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Performance of commercially available deformable image registration platforms for contour propagation using patient‐based computational phantoms: A multi‐institutional study
Author(s) -
Loi Gianfranco,
Fusella Marco,
Lanzi Eleonora,
Cagni Elisabetta,
Garibaldi Cristina,
Iacoviello Giuseppina,
Lucio Francesco,
Menghi Enrico,
Miceli Roberto,
Orlandini Lucia C.,
Roggio Antonella,
Rosica Federica,
Stasi Michele,
Strigari Lidia,
Strolin Silvia,
Fiandra Christian
Publication year - 2018
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.12737
Subject(s) - image registration , medical imaging , artificial intelligence , voxel , dicom , nuclear medicine , computer science , medical physics , algorithm , medicine , image (mathematics)
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, USA), VelocityAI and Smart Adapt (Varian Medical Systems, Palo Alto, CA, USA), 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 intercenter 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 centers. 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 centers using the same hybrid registration algorithm.