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P Stoica

Publications and source records attributed to P Stoica.

4 recordsLinked to original sources
PubMed2004

Subspace-based MRS data quantitation of multiplets using prior knowledge.

Accurate quantitation of Magnetic Resonance Spectroscopy (MRS) signals is an essential step before converting the estimated signal parameters, such as frequencies, damping factors, and amplitudes, into biochemical quantities (concentration, pH). Several subspace-based parameter estimators have been developed for this task, which are efficient and accurate time-domain algorithms. However, they suffer from a serious drawback: they allow only a limited inclusion of prior knowledge which is important for accuracy and resolution. In this paper, a new method is presented: KNOB-SVD and its improved variant KNOB-TLS. KNOB-SVD is a recently proposed method, based on the Singular Value Decomposition (SVD), which allows the use of more prior knowledge about the signal parameters than previously published subspace-based methods. We compare its performance in terms of robustness and accuracy with the performance of three commonly used methods for signal parameter estimation: HTLS, a subspace-based method which does not allow any inclusion of prior knowledge, except for the model order; HTLSPK(Delta fd(eq)), a subspace-based method obtained by incorporating in HTLS the prior information that the frequency differences between doublet components are known and the damping factors are equal; and AMARES, an interactive maximum likelihood method that allows the inclusion of a variety of prior knowledge. Extensive simulation and in vivo studies, using (31)P as well as proton MRS signals, show that the new method outperforms HTLS and HTLSPK(Delta fd(eq)) in robustness, accuracy, and resolution, and that it provides parameter estimates comparable to the AMARES ones.

Journal Article↗
PubMed2001

Nonparametric NMR spectroscopy.

The parametric (or model-based) approach to NMR spectroscopy suffers from two general problems: it is sensitive to modeling errors and requires knowledge of the number of resonances present in the compound(s) under analysis. The nonparametric approach has neither of these drawbacks and it may also be computationally simpler than the parametric approach. However, if not applied properly, the nonparametric approach may yield significantly less accurate spectroscopic results than the parametric approach. In this paper we introduce a high-resolution nonparametric methodology for NMR spectroscopy based on the adaptive filter bank approach. The main salient feature of the new approach is that it provides 2D spectra versus both frequency and damping, as opposed to the classical 1D frequency spectra routinely used in NMR spectroscopy. To show the power of our new nonparametric approach we compare its performance with the ultimate performance of the parametric approach. We use both simulated and real NMR signals in our numerical performance study.

Magnetic Resonance Spectroscopy↗
PubMed2000

Exact ML estimation of spectroscopic parameters

In a paper on spectroscopic imaging published in this journal Spielman et al. (J. Magn. Reson. 79, 66-77 (1988)) made the important point that a priori information about the compounds present can and should be incorporated into the estimation of spectroscopic signal parameters. They proposed using the maximum likelihood (ML) approach for parameter estimation, but failed to incorporate properly the full a priori information that was assumed to be available. Consequently they ended up with a spectroscopic imaging method that is only a suboptimal approximation of the ML method. In this paper we derive the exact ML method, present a computationally efficient implementation of it (which is much faster than the direct implementation suggested by Spielman et al. for their suboptimal method), and illustrate numerically the performance gain that can be achieved over the method of Spielman et al. Copyright 2000 Academic Press.

Journal Article↗