Purpose: The 4D-CT data used for comparing a patient's ventilation distributions before and after lung radiotherapy are acquired at different times. As a result, an additional variable - the tidal volume (TV) - can alter the results. Therefore, in this paper we propose to normalize the ventilation to the same TV to eliminate that uncertainty. Methods: Absolute ventilation (AV) data were generated for 6 stereotactic body radiation therapy (SBRT) cases before and after treatment, using the direct geometric algorithm and diffeomorphic morphons deformable image registration (DIR). Each pair of AV distributions was converted to TV-normalized, percentile ventilation (PV) and low-dose well-ventilated-normalized ventilation (LDWV) distributions. The ventilation change was calculated in various dose regions based on the treatment plans using the DIR-registered before and after treatment data sets. The ventilation change based on TV-normalized ventilation was compared with the AV as well as the data normalized by PV and LDWV. Results: AV change may be misleading when the TV differs before and after treatment, which was found to be up to 6.7%. All three normalization methods produced a similar trend in ventilation change: the higher the dose to a region of lung, the greater the degradation in ventilation. In low dose regions (<5 Gy), ventilation appears relatively improved after treatment due to the relative nature of the normalized ventilation. However, the LDWV may not be reliable when the ventilation in the low-dose regions varies. PV exhibited a similar ventilation change trend compared to the TV-normalized in all cases. However, by definition, the ventilation distribution in the PV is significantly different from the original distribution. Conclusion: Normalizing ventilation distributions by the TV is a simple and reliable method for evaluation of ventilation changes. © 2013 Latifi et al.
CITATION STYLE
Latifi, K., Feygelman, V., Moros, E. G., Dilling, T. J., Stevens, C. W., & Zhang, G. G. (2013). Normalization of ventilation data from 4D-CT to facilitate comparison between datasets acquired at different times. PLoS ONE, 8(12). https://doi.org/10.1371/journal.pone.0084083
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