Adaptation of therapy

IMPACT Team: Image and models for the adaptation of therapy

Improvement of therapy delivery requires the accurate control of interactions between the implantable or treatment devices and the soft tissues and associated pathologies. This work is mainly conducted within the framework of Labex CAMI. The issues are related to observation-action coupling, with the integration and identification of patient-specific tools / tissues interaction models, and to human-machine coupling with the elaboration of operational solution for augmented decision.


Image-guided endovascular therapy


The development of endovascular aortic procedures is growing with constant innovations on implantable devices (e.g. fenestrated stentgrafts, bi-lateral aortic stents surrounded with biostable polymer, multilayer flow modulator devices, transcatheter aortic valves). These mini-invasive techniques allow a reduction of surgical trauma, usually important in conventional open surgery. The technical limitations of endovascular repair are pushed to special aortic localizations which were in the past decade indication for open repair. Success and efficiency of such procedures are also determined by the development of new decision support solutions. In this context our work aimed at improving endovascular procedures thanks to a better utilization of available information (pre- and intra-operative imaging, modeling).



Augmented endovascular navigation: a) comparison between the real intraoperative shape of the vascular structure deformed by guidewires and the deformed shape given by the simulation, highlighting the existence of folds on the wall in both cases, b) Intra-operative fluroscopic images augmented with the visualization of the deformed structure and the stent-graft


    Some recent publications:



      • - Kaladji A, Lucas A, Cardon A, Haigron P. Computer-Aided Surgery: Concepts and Applications in Vascular Surgery. Perspect Vasc Surg Endovasc Ther. 2012;24(1):23-7.
      • - Kaladji, A., Cardon, A., Abouliatim, I., Campillo-Gimenez, B., Heautot, J.F. and Verhoye, J.-P., Preoperative predictive factors of aneurysmal regression using the reporting standards for endovascular aortic aneurysm repair. Journal of Vascular Surgery. 2012;55(5):1287-1295.
      • - Kaladji A, Spear R, Hertault A, Sobocinski J, Maurel B, Haulon S. Centerline is not as accurate as outer curvature length to estimate thoracic endograft length. European Journal of Vascular and Endovascular Surgery. 2013;46(1):82-6.
      • - Kaladji, A., Dumenil, A., Castro, M., Cardon, A., Becquemin, J.P., Bou-Said, B., Lucas, A. and Haigron, P., Prediction of deformations during endovascular aortic aneurysm repair using finite element simulation. Comput Med Imaging Graph 37(2): p. 142-9, 2013.
      • - Kaladji A, Dumenil A, Castro M, Haigron P, Heautot JF, Haulon S. Endovascular aortic repair of a postdissecting thoracoabdominal aneurysm using intraoperative fusion imaging. Journal of vascular surgery. 2013;57(4):1109-12.
      • - Kaladji, A., Dumenil, A., Mahe, G., Castro, M., Cardon, A., Lucas, A. and Haigron, P., Safety and Accuracy of Endovascular Aneurysm Repair Without Pre-operative and Intra-operative Contrast Agent. Eur J Vasc Endovasc Surg: p. 255-61, 2015.
      • - Duménil A., Kaladji A., Castro M., Esneault S., Lucas A., Rochette M., Goksu C., Haigron P., "Finite-Element-Based Matching of Pre- and Intraoperative Data for Image-Guided Endovascular Aneurysm Repair", IEEE Transactions on Biomedical Engineering. 2013;(60)5:1353-1362.
      • - Gindre J, Bel-Brunon A, Kaladji A, Duménil A, Rochette M, Lucas A, Haigron P, Combescure A. Finite element simulation of the insertion of guidewires during an EVAR procedure: example of a complex patient case, a first step toward patient-specific parameterized models. Internal Journal for Numerical Methods in Biomedical Engineering. 2015; 31(7).
      • - Gindre, J., Bel-Brunon, A., Combescure, A., Haigron, P., Rochette, M. and Lucas, A., Estimation of clinically relevant indicators for EVAR using patient-specific finite element simulation. Comput Methods Biomech Biomed Engin: p. 1-2, 2015.
      • - El-Fakdi A, Gamero F, Melendez J, Auffret V, Haigron P. eXiTCDSS: A framework for a workflow-based CBR for interventional Clinical Decision Support Systems and its application to TAVI. Expert Systems with Applications. 2014;41(2):284-94.
      • - Vy P, Auffret V, Badel P, Rochette M, Le Breton H, Haigron P, Avril S. Review of patient-specific simulations of transcatheter aortic valve implantation. International Journal of Advances in Engineering Sciences and Applied Mathematics, Springer, 2015, pp.1-32.

