Peter Seevinck
Associate Professor at Umc Utrecht
Based in Utrecht, Netherlands
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Seniority
Staff
Department
Education
Location
Utrecht
Industry
Hospitals and Health Care
Company size
11K
Contact information
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p•••••••@umcutrecht.nl
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Background
About Peter Seevinck
My objective is the development and clinical introduction of MR imaging methods for minimally invasive and safe patient-specific interventions and workflows, by facilitating MRI guided device visualization, MRI-only radiotherapy treatment planning and MRI-based bone imaging, reducing radiation burden and workflow complexity. Founder and co-owner of MRIguidance B.V, a company that commercializes Bone MRI, the first medical imaging technique that visualizing both bone and soft tissue, without the need for hazardous radiation. Responsible as PI for several research projects: PI – 2017: STW Smart Industry grant (€ 1M) MIRROR (MRI for peRsonalized pRinting in Orthopedics) on the development of a patient specific approach for treatment planning and implant design enabling improved accuracy of reconstructive surgery. The additive manufacturing will be based on the BoneMRI technology developed by MRIguidance B.V. PI – 2016/2017: STW Take-off phase I (€40k) and phase II (€250k) grants on Bone MRI. This helps us to further develop our MRI-based bone imaging approach which makes x-ray/ CT superfluous and facilitates efficient, accurate and radiation-free diagnosis, treatment guidance and evaluation. Co-applicant – 2014: ZonMW grant (€1.2M) on MRI-only treatment planning entitled “RASOR sharp: MRI-based radiotherapy planning using a single MRI sequence”. Co-applicant – 2013: LSH Impulse grant (€1.2M) MIGRATE (Targeted treatment by MRI guidance to optimize regenerative therapy for ischemic heart disease), led by Steven Chamuleau of the Department of Cardiology of the UMCU. PI – 2011: personal grant (VENI, €250k) to further develop my ideas on MRI-guided interventional oncology, focusing device visualization for MR-guided radiotherapy. PI – 2010: STW OTP Single Point Imaging project an acceleration of metal object visualization using MRI, exploiting undersampling in combination with iterative reconstruction methods (Compressed Sensing).
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