Vector-field dynamic x-ray (VF-DXR) using optical flow method in patients with chronic obstructive pulmonary disease.

Lung Optic flow Pulmonary disease (chronic obstructive) Radiography (thoracic) Respiratory function tests

Journal

European radiology experimental
ISSN: 2509-9280
Titre abrégé: Eur Radiol Exp
Pays: England
ID NLM: 101721752

Informations de publication

Date de publication:
31 01 2022
Historique:
received: 15 08 2021
accepted: 20 11 2021
entrez: 31 1 2022
pubmed: 1 2 2022
medline: 6 5 2022
Statut: epublish

Résumé

We assessed the difference in lung motion during inspiration/expiration between chronic obstructive pulmonary disease (COPD) patients and healthy volunteers using vector-field dynamic x-ray (VF-DXR) with optical flow method (OFM). We enrolled 36 COPD patients and 47 healthy volunteers, classified according to pulmonary function into: normal, COPD mild, and COPD severe. Contrast gradient was obtained from sequential dynamic x-ray (DXR) and converted to motion vector using OFM. VF-DXR images were created by projection of the vertical component of lung motion vectors onto DXR images. The maximum magnitude of lung motion vectors in tidal inspiration/expiration, forced inspiration/expiration were selected and defined as lung motion velocity (LMV). Correlations between LMV with demographics and pulmonary function and differences in LMV between COPD patients and healthy volunteers were investigated. Negative correlations were confirmed between LMV and % forced expiratory volume in one second (%FEV In the tidal inspiration, the lung parenchyma moved faster in COPD patients compared with healthy volunteers. VF-DXR was feasible for the assessment of lung parenchyma using LMV.

Sections du résumé

BACKGROUND
We assessed the difference in lung motion during inspiration/expiration between chronic obstructive pulmonary disease (COPD) patients and healthy volunteers using vector-field dynamic x-ray (VF-DXR) with optical flow method (OFM).
METHODS
We enrolled 36 COPD patients and 47 healthy volunteers, classified according to pulmonary function into: normal, COPD mild, and COPD severe. Contrast gradient was obtained from sequential dynamic x-ray (DXR) and converted to motion vector using OFM. VF-DXR images were created by projection of the vertical component of lung motion vectors onto DXR images. The maximum magnitude of lung motion vectors in tidal inspiration/expiration, forced inspiration/expiration were selected and defined as lung motion velocity (LMV). Correlations between LMV with demographics and pulmonary function and differences in LMV between COPD patients and healthy volunteers were investigated.
RESULTS
Negative correlations were confirmed between LMV and % forced expiratory volume in one second (%FEV
CONCLUSIONS
In the tidal inspiration, the lung parenchyma moved faster in COPD patients compared with healthy volunteers. VF-DXR was feasible for the assessment of lung parenchyma using LMV.

Identifiants

pubmed: 35099604
doi: 10.1186/s41747-021-00254-w
pii: 10.1186/s41747-021-00254-w
pmc: PMC8802288
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

4

Subventions

Organisme : NCI NIH HHS
ID : R01 CA203636
Pays : United States
Organisme : NCI NIH HHS
ID : U01 CA209414
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL135142
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL111024
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL130974
Pays : United States

Informations de copyright

© 2022. The Author(s) under exclusive licence to European Society of Radiology.

