Skip to content
2000
Volume 21, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
file format pdf download PDF
side by side viewer icon HTML

Abstract

Objective

This study aimed to compare automated three-dimensional Intrapulmonary Vessel Volume (IPVV) differences between lung and mediastinal windows in healthy individuals using quantitative measurements obtained from chest Computed Tomography (CT) plain scans.

Methods

A total of 258 participants (aged 21–83 years) with negative chest CT scans from routine physical examinations conducted between January to November 2023 were retrospectively enrolled. For each healthy participant, an algorithm was used to automatically extract total lung IPVVs as well as IPVVs for vessels of specific diameter. Differences in IPVVs were then compared between those extracted using the lung window and those extracted using the mediastinal window.

Results

The IPVVs for the entire lung, intrapulmonary arteries, intrapulmonary veins, and small pulmonary vessels (categorized by different diameters) extracted from the lung window were significantly higher than those extracted from the mediastinal window (<0.01). No significant sex-based differences in IPVV were observed for pulmonary arteries and veins with diameters between 0.8 and 1.6 mm, as well as pulmonary veins with diameters between 2.4 and 3.2 mm. However, in pulmonary arteries and veins with diameters between 1.6 and 2.4 mm, females had significantly higher IPVVs than males. In all other cases, IPVVs were larger in males than in females.

Conclusion

This method of automatic IPVV extraction and quantitative assessment has been proven to be feasible. Automated IPVV expression effectively identified morphological characteristics of intrapulmonary vessels. The study has concluded IPVVs extracted from the lung window to be generally larger than those extracted from the mediastinal window.

© 2025 The Author(s). Published by Bentham Science Publishers.
This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Loading

Article metrics loading...

/content/journals/cmir/10.2174/0115734056354924241115102310
2025-01-24
2025-07-29

  • From This Site

    /content/journals/cmir/10.2174/0115734056354924241115102310
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
The full text of this item is not currently available.

