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 journalArticlepeer-review

    65 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 languageEnglish
    Pages (from-to)3634-3636
    Number of pages3
    JournalMedical physics
    Volume33
    Issue number10
    DOIs
    StatePublished - 2006

    Keywords

    • 4D CT
    • Gated radiotherapy
    • Peak detection
    • Respiratory waveform

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