P
Peter R. Seevinck
Researcher at Utrecht University
Publications - 87
Citations - 2598
Peter R. Seevinck is an academic researcher from Utrecht University. The author has contributed to research in topics: Imaging phantom & Medicine. The author has an hindex of 23, co-authored 76 publications receiving 1982 citations. Previous affiliations of Peter R. Seevinck include University Medical Center Utrecht.
Papers
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Book ChapterDOI
Deep MR to CT synthesis using unpaired data
Jelmer M. Wolterink,Anna M. Dinkla,Mark H. F. Savenije,Peter R. Seevinck,Cornelis A. T. van den Berg,Ivana Išgum +5 more
TL;DR: This work proposes to train a generative adversarial network (GAN) with unpaired MR and CT images to synthesize CT images that closely approximate reference CT images, and was able to outperform a GAN model trained with paired MR andCT images.
Journal ArticleDOI
Superparamagnetic iron oxide nanoparticles encapsulated in biodegradable thermosensitive polymeric micelles: toward a targeted nanomedicine suitable for image-guided drug delivery.
Marina Talelli,Cristianne J.F. Rijcken,Twan Lammers,Peter R. Seevinck,Gert Storm,Cornelus F. van Nostrum,Wim E. Hennink +6 more
TL;DR: The ability of biodegradable thermosensitive polymeric micelles to stably encapsulate hydrophobic oleic-acid-coated SPIONs (diameter 5-10 nm) was investigated, to result in a system fulfilling the requirements for systemic administration.
Journal ArticleDOI
Dose evaluation of fast synthetic-CT generation using a generative adversarial network for general pelvis MR-only radiotherapy
Matteo Maspero,Mark H. F. Savenije,Anna M. Dinkla,Peter R. Seevinck,Martijn Intven,Ina M. Jürgenliemk-Schulz,Linda G W Kerkmeijer,Cornelis A. T. van den Berg +7 more
TL;DR: Accurate MR-based dose calculation using sCT images generated with a cGAN trained on prostate cancer patients is feasible for the entire pelvis, and the sCT generation was sufficiently fast for integration in an MR-guided radiotherapy workflow.
Journal ArticleDOI
Dose evaluation of fast synthetic-CT generation using a generative adversarial network for general pelvis MR-only radiotherapy
Matteo Maspero,Mark H. F. Savenije,Anna M. Dinkla,Peter R. Seevinck,Martijn Intven,Ina M. Jürgenliemk-Schulz,Linda G W Kerkmeijer,Cornelis A. T. van den Berg +7 more
TL;DR: In this article, a conditional generative adversarial network (cGAN) was trained on 2D transverse slices of 32 prostate cancer patients to generate sCT images for accurate MR-based dose calculations in the entire pelvis.
Journal ArticleDOI
MR-Only Brain Radiation Therapy: Dosimetric Evaluation of Synthetic CTs Generated by a Dilated Convolutional Neural Network
Anna M. Dinkla,Jelmer M. Wolterink,Matteo Maspero,Mark H. F. Savenije,Joost J.C. Verhoeff,Enrica Seravalli,Ivana Išgum,Peter R. Seevinck,Cornelis A. T. van den Berg +8 more
TL;DR: D dose calculations performed on the synthetic computed tomography images generated with a dilated convolutional neural network are accurate and can therefore be used for MR-only intracranial radiation therapy treatment planning, according to Dosimetric evaluation.