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Abstract #4371

Local Versus Global Low-Rank Promotion in Dynamic MRI Series Reconstruction

Joshua Trzasko1, Armando Manduca1

1Mayo Clinic, Rochester, MN, United States

Recent works have suggested that dynamic MRI series reconstructions can be significantly improved by promoting low-rank structure in the estimated image series. However, when there exists a significant discrepancy between the spatial and temporal dimensions of the image series, low-rank approximations begin to lose their efficacy, resulting in either inadequate noise removal or temporal blurring. In this work, we present a generalization of the low-rank recovery paradigm, which we call Locally Low Rank (LLR) image recovery, that promotes low-rank structure locally rather than globally. As demonstrated, this migration can improve both the efficacy of noise removal and temporal signal fidelity.