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 journalArticle

56 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 - Jan 1 2006

Keywords

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

Fingerprint Dive into the research topics of 'A semi-automatic method for peak and valley detection in free-breathing respiratory waveforms'. Together they form a unique fingerprint.

  • Cite this

    Lu, W., Nystrom, M. M., Parikh, P. J., Fooshee, D. R., Hubenschmidt, J. P., Bradley, J. D., & Low, D. A. (2006). A semi-automatic method for peak and valley detection in free-breathing respiratory waveforms. Medical physics, 33(10), 3634-3636. https://doi.org/10.1118/1.2348764