nach oben

International Journal of Computer Assisted Radiology and Surgery

Erschienen in:

03.08.2018 | Original Article

Extraction of open-state mitral valve geometry from CT volumes

verfasst von: Lennart Tautz, Mathias Neugebauer, Markus Hüllebrand, Katharina Vellguth, Franziska Degener, Simon Sündermann, Isaac Wamala, Leonid Goubergrits, Titus Kuehne, Volkmar Falk, Anja Hennemuth

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 11/2018

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Purpose

The importance of mitral valve therapies is rising due to an aging population. Visualization and quantification of the valve anatomy from image acquisitions is an essential component of surgical and interventional planning. The segmentation of the mitral valve from computed tomography (CT) acquisitions is challenging due to high variation in appearance and visibility across subjects. We present a novel semi-automatic approach to segment the open-state valve in 3D CT volumes that combines user-defined landmarks to an initial valve model which is automatically adapted to the image information, even if the image data provide only partial visibility of the valve.

Methods

Context information and automatic view initialization are derived from segmentation of the left heart lumina, which incorporates topological, shape and regional information. The valve model is initialized with user-defined landmarks in views generated from the context segmentation and then adapted to the image data in an active surface approach guided by landmarks derived from sheetness analysis. The resulting model is refined by user landmarks.

Results

For evaluation, three clinicians segmented the open valve in 10 CT volumes of patients with mitral valve insufficiency. Despite notable differences in landmark definition, the resulting valve meshes were overall similar in appearance, with a mean surface distance of \(1.62 \pm 2.10\) mm. Each volume could be segmented in 5–22 min.

Conclusions

Our approach enables an expert user to easily segment the open mitral valve in CT data, even when image noise or low contrast limits the visibility of the valve.

Vorheriger Artikel Long-term experience with setup and implementation of an IHE-based image management and distribution system in intersectoral clinical routine

Nächster Artikel Model checking for trigger loss detection during Doppler ultrasound-guided fetal cardiovascular MRI

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Nur mit Berechtigung zugänglich

Mozaffarian D, Benjamin EJ, Go AS, Arnett DK, Blaha MJ, Cushman M, Das SR, De Ferranti S, Desprs J-P, Fullerton HJ (2016) Heart disease and stroke statistics-2016 update: a report from the American heart association. Circulation 133(4):447–454CrossRefPubMed

Iung B, Vahanian A (2014) Epidemiology of acquired valvular heart disease. Can J Cardiol 30:962–970CrossRefPubMed

Beckmann A, Funkat A-K, Lewandowski J, Frie M, Ernst M, Hekmat K, Schiller W, Gummert J, Harringer W (2017) German heart surgery report 2016: the annual updated registry of the German society for thoracic and cardiovascular surgery. Thorac Cardiovasc Surg 65:505–518CrossRefPubMed

Klawki R, Schmidt K, Heinemann M (eds) (2016) Deutscher Herzbericht. Deutsche Herzstiftung (German)

Blausen.com staff (2014) Medical gallery of Blausen Medical 2014. WikiJ Med 1(2):5

De Bonis M, Al-Attar N, Antunes M, Borger M, Casselman F, Falk V, Folliguet T, Iung B, Lancellotti P, Lentini S, Maisano F, Messika-Zeitoun D, Muneretto C, Pibarot P, Pierard L, Punjabi P, Rosenhek R, Suwalski P, Vahanian A, Wendler O, Prendergast B (2016) Surgical and interventional management of mitral valve regurgitation: a position statement from the European Society of Cardiology Working Groups on cardiovascular surgery and valvular heart disease. Eur Heart J 37:133–139CrossRefPubMed

Kirişli HA, Schaap M, Klein S, Papadopoulou S-L, Bonardi M, Chen C-H, Weustink AC, Mollet NR, Vonken E-J, van der Geest RJ, van Walsum T, Niessen WJ (2010) Evaluation of a multi-atlas based method for segmentation of cardiac CTA data: a large-scale, multicenter, and multivendor study. Med Phys 37(12):6279–6291CrossRefPubMed

Cai K, Yang R, Chen H, Li L, Zhou J, Ou S, Liu F (2017) A framework combining window width-level adjustment and Gaussian filter-based multi-resolution for automatic whole heart segmentation. Neurocomputing 220:138–150CrossRef

Mortazi A, Burt J, Bagci U (2017) Multi-planar deep segmentation networks for cardiac substructures from MRI and CT. arXiv e-prints

10.

Larrey-Ruiz J, Morales-Snchez J, Bastida-Jumilla MC, Menchn-Lara RM, Verd-Monedero R, Sancho-Gmez JL (2014) Automatic image-based segmentation of the heart from CT scans. EURASIP J Image Video 2014:52CrossRef

11.

