Optimal filtering values in renogram deconvolution

Puchal, R.; Pavia, João Pedro; Gonzalez, A. Medina; Ros, De

doi:10.1088/0031-9155/33/7/007

Cited by 15 publications

(10 citation statements)

References 11 publications

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“…Moreover, APF may be superior to other non-linear ®lter techniques since they have been shown to be less effective than repetitive 3-point smoothing (Puchal et al, 1988). Therefore, we believe that the APF demonstrates the potential of preserving high-frequency parts and still accomplishing a satisfactory overall noise reduction.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, we believe that the APF demonstrates the potential of preserving high-frequency parts and still accomplishing a satisfactory overall noise reduction. Moreover, APF may be superior to other non-linear ®lter techniques since they have been shown to be less effective than repetitive 3-point smoothing (Puchal et al, 1988).…”

Section: Discussionmentioning

confidence: 99%

“…This is basically the model used by Puchal et al (1988). This is basically the model used by Puchal et al (1988).…”

Section: Renogram Simulationmentioning

confidence: 99%

“…where the ®rst term represents the vascular phase, the second term the uptake phase, and the third term the out¯ow phase. This is basically the model used by Puchal et al (1988). The physiological interpretation of the parameters de®ning the model is: · rf: renal plasma¯ow (ml min ±1 ) · E: renal extraction of the tracer · r v : standard deviation of distribution of vascular times due to different proximal branches of the renal artery (the vascular phase is modelled using the right part of a Gaussian-shaped function) (s) · r T : standard deviation of distribution of transit times within different regions of the kidney, modelled by the integral of a Gaussian-shaped function (s) · TT: mean transit time (s) · g: relative scale factor in order to achieve appropriate count levels The same blood activity curve B pat (t) can be used in these three simulations, provided that the total kidney function (i.e.…”

Section: Renogram Simulationmentioning

confidence: 99%

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Noise reduction of renograms: a new algorithm applied to simulated renograms for evaluation of the renal retention function

Ekman

Volkmann

Carlsson

1999

Clinical Physiology

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Physiological information about an organ may be assessed from the retention function as derived by deconvolution analysis. However, noise in data may cause distortion of the retention curve and potentially induce methodological errors. To take full advantage of all parts of the retention function, i.e. even the vascular part, we have developed a non-linear noise reduction algorithm. The algorithm is an adaptive polynomial fit (APF) in a sliding segment over the renogram to be deconvoluted. Each segment is modelled by the lowest polynomial resulting in a root of mean square error lower than a pre-set value. APF was tested in comparison with conventional repetitive 1:2:1 smoothing and rectangular window smoothing, using a set of simulated retention functions and corresponding renograms with superimposed artificial noise. The outcome was evaluated by comparing the generated retention functions with the simulated ones. The conventional smoothing algorithms, as well as APF, induced some distortion of the retention function, but the deviation from the true retention function is essentially lower in the case of APF. In addition, APF seems to be more robust in cases of essentially reduced renal function and thereby relative high noise levels. APF reduces noise in data, leading to retention functions with reliable information in terms of a tracer's first passage, uptake and outflow.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…This is basically the model used by Puchal et al (1988). This is basically the model used by Puchal et al (1988).…”

Section: Renogram Simulationmentioning

confidence: 99%

Section: Renogram Simulationmentioning

confidence: 99%

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Noise reduction of renograms: a new algorithm applied to simulated renograms for evaluation of the renal retention function

Ekman

Volkmann

Carlsson

1999

Clinical Physiology

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“…Transport functions have been estimated for the coronary vascular bed [l, 9], the hing [4,5] and the kidney [6,10]. This leads mathematically to a deconvolution.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Deconvolution Techniques by the Application of the Münchhausen Meta Algorithm - Optimierung von Entfaltungstechniken durch Anwendung des Münchhausen Meta-Algorithmus

Schröder¹,

Rösler²,

Frerichs³

et al. 1999

Biomedizinische Technik/Biomedical Engineering

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A deconvolution applied to disturbed data often gives poor results, due to fundamental difficulties associated with ill-posed problems. Many numerical and theoretical methods have been invented to circumvent this phenomenon. Their performance varies, depending on the given problem and data. The main aim of this paper is to provide a decision rule for choosing a method for deconvolution and application of this method to the same data. We have called this meta-algorithm Münchhausen. In this paper we introduce and describe for the first time the basic principle of artificial disturbance of the data in the set-up of deconvolution. We demonstrate some interesting features of the random procedure Münchhausen, such äs the non parametric set-up, robustness to disturbance of the data and last but not least good performance. Schlüsselwörter: Entfaltung, Volumen, mathematisches Verfahren Die Entfaltung führt bei gestörten Daten häufig zu sehr schlechten Ergebnissen (ill-posed problem). Viele numerische und theoretische Methoden sind bisher eingesetzt worden, um dieses Phänomen zu vermeiden. Die Ergebnisse variieren in Abhängigkeit vom gegebenen Problem und von den Daten. Das Hauptziel dieser Arbeit ist es, eine Entscheidungsregel vorzustellen, die eine Entfaltungsmethode anhand von Daten auswählt und die gefundene Methode auf dieselben Daten wieder anwendet. Dieser Meta-Algorithmus wird Münchhausen genannt. In dieser Arbeit wird zum ersten Mal das Grundprinzip beschrieben, Daten im Setup der Entfaltung künstlich zu stören. Es werden einige interessante Details der Münchhausen-Prozedur demonstriert, wie das nichtparametrische Setup, die Robustheit gegenüber gestörten Daten und die gute Anwendbarkeit. Biomedizinische Technik Band 44 Heft 11/1999 Application of the Münchhausen meta-algorithm 309 U Output 9 iurel _ .J it nolse ~"-* not observable Measurement d(u x , u x ) = Optimum

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Ionic dependence of the velocity of release of ATP from permeabilized cholinergic synaptic vesicles

González-Sistal¹,

Reigada²,

Puchal³

et al. 2007

Neuroscience

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Optimal filtering values in renogram deconvolution

Cited by 15 publications

References 11 publications

Noise reduction of renograms: a new algorithm applied to simulated renograms for evaluation of the renal retention function

Noise reduction of renograms: a new algorithm applied to simulated renograms for evaluation of the renal retention function

Optimizing Deconvolution Techniques by the Application of the Münchhausen Meta Algorithm - Optimierung von Entfaltungstechniken durch Anwendung des Münchhausen Meta-Algorithmus

Ionic dependence of the velocity of release of ATP from permeabilized cholinergic synaptic vesicles

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