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Treatment plan library based on population shape analysis for cervical adaptive radiotherapy
Author(s): Bastien Rigaud; Antoine Simon; Maxime Gobeli; Julie Leseur; Danièle Williaume; Oscar Acosta; Pascal Haigron; Renaud de Crevoisier
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Paper Abstract

External radiotherapy is extensively used to treat cervix carcinoma. It is based on the acquisition of a planning CT scan on which the treatment is optimized before being delivered over 25 fractions. However, large pertreatment anatomical variations, hamper the dose delivery accuracy, with a risk of tumor under-dose and healthy organs over-dose resulting to recurrence and toxicity. We propose to generate a patient-specific treatment library based on a population analysis. First, the cervix meshes of the population were registered towards a template anatomy using a deformable mesh registration (DMR). The DMR follows an iterative point matching approach based on the local shape context (histogram of cylindrical neighbor coordinates and normalized geodesic distance to the cervix base), a topology constraint filter, a thin-plate-spline interpolation and a Gaussian regularization. Second, a standard principal component analysis (PCA) model was generated to estimate the dominant deformation modes of the population. Posterior PCA was computed to generate different potential anatomies of the target. For a new patient, her cervix was registered towards the template and her pre-treatment library was modeled. This method was applied on the data of 19 patients (282 images), using a leave-one-patient-out. The DMR was evaluated using point-to-point distance (mean: 1.3 mm), Hausdorff distance (5.7 mm), dice coeffi- cient (0.96) and mean triangle area difference (0.49 mm2 ). The performances of two modeled libraries (2 and 6 modeled anatomies) were compared to a classic pre-treatment library based on 3 planning CTs, showing better results according to both target and healthy organs coverage.

Paper Details

Date Published: 13 March 2018
PDF: 7 pages
Proc. SPIE 10576, Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling, 105762J (13 March 2018); doi: 10.1117/12.2289581
Show Author Affiliations
Bastien Rigaud, Univ. de Rennes 1 (France)
INSERM (France)
Antoine Simon, Univ. de Rennes 1 (France)
INSERM (France)
Maxime Gobeli, Ctr. Eugène Marquis (France)
Julie Leseur, Ctr. Eugène Marquis (France)
Danièle Williaume, Ctr. Eugène Marquis (France)
Oscar Acosta, Univ. de Rennes 1 (France)
INSERM (France)
Pascal Haigron, Univ. de Rennes 1 (France)
INSERM (France)
Renaud de Crevoisier, Univ. de Rennes 1 (France)
INSERM (France)
Ctr. Eugène Marquis (France)


Published in SPIE Proceedings Vol. 10576:
Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling
Baowei Fei; Robert J. Webster, Editor(s)

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