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Sandgren, Niclas
Publications (10 of 18) Show all publications
Stoica, P. & Sandgren, N. (2007). Total-Variance Reduction via Thresholding: Application to cepstral analysis. IEEE Transactions on Signal Processing, 55(1), 66-72
Open this publication in new window or tab >>Total-Variance Reduction via Thresholding: Application to cepstral analysis
2007 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 55, no 1, p. 66-72Article in journal (Refereed) Published
National Category
Signal Processing
Identifiers
urn:nbn:se:uu:diva-25057 (URN)10.1109/TSP.2006.882073 (DOI)000243281200007 ()
Available from: 2007-02-09 Created: 2007-02-09 Last updated: 2018-10-01Bibliographically approved
Sandgren, N., Stoica, P. & Frigo, F. (2006). Area-Selective Signal Parameter Estimation for Two-Dimensional MR Spectroscopy Data. Journal of magnetic resonance, 183(1), 50-59
Open this publication in new window or tab >>Area-Selective Signal Parameter Estimation for Two-Dimensional MR Spectroscopy Data
2006 (English)In: Journal of magnetic resonance, ISSN 1090-7807, E-ISSN 1096-0856, Vol. 183, no 1, p. 50-59Article in journal (Refereed) Published
Abstract [en]

We consider the problem of parametric spectral analysis of two-dimensional (2D) magnetic resonance spectroscopy (MRS) data. Estimating the signal components from 2D MRS data is becoming common practice in many clinical MR applications. The most frequently used signal processing tool for this estimation problem is the non-parametric 2D-FFT. There are several alternative parametric methods available to perform this analysis, yet their computational complexity is generally rather high and it becomes prohibitive when the number of points in the measured data matrix is large. In this paper, we propose a novel signal parameter estimation technique which operates on a pre-specified sub-area of the 2D spectrum. This area-selective approach can be used either to estimate only the signal components of main interest in the data, or to compute signal parameter estimates of all present signal components as the computational burden for each sub-area is low. In the numerical example section we consider both simulated data and in vitro H-1 data acquired from a 1.5 T MR scanner.

Keywords
two-dimensional (2D) magnetic resonance spectroscopy, damped sinusoidal model, parametric area-selective analysis
National Category
Signal Processing
Identifiers
urn:nbn:se:uu:diva-19021 (URN)10.1016/j.jmr.2006.07.018 (DOI)000242131100006 ()
Available from: 2006-11-24 Created: 2006-11-24 Last updated: 2018-10-01Bibliographically approved
Stoica, P. & Sandgren, N. (2006). Cepstrum Thresholding Scheme for Nonparametric Estimation of Smooth Spectra. In: Proceedings of the IEEE Instrumentation and Measurement Technology Conference (IMTC).
Open this publication in new window or tab >>Cepstrum Thresholding Scheme for Nonparametric Estimation of Smooth Spectra
2006 (English)In: Proceedings of the IEEE Instrumentation and Measurement Technology Conference (IMTC), 2006Conference paper, Published paper (Refereed)
National Category
Signal Processing
Identifiers
urn:nbn:se:uu:diva-19026 (URN)
Available from: 2006-11-24 Created: 2006-11-24 Last updated: 2018-10-01
Sandgren, N. & Stoica, P. (2006). Frequency-Selective Magnetic Resonance Spectroscopy using Prior Information for Data with Low Signal to Noise Ratio. In: Proceedings of the 18th international BIOSIGNAL conference.
Open this publication in new window or tab >>Frequency-Selective Magnetic Resonance Spectroscopy using Prior Information for Data with Low Signal to Noise Ratio
2006 (English)In: Proceedings of the 18th international BIOSIGNAL conference, 2006Conference paper, Published paper (Refereed)
National Category
Signal Processing
Identifiers
urn:nbn:se:uu:diva-19029 (URN)
Available from: 2006-11-24 Created: 2006-11-24 Last updated: 2018-10-01
Sandgren, N. & Stoica, P. (2006). On nonparametric estimation of 2-D smooth spectra. IEEE Signal Processing Letters, 13(10), 632-635
Open this publication in new window or tab >>On nonparametric estimation of 2-D smooth spectra
2006 (English)In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 13, no 10, p. 632-635Article in journal (Refereed) Published
Abstract [en]

We consider the problem of smoothed nonparametric estimation of two-dimensional (2-D) spectra. In the one-dimensional (1-D) scenario, several methods have been developed in the past for the computation of nonparametric spectral estimates with lower variance than that of the standard periodogram. Some of these techniques can also be extended to the 2-D case. However, such methods usually require a careful selection of certain design parameters, which can be hard to make. The spectral estimator proposed here is based on cepstrum thresholding and is shown to have significantly lower variance than the standard 2-D periodogram. Moreover, the thresholding is performed in a simple and practically automatic manner without the requirement of extensive prior knowledge about the signal of interest.

