Analysis of CT Numbers in Thoracic CT Scan Image Segmentation and Spirometry on Pulmonary Vital Capacity
Abstract
This research aims to analyze the correct CT Number to calculate the capacity volume of vital lungs using process segmentation image CT Scans. Compare results from capacity volume vital lungs on segmentation with results measurement volume capacity vital from spirometry. The research method used is an applied experiment that compares the results of lung vital capacity volume in segmentation with the results of vital capacity volume measurements from spirometry. Testing is carried out by calculating each volume on the CT Number used in segmentation and then comparing it with results from spirometry. Analysis data use test Correlation Pearson And Test paired Q Test Results study show that application segmentation image CT Scans with CT Numbers -850 HU up to -950 HU is quite good in calculating the volume of vital lung capacity. There is no significant difference between the results of lung vital capacity volume in segmentation and the results of vital capacity volume measurements in spirometry with a value (p > 0.05) of 0.06. The conclusion of this research is that segmentation image CT Scans of Thorax with the use of CT Numbers -850 HU until -950 HU can considered as an alternative in calculating the volume of vital lung capacity in patients with COPD.
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Webb, W. R. (1989). High-resolution CT of the lung parenchyma. In Radiologic Clinics of North America. https://doi.org/10.1016/s0033-8389(22)01199-x
Wu, F., Chen, L., Huang, J., Fan, W., Yang, J., Zhang, X., Jin, Y., Yang, F., & Zheng, C. (2021). Total lung and lobar quantitative assessment based on paired inspiratory–Expiratory chest CT in healthy adults: Correlation with pulmonary ventilatory function. Diagnostics. https://doi.org/10.3390/diagnostics11101791
Badan Penelitian dan Pengembangan Kesehatan. (2013). Laporan Hasil Riset Kesehatan Dasar (Riskesdas) 2013. In Kementrian Kesehatan RI.
Bakhtiar, A., & Amran, W. S. (2019). Faal Paru Statis. Jurnal Respirasi. https://doi.org/10.20473/jr.v2-i.3.2016.91-98
Bakhtiar, A., & WS, A. (2016). Faal paru statis. Jurnal Respirasi, 2(3), 91.
Banik, S., Rangayyan, R. M., & Boag, G. S. (2009). Landmarking and Segmentation of 3D CT Images. Synthesis Lectures on Biomedical Engineering. https://doi.org/10.2200/S00185ED1V01Y200903BME030
Bronzino, J. D., & Peterson, D. R. (2018). The Biomedical Engineering Handbook. In The Biomedical Engineering Handbook. https://doi.org/10.1201/b18423
Camiciottoli, G., Bartolucci, M., Maluccio, N. M., Moroni, C., Mascalchi, M., Giuntini, C., & Pistolesi, M. (2006). Spirometrically gated high-resolution CT findings in COPD: Lung attenuation vs lung function and dyspnea severity. Chest. https://doi.org/10.1378/chest.129.3.558
Chuang, C. C., Chou, Y. H., Peng, S. L., Tai, J. E., Lee, S. C., Tyan, Y. S., & Shih, C. T. (2020). Calculating air volume fractions from computed tomography images for chronic obstructive pulmonary disease diagnosis. PLoS ONE. https://doi.org/10.1371/journal.pone.0231730
Coxson, H. O., Mayo, J., Lam, S., Santyr, G., Parraga, G., & Sin, D. D. (2009). New and current clinical imaging techniques to study chronic obstructive pulmonary disease. In American Journal of Respiratory and Critical Care Medicine. https://doi.org/10.1164/rccm.200901-0159PP
Ginting, M., Yunus, F., & Antariksa, B. (2015). Faal Paru pada Polisi Lalu Lintas Jakarta Pusat dan Faktor-Faktor yang Mempengaruhi. J Respir Indo.
