and Discussion
Application of MRF on MRSI has been performed for the first
time. This method aids in accelerated MRSI. Future work includes
generation of metabolite maps and incorporation of CS for denoising of MRSI
data.
Acknowledgements
This work was supported by Department of Science and Technology (DST), Govt. of India under the program Technology Systems Development (TSD) for the project “Novel acquisition and reconstruction strategies to accelerate magnetic resonance imaging using compressed sensing”, No: DST/TSG/NTS/2013/100-G.References
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187–192
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