216-577-0784

Applications:
Clinical Research, Healthcare Management and Outcomes, Human Subjects Trials and Intervention Studies, Medical Imaging, Physics, Precision Health, Public Health, Research Reproducibility
Methodologies:
Algorithms, Artificial Intelligence, Classification, Computational Tools for Data Science, Data Collection Design, Data Management, Data Mining, Data Quality, Data Security and Privacy, Data Visualization, Deep Learning, High-Dimensional Data Analysis, Human-Computer Interaction, Image Data Processing and Analysis, Machine Learning, Mathematics, Natural Language Processing, Optimization, Pattern Analysis and Classification, Predictive Modeling, Real-time Data Processing, Signal Processing, Sparse Data Analysis, Spatio-Temporal Data Analysis, Time Series Analysis
Relevant Projects:

Quantitative MRI in heart, prostate, brain, and liver; Optimization of MRI data acquisition methods; Real-time cardiac MRI; Radiology workflow optimization


Connections:

Jeff Fessler, Doug Noll, Vikas Gulani (UM), Sai Ravishanker (MSU), Miki Lustig (Berkeley), Krishna Nayak (USC)

Nicole Seiberlich

Associate Professor, Co-Director of Michigan Institute for Imaging Technology and Translation

Radiology


Affiliation(s):

Biomedical Engineering

My research involves developing novel data collection strategies and image reconstruction techniques for Magnetic Resonance Imaging. In order to accelerate data collection, we take advantage of features of MRI data, including sparsity, spatiotemporal correlations, and adherence to underlying physics; each of these properties can be leveraged to reduce the amount of data required to generate an image and thus speed up imaging time. We also seek to understand what image information is essential for radiologists in order to optimize MRI data collection and personalize the imaging protocol for each patient. We deploy machine learning algorithms and optimization techniques in each of these projects. In some of our work, we can generate the data that we need to train and test our algorithms using numerical simulations. In other portions, we seek to utilize clinical images, prospectively collected MRI data, or MRI protocol information in order to refine our techniques.

We seek to develop technologies like cardiac Magnetic Resonance Fingerprinting (cMRF), which can be used to efficiently collect multiple forms of information to distinguish healthy and diseased tissue using MRI. By using rapid methods like cMRF, quantitative data describing disease processes can be gathered quickly, enabling more and sicker patients can be assessed via MRI. These data, collected from many patients over time, can also be used to further refine MRI technologies for the assessment of specific diseases in a tailored, patient-specific manner.