Performance of commercially available deformable image registration platforms for contour propagation using patient-based computational phantoms: a multi-institutional study.

Performance of commercially available deformable image registration platforms for contour propagation using patient-based computational phantoms: a multi-institutional study.

Med Phys. 2017 Dec 20;:

Authors: Loi G, Fusella M, Lanzi E, Cagni E, Garibaldi C, Iacoviello G, Lucio F, Menghi E, Miceli R, Orlandini LC, Roggio A, Rosica F, Stasi M, Strigari L, Strolin S, Fiandra C

Abstract
PURPOSE: To investigate the performance of various algorithms for deformable image registration (DIR) to propagate regions of interest (ROIs) using multiple commercial platforms.
METHODS AND MATERIALS: Thirteen institutions participated in the study with six commercial platforms: RayStation (RaySearch Laboratories, Stockholm, Sweden), MIM (Cleveland, OH), VelocityAI and Smart Adapt (Varian Medical Systems, Palo Alto, CA), Mirada XD (Mirada Medical Ltd, Oxford, UK) and ABAS (Elekta AB, Stockholm, Sweden). The DIR algorithms were tested on synthetic images generated with the ImSimQA package (Oncology Systems Limited, Shrewsbury, UK) by applying two specific Deformation Vector Fields (DVF) to real patient data-sets. Head-and-neck (HN), thorax and pelvis sites were included. The accuracy of the algorithms was assessed by comparing the DIR-mapped ROIs from each center with those of reference, using the Dice Similarity Coefficient (DSC) and Mean Distance to Conformity (MDC) metrics. Statistical inference on validation results was carried out in order to identify the prognostic factors of DIR performances.
RESULTS: DVF intensity, anatomic site and participating center were significant prognostic factors of DIR performances. Sub-voxel accuracy was obtained in the HN by all algorithms. Large errors, with MDC ranging up to 6 mm, were observed in low-contrast regions that underwent significant deformation, such as in the pelvis, or large DVF with strong contrast, such as the clinical tumor volume (CTV) in the lung. Under these conditions, the hybrid DIR algorithms performed significantly better than the free-form intensity based algorithms and resulted robust against inter-center variability.
CONCLUSIONS: The performances of the systems proved to be site specific, depending on the DVF type and the platforms and the procedures used at the various centres. The pelvis was the most challenging site for most of the algorithms, which failed to achieve sub-voxel accuracy. Improved reproducibility was observed among the centres using the same hybrid registration algorithm. This article is protected by copyright. All rights reserved.

PMID: 29266262 [PubMed - as supplied by publisher]

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