We know a great deal about the biochemistry of cells because they can be isolated and studied. The biochemistry of the much more complex in vivo environment is more difficult to study because the only ways to quantitate concentrations is to sacrifice the animal or biopsy the tissue. Either method disrupts the environment profoundly and neither method allows longitudinal studies on the same individual. Methods of measuring chemical concentrations in vivo are very valuable alternatives to sacrificing groups of animals. We are developing microscopic magnetic nanoparticle (mNP) probes to measure the concentration of a selected molecule in vivo. The mNPs are targeted to bind the selected molecule and the resulting reduction in rotational freedom can be quantified remotely using magnetic spectroscopy. The mNPs must be contained in micrometer sized porous shells to keep them from migrating and to protect them from clearance by the immune system. There are two key issues in the development of the probes. First, we demonstrate the ability to measure concentrations in the porous walled alginate probes both in phosphate buffered saline and in blood, which is an excellent surrogate for the complex and challenging in vivo environment. Second, sensitivity is critical because it allows microscopic probes to measure very small concentrations very far away. We report sensitivity measurements on recently introduced technology that has allowed us to improve the sensitivity by two orders of magnitude, a factor of 200 so far.

An imaging system that simultaneously performs near infrared (NIR) tomography and magnetic resonance imaging (MRI) is used to study breast tissue phantoms and a healthy woman in vivo. An NIR image reconstruction that exploits the combined data set is presented that implements the MR structure as a soft-constraint in the NIR property estimation. The algorithm incorporates the MR spatially segmented regions into a regularization matrix that links locations with similar MR properties, and applies a Laplacian-type filter to minimize variation within each region. When prior knowledge of the structure of phantoms is used to guide NIR property estimation, root mean square (rms) image error decreases from 26 to 58%. For a representative in vivo case, images of hemoglobin concentration, oxygen saturation, water fraction, scattering power, and scattering amplitude are derived and the properties of adipose and fibroglandular breast tissue types, identified from MRI, are quantified. Fibroglandular tissue is observed to have more than four times as much water content as adipose tissue, almost twice as much blood volume, and slightly reduced oxygen saturation. This approach is expected to improve recovery of abnormalities within the breast, as the inclusion of structural information increases the accuracy of recovery of embedded heterogeneities, at least in phantom studies.

Experience suggests the existence of a connection between the contrast of a gray-scale image and the gradient magnitude of intensity edges in the neighborhood where the contrast is measured. This observation motivates the development of edge-based contrast enhancement techniques. We present a simple and effective method for image contrast enhancement based on the multiscale edge representation of images. The contrast of an image can be enhanced simply by stretching or upscaling the multiscale gradient maxima of the image. This method offers flexibility to selectively enhance features of different sizes and ability to control noise magnification. We present some experimental results from enhancing medical images and discuss the advantages of this wavelet approach over other edge-based techniques.