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 (T2*), and complex amplitudes of a multipeak system from a limited number of echoes (≤16). 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 T2* 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 (SNR) ≥5 for echo train lengths (ETLs) ≥4 using a fixed echo spacing of 3.3 ms. T2* 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 (<0.01 ppm) and amplitude (<1.0%) were obtained with ≥4 echoes and for T2* (<1.0%) with ≥7 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 ≤16 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 ∼1.6×1.6×4 mm3 in ≤5 s. © 2009 American Association of Physicists in Medicine.
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Taylor, B. A., Hwang, K. P., Hazle, J. D., & Stafford, R. J. (2009). Autoregressive moving average modeling for spectral parameter estimation from a multigradient echo chemical shift acquisition. Medical Physics, 36(3), 753–764. https://doi.org/10.1118/1.3075819
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