Low-cost structured light imaging of regional volume changes for use in assessing mechanical ventilation

Computer Methods and Programs in Biomedicine - Tập 226 - Trang 107176 - 2022
Cong Zhou1,2, J. Geoffrey Chase2
1School of Civil Aviation, Northwestern Polytechnical University, China
2Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Christchurch, New Zealand

Tài liệu tham khảo

2019 2019 Dasta, 2005, Daily cost of an intensive care unit day: the contribution of mechanical ventilation, Crit. Care Med., 33, 1266, 10.1097/01.CCM.0000164543.14619.00 Mehta, 2015, Epidemiological trends in invasive mechanical ventilation in the United States: a population-based study, J. Crit. Care, 30, 1217, 10.1016/j.jcrc.2015.07.007 Villar, 2014, The acute respiratory distress syndrome: incidence and mortality, has it changed?, Curr. Opin. Crit. Care, 20, 3, 10.1097/MCC.0000000000000057 Chase, 2018, Next-generation, personalised, model-based critical care medicine: a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them, Biomed. Eng. Online, 17, 1, 10.1186/s12938-018-0455-y Chiew, 2011, Model-based PEEP optimisation in mechanical ventilation, Biomed. Eng. Online, 10, 1, 10.1186/1475-925X-10-111 Morton, 2019, Optimising mechanical ventilation through model-based methods and automation, Ann. Rev. Control, 48, 369, 10.1016/j.arcontrol.2019.05.001 Zhou, 2021, Virtual patients for mechanical ventilation in the intensive care unit, Comput. Methods Prog. Biomed., 199, 10.1016/j.cmpb.2020.105912 Zhou, 2021, 54, 186 Sun, 2022, Over-distension prediction via hysteresis loop analysis and patient-specific basis functions in a virtual patient model, Comput. Biol. Med., 141, 10.1016/j.compbiomed.2021.105022 Sun, 2022, Prediction and estimation of pulmonary response and elastance evolution for volume-controlled and pressure-controlled ventilation, Biomed. Signal Process. Control, 72, 10.1016/j.bspc.2021.103367 Pulletz, 2012, Regional lung opening and closing pressures in patients with acute lung injury, J. Crit. care, 27, 323.e11, 10.1016/j.jcrc.2011.09.002 Puybasset, 2000, Regional distribution of gas and tissue in acute respiratory distress syndrome. III. Consequences for the effects of positive end-expiratory pressure. CT scan ARDS study group. Adult respiratory distress syndrome, Intensiv. Care Med., 26, 1215, 10.1007/s001340051340 Puybasset, 2000, Regional distribution of gas and tissue in acute respiratory distress syndrome. I. Consequences for lung morphology. CT scan ARDS study group, Intensiv. Care Med., 26, 857, 10.1007/s001340051274 Fleming, 2014, Determination of regional lung air volume distribution at mid-tidal breathing from computed tomography: a retrospective study of normal variability and reproducibility, BMC Med. Imaging, 14, 1, 10.1186/1471-2342-14-25 Fuld, 2008, CT-measured regional specific volume change reflects regional ventilation in supine sheep, J. Appl. Physiol., 104, 1177, 10.1152/japplphysiol.00212.2007 Puybasset, 1998, A computed tomography scan assessment of regional lung volume in acute lung injury, Am. J. Resp. Crit. Care Med., 158, 1644, 10.1164/ajrccm.158.5.9802003 Chase, 2014, When the value of gold is zero, BMC Res. Notes, 7, 404, 10.1186/1756-0500-7-404 He, 2021, Early individualized positive end-expiratory pressure guided by electrical impedance tomography in acute respiratory distress syndrome: a randomized controlled clinical trial, Crit. Care, 25, 1, 10.1186/s13054-021-03645-y Zhao, 2009, Evaluation of an electrical impedance tomography-based global inhomogeneity index for pulmonary ventilation distribution, Intensiv. Care Med., 35, 1900, 10.1007/s00134-009-1589-y Bikker, 2009, Lung volume calculated from electrical impedance tomography in ICU patients at different PEEP levels, Intensiv. Care Med., 35, 1362, 10.1007/s00134-009-1512-6 Frerichs, 2017, Chest electrical impedance tomography examination, data analysis, terminology, clinical use and recommendations: