Abstract
Multispectral tissue classification using magnetic resonance T 1, T 2, and ϱ images may be useful in diagnosing and locating certain pathology. Techniques for generating the T 1 images necessary for this classification scheme often require longer data collection and post processing times than are practical. As a consequence, further development of this classifiction scheme may be limited. This paper addresses an improvement in the post processing time required to generate T 1 images. A nonlinear least-squares algorithm is described for rapidly generating spinlattice relaxation time images from variable repetition time magnetic resonance images. The algorithm generates a 256 × 256 pixel T 1 image from nine variable repetition time images in approximately 60 sec on a VAX-6510 computer.
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