Références

Chatila WM, Thomashow BM, Minai OA, Criner GJ (2008) Comorbidities in chronic obstructive pulmonary disease. Proc Am Thorac Soc. https://doi.org/10.1513/pats.200709-148ET
Anthonisen NR, Connett JE, Enright PL, Manfreda J (2002) Hospitalizations and mortality in the Lung Health Study. Am J Respir Crit Care Med 166:333–339. https://doi.org/10.1164/rccm.2110093
doi: 10.1164/rccm.2110093 pubmed: 12153966
Jaitovich A, Barreiro E (2018) Skeletal muscle dysfunction in chronic obstructive pulmonary disease. What We Know and Can Do for Our Patients. Am J Respir Crit Care Med. https://doi.org/10.1164/rccm.201710-2140CI
Wu G, Wang Q, Lian J, Shen D (2013) Estimating the 4D respiratory lung motion by spatiotemporal registration and super-resolution image reconstruction. Med Phys 40:031710. https://doi.org/10.1118/1.4790689
doi: 10.1118/1.4790689 pubmed: 23464305 pmcid: 4108704
Rao F, Li WL, Yin ZP (2018) Non-rigid point cloud registration based lung motion estimation using tangent-plane distance. PLoS One 13:e0204492. https://doi.org/10.1371/journal.pone.0204492
doi: 10.1371/journal.pone.0204492 pubmed: 30256830 pmcid: 6157875
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
Chen D, Xie H, Zhang S, Gu L (2017) Lung respiration motion modeling: a sparse motion field presentation method using biplane x-ray images. Phys Med Biol. https://doi.org/10.1088/1361-6560/aa8841
Yamada Y, Ueyama M, Abe T, et al (2017) Time-resolved quantitative analysis of the diaphragms during tidal breathing in a standing position using dynamic chest radiography with a flat panel detector system (“Dynamic X-Ray Phrenicography”): initial experience in 172 volunteers. Acad Radiol 24:393–400. https://doi.org/10.1016/j.acra.2016.11.014
Yamada Y, Ueyama M, Abe T, et al (2017) Difference in diaphragmatic motion during tidal breathing in a standing position between COPD patients and normal subjects: time-resolved quantitative evaluation using dynamic chest radiography with flat panel detector system (“dynamic X-ray phrenicography”). Eur J Radiol 87:76–82. https://doi.org/10.1016/j.ejrad.2016.12.014
Hida T, Yamada Y, Ueyama M, et al (2019) Time-resolved quantitative evaluation of diaphragmatic motion during forced breathing in a health screening cohort in a standing position: dynamic chest phrenicography. Eur J Radiol 113:59–65. https://doi.org/10.1016/j.ejrad.2019.01.034
Hida T, Yamada Y, Ueyama M, et al (2019) Decreased and slower diaphragmatic motion during forced breathing in severe COPD patients: time-resolved quantitative analysis using dynamic chest radiography with a flat panel detector system. Eur J Radiol 112:28–36. https://doi.org/10.1016/j.ejrad.2018.12.023
Hino T, Hata A, Hida T, et al (2020) Projected lung areas using dynamic x-ray (DXR). Eur J Radiol Open 7:100263. https://doi.org/10.1016/j.ejro.2020.100263
Hiasa Y, Otake Y, Tanaka R, Sanada S, Sato Y (2019) Recovery of 3D rib motion from dynamic chest radiography and CT data using local contrast normalization and articular motion model. Med Image Anal 51:144–156. https://doi.org/10.1016/j.media.2018.10.002
doi: 10.1016/j.media.2018.10.002 pubmed: 30439674
Tanaka R, Sanada S, Suzuki M, et al (2004) Breathing chest radiography using a dynamic flat-panel detector combined with computer analysis. Med Phys 31:2254–2262. https://doi.org/10.1118/1.1769351
Hata A, Yamada Y, Tanaka R, et al (2021) Dynamic chest x-ray using a flat-panel detector system: technique and applications. Korean J Radiol. https://doi.org/10.3348/kjr.2020.1136
Horn BKP, Schunck BG (1981) Determining optical flow. Artif Intell 17:185–203. https://doi.org/10.1016/0004-3702(81)90024-2
doi: 10.1016/0004-3702(81)90024-2
Lin YH, Fu B, Xiao LC, Wang W, Liu PX (2013) A video smoke detection algorithm based on wavelet energy and optical flow eigen-values. J Softw 8:63–69. https://doi.org/10.4304/jsw.8.1.63-70
doi: 10.4304/jsw.8.1.63-70
Denman S, Fookes C, Sridharan S (2009) Improved Simultaneous computation of motion detection and optical flow for object tracking. In: Proceedings of DICTA: Digital Image Computing: Techniques and Applications. Melbourne: 2009 Digital Image Computing: Techniques and Applications.  https://doi.org/10.1109/DICTA.2009.35
Shukla D, Patel E (2012) Speed determination of moving vehicles using Lucas-Kanade algorithm. Int J Comput Appl Technol Res 2:32–36. https://doi.org/10.7753/IJCATR0201.1007
doi: 10.7753/IJCATR0201.1007
Hino T, Tsunomori A, Fukumoto T, et al (2021) Vector-field dynamic x-ray (VF-DXR) using optical flow method. Br J Radiol. https://doi.org/10.1259/bjr.20201210
Vogelmeier CF, Criner GJ, Martinez FJ, et al (2017) Global Strategy for the diagnosis, management and prevention of chronic obstructive lung disease 2017 report: GOLD executive summary. Respirology 22:575–601. https://doi.org/10.1164/rccm.201701-0218PP
Sanchez J, Meinhardt-Llopis E, Facciolo G (2013) TV-L1 optical flow estimation. Image Processing On Line 3:137−150. https://doi.org/10.5201/ipol.2013.26
OpenCV - 3.4.9. (2019) Open Source Computer Vision Library https://opencv.org/releases/page/2/ Accessed 23 Dec 2019
Kanda Y (2013) Investigation of the freely available easy-to-use software ‘EZR’ for medical statistics. Bone Marrow Transplant 48:452–458. https://doi.org/10.1038/bmt.2012.244
doi: 10.1038/bmt.2012.244 pubmed: 23208313
Core Team R (2020) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna  https://www.R-project.org/ . Accessed 25 May 2020.
Teo PT, Crow R, Van Nest S et al (2013) Tracking lung tumour motion using a dynamically weighted optical flow algorithm and electronic portal imaging device. Meas Sci Technol 24:074012. https://doi.org/10.1088/0957-0233/24/7/074012
doi: 10.1088/0957-0233/24/7/074012
Liu T, Merat A, Makhmalbaf MHM, Fajardo C, Merati P (2015) Comparison between optical flow and cross-correlation methods for extraction of velocity fields from particle images. Exp Fluids 56:166. https://doi.org/10.1007/s00348-015-2036-1
doi: 10.1007/s00348-015-2036-1
Ichiji K, Yoshida Y, Homma N, et al (2018) A key-point based real-time tracking of lung tumor in x-ray image sequence by using difference of Gaussians filtering and optical flow. Phys Med Biol 63:185007. https://doi.org/10.1088/1361-6560/aada71
doi: 10.1088/1361-6560/aada71 pubmed: 30109995
Aliverti A, Quaranta M, Chakrabarti B, Albuquerque AL, Calverley PM (2009) Paradoxical movement of the lower ribcage at rest and during exercise in COPD patients. Eur Respir J 33:49–60. https://doi.org/10.1183/09031936.00141607
doi: 10.1183/09031936.00141607 pubmed: 18799505
Koyama H, Ohno Y, Fujisawa Y et al (2016) 3D lung motion assessments on inspiratory/expiratory thin-section CT: capability for pulmonary functional loss of smoking-related COPD in comparison with lung destruction and air trapping. Eur J Radiol 85:352–359. https://doi.org/10.1016/j.ejrad.2015.11.026
doi: 10.1016/j.ejrad.2015.11.026 pubmed: 26781140