References

  1. PiennM. BurgardC. PayerC. AvianA. UrschlerM. StollbergerR. OlschewskiA. OlschewskiH. JohnsonT. MeinelF.G. BálintZ. Healthy lung vessel morphology derived from thoracic computed tomography.Front. Physiol.2018934610.3389/fphys.2018.0034629755360
     [Google Scholar]
  2. AaronC.P. HoffmanE.A. LimaJ.A.C. KawutS.M. BertoniA.G. Vogel-ClaussenJ. HabibiM. HueperK. JacobsD.R.Jr KalhanR. MichosE.D. PostW.S. PrinceM.R. SmithB.M. Ambale-VenkateshB. LiuC.Y. ZemrakF. WatsonK.E. BudoffM. BluemkeD.A. BarrR.G. Pulmonary vascular volume, impaired left ventricular filling and dyspnea: The MESA Lung Study.PLoS One2017124e017618010.1371/journal.pone.017618028426728
     [Google Scholar]
  3. HuangW YenRT McLaurineM BledsoeG Morphometry of the human pulmonary vasculature.J Appl Physiol1996815212333
     [Google Scholar]
  4. HorsfieldK. GordonW.I. Morphometry of pulmonary veins in man.Lung1981159121121810.1007/BF027139177289655
     [Google Scholar]
  5. SinghalS. HendersonR. HorsfieldK. HardingK. CummingG. Morphometry of the human pulmonary arterial tree.Circ. Res.197333219019710.1161/01.RES.33.2.1904727370
     [Google Scholar]
  6. SluimerI. SchilhamA. ProkopM. van GinnekenB. Computer analysis of computed tomography scans of the lung: A survey.IEEE Trans. Med. Imaging200625438540510.1109/TMI.2005.86275316608056
     [Google Scholar]
  7. NardelliP. Jimenez-CarreteroD. Bermejo-PelaezD. WashkoG.R. RahaghiF.N. Ledesma-CarbayoM.J. San Jose EsteparR. Pulmonary artery-vein classification in CT images using deep learning.IEEE Trans. Med. Imaging201837112428244010.1109/TMI.2018.283338529993996
     [Google Scholar]
  8. RestenA. MaitreS. MussetD. CT imaging of peripheral pulmonary vessel disease.Eur. Radiol.200515102045205610.1007/s00330‑005‑2740‑y15906039
     [Google Scholar]
  9. MaranoR. PirroF. SilvestriV. MerlinoB. SavinoG. RutiglianoC. MeduriA. NataleL. BonomoL. Comprehensive CT cardiothoracic imaging: A new challenge for chest imaging.Chest2015147253855110.1378/chest.14‑140325644907
     [Google Scholar]
  10. SatoY. NakajimaS. ShiragaN. AtsumiH. YoshidaS. KollerT. GerigG. KikinisR. Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images.Med. Image Anal.19982214316810.1016/S1361‑8415(98)80009‑110646760
     [Google Scholar]
  11. FrangiA.F. NiessenW.J. HoogeveenR.M. van WalsumT. ViergeverM.A. Model-based quantitation of 3-D magnetic resonance angiographic images.IEEE Trans. Med. Imaging1999181094695610.1109/42.81127910628954
     [Google Scholar]
  12. LesageD. AngeliniE.D. BlochI. Funka-LeaG. A review of 3D vessel lumen segmentation techniques: Models, features and extraction schemes.Med. Image Anal.200913681984510.1016/j.media.2009.07.01119818675
     [Google Scholar]
  13. ShahinY. AlabedS. AlkhanfarD. TschirrenJ. RothmanA.M.K. CondliffeR. WildJ.M. KielyD.G. SwiftA.J. Quantitative CT evaluation of small pulmonary vessels has functional and prognostic value in pulmonary hypertension.Radiology2022305243144010.1148/radiol.21048235819325
     [Google Scholar]
  14. van RikxoortE.M. van GinnekenB. Automated segmentation of pulmonary structures in thoracic computed tomography scans: A review.Phys. Med. Biol.20135817R187R22010.1088/0031‑9155/58/17/R18723956328
     [Google Scholar]
  15. SunX. MengX. ZhangP. WangL. RenY. XuG. YangT. LiuM. Quantification of pulmonary vessel volumes on low-dose computed tomography in a healthy male Chinese population: The effects of aging and smoking.Quant. Imaging Med. Surg.202212140641610.21037/qims‑21‑16034993089
     [Google Scholar]
