Quantification of Gaps in Ablation Lesions Around the Pulmonary Veins in Delayed Enhancement MRI

  • Marta Nuñez GarciaEmail author
  • Catalina Tobon-Gomez
  • Kawal Rhode
  • Bart Bijnens
  • Oscar Camara
  • Constantine Butakoff
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9126)


We propose a method for measuring the quality of pulmonary vein isolation in delayed enhancement MRI images for the patients that underwent atrial radiofrequency ablation. To that end we construct a graph from an anatomy independent representation of the atrium, where every node represents a scar lesion and the edges are the distances between the lesions. Subsequently we search for the shortest path in this graph. The total amount of gap between the scar lesions is measured as the fraction of the path’s length that passes through the healthy tissue. We illustrate the proposed technique using pre-segmented atria from a freely available database and show that the proposed approach is able to measure the amount of gaps in the scar isolating the PV’s as well as provide a meaningful definition of the gap in cases where the scar lesions are patchy and not continuous.


Radiofrequency ablation Left atrium Gaps Repeated ablation Graphs Shortest path 



This study was partially supported by the EU FP7 for research, technological development and demonstration under grant agreement VP2HF (no 611823) and by the Spanish Ministry of Science and Innovation (TIN2011-28067).


  1. 1.
    Calkins, H., Kuck, K.H., Cappato, R., Brugada, J., John Camm, A., et al.: HRS/EHRA/ECAS expert consensus statement on catheter and surgical ablation of atrial fibrillation: recommendations for patient selection, procedural techniques, patient management and follow-up, definitions, endpoints, and research trial design. J. Interv. Card. Electrophysiol. 33(2012), 171–257 (2012)CrossRefGoogle Scholar
  2. 2.
    Weerasooriya, R., Khairy, P., Litalien, J., Macle, L., Hocini, M., Sacher, F., Lellouche, N., Knecht, S., Wright, M., Nault, I., et al.: Catheter ablation for atrial fibrillation: are results maintained at 5 years of follow-up? J. Am. Coll. Cardiol. 57(2), 160–166 (2011)CrossRefGoogle Scholar
  3. 3.
    Peters, D.C., Wylie, J.V., Hauser, T.H., Nezafat, R., Han, Y., Woo, J.J., Taclas, J., Kissinger, K.V., Goddu, B., Josephson, M.E., et al.: Recurrence of atrial fibrillation correlates with the extent of post-procedural late gadolinium enhancement: a pilot study. JACC: Cardiovasc. Imaging 2(3), 308–316 (2009)Google Scholar
  4. 4.
    Bisbal, F., Guiu, E., Cabanas-Grandío, P., Berruezo, A., Prat-Gonzalez, S., Vidal, B., Garrido, C., Andreu, D., Fernandez-Armenta, J., Tolosana, J.M., et al.: CMR-guided approach to localize and ablate gaps in repeat af ablation procedure. JACC: Cardiovasc. Imaging 7(7), 653–663 (2014)Google Scholar
  5. 5.
    Harrison, J.L., Sohns, C., Linton, N.W., Karim, R., Williams, S.E., Rhode, K.S., Gill, J., Cooklin, M., Rinaldi, C.A., Wright, M., et al.: Repeat left atrial catheter ablation: cardiac magnetic resonance prediction of endocardial voltage and gaps in ablation lesion sets. Circ.: Arrhythm. Electrophysiol. 8(2), 270–278 (2015). CIRCEP-114Google Scholar
  6. 6.
    Nazarian, S., Beinart, R.: CMR-guided targeting of gaps after initial pulmonary vein isolation. JACC: Cardiovasc. Imaging 7(7), 664–666 (2014)Google Scholar
  7. 7.
    Tobon-Gomez, C., Zuluaga, M.A., Chubb, H., Williams, S.E., Butakoff, C., Karim, R., Camara, O., Ourselin, S., Rhode, K.: Standardised unfold map of the left atrium: regional definition for multimodal image analysis. J. Cardiovasc. Magn. Reson. 17(1), 1–3 (2015)CrossRefGoogle Scholar
  8. 8.
    Comprehensive arrhythmia research management center. University of Utah Health Sciences, January 2015.
  9. 9.
    Perry, D., Morris, A., Burgon, N., McGann, C., MacLeod, R., Cates, J.: Automatic classification of scar tissue in late gadolinium enhancement cardiac MRI for the assessment of left-atrial wall injury after radiofrequency ablation. In: SPIE Medical Imaging, pp. 83151D–83151D. International Society for Optics and Photonics (2012)Google Scholar
  10. 10.
    Slicer cardiac MRI extension for atrial fibrillation research and management comprehensive arrhythmia research and management center, January 2015.
  11. 11.
    3DSlicer, January 2015.
  12. 12.
    Fedorov, A., Beichel, R., Kalpathy-Cramer, J., Finet, J., Fillion-Robin, J.C., Pujol, S., Bauer, C., Jennings, D., Fennessy, F., Sonka, M., Buatti, J., Aylward, S., Miller, J.V., Pieper, S., Kikinis, R.: 3D slicer as an image computing platform for the quantitative imaging network. Magn. Reson. Imaging 30, 1323–1341 (2012)CrossRefGoogle Scholar
  13. 13.
    Lorensen, W.E., Cline, H.E.: Marching cubes: a high resolution 3D surface construction algorithm (1987)Google Scholar
  14. 14.
    Durrleman, S., Prastawa, M., Charon, N., Korenberg, J.R., Joshi, S., Gerig, G., Trouvé, A.: Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101, 35–49 (2014)CrossRefGoogle Scholar
  15. 15.
    Deformetrica, January 2015.
  16. 16.
    Graphite, January 2015.
  17. 17.
    Dijkstra, E.W.: A note on two problems in connexion with graphs. Numerische Mathematik 1, 269–271 (1959)zbMATHMathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Marta Nuñez Garcia
    • 1
    Email author
  • Catalina Tobon-Gomez
    • 2
  • Kawal Rhode
    • 2
  • Bart Bijnens
    • 1
    • 3
  • Oscar Camara
    • 1
  • Constantine Butakoff
    • 1
  1. 1.PhySense, DTICUniversitat Pompeu FabraBarcelonaSpain
  2. 2.Division of Imaging Sciences and Biomedical EngineeringKings College LondonLondonUK
  3. 3.ICREABarcelonaSpain

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