scholarly journals Pulmonary Perfusion Changes as Assessed by Contrast-Enhanced Dual-Energy Computed Tomography after Endoscopic Lung Volume Reduction by Coils

Respiration ◽  
2016 ◽  
Vol 92 (6) ◽  
pp. 404-413
Author(s):  
Frédéric Lador ◽  
Anne-Lise Hachulla ◽  
Olivia Hohn ◽  
Jérôme Plojoux ◽  
Maxime Ronot ◽  
...  
Diagnostics ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 870
Author(s):  
Saif Afat ◽  
Ahmed E. Othman ◽  
Konstantin Nikolaou ◽  
Sebastian Gassenmaier

To evaluate contrast-enhanced dual-energy computed tomography (DECT) chest examinations regarding pulmonary perfusion patterns and pulmonary opacities in patients with confirmed COVID-19 disease. Fourteen patients with 24 DECT examinations performed between April and May 2020 were included in this retrospective study. DECT studies were assessed independently by two radiologists regarding pulmonary perfusion defects, using a Likert scale ranging from 1 to 4. Furthermore, in all imaging studies the extent of pulmonary opacities was quantified using the same rating system as for perfusion defects. The main pulmonary findings were ground glass opacities (GGO) in all 24 examinations and pulmonary consolidations in 22 examinations. The total lung scores after the addition of the scores of the single lobes showed significantly higher values of opacities compared to perfusion defects, with a median of 12 (9–18) for perfusion defects and a median of 17 (15–19) for pulmonary opacities (p = 0.002). Furthermore, mosaic perfusion patterns were found in 19 examinations in areas with and without GGO. Further studies will be necessary to investigate the pathophysiological background of GGO with maintained perfusion compared to GGO with reduced perfusion, especially regarding long-term lung damage and prognosis.


Respiration ◽  
2021 ◽  
pp. 1-10
Author(s):  
Hester A. Gietema ◽  
Kim H.M. Walraven ◽  
Rein Posthuma ◽  
Cristina Mitea ◽  
Dirk-Jan Slebos ◽  
...  

<b><i>Background:</i></b> Endoscopic lung volume reduction (ELVR) using one-way endobronchial valves is a technique to reduce hyperinflation in patients with severe emphysema by inducing collapse of a severely destroyed pulmonary lobe. Patient selection is mainly based on evaluation of emphysema severity on high-resolution computed tomography and evaluation of lung perfusion with perfusion scintigraphy. Dual-energy contrast-enhanced CT scans may be useful for perfusion assessment in emphysema but has not been compared against perfusion scintigraphy. <b><i>Aims:</i></b> The aim of the study was to compare perfusion distribution assessed with dual-energy contrast-enhanced computed tomography and perfusion scintigraphy. <b><i>Material and Methods:</i></b> Forty consecutive patients with severe emphysema, who were screened for ELVR, were included. Perfusion was assessed with 99mTc perfusion scintigraphy and using the iodine map calculated from the dual-energy contrast-enhanced CT scans. Perfusion distribution was calculated as usually for the upper, middle, and lower thirds of both lungs with the planar technique and the iodine overlay. <b><i>Results:</i></b> Perfusion distribution between the right and left lung showed good correlation (<i>r</i> = 0.8). The limits of agreement of the mean absolute difference in percentage perfusion per region of interest were 0.75–5.6%. The upper lobes showed more severe perfusion reduction than the lower lobes. Mean difference in measured pulmonary perfusion ranged from −2.8% to 2.3%. Lower limit of agreement ranged from −8.9% to 4.6% and upper limit was 3.3–10.0%. <b><i>Conclusion:</i></b> Quantification of perfusion distribution using planar 99mTc perfusion scintigraphy and iodine overlays calculated from dual-energy contrast-enhanced CTs correlates well with acceptable variability.


2009 ◽  
Vol 35 (3) ◽  
pp. 403-407 ◽  
Author(s):  
Antonio D’Andrilli ◽  
Laura Vismara ◽  
Matilde Rolla ◽  
Mohsen Ibrahim ◽  
Federico Venuta ◽  
...  

2018 ◽  
Vol 48 (12) ◽  
pp. 1008-1019 ◽  
Author(s):  
Keitaro Sofue ◽  
Masakatsu Tsurusaki ◽  
Achille Mileto ◽  
Tomoko Hyodo ◽  
Kosuke Sasaki ◽  
...  

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