A semi-automatic method for peak and valley detection in free-breathing respiratory waveforms.
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.
Lu
Wei
W
Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri 63110, USA. wlu@radonc.wustl.edu
Nystrom
Michelle M
MM
Parikh
Parag J
PJ
Fooshee
David R
DR
Hubenschmidt
James P
JP
Bradley
Jeffrey D
JD
Low
Daniel A
DA
D000465:Algorithms; D001331:Automation; D005583:Fourier Analysis; D006801:Humans; D011857:Radiographic Image Interpretation, Computer-Assisted; D015203:Reproducibility of Results; D012119:Respiration; D012984:Software; D013899:Thoracic Neoplasms / Q000175:diagnosis / Q000473:pathology; D013997:Time Factors; D014057:Tomography, X-Ray Computed / Q000379:methods