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3D blood vessels reconstruction based on segmented CT data for further simulations of hemodynamic in human artery branches

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
We aimed at the reconstruction of the branches of human aortic arch for blood perfusion analysis used later in the Computational Fluid Dynamic (CFD). The reconstruction was performed based on segmentation results obtained from CT data. Two segmentation algorithms, region growing and level set were implemented. Obtained binary segmentation results were next evaluated by the expert and corrected if needed. The final reconstruction was used for preparation of a numerical grid and for further calculation of blood hemodynamic. The collected data composed of blood velocity and blood flow rate in function of time were compared with USG-Doppler data. Results demonstrate that proposed algorithm may be useful for initial reconstruction of human cardiac system, however its accuracy needs to be improved as further manual corrections are still needed.
Rocznik
Strony
359--371
Opis fizyczny
Bibliogr. 22 poz., fig., tab.
Twórcy
  • Department of Heat and Mass Transfer, Lodz University of Technology, Wólczańska 213, 90-924 Łódź, Poland
  • Lodz University of Technology, Wólczańska 211/215, 90-924 Łódź, Poland
autor
  • Lodz University of Technology, Wólczańska 211/215, 90-924 Łódź, Poland
autor
  • Department of Diagnostic Imaging Medical University of Lodz, Kopcińskiego 22, 90-154 Łódź, Poland
  • Department of Diagnostic Imaging Medical University of Lodz, Kopcińskiego 22, 90-154 Łódź, Poland
Bibliografia
  • [1] Auer M., Gasser T.C., Reconstruction and finite element mesh generation of abdominal aortic aneurysms from computerized tomography angiography data with minimal user interactions, IEEE Transactions on Medical Imaging, 29, 2010, 1022-1028.
  • [2] Chan T.F., Vese L.A., Active Contours Without Edges, IEEE Transaction on Image Processing, 10, 2001, 266-277.
  • [3] Cloutier G., Zimmer A., Yu F.T., Chiasson J.L., Increased shear rate resistance and fastest kinetics of erythrocyte aggregation in diabetes measured with ultrasound, Diabetes care, 31, 2008, 1400-1402.
  • [4] Dasari P., Venkatesan B., Thyagarajan C., Balan S., Expectant and medical management of placenta increta in a primiparous woman presenting with postpartum haemorrhage: The role of Imaging, Journal of Radiology Case Reports, 4, 2010, 32-40.
  • [5] Frangi F., Niessen W.J., Vincken K.L., Viergever M.A., Multi-scale Vessel Enhancement Filtering, in: Proc. of MICCAI 1998, 1998, 130-137.
  • [6] Gijsen F.J., van de Vosse F.N., Janssen J.D., The influence of the non-Newtonian properties of blood on the flow in large arteries: steady flow in a carotid bifurcation model, Journal of Biomechanics, 32, 1999, 601-608.
  • [7] Gonzales R., Woods R., Digital Image Processing, Addison-Wesley, 1983.
  • [8] Hoi Y., Meng H., Woodward S.H., Bendok B.R., Hanel R.A., Guterman L.R., Hopkins L.N., Effects of arterial geometry on aneurysm growth: three-dimensional computational fluid dynamics study, Journal of Neurosurgery, 101, 2004, 676-681.
  • [9] Klepaczko A., Szczypiński P., Dwojakowski G., Strzelecki M., Materka A., Computer simulation of magnetic resonance angiography imaging: Model description and validation, PLoS ONE, 9, 2014, DOI: 10.1371/journal.pone.0093689.
  • [10] Lee J., Smith N.P., The multi-scale modeling of coronary blood flow, Ann Biomed Eng., 40, 2012, 2399-2413.
  • [11] Lesage D., Angelini E.D., Bloch I., Funka-Lea G., A review of 3D vessel lumen segmentation techniques: models, features and extraction schemes, Medical image analysis, 13, 2009, 819-845.
  • [12] Polanczyk A., Podyma M., Stefanczyk L., Szubert W., Zbicinski I., A 3D model of thrombus formation in a stent-graft after implantation in the abdominal aorta, Journal of Biomechanics, 48, 2015, 425-431.
  • [13] Polanczyk A., Podyma M., Stefanczyk L., Zbicinski I., Effects of stent-graft geometry and blood hematocrit on hemodynamic in Abdominal Aortic Aneurysm, Chemical and Process Engineering, 33, 2012, 53-61.
  • [14] Polanczyk A., Podyma M., Trebinski L., Chrzastek J., Zbicinski I., Stefanczyk L., A Novel Attempt to Standardize Results of CFD Simulations Basing on Spatial Configuration of Aortic Stent-Grafts, PloS ONE, 2016, http://dx.doi.org/10.1371/journal.pone.0153332.
  • [15] Sato Y., Nakajima S., Atsumi H., Koller T., Gerig G., Yoshida S., Kikinis R., 3D Multi-Scale Line Filter for Segmentation and Visualization of Curvilinear Structures in Medical Image, in: Proc. of CVRMed-MRCAS'97, 1997, Lecture Notes in Computer Science, 1205, 213-222.
  • [16] Strzelecki M., Szczypinski P., Materka A., Kocinski M., Sankowski A., Level-set segmentation of noisy 3D images of numerically simulated blood vessels and vascular trees, in: Proc. of 6th International Symposium on Image and Signal Processing and Analysis, 2009, 742-747.
  • [17] Tadeusiewicz R., Śmietański J., Acquisition of Medical Images and their Processing, Analysis, Automatic Recognition and Diagnostic Interpretation [in Polish: Pozyskiwanie obrazów medycznych oraz ich przetwarzanie, analiza, automatyczne rozpoznawanie i diagnostyczna interpretacja], Wydawnictwo STN, Kraków, 2011.
  • [18] Thierfelder J., Sommer K.M., Baumann W.H., Klotz A.B., Meinel E., Strobl F.G., Nikolaou F.F., Reiser K., von Baumgarten M.F., Whole-brain CT perfusion: reliability and reproducibility of volumetric perfusion deficit assessment in patients with acute ischemic stroke, Neuroradiology, 55, 2013, 827-835.
  • [19] Valencia A., Figueroa H., Rivera R., Bravo E., Sensitivity analysis of fluid structure interaction in a cerebral aneurysm model to wall thickness and elastic modulus, Advances and Applications in Fluid Mechanics, 12, 2012, 49-66.
  • [20] Waite L.F., Applied Biofluid Mechanics, McGraw-Hill Professional, New York, 2007.
  • [21] Woźniak T., Strzelecki M., Segmentation of 3D magnetic resonance brain vessel images based on level set approaches, in: Proc. of IEEE SPA 2015, 2015, 56-61.
  • [22] Woźniak T., Strzelecki M., Stefańczyk L., Majos A., 3D vascular tree segmentation using a multi scale vesselness function and a level set approach, Bio cybernetics and Biomedical Engineering, 37, 2017, 66-77.
Uwagi
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-414586ef-fc75-423c-8970-56e81e017a74
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