Leticia Gallardo Estrella1, Esther Pompe2, Jan-Martin Kuhnigk3, David A Lynch4, Surya P Bhatt5,6,7, Bram van Ginneken1,3, Eva Marjolein van Rikxoort1,3. 1. Department of Radiology and Nuclear Medicine, Radboud University Medical Centre, Nijmegen, 6525 GA, The Netherlands. 2. Department of Respiratory Medicine, University Medical Center Utrecht, Utrecht, 3584 CX, The Netherlands. 3. Fraunhofer MEVIS, Institute for Medical Image Computing, Bremen, 28359, Germany. 4. Department of Medicine, National Jewish Health, Denver, CO 80206, USA. 5. Division of Pulmonary, Allergy and Critical Care Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA. 6. UAB Lung Health Center, University of Alabama at Birmingham, Birmingham, AL 35294, USA. 7. UAB Lung Imaging Core, University of Alabama at Birmingham, Birmingham, AL 35294, USA.
Abstract
PURPOSE: To present a method to automatically quantify tracheal morphology changes during breathing and investigate its contribution to airflow impairment when adding CT measures of emphysema, airway wall thickness, air trapping and ventilation. METHODS: Because tracheal abnormalities often occur localized, a method is presented that automatically determines the most abnormal trachea section based on automatically computed sagittal and coronal lengths. In this most abnormal section, trachea morphology is encoded using four equiangular rays from the center of the trachea and the normalized lengths of these rays are used as features in a classification scheme. Consequently, trachea measurements are used as input for classification into GOLD stages in addition to emphysema, air trapping and ventilation. A database of 200 subjects distributed across all GOLD stages is used to evaluate the classification with a k nearest neighbour algorithm. Performance is assessed in two experimental settings: (a) when only inspiratory scans are taken; (b) when both inspiratory and expiratory scans are available. RESULTS: Given only an inspiratory CT scan, measuring tracheal shape provides complementary information only to emphysema measurements. The best performing set in the inspiratory setting was a combination of emphysema and bronchial measurements. The best performing feature set in the inspiratory-expiratory setting includes measurements of emphysema, ventilation, air trapping, and trachea. Inspiratory and inspiratory-expiratory settings showed similar performance. CONCLUSIONS: The fully automated system presented in this study provides information on trachea shape at inspiratory and expiratory CT. Addition of tracheal morphology features improves the ability of emphysema and air trapping CT-derived measurements to classify COPD patients into GOLD stages and may be relevant when investigating different aspects of COPD.
PURPOSE: To present a method to automatically quantify tracheal morphology changes during breathing and investigate its contribution to airflow impairment when adding CT measures of emphysema, airway wall thickness, air trapping and ventilation. METHODS: Because tracheal abnormalities often occur localized, a method is presented that automatically determines the most abnormal trachea section based on automatically computed sagittal and coronal lengths. In this most abnormal section, trachea morphology is encoded using four equiangular rays from the center of the trachea and the normalized lengths of these rays are used as features in a classification scheme. Consequently, trachea measurements are used as input for classification into GOLD stages in addition to emphysema, air trapping and ventilation. A database of 200 subjects distributed across all GOLD stages is used to evaluate the classification with a k nearest neighbour algorithm. Performance is assessed in two experimental settings: (a) when only inspiratory scans are taken; (b) when both inspiratory and expiratory scans are available. RESULTS: Given only an inspiratory CT scan, measuring tracheal shape provides complementary information only to emphysema measurements. The best performing set in the inspiratory setting was a combination of emphysema and bronchial measurements. The best performing feature set in the inspiratory-expiratory setting includes measurements of emphysema, ventilation, air trapping, and trachea. Inspiratory and inspiratory-expiratory settings showed similar performance. CONCLUSIONS: The fully automated system presented in this study provides information on trachea shape at inspiratory and expiratory CT. Addition of tracheal morphology features improves the ability of emphysema and air trapping CT-derived measurements to classify COPDpatients into GOLD stages and may be relevant when investigating different aspects of COPD.
Authors: Keelin Murphy; Josien P W Pluim; Eva M van Rikxoort; Pim A de Jong; Bartjan de Hoop; Hester A Gietema; Onno Mets; Marleen de Bruijne; Pechin Lo; Mathias Prokop; Bram van Ginneken Journal: Med Phys Date: 2012-03 Impact factor: 4.071
Authors: Phillip M Boiselle; Carl R O'Donnell; Alexander A Bankier; Armin Ernst; Mary E Millet; Alexis Potemkin; Stephen H Loring Journal: Radiology Date: 2009-05-06 Impact factor: 11.105
Authors: Onno M Mets; Michael Schmidt; Constantinus F Buckens; Martijn J Gondrie; Ivana Isgum; Matthijs Oudkerk; Rozemarijn Vliegenthart; Harry J de Koning; Carlijn M van der Aalst; Mathias Prokop; Jan-Willem J Lammers; Pieter Zanen; Firdaus A Mohamed Hoesein; Willem PthM Mali; Bram van Ginneken; Eva M van Rikxoort; Pim A de Jong Journal: Respir Res Date: 2013-05-27