Auteurs

Takuya Hino (T)

Center for Pulmonary Functional Imaging, Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA. thino@bwh.harvard.edu.

Akinori Tsunomori (A)

R&D Promotion Division, Healthcare Business Headquarters, Konica Minolta, Inc., 2970 Ishikawa-machi, Hachioji-shi, Tokyo, Japan.

Akinori Hata (A)

Center for Pulmonary Functional Imaging, Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA.
Department of Radiology, Graduate School of Medicine, Osaka University, 2-2 Yamadaoka, Suita, Osaka, Japan.

Tomoyuki Hida (T)

Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, Fukuoka, Japan.

Yoshitake Yamada (Y)

Department of Diagnostic Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, Japan.

Masako Ueyama (M)

Department of Health Care, Fukujuji Hospital, Japan Anti-Tuberculosis Association, 3-1-24 Matsuyama, Kiyose, Tokyo, Japan.

Tsutomu Yoneyama (T)

R&D Promotion Division, Healthcare Business Headquarters, Konica Minolta, Inc., 2970 Ishikawa-machi, Hachioji-shi, Tokyo, Japan.

Atsuko Kurosaki (A)

Department of Diagnostic Radiology, Fukujuji Hospital, Japan Anti-Tuberculosis Association, 3-1-24 Matsuyama, Kiyose, Tokyo, Japan.

Takeshi Kamitani (T)

Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, Fukuoka, Japan.

Kousei Ishigami (K)

Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, Fukuoka, Japan.

Takenori Fukumoto (T)

R&D Promotion Division, Healthcare Business Headquarters, Konica Minolta, Inc., 2970 Ishikawa-machi, Hachioji-shi, Tokyo, Japan.

Shoji Kudoh (S)

Japan Anti-Tuberculosis Association, 1-3-12 Kanda-Misakicho, Chiyoda-ku, Tokyo, Japan.

Hiroto Hatabu (H)

Center for Pulmonary Functional Imaging, Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA.

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