A numerical method to optimize presaturation sequences
on multiexponential samples

POSTER by  aMirko Gombia, bStanislav Sykora, cVilliam Bortolotti, aElisa Vacchelli, and aPaola Fantazzini
a Department of Physics, University of Bologna, Viale Berti Pichat 6/2, 40127 Bologna, Italy,  b Extra Byte, Castano Primo, Italy, and
c DICMA, University of Bologna, Via Terracini 28, 40131 Bologna, Italy

presented at

9th International Bologna Conference on Magnetic Resonance Applications to Porous Media,
MRPM9, July 13-17, 2008, Cambridge MA, USA.

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We have developed a mathematical model to simulate the effects of pulse sequences on composite samples and coded it in Matlab. The object-oriented code accommodates pulse sequences and samples of any complexity, including ones with any distribution of relaxation rates and offsets. Of equal importance is the fact that the most common instrumental artifacts (B0 and B1 inhomogeneity) can be easily taken into account. The package permits to simulate the magnetization of a composite sample during the whole sequence by using Bloch equations to track the magnetization of each sample component. The software allows one to obtain three quality factors related to: the highest residual magnetization among all components (Q1); the square mean of all the residual magnetization components (Q2); the modulus of the total magnetization vector of the sample (Q3). Low values of Qi (i=1,2,3) indicate good zeroings of the sample residual magnetization. In particular, a low value of Q1 indicates good zeroing of all the sample residual magnetization components.

This approach has been applied to the problem of fast and efficient presaturation by a suitable Sample Magnetization Suppression pulse sequence (SMS) in the presence of a wide spread of offsets, relaxation rates, and magnetic field imperfections. This led us to the Logarithmically-distributed A-Periodic Saturation Recovery sequence (LAPSR) which comes as close as possible to suppressing the absolute magnetization of all sample components and is, in this respect, much better than the classical sequences Saturation Recovery (SR) and A-Periodic Saturation Recovery (APSR). LAPSR is characterized by the following pulse sequence:

P(α) - D - P(α) - D.q - P(α) - D.q2 - ... - P(α) - D.qn-2 - P(α) - D.qn-1 - P(α) - t - P(90°),

where D is the delay between the first two presaturating pulses and a the nutation angle of the magnetization vector for each pulse. We notice that delays between presaturating pulses decrease logarithmically by a factor q<1 to reach the value D.qn-1 between the last two pulses of the presaturating sequence. The code was also used both to optimize LAPSR parameters, i.e. the delay D, the angle a, the number n of presaturating pulses, the q-value, and to show the effects of field inhomogeneities.

The performance of LAPSR has been verified on a composite large-volume phantom.

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