This project is an effort to implement major changes in the way complicated imaging experiments are conducted. In a modern imaging experiment (e.g. confocal microscopy, CT, MRI, EM) the number of experimental parameters requiring adjustment is often large and the effects of altering one, much less several of them, is seldom obvious. Moreover, the resulting images can be large; are often multi-dimensional (2, 3, or 4D); and may be scalar, vector, or tensor valued. Teleological modeling is a facile way of dealing with the complexities of both data collection and analysis where the data collection and final image goals of the investigator are algorithmically incorporated into a set of interactive computational tools. By fashioning these tools into a "teleological pipeline", we will be developing a more efficient environment for optical and MRI data collection, analysis, rendering, and viewing. By a teleological pipeline we mean a computational and instrumental device in which the user's goals (i.e. the qualities they wish the final image to possess) guide the data collection and processing procedures. The user's goals are figuratively entered in one section of the pipeline and, ultimately, the images appear in another section where they can be evaluated by the user, the goals modified, and the process repeated. In this way, we are 'closing the loop' between the data collection, visualization, and interpretation phases of the imaging xperiment. The teleological pipeline is a robust process of converting goals into computational procedures. Its application in this project will not only greatly increase the efficacy with which data can be collected and analyzed, but will also point the way to its application in other complex methodologies used in the neurosciences.
At the analysis level our goal-directed approach will enable us to increase the information extracted from the multi-spectral MR and optical images via the development of new methods for identifying tissues, volumetrically rendering those different tissues, and creating animations. The same tools will be useful for developing new and faster MR imaging methods by specifying low level goals such as relationships between individual parts of an imaging sequence and the characteristics of the final image (e.g. the effect of TR and TE on image contrast, the relationship between the size of a diffusion gradient and the resulting amount of diffusion weighting in the image, partial volume effects due to under-sampling), or automatically compensating for hardware limitations (e.g. non-linear gradients, inhomogeneous B1 fields). Use of teleological modeling coupled with novel techniques of rendering vector/tensor data (e.g. painterly visualization) and the semi-immersive Responsive Workbench rendering environment will make complicated experiments and the resulting images truly accessible to the working neuroscientist.
Hierarchical Multi-Resolution Image Matching
The elaboration of structure and function in the brain takes place through a range of spatial scales (nanometers to centimeters) and temporal scales (microseconds to months). Imaging modalities are typically limited to one to three orders of magnitude in both time and space because of the physics of the phenomenon being applied. Thus, the collection and analysis of the diverse scales of information needed to produce a complete description of brain development requires multiple imaging modalities. A new thrust of our efforts is to investigate and implement methods of dealing with the problem of differing scales. We employ optical (confocal and two photon imaging) and MR imaging as examples of two complementary imaging modalities with different applicable scales of temporal and spatial resolution. Optical imaging techniques have high spatial and temporal resolution, but suffer from limited field of view and the inability to see deep into dense tissue. MR imaging is capable of wide fields of view and has the well recognized ability to image deep within dense tissue in a non-invasive manner, but it suffers from limited spatial and temporal resolution. "Hierarchical Multi-Resolution Image Matching" is a joint experimental and computational approach to melding optical and MR images of the same specimen into a single package of information. The fact that image contrast in these two modalities arises from qualitatively different mechanisms both complicates the analysis and enriches the final product. Biocompatible agents that are visible in both the optical and MR imaging modalities provide the experimental key to joining images from the different techniques. These bifunctional tagging agents provide high contrast targets for similar objects in both the optical and the MR image data.
After automatically identifying similar features in the two image types,
the high resolution optical data is then "projected" down to the resolution
of the MRI data using our dyadic wavelet pyramid approach. The images,
which are now effectively at the same resolution, will be geometrically
matched using these common features as matching tie-points. Tie-points
for geometric matching will be automatically generated using a Fourier
phase correlation technique. This method is particularly suited to the
image registration problems encountered in these efforts because it is
relatively insensitive to image differences which are introduced due to
the different imaging modalities used. We are developing this approach
specifically for optical and MR imaging, but it is generally applicable
to other imaging methods.
The PI's have ongoing and complex collaborative relationships - e.g.
Fraser and Jacobs work on MRI micro-imaging of frog development; Barr and
Fraser collaborate on imaging and modeling biological systems; Laidlaw
and Jacobs collaborate on MRI diffusion tensor imaging; Allman and
Barr collaborate on MR imaging and extracting information from measurements
on the human brain; Solomon and Fraser collaborate on automated cell tracking
algorithms; Jacobs and Allman collaborate on in vivo MR functional
imaging in rodents and small primates; Barr and Jacobs collaborate on multidimensional
rendering and model acquisition in MRI. There are also many three-way collaborations
that are just as prevalent as the two-way efforts (e.g. Meade, Fraser,
and Jacobs work together on in vivo MRI contrast agent development).
This core supports the design, synthesis, purification, and characterization of novel contrast agents. These agents are typically composed of two or more linked parts:
MRI 'active' part composed of a ligand which binds
Gd3+ (e.g. DTPA or DOTA);
For example, in neuronal tracing studies the targeting part is a lipid that is incorporated into the membrane of the growing neuron, while in cell lineage studies a high molecular weight dextran moiety is used because it is inert and membrane impermeant. In other cases it is desirable to have the separate parts of the agent interact with each other and to design this interaction to be dependent upon the local environment. In these situations, MRI contrast agent which are normally "silent" will be designed to "speak" in the presence of a metabolic or physiological function (e.g. high Ca2+ levels or the gene marker enzyme b-galactosidase), thus indicating the whereabouts of the function.
All items pertaining to animal acquisition, care, breeding, surgery, and disposal are dealt with through this Core. An experienced Animal Technologist oversees all aspects of this project that involve live animals. This includes being an integral part of the design and construction of all MRI hardware planned for in vivo animal imaging. The services of this core leave the investigators free to conduct their experiments, while ensuring that the animals are cared for in a humane manner consistent with Caltech and NIH guidelines.
This Core provides a stable and effective computational environment for the other Projects and Cores. It aids in implementing algorithms associated with goal-directed imaging experiments and dissemination of results; provide routine hardware and software support for computers, networks, Web page maintenance, and video environments. On-line digital archives of images, models, and data from the other Cores and Projects will be maintained so that all researchers in this Human Brain Project have access.
This Core performs routine maintenance on the current MRI hardware/software
(Bruker AMX500 11.7 Tesla 89mm bore NMR spectrometer) and aids in implementing
new hardware and software (e.g. implementing fMRI routines, the experimental
portion of the "teleological pipeline") in support of individual projects.
New developments are aimed at optimizing image contrast and increasing
spatial and temporal resolution. A significant amount of effort is
directed towards MR probe development in order to maximize signal/noise,
deliver stimuli synchronized with the MR experiment, and insure animal