Abstract
The eigenimage filter generates a composite image in which a desired feature is segmented. The signal-to-noise ratio (SNR) of the eigenimage is directly proportional to the dissimilarity between the desired and interfering features. Since image gray levels are analytical functions of magnetic resonance imaging (MRI) parameters, it is possible to maximize this dissimilarity by optimizing these parameters. For optimization, the authors consider four MRI pulse sequences: multiple spin-echo, spin-echo, inversion recovery, and gradient-echo. They use the mathematical expressions for MRI signals along with intrinsic tissue parameters to express the objective function in terms of MRI parameters. The objective function along with a set of diagnostic or instrumental constraints defines a multidimensional nonlinear constrained optimization problem, which is solved by the fixed point approach. The optimization technique is demonstrated through its application to phantom and brain images. >
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