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Parameter estimation using weighted total least squares in the two-compartment exchange model
Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper, Radiofysik.ORCID-id: 0000-0002-0532-232X
Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper, Radiofysik.ORCID-id: 0000-0001-7119-7646
2018 (Engelska)Ingår i: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 79, nr 1, s. 561-567Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Purpose

The linear least squares (LLS) estimator provides a fast approach to parameter estimation in the linearized two-compartment exchange model. However, the LLS method may introduce a bias through correlated noise in the system matrix of the model. The purpose of this work is to present a new estimator for the linearized two-compartment exchange model that takes this noise into account.

Method

To account for the noise in the system matrix, we developed an estimator based on the weighted total least squares (WTLS) method. Using simulations, the proposed WTLS estimator was compared, in terms of accuracy and precision, to an LLS estimator and a nonlinear least squares (NLLS) estimator.

Results

The WTLS method improved the accuracy compared to the LLS method to levels comparable to the NLLS method. This improvement was at the expense of increased computational time; however, the WTLS was still faster than the NLLS method. At high signal-to-noise ratio all methods provided similar precisions while inconclusive results were observed at low signal-to-noise ratio.

Conclusion

The proposed method provides improvements in accuracy compared to the LLS method, however, at an increased computational cost. Magn Reson Med, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

Ort, förlag, år, upplaga, sidor
John Wiley & Sons, 2018. Vol. 79, nr 1, s. 561-567
Nyckelord [en]
dynamic contrast-enhanced magnetic resonance imaging; parameter estimation, two-compartment exchange model, weighted total least squares
Nationell ämneskategori
Radiologi och bildbehandling
Identifikatorer
URN: urn:nbn:se:umu:diva-135670DOI: 10.1002/mrm.26677ISI: 000417926300055PubMedID: 28349618OAI: oai:DiVA.org:umu-135670DiVA, id: diva2:1104993
Tillgänglig från: 2017-06-02 Skapad: 2017-06-02 Senast uppdaterad: 2018-06-09Bibliografiskt granskad

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