Computed Tomography to Cone Beam Computed Tomography Deformable Image Registration for Contour Propagation Using Head and Neck, Patient-Based Computational Phantoms: A Multicenter Study
| Autor: | Sebastiano Menna, F. Rosica, Gianfranco Loi, Christian Fiandra, A. Roggio, Francesco Lucio, Marco Fusella, Lorenzo Radici, Cristina Garibaldi, Marina Piovesan, Pierfrancesco Franco, Elisabetta Cagni, E. Menghi, N. Maffei, C. Vecchi, Alessandro Savini, Chiara Romanò, Eva Gino, S. Strolin, Lidia Strigari |
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| Rok vydání: | 2020 |
| Předmět: |
Cone beam computed tomography
Similarity (geometry) business.industry Image quality medicine.medical_treatment Image registration Cone-Beam Computed Tomography Tomotherapy 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Software Oncology Head and Neck Neoplasms 030220 oncology & carcinogenesis Medical imaging Humans Medicine Radiology Nuclear Medicine and imaging Computer vision Noise (video) Artificial intelligence Tomography X-Ray Computed business |
| Zdroj: | Practical Radiation Oncology. 10:125-132 |
| ISSN: | 1879-8500 |
| Popis: | Purpose To investigate the performance of various algorithms for deformable image registration (DIR) for propagating regions of interest (ROIs) using multiple commercial platforms, from computed tomography to cone beam computed tomography (CBCT) and megavoltage computed tomography. Methods and Materials Fourteen institutions participated in the study using 5 commercial platforms: RayStation (RaySearch Laboratories, Stockholm, Sweden), MIM (Cleveland, OH), VelocityAI and SmartAdapt (Varian Medical Systems, Palo Alto, CA), and ABAS (Elekta AB, Stockholm, Sweden). Algorithms were tested on synthetic images generated with the ImSimQA (Oncology Systems Limited, Shrewsbury, UK) package by applying 2 specific deformation vector fields (DVF) to real head and neck patient datasets. On-board images from 3 systems were used: megavoltage computed tomography from Tomotherapy and 2 kinds of CBCT from a clinical linear accelerator. Image quality of the system was evaluated. The algorithms’ accuracy was assessed by comparing the DIR-mapped ROIs returned by each center with those of the reference, using the Dice similarity coefficient and mean distance to conformity metrics. Statistical inference on the validation results was carried out to identify the prognostic factors of DIR performance. Results Analyzing 840 DIR-mapped ROIs returned by the centers, it was demonstrated that DVF intensity and image quality were significant prognostic factors of DIR performance. The accuracy of the propagated contours was generally high, and acceptable DIR performance can be obtained with lower-dose CBCT image protocols. Conclusions The performance of the systems proved to be image quality specific, depending on the DVF type and only partially on the platforms. All systems proved to be robust against image artifacts and noise, except the demon-based software. |
| Databáze: | OpenAIRE |
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