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Technical note: development of a tidal volume surrogate that replaces spirometry for physiological breathing monitoring in 4D CT.

ABSTRACT: Spirometry exhibits baseline drift and frequent measurement errors so it cannot be used by itself to provide tidal volume-based image sorting or breathing motion modeling. Other breathing surrogates, in this study an abdominal bellows system, are drift free but do not measure tidal volume. Simultaneously using spirometry and the bellows system allows the user to convert the recorded bellows signal to tidal volume but still relies on spirometry measurements. The authors therefore propose to use CT-based air content, rather than a spirometer, to convert the bellows signal to tidal volume.41 4D CT data sets are acquired, while the breathing cycle is simultaneously measured using spirometry and an abdominal pressure bellows system. The assumptions underlying the conversion of the bellows measurement to tidal volume by CT-based air content are analyzed. This comprises of detailed correlation studies of the spirometry-measured tidal volume, the bellows signal, and CT-based air content.For 15/41 patients, the spirometry signals are not consistently acquired during the 4D CT session, so correlating spirometry to bellows measurements and CT-based air content leads to erroneous conversion coefficients. After introducing a minimum correlation threshold to remove these data, good correlations are obtained between the remaining breathing signals. The ratio of CT-based air content to tidal volume is measured to be 1.11 +/- 0.08; the expected value is 1.11 because room air is 11% more dense than air in the lungs.The observed problems of spirometry recording illustrate the challenges encountered when using spirometers as breathing surrogate for 4D CT acquisition. The high correlation between spirometry and bellows breathing signals and the verified factor of 1.11 between CT-based air content and tidal volume mean that the bellows measurement (or other equivalent surrogates) can be reliably converted to tidal volume using the CT-based air content, avoiding the need for a spirometer.

SUBMITTER: Werner R

PROVIDER: S-EPMC2816987 | biostudies-other | 2010 Feb

REPOSITORIES: biostudies-other

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Project description:To evaluate and compare the theoretically achievable accuracy of two families of two-parameter photon cross-section models: basis vector model (BVM) and modified parametric fit model (mPFM).The modified PFM assumes that photoelectric absorption and scattering cross-sections can be accurately represented by power functions in effective atomic number and/or energy plus the Klein-Nishina cross-section, along with empirical corrections that enforce exact prediction of elemental cross-sections. Two mPFM variants were investigated: the widely used Torikoshi model (tPFM) and a more complex "VCU" variant (vPFM). For 43 standard soft and bony tissues and phantom materials, all consisting of elements with atomic number less than 20 (except iodine), we evaluated the theoretically achievable accuracy of tPFM and vPFM for predicting linear attenuation, photoelectric absorption, and energy-absorption coefficients, and we compared it to a previously investigated separable, linear two-parameter model, BVM.For an idealized dual-energy computed tomography (DECT) imaging scenario, the cross-section mapping process demonstrates that BVM more accurately predicts photon cross-sections of biological mixtures than either tPFM or vPFM. Maximum linear attenuation coefficient prediction errors were 15% and 5% for tPFM and BVM, respectively. The root-mean-square (RMS) prediction errors of total linear attenuation over the 20 keV to 1000 keV energy range of tPFM and BVM were 0.93% (tPFM) and 0.1% (BVM) for adipose tissue, 0.8% (tPFM) and 0.2% (BVM) for muscle tissue, and 1.6% (tPFM) and 0.2% (BVM) for cortical bone tissue. With exception of the thyroid and Teflon, the RMS error for photoelectric absorption and scattering coefficient was within 4% for the tPFM and 2% for the BVM. Neither model predicts the photon cross-sections of thyroid tissue accurately, exhibiting relative errors as large as 20%. For the energy-absorption coefficients prediction error, RMS errors for the BVM were less than 1.5%, while for the tPFM, the RMS errors were as large as 16%.Compared to modified PFMs, BVM shows superior potential to support dual-energy CT cross-section mapping. In addition, the linear, separable BVM can be more efficiently deployed by iterative model-based DECT image-reconstruction algorithms.

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Technical note: development of a tidal volume surrogate that replaces spirometry for physiological breathing monitoring in 4D CT.

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