SUMMARY
Andy D. Missert, Ph.D., focuses his research on applying artificial intelligence (AI) and advanced computational methods to improve the quality, safety and efficiency of computerized tomography (CT) imaging. Trained as an experimental physicist, he leverages expertise in large‑scale data analysis, image reconstruction and AI to address clinical challenges in radiology.
Dr. Missert develops deep-learning tools that reduce image noise, sharpen fine anatomical detail and combine information from multiple CT image types into a single, more informative series. He also creates AI applications for 3D vessel mapping and organ segmentation to support vascular, oncologic and interventional procedures. Working closely with radiologists, technologists and informatics teams, he leads efforts to translate these algorithms into rigorously validated clinical software. Most recently, he has been deeply involved in developing agentic AI applications based on multimodal foundation models to support the radiology practice. AI tools he has authored are now integrated across Mayo Clinic and have significantly advanced patient care and operational efficiency.
Focus areas
- AI-based CT image noise and artifact reduction. Dr. Missert designs deep-learning methods that remove noise from CT images while preserving subtle anatomical structures. These tools enable lower radiation dose imaging and improve visualization in demanding clinical scenarios that require both low noise and high spatial resolution.
- Semantic segmentation and quantitative imaging. Dr. Missert develops AI applications for 3D semantic segmentation and quantitative measurement of complex vascular structures. These tools are designed to provide consistent measurements and maps that can be used for treatment planning, follow-up and clinical research.
- Agentic AI and operational efficiency. Dr. Missert leads a team of software engineers and data scientists who design, develop and implement agentic AI solutions that leverage multimodal foundation models. These innovations aim to transform radiology practice and enhance the quality and efficiency of patient care.
- Translational AI and workflow integration in radiology. Through the Framework for AI Software Technology Program, Dr. Missert leads projects that bring AI from concept to bedside. This includes software design, validation and deployment within the radiology workflow. His work also addresses operational and governance aspects of implementing AI across a large radiology practice.
Significance to patient care
Dr. Missert's research aims to make CT scans safer, clearer and more useful for patients and their care teams. By reducing image noise, his AI tools can maintain image quality even at lower radiation doses. This is especially important for people who need many scans over time. His methods to sharpen images and remove distortions help healthcare professionals see small tumors, blood vessels and other important structures more clearly.
By turning these tools into carefully tested clinical software, his work helps radiologists get consistent, high‑quality images on every scanner. It also supports complex procedures, such as image‑guided tumor treatments, and ensures that measurements are reliable from one scan to the next. These improvements lead to more-confident diagnoses, better treatment planning and more-personalized care.
Professional highlights
- Member, International Society for Computed Tomography, 2021-present.
- Mayo Clinic:
- Member, Enterprise Radiology Artificial Intelligence Subcommittee, Enterprise Radiology Analytics Committee, 2020-present.
- Member, Subcommittee Admin Group, Enterprise Radiology Analytics Committee, 2020-present.
- Co‑inventor, U.S. Patent: "Systems and Methods for Multi‑Kernel Synthesis and Kernel Conversion in Medical Imaging," Mayo Foundation for Medical Education and Research, 2024.
- Member, Society for Imaging Informatics in Medicine, 2020-present.
- Member, American Association of Physicists in Medicine, 2017-present.
- Top 10 Most-Cited Article, Journal of Computer Assisted Tomography, 20
23.
- Trainee Research Award, Radiological Society of North America, 2018.
- Breakthrough Prize in Fundamental Physics, Breakthrough Prize Foundation, 2015-2016.