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Post a Comment How CT reconstruction parameters effect measurement error of pulmonary nodules volume
- Authors: Alderov Z.A.1, Rozengauz E.V.2,3, Nesterov D.2,3,4
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Affiliations:
- Mytishchi City Clinical Hospital
- Central Research Institute of Roentgenology and Radiology named after Academician A.M. Granov
- North-Western State Medical University named after I.I. Mechnikov
- National Medical Research Center of Oncology named after N.N. Petrov
- Issue: Vol 12, No 3 (2020)
- Pages: 73-77
- Section: Original research
- URL: https://ogarev-online.ru/vszgmu/article/view/44920
- DOI: https://doi.org/10.17816/mechnikov44920
- ID: 44920
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Abstract
One of the the widely used way to follow up oncological disease is estimation of lesion size differences. Volumetry is one of the most accurate approaches of lesion size estimation. However, being highly sensitive, volumetric errors can reach 60%, which significantly limits the applicability of the method.
Purpose was to estimate the effect of reconstruction parameters on volumetry error.
Materials and methods. 32 patients with pulmonary metastases underwent a CT scanning with 326 foci detected. 326 pulmonary were segmented. Volumetry error was estimated for every lesion with each combination of slice thickness and reconstruction kernel. The effect was measured with linear regression analysis
Results. Systematic and stochastic errors are impacted by slice thickness, reconstruction kernel, lesion position and its diameter. FC07 kernel and larger slice thickness is associated with high systematic error. Both systematic and stochastic errors decrease with lesion enlargment. intrapulmonary lesions have the lowest error regardless the reconstruction parameters.
Lineal regression model was created to prognose error rate. Model standart error was 6.7%. There was corelation between model remnants deviation and slice thickness, reconstruction kernel, lesion position and its diameter.
Conclusion. The systematic error depends on the focal diameter, slice thickness and reconstruction kernel. It can be estimated using the proposed model with a 6% error. Stochastic error mainly depends on lesion size.
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##article.viewOnOriginalSite##About the authors
Zaur A. Alderov
Mytishchi City Clinical Hospital
Author for correspondence.
Email: zaurzz@rambler.ru
ORCID iD: 0000-0002-6255-1583
Russian Federation, Moscow region, Mytishchi
Evgeny V. Rozengauz
Central Research Institute of Roentgenology and Radiology named after Academician A.M. Granov; North-Western State Medical University named after I.I. Mechnikov
Email: rozengaouz@yandex.ru
Russian Federation, Saint Petersburg
Denis Nesterov
Central Research Institute of Roentgenology and Radiology named after Academician A.M. Granov; North-Western State Medical University named after I.I. Mechnikov; National Medical Research Center of Oncology named after N.N. Petrov
Email: cireto@gmail.com
Russian Federation, Saint Petersburg
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