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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