Hisanobu Koyama1, Yoshiharu Ohno2, Yasuko Fujisawa3, Shinichiro Seki4, Noriyuki Negi5, Tohru Murakami5, Takeshi Yoshikawa2, Naoki Sugihara3, Yoshihiro Nishimura6, Kazuro Sugimura4. 1. Division of Radiology, Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan. Electronic address: hkoyama@med.kobe-u.ac.jp. 2. Advanced Biomedical Imaging Research Center, Kobe University Graduate School of Medicine, Kobe, Japan; Division of Functional and Diagnostic Imaging Research, Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan. 3. Toshiba Medical Systems Corporation, Otawara, Tochigi, Japan. 4. Division of Radiology, Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan. 5. Center for Radiology and Radiation Oncology, Kobe University Hospital, Japan. 6. Division of Respiratory Medicine, Department of Internal Medicine, Kobe University Graduate School of Medicine, Kobe, Japan.
Abstract
PURPOSE: To evaluate the utility of three-dimensional (3D) lung motion on inspiratory and expiratory CT for pulmonary functional loss in smoking-related COPD in comparison with lung destruction and air trapping assessments. METHOD AND MATERIALS: Forty-four consecutive smokers and COPD patients prospectively underwent inspiratory and expiratory CT. A 3D motion vector map was generated from these CTs, and regional motion magnitudes were measured at the horizontal axis (X-axis), the ventrodorsal axis (Y-axis), and the craniocaudal axis (Z-axis). All mean magnitudes within the entire lung (MMLX, MMLY, and MMLZ) were normalized by expiratory CT lung volume. Moreover, CT-based functional lung volume (FLV) on inspiratory CT and air trapping lung volume (ATLV) on expiratory CT were assessed quantitatively. To evaluate the capability for pulmonary function loss assessment, all MMLs were correlated with pulmonary function tests. Then, discrimination analysis was performed to determine the concordance capability for clinical stage, and correct classification capabilities were compared by means of McNemar's test. RESULTS: Multiple regression analysis showed MMLY (β=0.657, p<0.001) and FLV (β=0.375, p=0.019) were correlated with percentage of predicted forced expiratory volume in 1 second. Correct classification capabilities using patient characteristics and MMLs (68.2 (30/44)%) were significantly higher than those obtained by patient characteristics, FLV, and ATLV (54.5 (24/44)%), p=0.031). CONCLUSION: 3D lung motion parameter assessment is useful for smoking-related COPD assessment as well as lung parenchymal destruction and/or air trapping evaluations.
PURPOSE: To evaluate the utility of three-dimensional (3D) lung motion on inspiratory and expiratory CT for pulmonary functional loss in smoking-related COPD in comparison with lung destruction and air trapping assessments. METHOD AND MATERIALS: Forty-four consecutive smokers and COPDpatients prospectively underwent inspiratory and expiratory CT. A 3D motion vector map was generated from these CTs, and regional motion magnitudes were measured at the horizontal axis (X-axis), the ventrodorsal axis (Y-axis), and the craniocaudal axis (Z-axis). All mean magnitudes within the entire lung (MMLX, MMLY, and MMLZ) were normalized by expiratory CT lung volume. Moreover, CT-based functional lung volume (FLV) on inspiratory CT and air trapping lung volume (ATLV) on expiratory CT were assessed quantitatively. To evaluate the capability for pulmonary function loss assessment, all MMLs were correlated with pulmonary function tests. Then, discrimination analysis was performed to determine the concordance capability for clinical stage, and correct classification capabilities were compared by means of McNemar's test. RESULTS: Multiple regression analysis showed MMLY (β=0.657, p<0.001) and FLV (β=0.375, p=0.019) were correlated with percentage of predicted forced expiratory volume in 1 second. Correct classification capabilities using patient characteristics and MMLs (68.2 (30/44)%) were significantly higher than those obtained by patient characteristics, FLV, and ATLV (54.5 (24/44)%), p=0.031). CONCLUSION: 3D lung motion parameter assessment is useful for smoking-related COPD assessment as well as lung parenchymal destruction and/or air trapping evaluations.