Image reconstruction is one of the main challenges for fluorescence tomography. For in vivo experiments on small
animals, in particular, the inhomogeneous optical properties and irregular surface of the animal make free-space image
reconstruction challenging because of the difficulties in accurately modeling the forward problem and the finite dynamic
range of the photodetector. These two factors are fundamentally limited by the currently available forward models and
photonic technologies. Nonetheless, both limitations can be significantly eased using a signal processing approach. We
have recently constructed a free-space panoramic fluorescence diffuse optical tomography system to take advantage of
co-registered microCT data acquired from the same animal. In this article, we present a data processing strategy that
adaptively selects the optical sampling points in the raw 2-D fluorescent CCD images. Specifically, the general sampling
area and sampling density are initially specified to create a set of potential sampling points sufficient to cover the region
of interest. Based on 3-D anatomical information from the microCT and the fluorescent CCD images, data points are
excluded from the set when they are located in an area where either the forward model is known to be problematic (e.g.,
large wrinkles on the skin) or where the signal is unreliable (e.g., saturated or low signal-to-noise ratio). Parallel Monte
Carlo software was implemented to compute the sensitivity function for image reconstruction. Animal experiments were
conducted on a mouse cadaver with an artificial fluorescent inclusion. Compared to our previous results using a finite
element method, the newly developed parallel Monte Carlo software and the adaptive sampling strategy produced
favorable reconstruction results.
In vivo quantitative studies of cardiac function in mouse models provide information about cardiac pathophysiology in
more detail than can be obtained in humans. Quantitative measurements of left ventricular (LV) volume at multiple
contractile phases are particularly important. However, the mouse heart's small size and rapid motion present challenges
for precise measurement in live animals. Researchers at Duke University's Center for In Vivo Microscopy (CIVM) have
developed noninvasive time-gated microcomputed tomography (micro-CT) techniques providing the temporal and
spatial resolutions required for in vivo characterization of cardiac structure and function. This paper describes analysis of
the resulting reconstructions to produce volume measurements and corresponding models of heart motion. We believe
these are the most precise noninvasive estimates of in vivo LV volume currently available. Our technique uses binary
mixture models to directly recover volume estimates from reconstructed datasets. Unlike methods using segmentation
followed by voxel counting, this approach provides statistical error estimates and maintains good precision at high noise
levels. This is essential for long term multiple session experiments that must simultaneously minimize contrast agent and
x-ray doses. The analysis tools are built into the Pittsburgh Supercomputing Center's Volume Browser (PSC-VB) that
provides networked multi-site data sharing and collaboration including analysis and visualization functions.
Beyond their involvement in ordinary surface rendering, the boundaries of organs in medical images have differential properties that make them quite useful for quantitative understanding. In particular, their geometry affords a framework for navigating the original solid, representing its R3 contents quite flexibility as multiple pseudovolumes R2 x T, where T is ar eal-valued parameter standing for screen time. A navigation is a smoothly parameterized series of image sections characterized by normal direction, centerpoint, scale and orientation. Such filmstrips represent a radical generalization of conventional medical image dynamics. The lances encountered in these navigations can be represented by constructs from classic differential geometry. Sequences of plane sections can be formalized as continuous pencils of planes, sets of cardinality (infinity) 1 that are sometimes explicitly characterized by a real-value parameter and sometimes defined implicitly as the intersection (curve of common elements) of a pair of bundles of (infinity) 2 planes. An example of the first type of navigation is the pencil of planes through the tangent line at one point of a curve; of the second type, the cone of planes through a point tangent to a surface. The further enhancements of centering, orienting, and rescaling in the medical context are intended to leave landmark points or boundary intersections invariant on the screen. Edgewarp, a publicly available software package, allows free play with pencils of planes like these as they section one single enormous medical data resource, the Visible Human data sets from the National Library of Medicine. This paper argues the relative merits of such visualizations over conventional surface-rendered flybys for understanding and communication of associated anatomical knowledge.
Prostate cancer is the second most common cause of cancer deaths and is the most frequently detected form of cancer of males in the US. Death rate scan be greatly reduced by early treatment. Consequently, it is important to understand the cause and progression of this disease in order to improve detection and treatment methods. As part of the Cancer Genome Anatomy Project work is underway to produce a 'molecular finger print' of prostate cancer.
As part of collaboration between the Pittsburgh Supercomputing Center and the University of Pittsburgh Medical Center we are developing methods for content based image retrieval to assist pathology diagnosis. We have been using Gleason grading of prostate tumor samples as an initial domain for evaluating the effectiveness of the method for specific tasks. In this application, the system does not attempt to directly reproduce pathologists' visual analysis. Rather, it relies on the comparison of image features from a sample image to key the retrieval of similar but previously graded images from a database. Appropriate features should be highly selective to architecture differences of the Gleason system so the grades of the retrieved images can be applied to the unknown sample. We have been investigating the usefulness of computational geometry structures, such as spanning trees, as components of feature sets providing accurate retrieval of matching grades.