Schneider RJ, Perrin DP, Vasilyev NV, Marx GR, del Nido PJ, Howe RD (2010) Mitral annulus segmentation from 3D ultrasound using graph cuts. IEEE Trans Med Imaging 29:1676–1687CrossRefPubMedPubMedCentral

12.

Lassen B, van Rikxoort EM, Schmidt M, Kerkstra S, van Ginneken B, Kuhnigk J-M (2013) Automatic segmentation of the pulmonary lobes from chest CT scans based on fissures, vessels, and bronchi. IEEE Trans Med Imaging 32:210–222CrossRefPubMed

13.

Descoteaux M, Audette M, Chinzei K, Siddiqi K (2006) Bone enhancement filtering: application to sinus bone segmentation and simulation of pituitary surgery. Comput Aided Surg 11(5):247–255CrossRefPubMed

14.

Votta E, Le TB, Stevanella M, Fusini L, Caiani EG, Redaelli A, Sotiropoulos F (2013) Toward patient-specific simulations of cardiac valves: state-of-the-art and future directions. J Biomech 46:217–228CrossRefPubMed

15.

Ionasec RI, Voigt I, Georgescu B, Wang Y, Houle H, Vega-Higuera F, Navab N, Comaniciu D (2010) Patient-specific modeling and quantification of the aortic and mitral valves from 4-D cardiac CT and TEE. IEEE Trans Med Imaging 29:1636–1651CrossRefPubMed

16.

Grbic S, Ionasec R, Vitanovski D, Voigt I, Wang Y, Georgescu B, Navab N, Comaniciu D (2012) Complete valvular heart apparatus model from 4D cardiac CT. Med Image Anal 16:1003–1014CrossRefPubMed

17.

Mansi T, Voigt I, Georgescu B, Zheng X, Mengue EA, Hackl M, Ionasec RI, Noack T, Seeburger J, Comaniciu D (2012) An integrated framework for finite-element modeling of mitral valve biomechanics from medical images: application to MitralClip intervention planning. Med Image Anal 16:1330–1346CrossRefPubMed

18.

Grbic S, Easley TF, Mansi T, Bloodworth CH, Pierce EL, Voigt I, Neumann D, Krebs J, Yuh DD, Jensen MO (2014) Multi-modal validation framework of mitral valve geometry and functional computational models. STACOM 2014:239–248

19.

Grbic S, Easley TF, Mansi T, Bloodworth CH, Pierce EL, Voigt I, Neumann D, Krebs J, Yuh DD, Jensen MO, Comaniciu D, Yoganathan AP (2017) Personalized mitral valve closure computation and uncertainty analysis from 3D echocardiography. Med Image Anal 35:238–249CrossRefPubMed

20.

Blanke P, Dvir D, Cheung A, Levine RA, Thompson C, Webb JG, Leipsic J (2015) Mitral annular evaluation with CT in the context of transcatheter mitral valve replacement. JACC Cardiovasc Imaging 8:612–615CrossRefPubMed

21.

Wang Q, Sun W (2013) Finite element modeling of mitral valve dynamic deformation using patient-specific multi-slices computed tomography scans. Ann Biomed Eng 41:142–153CrossRefPubMed

22.

Toma M, Jensen M, Einstein DR, Yoganathan AP, Cochran RP, Kunzelman KS (2016) Fluid-structure interaction analysis of papillary muscle forces using a comprehensive mitral valve model with 3D chordal structure. Ann Biomed Eng 44:942–953CrossRefPubMed

23.

Khalighi AH, Drach A, Bloodworth CH, Pierce EL, Yoganathan AP, Gorman RC, Gorman JH, Sacks MS (2017) Mitral valve chordae tendineae: topological and geometrical characterization. Ann Biomed Eng 45:378–393CrossRefPubMed

24.

Drach A, Gorman RC, Gorman JH, Sacks MS (2017) Multi-resolution geometric modeling of the mitral heart valve leaflets. Biomech Model Mechanobiol 17(2):351–366PubMed

25.

Gosnell J, Pietila T, Samuel BP, Kurup HKN, Haw MP, Vettukattil JJ (2016) Integration of computed tomography and three-dimensional echocardiography for hybrid three-dimensional printing in congenital heart disease. J Digit Imaging 29:665–669CrossRefPubMedPubMedCentral

26.

Vukicevic M, Puperi DS, Jane Grande-Allen K, Little SH (2017) 3D printed modeling of the mitral valve for catheter-based structural interventions. Ann Biomed Eng 45:508–519CrossRefPubMed

27.

Votta E, Caiani E, Veronesi F, Soncini M, Montevecchi FM, Redaelli A (2008) Mitral valve finite-element modelling from ultrasound data: a pilot study for a new approach to understand mitral function and clinical scenarios. Philos Trans A Math Phys Eng Sci 366:3411–3434CrossRefPubMed

28.

