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Keywords:
Lung, Respiratory system, Computer applications, Digital radiography, Fluoroscopy, Computer Applications-Detection, diagnosis, Diagnostic procedure, Chronic obstructive airways disease, Obstruction / Occlusion, Image registration
Authors:
R. Tanaka1, S. Sanada1, K. Sakuta1, H. Kawashima1, Y. Kishitani2; 1Kanazawa/JP, 2Chuoh-ku/JP
DOI:
10.1594/ecr2015/C-0239
Methods and materials
Subjects
The study population consisted of 61 (abnormal,
n=33; normal,
n=28) patients.
The abnormal cases had been diagnosed with pulmonary diseases such as emphysema,
asthma,
interstitial pneumonia,
pulmonary fibrosis,
and pleural adhesions based on clinical and examination findings,
including 5 patients with unilateral ventilatory impairments.
The normal controls had no underlying pulmonary diseases or smoking history,
and they were confirmed to be normal based on chest radiographs and the results of pulmonary functional test.
Image acquisition
Posterior–anterior (PA) dynamic chest radiographs consisting of 30 frames in 10 s were obtained during a whole respiration using a flat-panel detector system (110 kV,
80 mA,
6.3 msec,
3fps,
SID=2 m,
non-contrast).
The entrance surface dose for 30 frames,
measured in air without backscattering,
was approximately 0.4 mGy,
which was less than that in lateral chest radiography determined as the guidance level of the International Atomic Energy Agency (IAEA) (1.5 mGy) [5].
Image processing
Commercial bone suppression image-processing software (Clear Read Bone Suppression; Riverain Technologies,
Miamisburg,
OH) was applied to the dynamic chest radiographs to create corresponding soft images (Fig.
1,
Fig.
2).
The diaphragm movement was measured for use as an index of respiratory phase [6].
The frames in the maximum inspiratory and expiratory phases were also determined based on the diaphragm movement,
respectively.
The lung area was determined by edge detection using the first derivative technique and iterative contour-smoothing algorithm [7,8].
The hilar regions were excluded from the lung area manually to reduce the influence of circulation.
Average pixel values in each lung were measured so that the changes in pixel values were calculated between the maximum inspiratory and expiratory phases (Fig.
3).
To facilitate visual evaluation,
inter-frame differences were visualized as color maps.
The mapped images were compared to findings on pulmonary scintigrams and CT.