H. Michael Gach, PhD

Professor of Radiation Oncology


  • BS, Electrical Engineering: University of Florida, Gainesville, FL (1982)
  • ME, Nuclear Engineering Sciences: University of Florida, Gainesville, FL (1983)
  • MS, Physics: University of Pittsburgh, Pittsburgh, PA (1993)
  • PhD, Physics: University of Pittsburgh, Pittsburgh, PA (1998)


H. Michael Gach, PhD, is a professor of radiation oncology. He joined the faculty in 2015 from the University of Pittsburgh MR Research Center (MRRC) and the University of Pittsburgh Cancer Institute (UPCI). He earned a PhD in Physics from the University of Pittsburgh in 1998 where his research included particle physics and magnetic resonance imaging (MRI). His research and academic interests include MRI physics, pulse sequence development and image processing, MRI-guided radiotherapy and hyperthermia, quantitative hemodynamics, and MRI-based attenuation correction.


Dr. Gach is an R01 funded investigator. His primary clinical and research interests are the development and application of MRI techniques in image-guided radiotherapy. These activities include MRI-guided radiotherapy using the ViewRay MR-RT, MRI simulation, 4D MRI, motion compensation, metal artifact reduction, MR calculated attenuation (MRCAT), and QA. He is also interested in the use of imaging to quantify hemodynamics including perfusion, blood flow, and diffusion. He developed and integrated respiratory motion prediction for minimizing subtraction errors related to free-breathing arterial spin labeled (ASL) acquisitions. Dr. Gach performed preclinical research using gold nanoparticles, carbon nanotubes, gadolinium nanogels, and perfluorocarbon nanoemulsions. He led a team that functionalized nanoparticles using targeting ligands like folate for theranostic applications (e.g., targeted imaging and drug delivery). He investigated localized effects associated with photodynamic therapy (PDT) and RF hyperthermia. He is a co-investigator on the Cardiovascular Health Study Cognition Study that analyzes structural and perfusion magnetic resonance imaging (MRI) data, clinical data, and measures of beta amyloid from plasma from all of its participants (N=195). His team developed automatic region of interest analysis techniques to quantify perfusion and structural changes in regions of the brain associated with AD pathogenesis.


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