Hofmanninger, J., Prayer, F., Pan, J., Röhrich, S., Prosch, H., & Langs, G. (2020). Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem. European Radiology Experimental, 4(1). https://doi.org/10.1186/s41747-020-00173-2
Katherine, Rulaningtyas, R., & Ain, K. (2021). CT scan image segmentation based on hounsfield unit values using Otsu thresholding method. Journal of Physics: Conference Series. https://doi.org/10.1088/1742-6596/1816/1/012080
Mohamed Hoesein, F. A. A., Van Rikxoort, E., Van Ginneken, B., De Jong, P. A., Prokops, M., Lammers, J. W. J., & Zanen, P. (2012). Computed tomography-quantified emphysema distribution is associated with lung function decline. European Respiratory Journal. https://doi.org/10.1183/09031936.00186311
Moutafidis, D., Gavra, M., Golfinopoulos, S., Kattamis, A., Chrousos, G., Kanaka-Gantenbein, C., & Kaditis, A. G. (2021). Low-and high-attenuation lung volume in quantitative chest CT in children without lung disease. Children. https://doi.org/10.3390/children8121172
Price, D. B., Tinkelman, D. G., Nordyke, R. J., Isonaka, S., & Halbert, R. J. (2006). Scoring system and clinical application of COPD diagnostic questionnaires. Chest. https://doi.org/10.1378/chest.129.6.1531
Romans, L. E. (2018). Computer tomography for technologist. In 2010.
Ryan, D. J., Kavanagh, R. G., Joyce, S., O’Callaghan Maher, M., Moore, N., McMahon, A., Hussey, D., O’Sullivan, M. G. J., Wyse, G., Fanning, N., O’Connor, O. J., & Maher, M. M. (2021). Development and implementation of an ultralow-dose CT protocol for the assessment of cerebrospinal shunts in adult hydrocephalus. European Radiology Experimental. https://doi.org/10.1186/s41747-021-00222-4
Simargi, Y., Seprianto, J., & Santi, B. T. (2021). HUBUNGAN LAMA PAJANAN POLUSI UDARA DENGAN KEJADIANEMFISEMA BERDASARKAN PEMERIKSAAN CT SCAN TORAKSPADA SOPIR ANGKUTAN KOTA. Jurnal Respirologi Indonesia, 41(2).
Sørensen, L., Nielsen, M., Petersen, J., Pedersen, J. H., Dirksen, A., & De Bruijne, M. (2020). Chronic obstructive pulmonary disease quantification using CT texture analysis and densitometry: Results from the Danish Lung Cancer Screening Trial. American Journal of Roentgenology. https://doi.org/10.2214/AJR.19.22300
Spillane, R. M., Shepard, J. A. O., & Deluca, S. A. (1993). High-resolution CT of the lungs. In American Family Physician. https://doi.org/10.1148/radiology.184.2.450-a
Sun, J., Peng, Y., Duan, X., Yu, T., Zhang, Q., Liu, Y., & Hu, D. (2014). Image quality in children with low-radiation chest CT using adaptive statistical iterative reconstruction and model-based iterative reconstruction. PLoS ONE. https://doi.org/10.1371/journal.pone.0096045
Sun, J., Yu, T., Liu, J., Duan, X., Hu, D., liu, Y., & Peng, Y. (2017). Image quality improvement using model-based iterative reconstruction in low dose chest CT for children with necrotizing pneumonia. BMC Medical Imaging. https://doi.org/10.1186/s12880-017-0177-9
Webb, W. R. (1989). High-resolution CT of the lung parenchyma. In Radiologic Clinics of North America. https://doi.org/10.1016/s0033-8389(22)01199-x
Wu, F., Chen, L., Huang, J., Fan, W., Yang, J., Zhang, X., Jin, Y., Yang, F., & Zheng, C. (2021). Total lung and lobar quantitative assessment based on paired inspiratory–Expiratory chest CT in healthy adults: Correlation with pulmonary ventilatory function. Diagnostics. https://doi.org/10.3390/diagnostics11101791
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