A multidimensional partition analysis of SSFP image pulse sequences

The k-space description, of MRI pulse sequences, has been combined with a partition model in order to model the image reconstruction and the contrast behaviour found in SSFP pulse sequences. A partition represents the magnetisation created, due to excitation by a given rf pulse. In the present model...

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Bibliographic Details
Published in:Magnetic resonance imaging 1997, Vol.15 (4), p.451-467
Main Authors: PETERSSON, J. S, CHRISTOFFERSSON, J.-O
Format: Article
Language:English
Subjects:
Biological and medical sciences
Computer Simulation
Humans
Image Processing, Computer-Assisted
Investigative techniques, diagnostic techniques (general aspects)
Magnetic Resonance Imaging - methods
Medical sciences
Miscellaneous. Technology
Phantoms, Imaging
Radiodiagnosis. Nmr imagery. Nmr spectrometry
Signal Processing, Computer-Assisted
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Summary:The k-space description, of MRI pulse sequences, has been combined with a partition model in order to model the image reconstruction and the contrast behaviour found in SSFP pulse sequences. A partition represents the magnetisation created, due to excitation by a given rf pulse. In the present model, it is visualised as a set of parameters rather than a vector sum taken over a collection of spins. A multidimensional parameter space, where each dimension is associated with one of the partition parameters, is introduced in order to describe the interaction between partitions and pulse sequence events (e.g., rf pulses and gradients). The three k-space dimensions form the first three dimensions and higher orders are used to handle phase dispersions due to diffusion and main field inhomogeneities. The model makes it possible to perform fast simulation of images resulting from general SSFP pulse sequences. A computer implementation generates images (256 matrix), containing more than 10 different T1/T2 combinations, in less than 45 s on a 120 MHz Pentium computer. The contrast behaviour and signal intensities found in simulated images show excellent agreement with data generated using a clinical MRI scanner system.
ISSN:0730-725X
1873-5894
DOI:10.1016/S0730-725X(96)00369-4