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A semiadiabatic spectral-spatial spectroscopic imaging (SASSI) sequence for improved high-field MR spectroscopic
Rebecca E Feldman1, Priti Balchandani2
1Translational and Molecular Imaging Institution, Icahn School of Medicine at Mount Sinai, New York, New York, USA. rebecca.feldman2@mountsinai.org.
Magnetic Resonance in Medicine
|November 1, 2015
Summary
We developed a new semiadiabatic spectral-spatial spectroscopic imaging (SASSI) pulse sequence for 7T MRI. SASSI provides more uniform brain metabolite signal-to-noise ratio (SNR) with significantly reduced radiofrequency power deposition (SAR).
Area of Science:
- Magnetic Resonance Imaging
- Spectroscopic Imaging
- Metabolite Analysis
Background:
- 7T MR Spectroscopic Imaging (MRSI) offers enhanced signal-to-noise ratio (SNR) and spectral separation.
- Conventional MRSI at 7T suffers from B1 field inhomogeneity and chemical shift errors, alongside increased radiofrequency (RF) power deposition (SAR).
- Adiabatic RF pulses can potentially overcome B1 variation issues.
Purpose of the Study:
- To design and implement a semiadiabatic spectral-spatial spectroscopic imaging (SASSI) pulse sequence for 7T MRSI.
- To achieve more uniform spectral data at 7T.
- To reduce RF power deposition (SAR) at 7T.
Main Methods:
- Utilized the adiabatic Shinnar-Le Roux algorithm to generate a 180° adiabatic spectral-spatial (SPSP) pulse for metabolite spectral range capture.
- Employed a pair of 180° SPSP pulses to refocus signal excited by a 90° SPSP pulse for 3D volume selection within the SASSI sequence.
Main Results:
- The SASSI pulse sequence demonstrated more uniform brain metabolite SNR compared to conventional nonadiabatic MRSI sequences.
- SASSI achieved SNR comparable to the semi-LASER technique.
- SASSI utilized one-third of the SAR compared to semi-LASER.
Conclusions:
- The developed SASSI pulse sequence effectively provides uniform spectral data at 7T.
- SASSI offers a promising alternative for 7T MRSI, balancing SNR with reduced SAR.
- This technique may mitigate B1 field variations and improve spectral localization accuracy.
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