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Int J Comput Assist Radiol Surg. 2011 Jan;6(1):103-10. doi: 10.1007/s11548-010-0491-y. Epub 2010 Jun 13.

Ventilatory impairment detection based on distribution of respiratory-induced changes in pixel values in dynamic chest radiography: a feasibility study.

Author information

1
Department of Radiological Technology, School of Health Sciences, College of Medical, Pharmaceutical and Health Sciences, Kanazawa University, 5-11-80 Kodatsuno, Kanazawa 920-0942, Japan. rie44@mhs.mp.kanazawa-u.ac.jp

Abstract

PURPOSE:

Decreased ventilation is observed on chest radiographs as small changes in X-ray translucency, and ventilatory impairments can therefore be detected by analyzing the distribution of respiratory-induced changes in pixel value. This study was performed to develop a ventilatory impairment detection method based on the distribution of respiratory-induced changes in pixel values.

METHODS:

Sequential chest radiographs during respiration were obtained using a dynamic flat panel detector system. Respiratory-induced changes in pixel value were measured in each local area and then compared for symmetrical positions in both lungs, which were located at the same distance from the axis of the thorax at the same level. The right-left symmetry was assessed in 20 clinical cases (Abnormal, 14; Normal, 6).

RESULTS:

In normal controls, the distribution was symmetrical, and there were good correlations of the pixel value changes in both lungs at symmetrical positions (r = 0.66 ± 0.05). In contrast, abnormal cases did not show a symmetrical distribution of pixel value changes (r = 0.40 ± 0.23) due to ventilation abnormalities observed as reductions in pixel value changes.

CONCLUSIONS:

Ventilatory impairment could be detected as deviation from the right-left symmetry of respiratory-induced changes in pixel value. In particular, the present method could be useful for detecting unilateral abnormalities. However, to detect bilateral abnormalities, further studies are required to develop multilevel detection methods combined with several methods of pattern analysis.

PMID:
20549376
DOI:
10.1007/s11548-010-0491-y
[Indexed for MEDLINE]

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