Abstract #2499
Efficient Dictionary Design for MR Fingerprinting using Tree-Structured Vector Quantization
Zhitao Li 1 , Benjamin Paul Berman 2 , Diego R Martin 3 , Maria I Altbach 3 , and Ali Bilgin 1,4
1
Electrical and Computer Engineering,
University of Arizona, Tucson, Arizona, United States,
2
Applied
Mathematics, University of Arizona, Tucson, Arizona,
United States,
3
Department of Medical Imaging,
University of Arizona, Tucson, Arizona, United States,
4
Biomedical
Engineering, University of Arizona, Tucson, Arizona,
United States
Accurate parameter estimation in MR Fingerprinting (MRF)
requires large dictionaries with many atoms, each with
thousands of time points. Storage of such dictionaries
require large memory and the matching process becomes
increasingly demanding with increasing dictionary size.
We propose a Tree Structured Vector Quantizer based
clustering approach for MRF dictionary design. The
proposed approach allows significant reduction in
dictionary dimensions and can enable clinically relevant
reconstruction accuracy and time which is a major
bottleneck for clinical usefulness of MRF.
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