CSCI Research


Dr. Frank provides the expertise and leadership to make the Center a central research resource for the UCSD research community and drive CSCI's core research program focusing on advancing MR computation and imaging. The CSCI is an interdisciplinary endeavor, involving physicists, radiologists, neuroscientists, neuropsychiatrists, biologists, and others. CSCI is involved in a wide network of collaborations with other investigators at UCSD and at affiliated institutions, such as the San Diego Veterans Affairs Healthcare System, SIO, Birch Aquarium, and SDSU.

Active Grants

(NSF) SI2-SSE: Wavelet Enabled Progressive Data Access and Storage Protocol (WASP)
The major goal of this project is to develop a common software framework for supporting a multi scale progressive data refinement method, based upon the representation of data as a wavelet expansion, that enables interactive exploration of very large data sets for the bio- and geo-sciences communities. This will facilitate the multi-scale analysis, storage, and visualization of 'big data' collected in a wide range of disciplines and on a multitude of platforms, from high end computing systems, to personal laptop computers used by students and researchers out in the field.
Principal Investigator: Lawrence Frank; Co-PI: John Clyne (NCAR, Boulder, CO)

(NIH) Diffusion Imaging in Gray Matter
The major goals of this project are to extend idealized theoretic models of brain white matter (WM) and grey matter (GM) to more realistic physiological models through numerical simulations; 2) Performing high field experiments on well- characterized WM and GM phantoms to validate both our extended theoretical and simulation models, and on excised normal rat brains to assess variations from idealized models; 3) Developing a clinical double pulsed field gradient (dPFG) pulse sequence strategy to be tested on normal humans. Our central hypothesis is that the dPFG method is sensitive to sub-voxel tissue structure manifest in three measurable forms of diffusion anisotropy: microscopic anisotropy (uA), compartment shape anisotropy (CSA), and ensemble anisotropy (EA), which can be used in-vivo for the quantitative assessment of GM architecture in humans.
Principal Investigator: Lawrence R. Frank, Ph.D.

(NSF) Collaborative Research: Shape Analysis for Phenomics with 3D Imaging Data (SAPID)
The major goal of this project is to develop the Shape Analysis for Phenomics with 3D Imaging Data (SAPID) Toolkit, or STK, for quantitative morphological analysis of 3D volumetric imaging data, primarily focusing on MRI and CT data acquired from fishes and other zoological specimens. Our major goals specifically include; 1) Development of semiautomated geometric morphometry systems for noisy 3D MRI data based on diffeomorphic spatial normalization and geometric metamorphosis methods, and production of software useable by the biological community; 2). Development of a robust and efficient automated system for signature-based shape analysis for 3D noisy MRI image data; and, 3) Application of these new shape description methods to two classic and outstanding problems in evolutionary and comparative morphology.
Principal Investigator: Dr Lawrence Frank; Co-PI: Dr Kathyrn Dickson (Cal State Fullerton)