Dataset Information

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

ACCESS DATA

Similar Datasets

Project description:PurposeThe computed tomography (CT)-derived ventilation imaging methodology employs deformable image registration (DIR) to recover respiratory motion-induced volume changes from an inhale/exhale CT image pair, as a surrogate for ventilation. The Integrated Jacobian Formulation (IJF) and Mass Conserving Volume Change (MCVC) numerical methods for volume change estimation represent two classes of ventilation methods, namely transformation based and intensity (Hounsfield Unit) based, respectively. Both the IJF and MCVC methods utilize subregional volume change measurements that satisfy a specified uncertainty tolerance. In previous publications, the ventilation images resulting from this numerical strategy demonstrated robustness to DIR variations. However, the reduced measurement uncertainty comes at the expense of measurement resolution. The purpose of this study was to examine the spatial correlation between robust CT-ventilation images and single photon emission CT-ventilation (SPECT-V).MethodsPreviously described implementations of IJF and MCVC require the solution of a large scale, constrained linear least squares problem defined by a series of robust subregional volume change measurements. We introduce a simpler parameterized implementation that reduces the number of unknowns while increasing the number of data points in the resulting least squares problem. A parameter sweep of the measurement uncertainty tolerance, τ , was conducted using the 4DCT and SPECT-V images acquired for 15 non-small cell lung cancer patients prior to radiotherapy. For each test case, MCVC and IJF CT-ventilation images were created for 30 different uncertainty parameter values, uniformly sampled from the range 0.01,0.25 . Voxel-wise Spearman correlation between the SPECT-V and the resulting CT-ventilation images was computed.ResultsThe median correlations between MCVC and SPECT-V ranged from 0.20 to 0.48 across the parameter sweep, while the median correlations for IJF and SPECT-V ranged between 0.79 and 0.82. For the optimal IJF tolerance τ=0.07 , the IJF and SPECT-V correlations across all 15 test cases ranged between 0.12 and 0.90. For the optimal MCVC tolerance τ=0.03 , the MCVC and SPECT-V correlations across all 15 test cases ranged between -0.06 and 0.84.ConclusionThe reported correlations indicate that robust methods generate ventilation images that are spatially consistent with SPECT-V, with the transformation-based IJF method yielding higher correlations than those previously reported in the literature. For both methods, overall correlations were found to marginally vary for τ∈[0.03,0.15] , indicating that the clinical utility of both methods is robust to both uncertainty tolerance and DIR solution.

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.

Project description:To determine the mean and range of location-averaged breast skin thickness using high-resolution dedicated breast CT for use in Monte Carlo-based estimation of normalized glandular dose coefficients.This study retrospectively analyzed image data from a clinical study investigating dedicated breast CT. An algorithm similar to that described by Huang et al. ["The effect of skin thickness determined using breast CT on mammographic dosimetry," Med. Phys. 35(4), 1199-1206 (2008)] was used to determine the skin thickness in 137 dedicated breast CT volumes from 136 women. The location-averaged mean breast skin thickness for each breast was estimated and the study population mean and range were determined. Pathology results were available for 132 women, and were used to investigate if the distribution of location-averaged mean breast skin thickness varied with pathology. The effect of surface fitting to account for breast curvature was also studied.The study mean (± interbreast SD) for breast skin thickness was 1.44 ± 0.25 mm (range: 0.87-2.34 mm), which was in excellent agreement with Huang et al. Based on pathology, pair-wise statistical analysis (Mann-Whitney test) indicated that at the 0.05 significance level, there were no significant difference in the location-averaged mean breast skin thickness distributions between the groups: benign vs malignant (p = 0.223), benign vs hyperplasia (p = 0.651), hyperplasia vs malignant (p = 0.229), and malignant vs nonmalignant (p = 0.172).Considering this study used a different clinical prototype system, and the study participants were from a different geographical location, the observed agreement between the two studies suggests that the choice of 1.45 mm thick skin layer comprising the epidermis and the dermis for breast dosimetry is appropriate. While some benign and malignant conditions could cause skin thickening, in this study cohort the location-averaged mean breast skin thickness distributions did not differ significantly with pathology. The study also underscored the importance of considering breast curvature in estimating breast skin thickness.

Dataset Information

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets