Optimization Techniques In Magnetic Resonance Imaging

optimization Techniques in Magnetic Resonance ImagingNola M. Hylton, Douglas A. Ortenaahl, Leon Kaufman ana Lawrence CrooksUniversity of California, San FranciscoRadiologic imaging Laboratory400 Grandview DriveSouth San Francisco, California 94080AbstractWith a minimally complete set of acquired patient data, computational techniques canbe used to extract intrinsic quantities and reconstruct magnetic resonance (MR) images.The effects of various sequences can be modeled for the purpose of understanding thefunctional behavior of the NMR response to a variety or changing parameters, as well asfor the generation of new images without the need of actual acquisition. Maps areconstructed which plot the signal difference between two tissues as a function of NMRparameters. These maps can be used as a guide to acquisition parameters for bestcontrast. Noise in calculated images resulting from the propagation of noise in thecalculational process can be accounted for by displaying signal difference -to -noise as afunction of NMR parameters. The effects of magnetic field strength changes on relaxationtimes are modeled using empirical data. These models are used to predict the effects ontissue contrast using difference maps and calculated images that have been extrapolated tonew values of field strength.IntroductionThe novelty and promise of magnetic resonance as an imaging technique derives from itssensitivity to multiple parameters. The ability to distinguish tissue and characterizedisease with hydrogen NMR imaging methods is based on the fact that in general,differences between tissues are manifested by a difference in the hydrogen density N(H) orin one or both of the relaxation constants, Tl or T2. Differences in the relaxation timesoccur since these times reflect the makeup of the local molecular environment. NMRacquisition techniques must be designed to best exploit these changes in order that anyabnormalities are ultimately detected in the resulting image.MRI methods measure the transient response of the magnetization to controlledelectronic stimulations. The object being imaged represents a three -dimensional spatialdistribution of each of the three quantities Ti, T2 ana N(H); flow factors neglected.Extrinsic parameters of the imaging technique such as the pulse repetition interval, TR,or the delay between excitation and signal readout, TE, add ari additional set ofsignal -affecting variables creating a large number of parameters which can changeindependently. The cumulative effect on the signal intensity and contrast reiationshipsof several of these parameters changing simultaneously yield non- intuitive results.Altering the timing of the sequence can create an infinite number of patterns ofexcitation.We present here analytical methods for the simulation and evaluation ci imagingmethods. Such techniques for retrospective evaluation of images enable a more exhaustiveanalysis of the data than time and other practical constraints allow during the actualimaging procedure (1).Modeling the Signal IntensityIn addition to its dependence on the hydrogen density distribution, the NMR signalyields information of the state of the spin system. The recovery of equilibriummagnetization after radiofrequency (rf) excitation occurs via two mechanisms; the transferof energy out of the system from the spin to its surrounding lattice, allowing the systemto return to equilibrium, and the interactions between spins which diminishes the order orcoherence of the spins. The rates of these two relaxation mechanisms, described by theconstants Tl and T2, will vary with tissue composition and will be affected by a number offactors including the strength of the static magnetic field and temperature.The NMR signal is observed as a voltage in a surrounding rf receiver coil due to bulkmagnetization in the transverse plane (orthogonal to the static magnetic field). Thesignal intensity can be modeled as the solution to the Bloch equations subject to theparticular boundary conditions of the sequence. These equations describe the motion ofthe magnetization vector, including the effects of relaxation. We have modeled thesignal intensity for two sequences presently implemented in our laboratory; a modified