Radiology > Biomedical Image Computing Group > Research projects and areas of interest

Research projects and areas of interest

Image analysis methods development

Fetal MR image reconstruction

We have previously proposed the first practical approach to creating true 3D images of the fetal brain from in-utero clinical MRI data using registration of multiple slice-stacks. In this now funded project we are developing improved approaches to recovering motion trajectories and methods of forming improved resolution MR images from the clinically collected image data at UCSF.

Automatic segmentation of the fetal brain from in utero clinical MRI

The fetal brain consists of a mixture of developing white matter (WM) and grey matter (GM) regions and transient tissue types related to brain growth. We have extended conventional approaches to adult brain segmentation by including these transient tissue types in the segmentation model and statistical priors. We have also created a statistical model of the developing fetal brain that aims at capturing its unique laminar structure. This allows us to accurately delineate various types of developing brain tissues from reconstructed fetal MRI with particular focus on the germinal matrix (GMAT), a deep brain region of developing cells adjacent to ventricles (VENT).

Spatio-temporal modeling of tissue distribution in the fetal brain

In order to meaningfully label tissues present in a given brain image, it is necessary to interpret the anatomy in relation to its developmental stage. We have developed an approach to modeling of a complete 4-dimensional atlas of tissue distribution within the human fetal brain through temporally parametrized probability models for each voxel of the brain. This spatio-temporal model can describe not only the local variation in the presence of developing tissues such as white matter (WM), but also the regional appearance and complete disappearance of transient tissue classes such as the germinal matrix (GMAT) over time.

Modeling of brain growth in fetuses and premature neonates

In this initial work with collaborators in pediatric radiology at UCSF we have been developing new methods of studying brain growth from clinical MRI scans of premature neonates. This work is aimed at building statistical models of both surface folding and local tissue volume increases in the developing human brain in order to create markers which can predict poor neurological development in premature babies.

  • C. E. Rodriguez-Carranza, P. Mukherjee, D. Vigneron, A. J. Barkovic, and C. Studholme, "A framework for in vivo quantification of regional brain folding in premature neonates," Neuroimage, vol. 41, no. 2, pp. 462-478, June 2008.
  • C. Studholme, C. E. Rodriguez-Carranza, V. A. Cardenas, B. Iordanova, S. Miller, P. Mukherjee, O. A. Glenn, D. Vigneron, and A. J. Barkovich, "A deformation morphometry study of the influences on the pattern of brain tissue development in premature neonates," in Proc. 11th Anuual Meeting of the Organization for Human Brain Mapping, June 2005.
Serial MRI morphometry of tissue volume changes

This work focuses on developing improved methods to map and quantify subtle, focal losses of tissue from repeated MRI scans of an individuals brain anatomy. We use fluid registration based deformation tensor morphometry that allows us to separate regional shape changes (such as the collapse of gyral structures) from true tissue volume changes providing a more direct physical measurement of tissue loss. We have specifically developed new methods of robust fluid registration using regionally adapted mutual information. This allows the separation local changes in tissue contrast (due to disease induced tissue integrity changes) from true volume changes.

Cross-sectional deformation tensor morphometry of tissue volume differences

This work focuses on developing methods to create and statistically analyze maps of tissue volume differences across and between populations of brain anatomies in order to detect characteristic patterns of tissue loss from single MRI scans of subjects. We have developed approaches to fine scale spatial normalisation of brain anatomy, groupwize registration and methods for the analysis of deformation tensor data using voxel-wise general linear modeling.

 

Clinical and neuroscience applications of brain morphometry

Patterns of tissue loss in dementia and aging

This aspect of our work refines and applies our deformation morphometry methodology to the study of dementia. We work with clinical collaborators to look for patterns of tissue volume differences and changes that are related to basic clinical diagnosis and to cognitive and neuropsychological measures.

  • V. A. Cardenas, A. L. Boxer, L. L. Chao, M. L. Gorno-Tempini, B. L. Miller, M. W. Weiner, and C. Studholme, "Deformation-based morphometry reveals brain atrophy in frontotemporal dementia," Arch. Neurol., vol. 64, no. 6, pp. 873-877, June 2007.
  • C. Studholme, V. A. Cardenas, R. Blumenfeld, N. Schuff, H. J. Rosen, B. Miller, and M. W. Weiner, "Deformation tensor morphometry of semantic dementia with quantitative validation," Neuroimage, vol. 21, no. 4, pp. 1387-1398, April 2004.
Modeling of brain changes in substance abuse

Here we have worked with collaborators studying substance abuse, to apply methods of serial MRI morphometry to create statistical maps of brain volume changes in groups of recoverying and relapsing alcoholics.

 

Others

MIDAS

For project description see midas.med.miami.edu