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Abstract #3560

Improved estimation of renal perfusion with multiple inversion-time acquisitions in arterial spin labeling

Jeff L Zhang 1 , Christopher C Conlin 1 , Jason Mendes 1 , Niels Oesingmann 2 , and Vivian S Lee 1

1 Department of Radiology, University of Utah, Salt Lake City, Utah, United States, 2 Siemens Medical Solutions USA, Inc., New York, United States

Conventional perfusion quantification model for renal ASL data ignores transit delay from tagging site to tissue voxels. In this study, we compared a convolution-based model incorporating transit delay to the conventional approach using both simulated and human kidney ASL data. We found that the conventional method to estimate renal perfusion from ASL data was sensitive to the selection of inversion time (TI), while by acquiring signals at multiple TIs and analyzing them with a convolution-based model, we can estimate renal perfusion with much lower variability.

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