Right main pulmonary artery distensibility on dynamic ventilation CT and its association with respiratory function
Tóm tắt
Heartbeat-based cross-sectional area (CSA) changes in the right main pulmonary artery (MPA), which reflects its distensibility associated with pulmonary hypertension, can be measured using dynamic ventilation computed tomography (DVCT) in patients with and without chronic obstructive pulmonary disease (COPD) during respiratory dynamics. We investigated the relationship between MPA distensibility (MPAD) and respiratory function and how heartbeat-based CSA is related to spirometry, mean lung density (MLD), and patient characteristics. We retrospectively analyzed DVCT performed preoperatively in 37 patients (20 female and 17 males) with lung cancer aged 70.6 ± 7.9 years (mean ± standard deviation), 18 with COPD and 19 without. MPA-CSA was separated into respiratory and heartbeat waves by discrete Fourier transformation. For the cardiac pulse-derived waves, CSA change (CSAC) and CSA change ratio (CSACR) were calculated separately during inhalation and exhalation. Spearman rank correlation was computed. In the group without COPD as well as all cases, CSACR exhalation was inversely correlated with percent residual lung volume (%RV) and RV/total lung capacity (r = -0.68, p = 0.003 and r = -0.58, p = 0.014). In contrast, in the group with COPD, CSAC inhalation was correlated with MLDmax and MLD change rate (MLDmax/MLDmin) (r = 0.54, p = 0.020 and r = 0.64, p = 0.004) as well as CSAC exhalation and CSACR exhalation. In patients with insufficient exhalation, right MPAD during exhalation was decreased. Also, in COPD patients with insufficient exhalation, right MPAD was reduced during inhalation as well as exhalation, which implied that exhalation impairment is a contributing factor to pulmonary hypertension complicated with COPD. Assessment of MPAD in different respiratory phases on DVCT has the potential to be utilized as a non-invasive assessment for pulmonary hypertension due to lung disease and/or hypoxia and elucidation of its pathogenesis. • There are no previous studies analyzing all respiratory phases of right main pulmonary artery distensibility (MPAD). • Patients with exhalation impairment decreased their right MPAD. • Analysis of MPAD on dynamic ventilation computed tomography contributes to understanding the pathogenesis of pulmonary hypertension due to lung disease and/or hypoxia in patients with expiratory impairment.
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Tài liệu tham khảo
Rabe KF, Watz H (2017) Chronic obstructive pulmonary disease. Lancet 389:1931–1940. https://doi.org/10.1016/S0140-6736(17)31222-9
Cavaillès A, Brinchault-Rabin G, Dixmier A et al (2013) Comorbidities of COPD. Eur Respir Rev 22:454–475. https://doi.org/10.1183/09059180.00008612
Kang KW, Chang HJ, Kim YJ et al (2011) Cardiac magnetic resonance imaging-derived pulmonary artery distensibility index correlates with pulmonary artery stiffness and predicts functional capacity in patients with pulmonary arterial hypertension. Circ J 75:2244–2251. https://doi.org/10.1253/circj.cj-10-1310
Johns CS, Rajaram S, Capener DA et al (2018) Non-invasive methods for estimating mPAP in COPD using cardiovascular magnetic resonance imaging. Eur Radiol 28(4):1438–1448. https://doi.org/10.1007/s00330-017-5143-y
Nagatani Y, Takahashi M, Nitta N et al (2008) Quantitative assessment of arterial stiffness by multiphase analysis in retrospectively electrocardiogram-gated multidetector row computed tomography: comparison between patients under chronic hemodialysis and age-matched controls. Invest Radiol 43(3):195–201. https://doi.org/10.1097/RLI.0b013e31815cd987
