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MRI On the Fly: Accelerating MRI Imaging Using LDA Classification with LDB Feature Extraction
Y. Joy Ko, Michael B. Taylor
Dartmouth PCS-TR96-290


To improve MRI acquisition time, we explored the uses of linear discriminant analysis (LDA), and local discriminant bases (LDB) for the task of classifying MRI images using a minimal set of signal acquisitions.

Our algorithm has both off-line and on-line components. The off-line component uses the k-basis algorithm to partition a set of training images (all from a particular region of a patient) into classes. For each class, we find a basis by applying the best basis algorithm on the images in that class. We keep these bases to be used by the on-line process. We then apply LDB to the training set with the class assignments, determining the best discriminant basis for the set. We rank the basis coordinates according to discriminating power, and retain the top M coordinates for the on-line algorithm. We keep the top M coordinates, which index the basis functions with the most discriminating capability, for on-line purposes. Finally, we train LDA on these transformed coordinates, producing a classifier for the images.

With the off-line requirements complete, we can take advantage of the simplicity and speed of the on-line mechanism to acquire an image in a similar region of the patient. We need acquire only the M important coordinates of the image in the discriminant basis to create a ``scout image.'' This image, which can be acquired quickly since M is much much smaller than the number of measurements needed to fill in the values of the 256 by 256 pixels, is then sent through the map furnished by LDA which in turn assigns a class to the image. Returning to the list of bases that we kept from the k-bases algorithm, we find the optimal basis for the particular class at hand. We then acquire the image using that optimal basis, omitting the coefficients with the least truncation error. The complete image can then be quickly reconstructed using the inverse wavelet packet transform.

The power of our algorithm is that the on-line task is fast and simple, while the computational complexity lies mostly in the off-line task that needs to be done only once for images in a certain region. In addition, our algorithm only makes use of the flexibility of MRI hardware, so no modifications in hardware design are needed.

Note: A Senior Undergraduate Honors Thesis in Computer Science.

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   Y. Joy Ko and Michael B. Taylor, "MRI On the Fly: Accelerating MRI Imaging Using LDA Classification with LDB Feature Extraction." Dartmouth Computer Science Technical Report PCS-TR96-290, June 1996.

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