Any automated, computerized assessment of disease requires establishment of healthy norms against which a test subject can be compared. A high-quality, carefully designed image database of healthy subjects could be of value to many groups for creation of healthy atlases, for assessment of disease, and for evaluation of the effects of both gender and healthy aging.

We have completed collection of high-resolution, 3T MR images of 100 healthy subjects aged 20-60+, with 20 subjects per decade divided equally by gender. All subjects were screened for the presence of disease. Images include T1, T2, MRA, and diffusion tensor. These images, with the consent of all participants, are now publicly available for download from the MIDAS Data Server at Kitware, Inc at http://hdl.handle.net/1926/594.

These standardized images can be used to assess the effects of healthy aging as perceived by MR. Early evaluation indicates significant differences between males and females and between youthful and aged subjects. These results, while interesting in themselves, suggest that analysis of subtle disease may require atlases tailored to the sex and/or age of the subject in question. Our designed database allows creation of an arbitrary number of tailored atlases.

We are also beginning to address the issue as to how much of the face must be deformed in order to preclude identification of an individual via a 3D surface rendering so as to meet United States HIPAA requirements while also preserving as much as the underlying anatomy as possible. This issue is important when aiming to make publicly available a large image database under conditions in which some subjects may not have provided explicit consent to make their images publicly available. We are aiming to make a large image database of brain tumor subjects publicly available in the near future.

Supported by NIH-NIBIB R01-EB000219

Mortamet B, Zeng D, Gerig G, Prastawa M, Bullitt E (2005) Effects of healthy aging measured by intracranial compartment volumes using a designed MR database. MICCAI 2005; LNCS 3749: 383-391.<pdf>

Aylward S, Jomier J, Vivert C, Ledigarcher V, Bullitt E (2005) Spatial graphs for intracranial vascular network characterization, generation, and discrimination. MICCAI 2005; LNCS 3749: 59-66.<pdf>

Davis B, Fletcher PT, Bullitt E, Joshi S (2007) Population Shape Regression from Random Design Data. ICCV 2007. Marr Prize winner 2007. <pdf>

Budin F, Zeng D, Ghosh A, Bullitt E (2008) Preventing facial recognition when rendering MR images of the head in three dimensions. In press MedIA. <pdf>

Last updated Dec 2007