consensus statement of the TRanslational EIT developmeNT stuDy group, Thorax, 72, 83, 10.1136/thoraxjnl-2016-208357 Kalantri, 2007, Accuracy and reliability of physical signs in the diagnosis of pleural effusion, Respir. Med., 101, 431, 10.1016/j.rmed.2006.07.014 Do Shellenberger, 2017, Diagnostic value of the physical examination in patients with dyspnea, Clevel. Clin. J. Med., 84, 943, 10.3949/ccjm.84a.16127 Scholten, 2017, Treatment of ARDS with prone positioning, Chest, 151, 215, 10.1016/j.chest.2016.06.032 Chase, 2006, A novel mechanical lung model of pulmonary diseases to assist with teaching and training, BMC Pulm. Med., 6, 1, 10.1186/1471-2466-6-21 Hartley, 2003 Geng, 2011, Structured-light 3D surface imaging: a tutorial, Adv. Opt. Photonics, 3, 128, 10.1364/AOP.3.000128 Lin, 2016, A single-shot structured light means by encoding both color and geometrical features, Pattern Recognit., 54, 178, 10.1016/j.patcog.2015.12.013 Desjardins, 2007, Dense stereo range sensing with marching pseudo-random patterns Bay, 2006, Surf: speeded up robust features Rublee, 2011, ORB: an efficient alternative to SIFT or SURF Salvi, 1998, A robust-coded pattern projection for dynamic 3D scene measurement, Pattern Recognit. Lett., 19, 1055, 10.1016/S0167-8655(98)00085-3 Chen, 2008, Vision processing for realtime 3-D data acquisition based on coded structured light, IEEE Trans. Image Process., 17, 167, 10.1109/TIP.2007.914755 Salvi, 2004, Pattern codification strategies in structured light systems, Pattern Recognit., 37, 827, 10.1016/j.patcog.2003.10.002 Salvi, 2010, A state of the art in structured light patterns for surface profilometry, Pattern Recognit., 43, 2666, 10.1016/j.patcog.2010.03.004 Mikolajczyk, 2001, Indexing based on scale invariant interest points Zhang, 1999, Flexible camera calibration by viewing a plane from unknown orientations Wilson, 2015, Compartmental models of the chest wall and the origin of Hoover's sign, Respirat. Physiol. Neurobiol., 210, 23, 10.1016/j.resp.2015.01.010 Loring, 1982, Action of the diaphragm on the rib cage inferred from a force-balance analysis, J. Appl. Physiol., 53, 756, 10.1152/jappl.1982.53.3.756 De Troyer, 2011, The action of the canine diaphragm on the lower ribs depends on activation, J. Appl. Physiol., 111, 1266, 10.1152/japplphysiol.00402.2011 Ward, 1992, Analysis of human chest wall motion using a two-compartment rib cage model, J. Appl. Physiol., 72, 1338, 10.1152/jappl.1992.72.4.1338 Major, 2018, Biomedical engineer's guide to the clinical aspects of intensive care mechanical ventilation, Biomed. Eng. Online, 17, 169, 10.1186/s12938-018-0599-9 Bates, 2006, The estimation of lung mechanics parameters in the presence of pathology: a theoretical analysis, Ann. Biomed. Eng., 34, 384, 10.1007/s10439-005-9056-6 Mertens, 2009, Alveolar dynamics in acute lung injury: heterogeneous distension rather than cyclic opening and collapse*, Crit. Care Med., 37, 2604, 10.1097/CCM.0b013e3181a5544d Holder-Pearson, 2021, e00227 Amato, 1998, Effect of a protective-ventilation strategy on mortality in the acute respiratory distress syndrome, N. Engl. J. Med., 338, 347, 10.1056/NEJM199802053380602 2000, Ventilation with lower tidal volumes as compared with traditional tidal volumes for acute lung injury and the acute respiratory distress syndrome, N. Engl. J. Med., 342, 1301, 10.1056/NEJM200005043421801 Chase, 2021, 54, 310 Moorhead, 2013, NAVA enhances tidal volume and diaphragmatic electro-myographic activity matching: a Range90 analysis of supply and demand, J. Clin.Monit. Comput., 27, 61, 10.1007/s10877-012-9398-1 Lee, 2021, Stochastic modelling of respiratory system elastance for mechanically ventilated respiratory failure patients, Ann. Biomed. Eng., 49, 3280, 10.1007/s10439-021-02854-4 Zhou, 2022, Reconstructing asynchrony for mechanical ventilation using a hysteresis loop virtual patient model, Biomed. Eng. Online, 21, 1, 10.1186/s12938-022-00986-9