Pouch AM, Xu C, Yushkevich PA, Jassar AS, Vergnat M, Gorman JH, Gorman RC, Sehgal CM, Jackson BM (2012) Semi-automated mitral valve morphometry and computational stress analysis using 3D ultrasound. J Biomech 45:903–907CrossRefPubMedPubMedCentral

29.

Pouch AM, Wang H, Takabe M, Jackson B, Gorman J, Gorman R, Yushkevich P, Sehgal C (2014) Fully automatic segmentation of the mitral leaflets in 3D transesophageal echocardiographic images using multi-atlas joint label fusion and deformable medial modeling. Med Image Anal 18:118–129CrossRefPubMed

30.

Schneider RJ, Perrin DP, Vasilyev NV, Marx GR, del Nido PJ, Howe RD (2012) Mitral annulus segmentation from four-dimensional ultrasound using a valve state predictor and constrained optical flow. Med Image Anal 16:497–504CrossRefPubMed

31.

Graser B, Wald D, Al-Maisary S, Grossgasteiger M, de Simone R, Meinzer H-P, Wolf I (2013) Using a shape prior for robust modeling of the mitral annulus on 4D ultrasound data. Int J Comput Assist Radiol Surg 9(4):635–644

32.

Wenk JF, Zhang Z, Cheng G, Malhotra D, Acevedo-Bolton G, Burger M, Suzuki T, Saloner DA, Wallace AW, Guccione JM (2010) First finite element model of the left ventricle with mitral valve: insights into ischemic mitral regurgitation. Ann Thorac Surg 89(5):1546–1553CrossRefPubMedPubMedCentral

33.

Stevanella M, Maffessanti F, Conti CA, Votta E, Arnoldi A, Lombardi M, Parodi O, Caiani EG, Redaelli A (2011) Mitral valve patient-specific finite element modeling from cardiac MRI: application to an annuloplasty procedure. Cardiovasc Eng Technol 2:66–76CrossRef

34.

Mosaliganti K, Gelas A, Cowgill P, Megason S (2009) An optimized N-dimensional hough filter for detecting spherical image objects. Insight J

35.

Lassen B, Kuhnigk J-M, Schmidt M, Krass S, Peitgen H-O (2011) Lung and lung lobe segmentation methods at Fraunhofer MEVIS. In: Fourth international workshop on pulmonary image analysis, vol 2011, pp 185–200

36.

Antiga L, Piccinelli M, Botti L, Ene-Iordache B, Remuzzi A, Steinman DA (2008) An image-based modeling framework for patient-specific computational hemodynamics. Med Biol Eng Comput 46:1097CrossRefPubMed

37.

Ritter F, Boskamp T, Homeyer A, Laue H, Schwier M, Link F, Peitgen H (2011) Medical image analysis. IEEE Pulse 2:60–70CrossRefPubMed

38.

Wolak A, Gransar H, Thomson LE, Friedman JD, Hachamovitch R, Gutstein A, Shaw LJ, Polk D, Wong ND, Saouaf R, Hayes SW, Rozanski A, Slomka PJ, Germano G, Berman DS (2008) Aortic size assessment by noncontrast cardiac computed tomography: normal limits by age, gender, and body surface area. JACC Cardiovasc Imaging 1:200–209CrossRefPubMed

39.

Klues HG, Proschan MA, Dollar AL, Spirito P, Roberts WC, Maron BJ (1993) Echocardiographic assessment of mitral valve size in obstructive hypertrophic cardiomyopathy. Anatomic validation from mitral valve specimen. Circulation 88(2):548–555CrossRefPubMed

40.

Kunzelman K, Cochran R, Verrier E, Eberhart R (1994) Anatomic basis for mitral valve modelling. J Heart Valve Dis 3(5):491–496PubMed

Titel: Extraction of open-state mitral valve geometry from CT volumes
verfasst von: Lennart Tautz
Mathias Neugebauer
Markus Hüllebrand
Katharina Vellguth
Franziska Degener
Simon Sündermann
Isaac Wamala
Leonid Goubergrits
Titus Kuehne
Volkmar Falk
Anja Hennemuth
Publikationsdatum: 03.08.2018
Verlag: Springer International Publishing
Erschienen in: International Journal of Computer Assisted Radiology and Surgery / Ausgabe 11/2018
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-018-1831-6

Springer Professional

Abstract

Purpose

Methods

Results

Conclusions

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 11/2018

AR in VR: assessing surgical augmented reality visualizations in a steerable virtual reality environment

Autonomous neuro-registration for robot-based neurosurgery

Semiautomatic neck curve reconstruction for intracranial aneurysm rupture risk assessment based on morphological parameters

A patient-specific haptic drilling simulator based on virtual reality for dental implant surgery

Model checking for trigger loss detection during Doppler ultrasound-guided fetal cardiovascular MRI

Accuracy of computer-aided design models of the jaws produced using ultra-low MDCT doses and ASIR and MBIR

Premium Partner