Colin GC, Verlynde G, Pouleur AC et al (2020) Pulmonary hypertension due to left heart disease: diagnostic value of pulmonary artery distensibility. Eur Radiol 30(11):6204–6212. https://doi.org/10.1007/s00330-020-06959-7
Tanaka R, Matsumoto I, Tamura M et al (2021) Dynamic chest radiography: clinical validation of ventilation and perfusion metrics derived from changes in radiographic lung density compared to nuclear medicine imaging. Quant Imaging Med Surg. 11(9):4016–4027. https://doi.org/10.21037/qims-20-1217
Yamada Y, Ueyama M, Abe T et al (2017) Difference in the craniocaudal gradient of the maximum pixel value change rate between chronic obstructive pulmonary disease patients and normal subjects using sub-mGy dynamic chest radiography with a flat panel detector system. Eur J Radiol 92:37–44. https://doi.org/10.1016/j.ejrad.2017.04.016
Nagatani Y, Takahashi M, Murata K et al (2015) Lung nodule detection performance in five observers on computed tomography (CT) with adaptive iterative dose reduction using three-dimensional processing (AIDR 3D) in a Japanese multicenter study: comparison between ultra-low-dose CT and low-dose CT by receiver-operating characteristic analysis. Eur J Radiol 84(7):1401–1412. https://doi.org/10.1016/j.ejrad.2015.03.012
Neroladaki A, Botsikas D, Boudabbous S, Becker CD, Montet X (2013) Computed tomography of the chest with model-based iterative reconstruction using a radiation exposure similar to chest X-ray examination: preliminary observations. Eur Radiol 23(2):360–366. https://doi.org/10.1007/s00330-012-2627-7
Nagatani Y, Takahashi M, Ikeda M et al (2017) Sub-solid nodule detection performance on reduced-dose computed tomography with iterative reduction: comparison between 20 mA (7 mAs) and 120 mA (42 mAs) regarding nodular size and characteristics and association with size-specific dose estimate. Acad Radiol 24(8):995–1007. https://doi.org/10.1016/j.acra.2017.01.004
Katsura M, Matsuda I, Akahane M et al (2013) Model-based iterative reconstruction technique for ultralow-dose chest CT: comparison of pulmonary nodule detectability with the adaptive statistical iterative reconstruction technique. Invest Radiol 48(4):206–212. https://doi.org/10.1097/RLI.0b013e31827efc3a
Nagatani Y, Moriya H, Noma S et al (2018) Association of focal radiation dose adjusted on cross sections with subsolid nodule visibility and quantification on computed tomography images using AIDR 3D: comparison among scanning at 84 mAs, 42 mAs and 7 mAs. Acad Radiol 25:1156–1166. https://doi.org/10.1016/j.acra.2018.01.024
Sakuma K, Yamashiro T, Moriya H et al (2017) Parietal pleural invasion/adhesion of subpleural lung cancer: quantitative 4-dimensional CT analysis using dynamic-ventilatory scanning. Eur J Radiol 87:36–44. https://doi.org/10.1016/j.ejrad.2016.12.004
Hashimoto M, Nagatani Y, Oshio Y et al (2018) Preoperative assessment of pleural adhesion by four-dimensional ultra-low-dose computed tomography (4D-ULDCT) with adaptive iterative dose reduction using three-dimensional processing (AIDR-3D). Eur J Radiol. 98(1):179–186. https://doi.org/10.1016/j.ejrad.2017.11.011
Nagatani Y, Hashimoto M, Oshio Y et al (2020) Preoperative assessment of localized pleural adhesion: utility of software-assisted analysis on dynamic-ventilation computed tomography. Eur J Radiol 133:109347. https://doi.org/10.1016/j.ejrad.2020.109347
Yamashiro T, Moriya H, Matsuoka, et al (2017) Asynchrony in respiratory movements between the pulmonary lobes in patients with COPD: continuous measurement of lung density by 4-dimensional dynamic-ventilation CT. Int J Chron Obstruct Pulmon Dis 12:2101–2109. https://doi.org/10.2147/COPD.S140247
