J. Webster Stayman

J. Webster Stayman is an Associate Professor in both the Department of Biomedical Engineering and the Department of Electrical & Computer Engineering at Johns Hopkins University. He also serves as Co-Director of the Biomedical Engineering Master's Program and leads the Advanced Imaging Algorithms and Instrumentation Lab. His research bridges engineering principles with medical imaging applications, focusing on developing advanced imaging systems and algorithms that optimize diagnostic outcomes while minimizing radiation exposure.

Dr. Stayman received his PhD in Electrical Engineering from the University of Michigan in 2003, following an MS in Electrical Engineering from the same institution in 1998. He completed his undergraduate education with a BS in Computer & Systems Engineering from Rensselaer Polytechnic Institute in 1995.

His research focuses on medical imaging systems modeling, design, and optimization, particularly for x-ray CT, cone-beam CT, and phase-contrast CT. A key aspect of his work involves integrating patient-, task-, and device-specific information into imaging workflows to optimize image quality for specific clinical tasks. He applies signal processing, estimation theory, and optimization techniques to develop sophisticated image reconstruction algorithms that can produce high-quality images from low-fidelity or sparse data.

Analysis of his recent publications (2023-2025) reveals a strong trend toward incorporating advanced machine learning techniques, particularly diffusion models, into medical imaging. His work spans material decomposition, artifact reduction, spectral CT, and optimization of imaging systems. Many papers focus on improving CT reconstruction through novel algorithms that leverage deep learning while maintaining physical accuracy of the imaging process. His research shows increasing emphasis on quantitative imaging, uncertainty quantification, and developing tools that bridge the gap between machine learning approaches and traditional model-based reconstruction.

Dr. Stayman's research has been supported by significant NIH funding, including a $2.6 million grant in 2015 to develop improved CT imaging hardware and software for patient-specific, low-dose CT scans. During the pandemic, he adapted his "Build an Imager" course for virtual delivery, demonstrating innovation in both research and education. He has made notable contributions to 3D printing of patient-specific phantoms for CT validation studies, advancing the field's ability to assess image quality and algorithm performance.