OBJECTIVES: Lung hyperinflation may be assessed by computed tomography (CT). As shown for patients with emphysema, however, CT image reconstruction affects quantification of hyperinflation. We studied the impact of reconstruction parameters on hyperinflation measurements in mechanically ventilated (MV) patients. DESIGN: Observational analysis. SETTING: A University hospital-affiliated research Unit. PATIENTS: The patients were MV patients with injured (n = 5) or normal lungs (n = 6), and spontaneously breathing patients (n = 5). INTERVENTIONS: None. MEASUREMENTS AND RESULTS: Eight image series involving 3, 5, 7, and 10 mm slices and standard and sharp filters were reconstructed from identical CT raw data. Hyperinflated (V(hyper)), normally (V(normal)), poorly (V(poor)), and nonaerated (V(non)) volumes were calculated by densitometry as percentage of total lung volume (V(total)). V(hyper) obtained with the sharp filter systematically exceeded that with the standard filter showing a median (interquartile range) increment of 138 (62-272) ml corresponding to approximately 4% of V(total). In contrast, sharp filtering minimally affected the other subvolumes (V(normal), V(poor), V(non), and V(total)). Decreasing slice thickness also increased V(hyper) significantly. When changing from 10 to 3 mm thickness, V(hyper) increased by a median value of 107 (49-252) ml in parallel with a small and inconsistent increment in V(non) of 12 (7-16) ml. CONCLUSIONS: Reconstruction parameters significantly affect quantitative CT assessment of V(hyper) in MV patients. Our observations suggest that sharp filters are inappropriate for this purpose. Thin slices combined with standard filters and more appropriate thresholds (e.g., -950 HU in normal lungs) might improve the detection of V(hyper). Different studies on V(hyper) can only be compared if identical reconstruction parameters were used.
OBJECTIVES:Lung hyperinflation may be assessed by computed tomography (CT). As shown for patients with emphysema, however, CT image reconstruction affects quantification of hyperinflation. We studied the impact of reconstruction parameters on hyperinflation measurements in mechanically ventilated (MV) patients. DESIGN: Observational analysis. SETTING: A University hospital-affiliated research Unit. PATIENTS: The patients were MV patients with injured (n = 5) or normal lungs (n = 6), and spontaneously breathing patients (n = 5). INTERVENTIONS: None. MEASUREMENTS AND RESULTS: Eight image series involving 3, 5, 7, and 10 mm slices and standard and sharp filters were reconstructed from identical CT raw data. Hyperinflated (V(hyper)), normally (V(normal)), poorly (V(poor)), and nonaerated (V(non)) volumes were calculated by densitometry as percentage of total lung volume (V(total)). V(hyper) obtained with the sharp filter systematically exceeded that with the standard filter showing a median (interquartile range) increment of 138 (62-272) ml corresponding to approximately 4% of V(total). In contrast, sharp filtering minimally affected the other subvolumes (V(normal), V(poor), V(non), and V(total)). Decreasing slice thickness also increased V(hyper) significantly. When changing from 10 to 3 mm thickness, V(hyper) increased by a median value of 107 (49-252) ml in parallel with a small and inconsistent increment in V(non) of 12 (7-16) ml. CONCLUSIONS: Reconstruction parameters significantly affect quantitative CT assessment of V(hyper) in MV patients. Our observations suggest that sharp filters are inappropriate for this purpose. Thin slices combined with standard filters and more appropriate thresholds (e.g., -950 HU in normal lungs) might improve the detection of V(hyper). Different studies on V(hyper) can only be compared if identical reconstruction parameters were used.
Authors: Fernando Suarez-Sipmann; Stephan H Böhm; Gerardo Tusman; Tanja Pesch; Oliver Thamm; Hajo Reissmann; Andreas Reske; Anders Magnusson; Göran Hedenstierna Journal: Crit Care Med Date: 2007-01 Impact factor: 7.598
Authors: Pier Paolo Terragni; Giulio Rosboch; Andrea Tealdi; Eleonora Corno; Eleonora Menaldo; Ottavio Davini; Giovanni Gandini; Peter Herrmann; Luciana Mascia; Michel Quintel; Arthur S Slutsky; Luciano Gattinoni; V Marco Ranieri Journal: Am J Respir Crit Care Med Date: 2006-10-12 Impact factor: 21.405
Authors: David G Parr; Berend C Stoel; Jan Stolk; Peter G Nightingale; Robert A Stockley Journal: Am J Respir Crit Care Med Date: 2004-07-21 Impact factor: 21.405
Authors: A W Reske; A P Reske; H A Gast; M Seiwerts; A Beda; U Gottschaldt; C Josten; D Schreiter; N Heller; H Wrigge; M B Amato Journal: Intensive Care Med Date: 2010-08-06 Impact factor: 17.440
Authors: Spyros D Mentzelopoulos; Maria Theodoridou; Sotirios Malachias; Sotiris Sourlas; Demetrios N Exarchos; Demetrios Chondros; Charis Roussos; Spyros G Zakynthinos Journal: Intensive Care Med Date: 2011-03-03 Impact factor: 17.440
Authors: Eduardo L V Costa; Guido Musch; Tilo Winkler; Tobias Schroeder; R Scott Harris; Hazel A Jones; Jose G Venegas; Marcos F Vidal Melo Journal: Anesthesiology Date: 2010-03 Impact factor: 7.892
Authors: Andreas W Reske; Alexander P Reske; Till Heine; Peter M Spieth; Anna Rau; Matthias Seiwerts; Harald Busse; Udo Gottschaldt; Dierk Schreiter; Silvia Born; Marcelo Gama de Abreu; Christoph Josten; Hermann Wrigge; Marcelo B P Amato Journal: Crit Care Date: 2011-02-25 Impact factor: 9.097
Authors: Andreas W Reske; Anna Rau; Alexander P Reske; Manja Koziol; Beate Gottwald; Michaele Alef; Jean-Claude Ionita; Peter M Spieth; Pierre Hepp; Matthias Seiwerts; Alessandro Beda; Silvia Born; Gerik Scheuermann; Marcelo B P Amato; Hermann Wrigge Journal: Crit Care Date: 2011-11-23 Impact factor: 9.097
Authors: Massimo Antonelli; Elie Azoulay; Marc Bonten; Jean Chastre; Giuseppe Citerio; Giorgio Conti; Daniel De Backer; François Lemaire; Herwig Gerlach; Johan Groeneveld; Goran Hedenstierna; Duncan Macrae; Jordi Mancebo; Salvatore M Maggiore; Alexandre Mebazaa; Philipp Metnitz; Jerôme Pugin; Jan Wernerman; Haibo Zhang Journal: Intensive Care Med Date: 2009-01-06 Impact factor: 17.440