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Injury degree and spatial distribution of pulmonary lobe of chronic obstructive pulmonary disease using voxel-based morphometry / 中国医学影像技术
Chinese Journal of Medical Imaging Technology ; (12): 340-344, 2020.
Article in Chinese | WPRIM | ID: wpr-861072
ABSTRACT

Objective:

To exlpore the value of voxel-based quantitative CT in evaluation on injury degree and spatial distribution in different lobes of chronic obstructive pulmonary disease (COPD) patients.

Methods:

COPD patients who underwent inspired and expired CT scanning were consecutively recruited from the multi-center study named Digital Lung.Quantitative parametres of functional small-airway disease (fSAD) and emphysema (Emph) were measured. The degree and distribution of pulmonary lobar injury were analyzed according to Emph and fSAD among different pulmonary lobes. At the same time, the correlation of quantitative CT distribution and lung function of each lobe was analyzed.

Results:

A total of 50 COPD patients were eventually included. Emph and fSAD of the right middle lobe (RML) was the most severe, followed by the upper lobes of the two lungs, while the lowest degree of injury were seen in both lower lobes of the lungs. Emph and fSAD were both mainly distributed in the left upper lobe (LUL), followed by in the right upper lobe (RUL), while in the right middle lobe (RML) was the least. Either fSAD or Emph showed better correlations with FEV1% in both lower lobes.

Conclusion:

The injury of the lobes in COPD patients mainly distribute in the RML and both upper lobes, especially the former. Emph and fSAD mainly distribute in the LUL, followed by the RUL. The lower lobes of the lungs have greater influence on pulmonary function in COPD patients.

Full text: Available Index: WPRIM (Western Pacific) Type of study: Controlled clinical trial Language: Chinese Journal: Chinese Journal of Medical Imaging Technology Year: 2020 Type: Article

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Full text: Available Index: WPRIM (Western Pacific) Type of study: Controlled clinical trial Language: Chinese Journal: Chinese Journal of Medical Imaging Technology Year: 2020 Type: Article