Application case study—non-Cartesian magnetic resonance imaging
An introduction to statistical estimation methods
Abstract
This chapter presents an application study on using CUDA C and GPU computing to accelerate an iterative solver for reconstruction of an MRI image from Non-Cartesian scan data. It covers the process of identifying the appropriate type of parallelism, loop transformations, mapping data into constant memory, mapping data into registers, data layout transformations, using special hardware instructions, and experimental tuning. It also demonstrates a process of validating the design choices with domain-specific criteria.
Keywords
Statistical estimation methods; matrix–vector multiplication; linear solvers; iterative methods; ...
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