Keywords
nonparametric two-dimensional (2-D) spectral estimation, spectral smoothing, thresholding
National Category
Signal Processing
Identifiers
urn:nbn:se:uu:diva-19020 (URN)10.1109/LSP.2006.876320 (DOI)000240756500014 ()
Available from: 2006-11-24 Created: 2006-11-24 Last updated: 2018-10-01Bibliographically approved
Stoica, P. & Sandgren, N. (2006). On Total-Variance Reduction via Thresholding-Based Spectral Analysis. In: Proceedings of the 31st International Conference on Acoustics, Speech, and Signal Processing (ICASSP).
Open this publication in new window or tab >>On Total-Variance Reduction via Thresholding-Based Spectral Analysis
2006 (English)In: Proceedings of the 31st International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2006Conference paper, Published paper (Refereed)
National Category
Signal Processing
Identifiers
urn:nbn:se:uu:diva-19027 (URN)
Available from: 2006-11-24 Created: 2006-11-24 Last updated: 2018-10-01
Sandgren, N., Frigo, F. & Stoica, P. (2006). Signal Parameter Estimation using Multichannel Magnetic Resonance Spectroscopy Analysis of In-Vivo 1H Data. In: Proceedings of the 18th international BIOSIGNAL conference.
Open this publication in new window or tab >>Signal Parameter Estimation using Multichannel Magnetic Resonance Spectroscopy Analysis of In-Vivo 1H Data
2006 (English)In: Proceedings of the 18th international BIOSIGNAL conference, 2006Conference paper, Published paper (Refereed)
National Category
Signal Processing
Identifiers
urn:nbn:se:uu:diva-19028 (URN)
Available from: 2006-11-24 Created: 2006-11-24 Last updated: 2018-10-01
Stoica, P. & Sandgren, N. (2006). Smoothed nonparametric spectral estimation via cepsturm thresholding. IEEE signal processing magazine (Print), 23(6), 34-45
Open this publication in new window or tab >>Smoothed nonparametric spectral estimation via cepsturm thresholding
2006 (English)In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 23, no 6, p. 34-45Article in journal (Refereed) Published
National Category
Signal Processing
Identifiers
urn:nbn:se:uu:diva-19023 (URN)000241566200005 ()
Available from: 2006-11-24 Created: 2006-11-24 Last updated: 2018-10-01Bibliographically approved
Stoica, P. & Sandgren, N. (2006). Spectral Analysis of Irregularly-Sampled Data: Paralleling the regularly-sampled data approaches. Digital signal processing (Print), 16(6), 712-734
Open this publication in new window or tab >>Spectral Analysis of Irregularly-Sampled Data: Paralleling the regularly-sampled data approaches
2006 (English)In: Digital signal processing (Print), ISSN 1051-2004, E-ISSN 1095-4333, Vol. 16, no 6, p. 712-734Article in journal (Refereed) Published
Abstract [en]

The spectral analysis of regularly-sampled (RS) data is a well-established topic, and many useful methods are available for performing it under different sets of conditions. The same cannot be said about the spectral analysis of irregularly-sampled (IS) data: despite a plethora of published works on this topic, the choice of a spectral analysis method for IS data is essentially limited, on either technical or computational grounds, to the periodogram and its variations. In our opinion this situation is far from satisfactory, given the importance of the spectral analysis of IS data for a considerable number of applications in such diverse fields as engineering, biomedicine, economics, astronomy, seismology, and physics, to name a few. In this paper we introduce a number of IS data approaches that parallel the methods most commonly used for spectral analysis of RS data: the periodogram (PER), the Capon method (CAP), the multiple-signal characterization method (MUSIC), and the estimation of signal parameters via rotational invariance technique (ESPRIT). The proposed IS methods are as simple as their RS counterparts, both conceptually and computationally. In particular, the fast algorithms derived for the implementation of the RS data methods can be used mutatis mutandis to implement the proposed parallel IS methods. Moreover, the expected performance-based ranking of the IS methods is the same as that of the parallel RS methods: all of them perform similarly on data consisting of well-separated sinusoids in noise, MUSIC and ESPRIT outperform the other methods in the case of closely-spaced sinusoids in white noise, and CAP outperforms PER for data whose spectrum has a small-to-medium dynamic range (MUSIC and ESPRIT should not be used in the latter case).

Keywords
CARMA signals, Irregular sampling, Nonuniform sampling, Sinusoids in noise, Spectral analysis
National Category
Signal Processing
Identifiers
urn:nbn:se:uu:diva-19498 (URN)10.1016/j.dsp.2006.08.012 (DOI)000243346900006 ()
Available from: 2006-11-29 Created: 2006-11-29 Last updated: 2018-10-01Bibliographically approved
Sandgren, N. & Stoica, P. (2005). Frequency-Selective Analysis of Multichannel Magnetic Resonance Spectroscopy Data. In: Proceedings of the 27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
Open this publication in new window or tab >>Frequency-Selective Analysis of Multichannel Magnetic Resonance Spectroscopy Data
2005 (English)In: Proceedings of the 27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2005Conference paper, Published paper (Refereed)
Abstract [en]

In several practical magnetic resonance spectroscopy (MRS) applications the user is interested only in the spectral content of a specific frequency band of the spectrum. A frequency-selective (or sub-band) method estimates only the parameters of those spectroscopic components that lie in a preselected frequency band of the spectrum in a computationally efficient manner. Multichannel MRS is a technique that employs phased-array receive coils to increase the signal-to-noise ratio (SNR) in the spectra by combining several simultaneous measurements of the magnetic resonance (MR) relaxation of an excited sample. In this paper we suggest a frequency-selective multichannel parameter estimation approach that combines the appealing features (high speed and improved SNR) of the two techniques above. The presented method shows parameter estimation accuracies comparable to those of existing full-band multichannel techniques in the high SNR case, but at a considerably lower computational complexity, and significantly better parameter estimation accuracies in low SNR scenarios.

Identifiers
urn:nbn:se:uu:diva-76430 (URN)
Available from: 2006-03-08 Created: 2006-03-08 Last updated: 2018-10-01
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