2006
DOI: 10.1016/j.jtbi.2005.10.005
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Signal analysis of impulse response functions in MR and CT measurements of cerebral blood flow

Abstract: The impulse response function (IRF) of a localized bolus in cerebral blood flow codes important information on the tissue type. It is indirectly accessible both from MR and CT imaging methods, at least in principle. In practice, however, noise and limited signal resolution render standard deconvolution techniques almost useless. Parametric signal descriptions look more promising, and it is the aim of this contribution to develop some improvements along this line.

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Cited by 5 publications
(3 citation statements)
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“…It has, however, been pointed out that simultaneous modelling of both the arterial and the tissue concentration curves by gamma densities is theoretically inappropriate [100]. Benner et al [101] investigated the accuracy of gamma-variate fits for different SNRs, temporal resolutions and maximum signal drops.…”
Section: Recirculationmentioning
confidence: 99%
“…It has, however, been pointed out that simultaneous modelling of both the arterial and the tissue concentration curves by gamma densities is theoretically inappropriate [100]. Benner et al [101] investigated the accuracy of gamma-variate fits for different SNRs, temporal resolutions and maximum signal drops.…”
Section: Recirculationmentioning
confidence: 99%
“…An input may be single or multiple, perfect or noisy, as well as an output may be. The areas of application of these models are quite different, varying from civil engineering (Engberg and Larsen 1995, Chapter 9), (Ren, Zhao and Harik 2004), (Spiridonakos and Chatzi 2014), communications and networks (Demir and Sangiovanni-Vintcentelli 1998, Sections 2.3-2.5), signal and image processing (Camps-Valls, Rojo-Álvarez, and Martínez-Ramón 2007), (Ogunfumni 2007, Chapters 3-5), (Gao et al 2014), (Prabhu 2014, Chapters 7-9), system identification and control (Söderström and Stoica 1988, Chapters 5-9), (Sjöberg et al 1995), (Chen, Ohlsson and Ljung 2012), applications to biology (Rost, Geske and Baake 2006) and finance (Hatemi-J 2012(Hatemi-J , 2014. Either in parametric of non-parametric framework, the above mentioned models use the feature of any linear system to be uniquely identified by means of the impulse response function (IRF).…”
Section: Introductionmentioning
confidence: 99%
“…The impulse response analysis is widely used in studying engineering structures, hydrology, biology, acoustics, and so on. [19][20][21][22][23][24] For instance, Volkmer et al 25 provided the impulse response function to study the physiological flows. Todorovska and Trifunac 26 analyzed wave travel times from impulse response functions to detect earthquake damage.…”
Section: Introductionmentioning
confidence: 99%