Chet Ford

Chet Ford, Ph.D., serves as Clinical Associate Professor in the Department of Radiation Oncology at the University of Miami, where he integrates clinical medical physics practice with translational research in MRI-guided radiation therapy. Board-certified by the American Board of Radiology in Therapeutic Radiological Physics, his expertise bridges nuclear magnetic resonance physics, clinical oncology, and medical imaging innovation.

His educational foundation includes:

• PhD in Physics, University of Connecticut (1988)
• MS in Physics, University of Connecticut (1983)
• BS in Physics, Virginia Commonwealth University (1981)
• MBA, Drexel University (2003)

Postdoctoral training at the University of Pennsylvania (1990) and Virginia Commonwealth University (2010) established his specialization in MRI physics and clinical radiation oncology. His research pioneers the application of quantitative MRI to model tumor physiology and predict radiation response, leveraging daily imaging from hybrid MRI/radiotherapy systems like ViewRay. Current projects focus on machine learning-driven radiomic analysis of prostate cancer and glioblastoma evolution during treatment, with emphasis on adaptive therapy optimization.

Recent publications (2023-2025) reveal a cohesive trajectory in exploiting low-field MR-LINAC technology for real-time treatment adaptation. Key themes include radiomic feature stability assessment, deep learning segmentation for tumor tracking, and multi-parametric MRI analysis to decode treatment resistance mechanisms—primarily in prostate cancer and glioblastoma contexts.

Award highlights include:

• NIH Shannon Award
• Association of University Radiologists Stauffer Award
• Doctoral Dissertation Fellowship Award
• Sigma Pi Sigma Physics Honor Society recognition

Dr. Ford mentors physics residents, a biomedical engineering graduate student, and postdoctoral researchers in MRI device development. His grant portfolio features an NIH Shared Instrumentation Grant as Principal Investigator and EPA funding for an MR-based water monitoring device, demonstrating cross-disciplinary innovation. Laboratory efforts center on building small-animal MRI platforms and establishing clinical data registries for ViewRay treatment optimization.