  16. AlkhanfarD. ShahinY. AlandejaniF. DwivediK. AlabedS. JohnsC. LawrieA. ThompsonA.A.R. RothmanA.M.K. TschirrenJ. UthoffJ.M. HoffmanE. CondliffeR. WildJ.M. KielyD.G. SwiftA.J. Severe pulmonary hypertension associated with lung disease is characterised by a loss of small pulmonary vessels on quantitative computed tomography.ERJ Open Res.20228200503-202110.1183/23120541.00503‑202135586449
     [Google Scholar]
  17. MelzigC. WörzS. EgenlaufB. PartoviS. RohrK. GrünigE. KauczorH.U. HeusselC.P. RengierF. Combined automated 3D volumetry by pulmonary CT angiography and echocardiography for detection of pulmonary hypertension.Eur. Radiol.201929116059606810.1007/s00330‑019‑06188‑730963276
     [Google Scholar]
  18. HuangX. YinW. ShenM. WangX. RenT. WangL. LiuM. GuoY. Contributions of emphysema and functional small airway disease on intrapulmonary vascular volume in COPD.Int. J. Chron. Obstruct. Pulmon. Dis.2022171951196110.2147/COPD.S36897436045693
     [Google Scholar]
  19. ChoY.H. LeeS.M. SeoJ.B. KimN. BaeJ.P. LeeJ.S. OhY.M. Do-LeeS. Quantitative assessment of pulmonary vascular alterations in chronic obstructive lung disease: Associations with pulmonary function test and survival in the KOLD cohort.Eur. J. Radiol.201810827628210.1016/j.ejrad.2018.09.01330396668
     [Google Scholar]
  20. LinsM. VandevenneJ. ThillaiM. LavonB.R. LanclusM. BonteS. GodonR. KendallI. De BackerJ. De BackerW. Assessment of small pulmonary blood vessels in COVID-19 patients using HRCT.Acad. Radiol.202027101449145510.1016/j.acra.2020.07.01932741657
     [Google Scholar]
  21. DierckxW De BackerW LinsM CT-derived measurements of pulmonary blood volume in small vessels and the need for supplemental oxygen in COVID-19 patients.J Appl Physiol2022133612959
     [Google Scholar]
  22. RatanawatkulP. OhA. RichardsJ.C. SwigrisJ.J. Performance of pulmonary artery dimensions measured on high-resolution computed tomography scan for identifying pulmonary hypertension.ERJ Open Res.20206100232-201910.1183/23120541.00232‑201932055634
     [Google Scholar]
  23. KarazincirS. BalciA. SeyfeliE. AkoğluS. BabayiğitC. AkgülF. YalçinF. EğilmezE. CT assessment of main pulmonary artery diameter.Diagn. Interv. Radiol.2008142727418553279
     [Google Scholar]
  24. Du BoisD. Du BoisE.F. A formula to estimate the approximate surface area if height and weight be known. 1916.Nutrition1989553033112520314
     [Google Scholar]
  25. PayerC. PiennM. BálintZ. ShekhovtsovA. TalakicE. NagyE. OlschewskiA. OlschewskiH. UrschlerM. Automated integer programming based separation of arteries and veins from thoracic CT images.Med. Image Anal.20163410912210.1016/j.media.2016.05.00227189777
     [Google Scholar]
  26. LuZ. LongF. HeX. Classification and segmentation algorithm in benign and malignant pulmonary nodules under different CT reconstruction.Comput. Math. Methods Med.202220221610.1155/2022/349046335495882
     [Google Scholar]
  27. BakS.H. LeeH.Y. KimJ.H. UmS.W. KwonO.J. HanJ. KimH.K. KimJ. LeeK.S. Quantitative CT scanning analysis of pure ground-glass opacity nodules predicts further CT scanning change.Chest2016149118019110.1378/chest.15‑003426313232
     [Google Scholar]
  28. YaoG. Value of window technique in diagnosis of the ground glass opacities in patients with non-small cell pulmonary cancer.Oncol. Lett.20161253933393510.3892/ol.2016.513327895751
     [Google Scholar]
  29. GrillsI.S. FitchD.L. GoldsteinN.S. YanD. ChmielewskiG.W. WelshR.J. KestinL.L. Clinicopathologic analysis of microscopic extension in lung adenocarcinoma: Defining clinical target volume for radiotherapy.Int. J. Radiat. Oncol. Biol. Phys.200769233434110.1016/j.ijrobp.2007.03.02317570609
     [Google Scholar]
  30. LuH. KimJ. QiJ. LiQ. LiuY. SchabathM.B. YeZ. GilliesR.J. BalagurunathanY. Multi-window CT based radiological traits for improving early detection in lung cancer screening.Cancer Manag. Res.202012122251223810.2147/CMAR.S24660933273859
