AGeneticAlgorithmforOptimalDesignofSpectrallySelective.ppt

A Genetic Algorithm for Optimal Design of Spectrally Selective k-Space Douglas C. Noll, Ph.D. Depts. of Biomedical Engineering and Radiology University of Michigan, Ann Arbor Supported by NIH Grant NS32756 Acknowledge the assistance of Sangwoo Lee Outline Background on Spectral-Spatial Imaging Optimization using Genetic Algorithms Optimization Results Experimental Findings Summary Stochastic Acquisitions Sheffler and Hennig (MRM, 35:569-576, 1996) Recognition that particular acquisitions could be spectrally and spatially selective Spectral bandwidth ~ 1/Tread Rosette Acquisitions Spectral properties similar to stochastic imaging, but: Extra suppression of low spatial frequencies Simple parameterization No sharp corners in k-space (reduced slew req.) SMART Imaging Simultaneous Multislice Acquisition using Rosette Trajectories (SMART) Excitation of several (e.g. 3) slices Use of slice gradient to modulate slices to different frequencies Use of spectral properties of acquisition to differentiate slices Demodulation of raw data shifts from one slice to another SMART Imaging The Rosette k-space Trajectory K-space can be described by:k(t) = A sin(w1 t)exp(i w2 t) w1 = oscillation frequency w2 = rotation frequency Peak gradient and slew rate constraints:gmax = (2p/g) A w1 smax = (2p/g) A (w12 + w22) Stochastic Rosettes Rosette acquisitions can be randomized by treating each petal as a separate unit Each petal can be characterized by two random numbers Method: Randomly select A from [0.9, 1.1]xA0 Determine w1 from gmax equation Determine w2,max from smax equation Randomly select w2 from [0.5, 1.0]x w2,max Stochastic Rosettes Petals are spliced together so that there are no discontinuities in the gradient waveforms Challenge: Optimization Stochastic rosette acquisitions: Easy to design Large number of parameters No obvious relationship between parameters and acquisition performance There are an infinite choice of parameters for stochastic rosette acquisiti