Yamashiro T, Moriya H, Tsubakimoto M, Nagatani Y, Kimoto T, Murayama S, ACTIve Study Group investigators (2019) Preoperative assessment of parietal pleural invasion/adhesion of subpleural lung cancer: advantage of software-assisted analysis of 4-dimensional dynamic-ventilation computed tomography. Eur Radiol. 29(10):5247–5252. https://doi.org/10.1007/s00330-019-06131-w
Xu Y, Yamashiro T, Moriya H et al (2018) Strain measurement on four-dimensional dynamic-ventilation CT: quantitative analysis of abnormal respiratory deformation of the lung in COPD. Int J Chron Obstruct Pulmon Dis 14:65–72. https://doi.org/10.2147/COPD.S183740
Nagatani Y, Hashimoto M, Nitta N et al (2018) Continuous quantitative measurement of the main bronchial dimensions and lung density in the lateral position by four-dimensional dynamic-ventilation CT in smokers and COPD patients. Int J Chron Obstruct Pulmon Dis 13:3845–3856. https://doi.org/10.2147/COPD.S178836
Uemura R, Nagatani Y, Hashimoto M et al (2023) Association of respiratory functional indices and smoking with pleural movement and mean lung density assessed using four-dimensional dynamic-ventilation computed tomography in smokers and patients with COPD. Int J Chron Obstruct Pulmon Dis 15(18):327–339. https://doi.org/10.2147/COPD.S389075
Xu Y, Yamashiro T, Moriya H et al (2017) Hyperinflated lungs compress the heart during expiration in COPD patients: a new finding on dynamic-ventilation computed tomography. Int J Chron Obstruct Pulmon Dis 12:3123–3131. https://doi.org/10.2147/COPD.S145599
Miller MR, Hankinson J, Brusasco V et al (2005) Standardisation of spirometry. Eur Respir J 26:319–338. https://doi.org/10.1183/09031936.05.00034805
Sato S, Nagatani Y, Hashimoto M et al (2021) Usability of the lateral decubitus position on 4-dimensional ultra-low dose computed tomography for the detection of localized pleural adhesion in the pulmonary apical region. Acta Radiol 62(4):426–473. https://doi.org/10.1177/0284185120930611
Shrimpton PC, Hillier MC, Lewis MA et al (2006) National survey of doses from CT in the UK: 2003. Br J Radiol 79(948):968–980. https://doi.org/10.1259/bjr/93277434
Kageyama K, Yamamoto A, Jogo A et al (2021) Visualization of flow dynamics in the portal circulation using 320-detector-row computed tomography: a feasibility study. Eur Radiol Exp 5(1):1. https://doi.org/10.1186/s41747-020-00197-8
Goddard PR, Nicholson EM, Laszlo G, Watt I (1982) Computed tomography in pulmonary emphysema. Clin Radiol 33:379–387. https://doi.org/10.1016/s0009-9260(82)80301-2
Miyati T, Mase M, Banno T et al (2003) Frequency analyses of CSF flow on cine MRI in normal pressure hydrocephalus. Eur Radiol 13(5):1019–1024. https://doi.org/10.1007/s00330-002-1697-3
Bondesson D, Schneider MJ, Gaass T et al (2019) Nonuniform Fourier-decomposition MRI for ventilation- and perfusion-weighted imaging of the lung. Magn Reson Med 82:1312–1321. https://doi.org/10.1002/mrm.27803
Yamashiro T, Matsuoka S, Bartholmai BJ et al (2010) Collapsibility of lung volume by paired inspiratory and expiratory CT scans: correlations with lung function and mean lung density. Acad Radiol 17(4):489–495. https://doi.org/10.1016/j.acra.2009.11.004
Xie X, de Jong PA, Oudkerk M et al (2012) Morphological measurements in computed tomography correlate with airflow obstruction in chronic obstructive pulmonary disease: systematic review and meta-analysis. Eur Radiol 22(10):2085–2093. https://doi.org/10.1007/s00330-012-2480-8
Bottai M, Pistelli F, Di Pede F et al (2002) Longitudinal changes of body mass index, spirometry and diffusion in a general population. Eur Respir J. 20(3):665–73. https://doi.org/10.1183/09031936.02.01282001
Suzuki S, Machida H, Tanaka I, Ueno E (2013) Vascular diameter measurement in CT angiography: comparison of model-based iterative reconstruction and standard filtered back projection algorithms in vitro. AJR Am J Roentgenol 00(3):652–657. https://doi.org/10.2214/AJR.12.8689