     [Google Scholar]
  31. MatsuokaS. WashkoG.R. DransfieldM.T. YamashiroT. San Jose EsteparR. DiazA. SilvermanE.K. PatzS. HatabuH. Quantitative CT measurement of cross-sectional area of small pulmonary vessel in COPD: correlations with emphysema and airflow limitation.Acad. Radiol.2010171939910.1016/j.acra.2009.07.02219796970
     [Google Scholar]
  32. TakayanagiS. KawataN. TadaY. IkariJ. MatsuuraY. MatsuokaS. MatsushitaS. YanagawaN. KasaharaY. TatsumiK. Longitudinal changes in structural abnormalities using MDCT in COPD: Do the CT measurements of airway wall thickness and small pulmonary vessels change in parallel with emphysematous progression?Int. J. Chron. Obstruct. Pulmon. Dis.20171255156010.2147/COPD.S12140528243075
     [Google Scholar]
  33. MatsuokaS. WashkoG.R. YamashiroT. EsteparR.S.J. DiazA. SilvermanE.K. HoffmanE. FesslerH.E. CrinerG.J. MarchettiN. ScharfS.M. MartinezF.J. ReillyJ.J. HatabuH. Pulmonary hypertension and computed tomography measurement of small pulmonary vessels in severe emphysema.Am. J. Respir. Crit. Care Med.2010181321822510.1164/rccm.200908‑1189OC19875683
     [Google Scholar]
  34. JacobJ. BartholmaiB.J. RajagopalanS. van MoorselC.H.M. van EsH.W. van BeekF.T. StruikM.H.L. KokosiM. EgashiraR. BrunA.L. NairA. WalshS.L.F. CrossG. BarnettJ. de LauretisA. JudgeE.P. DesaiS. KarwoskiR. OurselinS. RenzoniE. MaherT.M. AltmannA. WellsA.U. Predicting outcomes in idiopathic pulmonary fibrosis using automated computed tomographic analysis.Am. J. Respir. Crit. Care Med.2018198676777610.1164/rccm.201711‑2174OC29684284
     [Google Scholar]
  35. KauffmannC. TangA. TherasseÉ. GirouxM.F. ElkouriS. MelansonP. MelansonB. OlivaV.L. SoulezG. Measurements and detection of abdominal aortic aneurysm growth: Accuracy and reproducibility of a segmentation software.Eur. J. Radiol.20128181688169410.1016/j.ejrad.2011.04.04421601403
     [Google Scholar]
  36. KontopodisN. MetaxaE. PapaharilaouY. GeorgakarakosE. TsetisD. IoannouC.V. Value of volume measurements in evaluating abdominal aortic aneurysms growth rate and need for surgical treatment.Eur. J. Radiol.20148371051105610.1016/j.ejrad.2014.03.01824768189
     [Google Scholar]
  37. RengierF. WörzS. MelzigC. LeyS. FinkC. BenjaminN. PartoviS. von Tengg-KobligkH. RohrK. KauczorH.U. GrünigE. Automated 3D volumetry of the pulmonary arteries based on magnetic resonance angiography has potential for predicting pulmonary hypertension.PLoS One2016119e016251610.1371/journal.pone.016251627626802
     [Google Scholar]
  38. DwivediK. SharkeyM. CondliffeR. UthoffJ.M. AlabedS. MetherallP. LuH. WildJ.M. HoffmanE.A. SwiftA.J. KielyD.G. Pulmonary hypertension in association with lung disease: Quantitative CT and artificial intelligence to the rescue? State-of-the-Art rev.Diagnostics202111467910.3390/diagnostics1104067933918838
     [Google Scholar]
/content/journals/cmir/10.2174/0115734056354924241115102310
Loading
Automated 3D Quantitative Analysis of Intrapulmonary Vessel Volume on Non-contrast CT in Healthy Individuals
Curr. Med. Imaging 21, e15734056354924 (2025); https://doi.org/10.2174/0115734056354924241115102310
/content/journals/cmir/10.2174/0115734056354924241115102310
/content/journals/cmir/10.2174/0115734056354924241115102310
Loading

Data & Media loading...

Supplements

Supplementary material is available on the Publisher’s website.

file format download Download

  • Article Type:     Research Article
Keyword(s): Blood vessels; Computed tomography; Lung; Quantitative analysis; Three-dimensional; Window technique

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test