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Abstract #4273

View-sharing and Compressed Sensing in Two-Point Dixon-based DCE-MRI

Evan Levine 1,2 , Brady Quist 1,2 , Bruce Daniel 2 , Brian Hargreaves 2 , and Manojkumar Saranathan 2

1 Electrical Engineering, Stanford University, Palo Alto, CA, United States, 2 Radiology, Stanford University, Palo Alto, CA, United States

High spatiotemporal resolution multiphasic DCE-MRI entails tradeoffs of spatial and temporal resolution. To address the tradeoff, view-sharing schemes have been proposed, some of which use stochastic k-space trajectories. Compressed sensing offers a way to reduce view-sharing and temporal footprint. We propose a novel method of integrating view-sharing, compressed sensing, parallel imaging, and two-point Dixon based fat water separation that allows new tradeoffs in spatial and temporal resolution. A combination of CS, parallel imaging, and partial view-sharing is a suitable alternative to conventional view-sharing, especially when restricted to the water image using a two-point Dixon model.

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