A semi-automatic method for peak and valley detection in free-breathing respiratory waveforms

Wei Lu; Michelle M. Nystrom; Parag J. Parikh; David R. Fooshee; James P. Hubenschmidt; Jeffrey D. Bradley; Daniel A. Low

doi:10.1118/1.2348764

A semi-automatic method for peak and valley detection in free-breathing respiratory waveforms

Wei Lu, Michelle M. Nystrom, Parag J. Parikh, David R. Fooshee, James P. Hubenschmidt, Jeffrey D. Bradley, Daniel A. Low

Research output: Contribution to journal › Article › peer-review

71 Scopus citations

Abstract

The existing commercial software often inadequately determines respiratory peaks for patients in respiration correlated computed tomography. A semi-automatic method was developed for peak and valley detection in free-breathing respiratory waveforms. First the waveform is separated into breath cycles by identifying intercepts of a moving average curve with the inspiration and expiration branches of the waveform. Peaks and valleys were then defined, respectively, as the maximum and minimum between pairs of alternating inspiration and expiration intercepts. Finally, automatic corrections and manual user interventions were employed. On average for each of the 20 patients, 99% of 307 peaks and valleys were automatically detected in 2.8 s. This method was robust for bellows waveforms with large variations.

Original language	English
Pages (from-to)	3634-3636
Number of pages	3
Journal	Medical physics
Volume	33
Issue number	10
DOIs	https://doi.org/10.1118/1.2348764
State	Published - 2006

Keywords

4D CT
Gated radiotherapy
Peak detection
Respiratory waveform

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10.1118/1.2348764

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title = "A semi-automatic method for peak and valley detection in free-breathing respiratory waveforms",

abstract = "The existing commercial software often inadequately determines respiratory peaks for patients in respiration correlated computed tomography. A semi-automatic method was developed for peak and valley detection in free-breathing respiratory waveforms. First the waveform is separated into breath cycles by identifying intercepts of a moving average curve with the inspiration and expiration branches of the waveform. Peaks and valleys were then defined, respectively, as the maximum and minimum between pairs of alternating inspiration and expiration intercepts. Finally, automatic corrections and manual user interventions were employed. On average for each of the 20 patients, 99% of 307 peaks and valleys were automatically detected in 2.8 s. This method was robust for bellows waveforms with large variations.",

keywords = "4D CT, Gated radiotherapy, Peak detection, Respiratory waveform",

author = "Wei Lu and Nystrom, {Michelle M.} and Parikh, {Parag J.} and Fooshee, {David R.} and Hubenschmidt, {James P.} and Bradley, {Jeffrey D.} and Low, {Daniel A.}",

note = "Funding Information: This work was supported in part by National Institute of Health, Grant No. R01 CA 096679. The authors thank Kristen Lechleiter for peak detection with LABVIEW.",

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doi = "10.1118/1.2348764",

language = "English",

volume = "33",

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T1 - A semi-automatic method for peak and valley detection in free-breathing respiratory waveforms

AU - Lu, Wei

AU - Nystrom, Michelle M.

AU - Parikh, Parag J.

AU - Fooshee, David R.

AU - Hubenschmidt, James P.

AU - Bradley, Jeffrey D.

AU - Low, Daniel A.

N1 - Funding Information: This work was supported in part by National Institute of Health, Grant No. R01 CA 096679. The authors thank Kristen Lechleiter for peak detection with LABVIEW.

PY - 2006

Y1 - 2006

N2 - The existing commercial software often inadequately determines respiratory peaks for patients in respiration correlated computed tomography. A semi-automatic method was developed for peak and valley detection in free-breathing respiratory waveforms. First the waveform is separated into breath cycles by identifying intercepts of a moving average curve with the inspiration and expiration branches of the waveform. Peaks and valleys were then defined, respectively, as the maximum and minimum between pairs of alternating inspiration and expiration intercepts. Finally, automatic corrections and manual user interventions were employed. On average for each of the 20 patients, 99% of 307 peaks and valleys were automatically detected in 2.8 s. This method was robust for bellows waveforms with large variations.

AB - The existing commercial software often inadequately determines respiratory peaks for patients in respiration correlated computed tomography. A semi-automatic method was developed for peak and valley detection in free-breathing respiratory waveforms. First the waveform is separated into breath cycles by identifying intercepts of a moving average curve with the inspiration and expiration branches of the waveform. Peaks and valleys were then defined, respectively, as the maximum and minimum between pairs of alternating inspiration and expiration intercepts. Finally, automatic corrections and manual user interventions were employed. On average for each of the 20 patients, 99% of 307 peaks and valleys were automatically detected in 2.8 s. This method was robust for bellows waveforms with large variations.

KW - 4D CT

KW - Gated radiotherapy

KW - Peak detection

KW - Respiratory waveform

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DO - 10.1118/1.2348764

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A semi-automatic method for peak and valley detection in free-breathing respiratory waveforms

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Keywords

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