Gordon Kindlmann

Faculty, Fellow


I research scientific visualization and image analysis to improve the biomedical applications of three-dimensional imaging modalities (like MRI and CT). My past research simplified the work of making informative direct volume renderings, inspired by traditional techniques of edge detection. I continue to explore ways of translating mathematical principles of image processing and computer vision to practical methods of detecting, measuring, and understanding biological and anatomical structure in modern imaging data.

My doctoral and post-doctoral work focused on diffusion MRI, including data inspection, iber tractography, feature detection, and tensor analysis. Current work with diffusion MRI is with my post-doc Thomas Schultz. Recent work is focusing on building image analysis tools that work usefully on the variety of imaging modalities studied by colleagues in the Biological Sciences Division (microCT, clinical CT, spectral MRI). I am also involved in the creation of the Diderot language, which will simplify the work of translating analysis and visualization algorithms into high-performance GPU-based computation. All my research software is open-source, which is vital for creating reproducible methods of computational science.