Mark Bangert

Mark Bangert is a researcher in the Department of Medical Physics in Radiation Oncology at the German Cancer Research Center (DKFZ) in Heidelberg, Germany. His work focuses on optimizing radiation therapy treatments through advanced computational methods and physics principles, particularly for complex cases involving critical organs at risk.

Dr. Bangert completed his PhD at Heidelberg University in 2011 with a dissertation titled 'New concepts for beam angle selection in IMRT treatment planning: From heuristics to combinatorial optimization.' His doctoral work was supervised by Prof. Uwe Oelfke and Prof. Wolfgang Schlegel, established experts in medical physics and radiation oncology.

His research spans radiation therapy physics, intensity-modulated radiation therapy (IMRT), beam angle optimization, proton therapy, and treatment planning. Dr. Bangert pioneered methods for selecting optimal beam angles in IMRT, demonstrating that noncoplanar beam configurations could significantly reduce radiation doses to organs at risk while maintaining target coverage. His early work established the importance of integrating fluence optimization into beam angle selection.

Analysis of his publication history reveals an evolution from traditional IMRT optimization to sophisticated multi-modality treatment approaches combining photons with particle therapies (protons, carbon ions, and helium). His recent work emphasizes probabilistic modeling to address range uncertainties in particle therapy, analytical dose calculation methods, and the integration of biological optimization into treatment planning. He has made significant contributions to open-source software development for radiation therapy planning education.

Dr. Bangert has played a key role in developing matRad, an open-source multi-modality treatment planning toolkit designed for educational purposes. This software provides accessible tools for students and researchers to explore radiation therapy planning concepts without commercial constraints. His work bridges theoretical optimization methods with practical clinical applications, improving treatment precision while minimizing damage to healthy tissues.