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• W2128173942 abstract "The authors investigated the performance of the iterative Steiglitz–McBride (SM) algorithm on an autoregressive moving average (ARMA) model of signals from a fast, sparsely sampled, multiecho, chemical shift imaging (CSI) acquisition using simulation, phantom, ex vivo , and in vivo experiments with a focus on its potential usage in magnetic resonance (MR)‐guided interventions. The ARMA signal model facilitated a rapid calculation of the chemical shift, apparent spin‐spin relaxation time , and complex amplitudes of a multipeak system from a limited number of echoes . Numerical simulations of one‐ and two‐peak systems were used to assess the accuracy and uncertainty in the calculated spectral parameters as a function of acquisition and tissue parameters. The measured uncertainties from simulation were compared to the theoretical Cramer–Rao lower bound (CRLB) for the acquisition. Measurements made in phantoms were used to validate the estimates and to validate uncertainty estimates made from the CRLB. We demonstrated application to real‐time MR‐guided interventions ex vivo by using the technique to monitor a percutaneous ethanol injection into a bovine liver and in vivo to monitor a laser‐induced thermal therapy treatment in a canine brain. Simulation results showed that the chemical shift and amplitude uncertainties reached their respective CRLB at a signal‐to‐noise ratio for echo train lengths using a fixed echo spacing of 3.3 ms. estimates from the signal model possessed higher uncertainties but reached the CRLB at larger SNRs and/or ETLs. Highly accurate estimates for the chemical shift and amplitude were obtained with echoes and for with echoes. We conclude that, over a reasonable range of SNR, the SM algorithm is a robust estimator of spectral parameters from fast CSI acquisitions that acquire echoes for one‐ and two‐peak systems. Preliminary ex vivo and in vivo experiments corroborated the results from simulation experiments and further indicate the potential of this technique for MR‐guided interventional procedures with high spatiotemporal resolution in ." @default.
• W2128173942 created "2016-06-24" @default.
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• W2128173942 date "2009-02-12" @default.
• W2128173942 modified "2023-09-26" @default.
• W2128173942 title "Autoregressive moving average modeling for spectral parameter estimation from a multigradient echo chemical shift acquisition" @default.
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• W2128173942 doi "https://doi.org/10.1118/1.3075819" @default.
• W2128173942 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/2736745" @default.
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