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Naji, M. |
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Motta, Antonella |
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Aletan, Dirar |
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Mohamed, Tarek |
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Ertürk, Emre |
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Taccardi, Nicola |
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Kononenko, Denys |
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Petrov, R. H. | Madrid |
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Alshaaer, Mazen | Brussels |
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Bih, L. |
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Casati, R. |
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Muller, Hermance |
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Kočí, Jan | Prague |
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Šuljagić, Marija |
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Kalteremidou, Kalliopi-Artemi | Brussels |
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Azam, Siraj |
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Ospanova, Alyiya |
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Blanpain, Bart |
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Ali, M. A. |
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Popa, V. |
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Rančić, M. |
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Ollier, Nadège |
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Azevedo, Nuno Monteiro |
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Landes, Michael |
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Rignanese, Gian-Marco |
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Fisher, Kristen
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article
An MRI based workflow for prostate radiation therapy planning
Abstract
- Prostate radiation therapy dose planning is performed using computed tomography (CT) scan images which contain electron density information needed for patient dose calculations.- However magnetic resonance imaging (MRI) images have vastly superior soft-tissue contrast for visualising the prostate and determining the target volume for treatment. Currently MRI images can not be used for dose planning as they lack the electron density information needed. The objective of this work is to develop an alternative and efficient MRI-alone image based workflow enabling both organ delineation and dose planning to be performed using MRI images alone.- This paper describes the high precision MRI guided prostate radiotherapy collaborative project between the clinical and medical physics group at the Calvary Mater Newcastle Hospital/University of Newcastle and the biomedical image processing group at the CSIRO Australian e-Health Research Centre. This collaborative aims to develop the first feasible implementation of MRI-based prostate radiation therapy planning.- Translating to an MRI based workflow involves a number of significant research issues to be addressed, including (i) the development of software for data transfer between the clinical treatment planning system and research software platforms; (ii) methods to automatically segment organs of interest from MRI;(iii) tools to automatically assign electron density information to MR scans for radiotherapy dose calculations for treatment planning; and (iv) the automatic identification of fiducial markers from MR scans.