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Abstract #4540

Study of key properties behind a good undersampling pattern for quantitative estimation of tissue parameters

Riwaj Byanju1, Stefan Klein1, Alexandra Cristobal Huerta2, Juan A. Hernandez Tamames2, and Dirk H. J. Poot1

1Departments of Medical Informatics and Radiology, Erasmus MC, Rotterdam, Netherlands, 2Departments of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, Netherlands

Quantitative MR (qMRI) at present is clinically unfeasible due to long scan time. Jointly performing image reconstruction and parameters estimation is expected to allow increased acceleration. In this work, we investigate properties of undersampling patterns that are most relevant for parameter estimation using a Cramer-Rao-Lower-Bound (CRLB) based metric for such an approach. We compare key properties of undersampling patterns and conclude that one of these properties, namely low discrepancy, is most relevant for achieving time-efficient qMRI.

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