Abstract
Breathing waveform extracted via nasal thermistor is the most common method to study respiratory function in sleep studies. In essence, this is a temporal waveform of mean temperatures in the nostril region that at every time step collapses two-dimensional data into a single point. Hence, spatial heat distribution in the nostrils is lost along with valuable functional and anatomical cues. This article presents the construction and experimental validation of a spatiotemporal profile for the breathing function via thermal imaging of the nostrils. The method models nasal airflow advection by using a front-propagating level set algorithm with optimal parameter selection. It is the first time that the full two-dimensional advantage of thermal imaging is brought to the fore in breathing computation. This new multi-dimensional measure is likely to bring diagnostic value in sleep studies and beyond.
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Duong, D., Shastri, D., Tsiamyrtzis, P., Pavlidis, I. (2012). Spatiotemporal Reconstruction of the Breathing Function. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. MICCAI 2012. Lecture Notes in Computer Science, vol 7510. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33415-3_19
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DOI: https://doi.org/10.1007/978-3-642-33415-3_19
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