R. Poel
Paul Scherrer Institute
16 Papers
5 Citations
R. Poel is an academic researcher from Paul Scherrer Institute. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 3, co-authored 5 publications. Previous affiliations of R. Poel include University of Zurich.
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Papers
Early results and volumetric analysis after spot-scanning proton therapy with concomitant hyperthermia in large inoperable sacral chordomas.
Sebastien Tran,Emsad Puric,M. Walser,R. Poel,Niloy R. Datta,Juerg Heuberger,Alessia Pica,Dietmar Marder,Nicoletta Lomax,Alessandra Bolsi,Petra Morach,Barbara Bachtiary,Beatrice Seddon,Ralf Schneider,Stephan Bodis,Damien C. Weber,Damien C. Weber,Damien C. Weber +17 more
TL;DR: Combining PT and HT in large inoperable sacral chordomas is feasible and causes acceptable toxicity, and volumetric analysis shows promising early results, warranting confirmation in the framework of a prospective trial.
Feasibility of postoperative spine stereotactic body radiation therapy in proximity of carbon and titanium hybrid implants using a robotic radiotherapy device
D. Henzen,Daniel Schmidhalter,G. Guyer,A. Stenger-Weisser,Ekin Ermiş,R. Poel,M. Deml,Michael K. Fix,Peter Manser,Daniel M. Aebersold,Hossein Hemmatazad +10 more
TL;DR: In this paper , the authors evaluated the feasibility of postoperative stereotactic body radiation therapy (SBRT) for patients with hybrid implants consisting of carbon fiber reinforced polyetheretherketone and titanium (CFP-T) using CyberKnife.
Impact of random outliers in auto-segmented targets on radiotherapy treatment plans for glioblastoma
R. Poel,E. Rüfenacht,Ekin Ermiş,Michael Müller,Michael K. Fix,Daniel M. Aebersold,Peter Manser,Mauricio Reyes +7 more
TL;DR: In this paper , a set of controlled experiments on synthetically generated outliers on the CT of a glioblastoma (GBM) patient was performed, and the dosimetric impact on outliers with different location, shape, absolute size and relative size to the main target was analyzed.
How Sensitive Are Deep Learning Based Radiotherapy Dose Prediction Models To Variability In Organs At Risk Segmentation?
Amith J. Kamath,R. Poel,J. S. Willmann,Nicolaus Andratschke,Mauricio Reyes +4 more
- 18 Apr 2023
TL;DR: Encouraging results show the potential of employing a Cascaded 3D UNet deep neural network for dose prediction in brain tumors within a broader automated quality assurance system in the radiotherapy planning workflow.
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PyRaDiSe: A Python package for DICOM-RT-based auto-segmentation pipeline construction and DICOM-RT data conversion
E. Rüfenacht,Amith J. Kamath,Yannick Suter,R. Poel,Ekin Ermiş,Stefan Scheib,Mauricio Reyes +6 more
- 01 Jan 2023
TL;DR: PyRaDiSe as mentioned in this paper is an open-source, deep learning framework independent Python package, which provides a framework for building auto-segmentation solutions feasible to operate directly on DICOM data.