Evaluation of the Block Matching deformable registration algorithm in the field of head-and-neck adaptive radiotherapy

Huger, S.; Gräff, P.; Harter, Valentin; Marchesi, V.; Royer, P.; Diaz, Jean-Christophe; Aouadi, Souha; Peiffert, D.; Noël, Alain

doi:10.1016/j.ejmp.2013.09.001

Cited by 18 publications

(12 citation statements)

References 31 publications

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“…Approaches used to quantify the performance of distinct DIR in these studies include landmark identification (12), ROI-based comparison (3, 10, 13, 34, 35), or computational phantom deformation (8, 9, 36, 37); each of these methods have their specific caveats and limitations of application. In the examined setting of DxCT-SimCT co-registration, the application of an evenly and densely distributed matrix of anatomic landmark points is intuitively understandable, and, with sufficient point placement, exceptionally spatially accurate and statistically robust as a validation method (24).…”

Section: Discussionmentioning

confidence: 99%

“…DIR, as a tool for motion assessment/correction in tumors that move with respiration, as well as for adaptive re-contouring of target or anatomic volumes that alter over time, is becoming more widely utilized, as vendors integrate DIR solutions into commercial software packages (4–9). Additionally, emerging data suggest that, for head and neck cancers, DIR has demonstrable technical performance gain compared to rigid image registration (RIR) for adaptive radiotherapy, wherein a simulation CT dataset is co-registered with on-treatment CT or cone beam CT (10–13). …”

Section: Introductionmentioning

confidence: 99%

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Quality Assurance Assessment of Diagnostic and Radiation Therapy–Simulation CT Image Registration for Head and Neck Radiation Therapy: Anatomic Region of Interest–based Comparison of Rigid and Deformable Algorithms

Mohamed¹,

Ruangskul²,

Awan³

et al. 2015

Radiology

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Purpose To develop a quality assurance (QA) workflow using a robust, curated, manually-segmented anatomic ROI library as a benchmark for quantitative assessment of different image registration techniques used for head and neck radiation therapy-simulation CT (SimCT) to diagnostic CT (DxCT) co-registration. Materials and Methods SimCTs and DxCTs of twenty patients with head and neck squamous cell carcinoma treated with curative-intent intensity modulated radiotherapy (IMRT) between August 2011 and May 2012 were retrospectively retrieved under an institutional review board approval. 68 reference anatomic regions of interest (ROIs) in addition to gross tumor and nodal targets were then manually contoured on each scan. DxCT was registered to SimCT rigidly, and through 4 different deformable image registration (DIR) algorithms; Atlas-based, B-spline, demons, and optical flow. The resultant deformed ROIs were compared with manually contoured reference ROIs using similarity coefficient metrics (i.e. Dice similarity coefficient) and surface distance metrics (i.e. 95% maximum Hausdorff distance). Non-parametric Steel test with control was used to compare different DIR algorithms to rigid registration (RIR) with post hoc Wilcoxon rank test for stratified metric comparison. Results A total of 2720 anatomic and 50 tumor/nodal ROIs were delineated. All DIR algorithms showed improved performance over RIR for both anatomic and target ROIs conformance as shown for the majority of comparison metrics (Steel test, p-value <0.008 after Bonferroni correction). The performance of different algorithms varied substantially with stratification by specific anatomic structures/category, and SimCT image slice thickness. Conclusion Development of a formal ROI-based QA workflow for registration assessment revealed improved performance with DIR techniques over RIR. After QA, DIR implementation should be the standard for head and neck DxCT-SimCT allineation, especially for target delineation.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quality Assurance Assessment of Diagnostic and Radiation Therapy–Simulation CT Image Registration for Head and Neck Radiation Therapy: Anatomic Region of Interest–based Comparison of Rigid and Deformable Algorithms

Mohamed¹,

Ruangskul²,

Awan³

et al. 2015

Radiology

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“…However, the results revealed in this study (mean DSC ranging from 7.05 to 7.65) are comparable with other studies on CT-CBCT DIR. For example, Huger et al [23] reported a mean DSC of 7.0 for SMG on ten H & N patients based on a block matching DIR algorithm. With the similar DIR strategy, Veiga et al [24] obtained a mean DSC of 0.79 for normal tissues.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of deformable image registration for contour propagation between CT and cone-beam CT images in adaptive head and neck radiotherapy

Zhang

Shi

et al. 2016

THC

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Abstract. Deformable image registration (DIR) is a critical technic in adaptive radiotherapy (ART) to propagate contours between planning computerized tomography (CT) images and treatment CT/Cone-beam CT (CBCT) image to account for organ deformation for treatment re-planning. To validate the ability and accuracy of DIR algorithms in organ at risk (OAR) contours mapping, seven intensity-based DIR strategies are tested on the planning CT and weekly CBCT images from six Head & Neck cancer patients who underwent a 6 ∼ 7 weeks intensity-modulated radiation therapy (IMRT). Three similarity metrics, i.e. the Dice similarity coefficient (DSC), the percentage error (PE) and the Hausdorff distance (HD), are employed to measure the agreement between the propagated contours and the physician delineated ground truths. It is found that the performance of all the evaluated DIR algorithms declines as the treatment proceeds. No statistically significant performance difference is observed between different DIR algorithms (p > 0.05), except for the double force demons (DFD) which yields the worst result in terms of DSC and PE. For the metric HD, all the DIR algorithms behaved unsatisfactorily with no statistically significant performance difference (p = 0.273). These findings suggested that special care should be taken when utilizing the intensitybased DIR algorithms involved in this study to deform OAR contours between CT and CBCT, especially for those organs with low contrast.

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“…Beyond these checks, in Adaptive Radiation Therapy (ART) deformable transformation models should be taken into account to follow the organ motion and warping [5]. A daily contouring of organ at risk (OAR) and tumor might allow distributing the dose to the observed deformations [6].…”

Section: Introductionmentioning

confidence: 99%

A support vector machine tool for adaptive tomotherapy treatments: Prediction of head and neck patients criticalities

et al. 2015

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Evaluation of the Block Matching deformable registration algorithm in the field of head-and-neck adaptive radiotherapy

Cited by 18 publications

References 31 publications

Quality Assurance Assessment of Diagnostic and Radiation Therapy–Simulation CT Image Registration for Head and Neck Radiation Therapy: Anatomic Region of Interest–based Comparison of Rigid and Deformable Algorithms

Quality Assurance Assessment of Diagnostic and Radiation Therapy–Simulation CT Image Registration for Head and Neck Radiation Therapy: Anatomic Region of Interest–based Comparison of Rigid and Deformable Algorithms

Evaluation of deformable image registration for contour propagation between CT and cone-beam CT images in adaptive head and neck radiotherapy

A support vector machine tool for adaptive tomotherapy treatments: Prediction of head and neck patients criticalities

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