Adaptive RN in HN cancerComparison of 12 deformable registration strategies in adaptive radiation therapy for the treatment of head and neck tumors☆,☆☆
Section snippets
Patients and image acquisition
The sets of images used were acquired in a previously reported clinical study [4]. Briefly, contrast-enhanced CT scans were acquired for 10 patients before the start of the treatment and during concomitant chemo-radiotherapy, after mean prescribed doses of 14, 25, 35 and 45 Gy. For all the acquisitions, patients were immobilized with a customized thermoplastic mask (Sinmed, Reuwijk, The Netherlands) fixed to a flat tabletop. Contrast-enhanced CT was performed on a spiral CT scanner (MX 8000 IDT,
Image registration accuracy
As illustrated in Fig. 3, all DR strategies significantly improved the DSI compared to the rigid-body registration (Kruskal–Wallis Z test, p < 0.05). On the other hand, 4 out of the 6 DR strategies (LS vs fLS, LSMRvs fLSMR, DMR-LS vs fDMR-LS and F3CV-LS vs fF3CV-LS) showed that using an edge-preserving filter before the registration improved the volume criteria. However, such improvement was very mild and not significant (Kruskal–Wallis Z test, p > 0.05). Comparison of the median values of DSI
Discussion
In this study, we attempted to compare different DR strategies within the framework of adaptive radiotherapy for the treatment of patients with Head and Neck tumors. The registrations were performed in “extreme” conditions, i.e. on images acquired after having delivered 36.8 Gy on average, and thus presenting a large variation in external and internal contours. In summary, fDMR-LS showed better and more consistent results than the other strategies; it was the best strategy according to
Conclusions
In this paper we presented a methodological study for comparing 12 DR strategies within the framework of adaptive radiotherapy for head and neck tumors. The comparisons were based on volume matching estimation and voxel-intensity correspondance. We demonstrated that fDMR-LS is the best compromise and is effective in deforming CT images of a same patient at different phases of his treatment course. Even though it is less robust in terms of voxel-intensity correlation, DMR seems to be an
Acknowledgements
The authors wish to gratefully thank Weiguo Lu, Kenneth J. Ruchala and Gustavo H. Olivera from Tomotherapy Inc. for sharing their knowledge, algorithms and software.
References (36)
- et al.
Quantification of volumetric and geometric changes occurring during fractionated radiotherapy for head-and-neck cancer using an integrated CT/linear accelerator system
Int J Radiat Oncol Biol Phys
(2004) - et al.
Impact of the type of imaging modality on target volumes delineation and dose distribution in pharyngo-laryngeal squamous cell carcinoma: comparison between pre- and per-treatment studies
Radiother Oncol
(2006) - et al.
Adaptive biological image-guided IMRT with anatomic and functional imaging in pharyngo-laryngeal tumors: impact on target volume delineation and dose distribution using helical tomotherapy
Radiother Oncol
(2007) - et al.
A survey of medical image registration
Med Image Anal
(1998) - et al.
Computational challenges for image-guided radiation therapy: framework and current research
Semin Radiat Oncol
(2007) 4-dimensional computed tomography imaging and treatment planning
Semin Radiat Oncol
(2004)- et al.
Tumour delineation and cumulative dose computation in radiotherapy based on deformable registration of respiratory correlated CT images of lung cancer patients
Radiother Oncol
(2007) - et al.
CT-based delineation of lymph node levels and related CTVs in the node-negative neck: DAHANCA, EORTC, GORTEC, NCIC,RTOG consensus guidelines
Radiother Oncol
(2003) - et al.
Use of deformed intensity distributions for on-line modification of image-guided IMRT to account for interfractional anatomic changes
Int J Radiat Oncol Biol Phys
(2005) Image matching as a diffusion process: an analogy with Maxwell’s demons
Med Image Anal
(1998)
Implementation and validation of a three-dimensional deformable registration algorithm for targeted prostate cancer radiotherapy
Int J Radiat Oncol Biol Phys
Multiresolution elastic matching
Comput Vis Graph Image Process
Image registration via level-set motion: applications to atlas-based segmentation
Med Image Anal
A new Hausdorff distance for image matching
Pattern Recognit Lett
Automatic delineation of on-line head-and-neck computed tomography images: toward on-line adaptive radiotherapy
Int J Radiat Oncol Biol Phys
Reduce in variation and improve efficiency of target volume delineation by a computer-assisted system using a deformable image registration approach
Int J Radiat Oncol Biol Phys
Four-dimensional image-based treatment planning: target volume segmentation and dose calculation in the presence of respiratory motion
Int J Radiat Oncol Biol Phys
Parotid gland dose in intensity-modulated radiotherapy for head and neck cancer: is what you plan what you get?
Int J Radiat Oncol Biol Phys
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Financial support: This work was supported by a grant from the Fonds National pour la Recherche Scientifique (FNRS) of Belgium (convention # 7.4583.07), by a grant from the Belgian Federation against Cancer (convention #SCIE 2003-23FR), by a grant from the “Cancéropôle du Nord-Ouest (France)”, by a grant from the Région wallonne of Belgium (convention PAINTER) and by the “Fonds J. Maisin” of the Université catholique de Louvain. John A. Lee is a Postdoctoral Researcher with the FNRS. The authors have no financial relationship with the organizations that sponsored the research.
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Statement: The authors have had full control of all primary data and agree to allow the journal to review their data if requested.
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These authors have equally contributed to this paper.