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dr. ir. P.R. Seevinck

Associate Professor

  • Image Sciences Institute

  1. Contact
  2. Research Programs
  3. Biography
  4. Research Output
dr. ir. P.R. Seevinck

Contact

E

Research Programs

Biography

Peter Seevinck graduated Biomedical Engineering at the Eindhoven University of Technology in 2005. In 2009, he received his PhD in Medical Imaging at Utrecht University after defending his thesis entitled “Multimodal imaging of holmium-loaded microspheres for internal radiation therapy”.

Currently Peter is appointed Associate Professor (UHD) at the Image Sciences Institute in the Imaging Division of UMC Utrecht. He acts as a coordinator and lecturer in several courses in the field of MRI. His research is focusing on the development and clinical introduction of novel MR imaging methods and advanced machine learning algorithms for minimally invasive and safe personalized image-guided diagnosis and therapy. Examples of these novel approaches include MRI-guided device visualization for cardiac stem cell therapy and brachytherapy, MRI-only radiotherapy treatment planning for more efficient and shorter treatment workup and MRI-based bone imaging (BoneMRI), reducing radiation burden and workflow complexity. He has >50 publications in this field, and has been awarded several (personal) scientific and valorisation grants including VENI (interventional MRI), IMDI ZonMw (MRI-based radiotherapy planning), TTW Take-off phase I and II (commercialization of BoneMRI) and TTW Smart Industry (Deep learning-based image synthesis for orthopedics), NWO KIEM (BoneMRI in the shoulder) and Eurostars (additive manufacturing for orthopedics).

Peter Seevinck is co-founder of MRIguidance B.V., a company that commercializes BoneMRI, the first medical imaging technique that visualizes both bone and soft tissue, without the need for hazardous radiation. BoneMRI renders MRI a one-stop-shop workflow both for diagnostic/prognostic purposes as well as for innovative surgical purposes (e.g. pre-operative planning, navigation, virtual/augmented reality and robotics). For the patient such a one-stop-shop approach could lead to less radiological examinations, less hospital visits and a lower radiation burden.  “With the CE marking of BoneMRI in 2019 an important milestone has been reached, as this opens the door towards widespread usage for the benefit of the patient”.

Research Output (75)

Magnetic resonance imaging-based synthetic computed tomography of the lumbar spine for surgical planning:a clinical proof-of-concept

Staartjes Victor E, Seevinck Peter R, Vandertop W Peter, van Stralen Marijn, Schröder Marc L jan 2021, In: Neurosurgical Focus [E]. 50 , p. E13

Deep learning-enabled MRI-only photon and proton therapy treatment planning for paediatric abdominal tumours

Florkow Mateusz C, Guerreiro Filipa, Zijlstra Frank, Seravalli Enrica, Janssens Geert O, Maduro John H, Knopf Antje C, Castelein René M, van Stralen Marijn, Raaymakers Bas W, Seevinck Peter R 7 okt 2020, In: Radiotherapy & Oncology. 153 , p. 220-227 8 p.

Deep learning-based MR-to-CT synthesis:The influence of varying gradient echo-based MR images as input channels

Florkow Mateusz C, Zijlstra Frank, Willemsen Koen, Maspero Matteo, van den Berg Cornelis A T, Kerkmeijer Linda G W, Castelein René M, Weinans Harrie, Viergever Max A, van Stralen Marijn, Seevinck Peter R apr 2020, In: Magnetic Resonance in Medicine. 83 , p. 1429-1441 13 p.

Visualization of gold fiducial markers in the prostate using phase-cycled bSSFP imaging for MRI-only radiotherapy

Shcherbakova Yulia, Bartels Lambertus Wilbert, Mandija Stefano, Beld Ellis, Seevinck Peter R, van der Voort van Zyp Jochem R N, Kerkmeijer Linda G W, Moonen Chrit T W, Lagendijk Jan J W, Van den Berg Cornelis A T 11 sep 2019, In: Physics in Medicine and Biology. 64 , p. 185001 1 p.

Dosimetric evaluation of synthetic CT for head and neck radiotherapy generated by a patch-based three-dimensional convolutional neural network

Dinkla Anna M, Florkow Mateusz C, Maspero Matteo, Savenije Mark H F, Zijlstra Frank, Doornaert Patricia A H, van Stralen Marijn, Philippens Marielle E P, van den Berg Cornelis A T, Seevinck Peter R sep 2019, In: Medical Physics. 46 , p. 4095-4104 10 p.

SMART tracking: Simultaneous anatomical imaging and real-time passive device tracking for MR-guided interventions

Zijlstra Frank, Viergever Max A., Seevinck Peter R. 1 aug 2019, In: Physica medica. 64 , p. 252-260 9 p.

Reducing distortions in echo-planar breast imaging at ultrahigh field with high-resolution off-resonance maps

van Rijssel Michael J, Zijlstra Frank, Seevinck Peter R, Luijten Peter R, Gilhuijs Kenneth G A, Klomp Dennis W J, Pluim Josien P W jul 2019, In: Magnetic Resonance in Medicine. 82 , p. 425-435 11 p.

MRI artifact simulation for clinically relevant MRI sequences for guidance of prostate HDR brachytherapy

Beld Ellis, Moerland Marinus A., van Zyp Jochem R. N. van der Voort, Viergever Max A., Lagendijk Jan J. W., Seevinck Peter R. 26 apr 2019, In: Physics in Medicine and Biology. 64 , p. 095006

GANs covert CBCT to CT for head-neck, lung and breast: paired vs unpaired; single-site vs generic

Maspero M., Savenije M. H. F., Van Heijst T. C. F., Kotte A. N. T. J., Houweling A. C., Verhoeff J. J. C., Seevinck P. R., Van den Berg C. A. T. apr 2019, In: Radiotherapy and Oncology. 133 , p. S1105-S1106

Synthetic CT generation for Head and Neck radiotherapy by a 3D convolutional neural network

Dinkla A., Florkow M., Maspero M., Savenije M., Zijlstra F., Doornaert P., Van Stralen M., Philippens M., Seevinck P., Van den Berg N. apr 2019, In: Radiotherapy and Oncology. 133 , p. S268-S269

All Research Output (75)