        Image-guided radiation therapy


        External Beam Radiation Therapy (EBRT) is one of the major treatments for cancer, which may be also combined with chemotherapy (as concomitant and/or adjuvant treatment) and surgery (before or after radiation delivery). The main goal in radiotherapy is to deliver a high amount of dose to the tumor, while sparing the nearby organs at risk and surrounding healthy tissues thereby limiting side effects. The accuracy of the dose delivery systems (linear accelerators, LINAC) is increasingly improving and nowadays they are able to deliver highly concentrated and conformal dose to complex and sometimes moving targets in real time. Modern systems tend to use intraoperative images to guide the dose delivery, in order to adjust the dose plan to the real anatomy (Image Guided Radiotherapy, IGRT). One of the main issues we addressed in this context was related to dose monitoring from pertreatment CBCT imaging.

        Dose monitoring in deformable organs at risk. Estimation of the deviation between the dose planned from the pre-treatment CT and the dose cumulated from the non-rigid registration of per-treatment CBCT images.

          Some recent publications:


          • - Cazoulat, G., Simon, A., Dumenil, A., Gnep, K., de Crevoisier, R., Acosta-Tamayo, O. and Haigron, P., Surface-Constrained Nonrigid Registration for Dose Monitoring in Prostate Cancer Radiotherapy. IEEE Transactions on Medical Imaging. 2014;33(7): 1464-1474.
          • - Rigaud B, Simon A, Castelli J, Gobeli M, Ospina Arango JD, Cazoulat G, Henry O, Haigron P, de Crevoisier R. Evaluation of deformable image registration methods for dose monitoring in head and neck radiotherapy. BioMed Research International (previously Journal of Biomedicine and Biotechnology). 2015;Article ID:726268.
          • - Castelli, J., Simon, A., Louvel, G., Henry, O., Chajon, E., Nassef, M., Haigron, P., Cazoulat, G., Ospina, J., Jegoux, F., Benezery, K., de Crevoisier, R. Impact of head and neck cancer adaptive radiotherapy to spare the parotid glands and decrease the risk of xerostomia. Radiation Oncology 2015;10:A6.
          • - Zhang P, De Crevoisier R, Simon A, Haigron P, Coatrieux JL, Li B, Shu H. A new deconvolution approach to robust fluence for intensity modulation under geometrical uncertainty. Physics in Medicine and Biology. 2013;58(17):6095-110.
          • - Zhang P, Simon A, De Crevoisier R, Haigron P, Nassef MH, Li B, Shu H. A new pencil beam model for photon dose calculations in heterogeneous media. Physica Medica. 2014;30(7):765-773.
          • - Lafond C, Jouyaux F, Bellec J, Henry O, Perdrieux M, Chajon E, Le Prisé E, De Crevoisier R, Manens JP. Quelle RCMI ? De la technique « step and shoot » à la RCMI en arcthérapie : point de vue du physicien. Cancer Radiothérapie. 2010;14(6-7):539-49.
          • - Lafond C, Chajon E, Devillers A, Louvel G, Toublanc S, Olivier M, Simon A, de Crevoisier R, Manens JP. Impact of MLC leaf width on Volumetric Modulated Arc Therapy planning for head and neck cancers. Journal of Applied Clinical Medical Physics. 2013;14(6):40-52.
          • - Lafond C, Gassa F, Odin C, Drean G, Even J, De Crevoisier R, Pommier P, Manens JP, Biston MC. Comparison between Two Treatment Planning Systems for Volumetric Modulated Arc Therapy Optimization for Prostate Cancer. Physica Medica-European Journal of Medical Physics. 2014;30(1):2-9.