PARAMETERS AFFECTING THE IMAGING AND DETECTION OF MICROBUBBLES IN BLOOD

Stabilized micron-sized bubbles, known as contrast agents, are often injected into the body to enhance ultrasound imaging of blood flow. The ability to detect such bubbles in blood depends on the relative magnitude of the acoustic power backscattered from the microbubbles (‘signal’) to the power backscattered from the red blood cells (‘noise’). Erythrocytes are acoustically small (Rayleigh regime), weak scatterers, and therefore the backscatter coefficient (BSC) of blood increases as the fourth power of frequency throughout the diagnostic frequency range. Microbubbles, on the other hand, are either resonant or super-resonant in the range 5-30 MHz. Above resonance, their total scattering cross-section remains constant with increasing frequency. In the present thesis, a theoretical model of the BSC of a suspension of red blood cells is presented and compared to the BSC of Optison® contrast agent microbubbles. It is predicted that, as the frequency increases, the BSC of red blood cell suspensions eventually exceeds the BSC of the strong scattering microbubbles, leading to a dramatic reduction in signal-to-noise ratio (SNR). This decrease in SNR with increasing frequency was also confirmed experimentally by use of an active cavitation detector for different concentrations of Optison® microbubbles in erythrocyte suspensions of different hematocrits. The magnitude of the observed decrease in SNR correlated well with theoretical predictions in most cases, except for very dense suspensions of red blood cells, where it is hypothesized that the close proximity of erythrocytes inhibits the acoustic response of the microbubbles.

Retrieval by Shape Population: An Index Tree Approach

Based on our previous work in deformable shape model-based object detection, a new method is proposed that uses index trees for organizing shape features to support content-based retrieval applications. In the proposed strategy, different shape feature sets can be used in index trees constructed for object detection and shape similarity comparison respectively. There is a direct correspondence between the two shape feature sets. As a result, application-specific features can be obtained efficiently for shape-based retrieval after object detection. A novel approach is proposed that allows retrieval of images based on the population distribution of deformed shapes in each image. Experiments testing these new approaches have been conducted using an image database that contains blood cell micrographs. The precision vs. recall performance measure shows that our method is superior to previous methods.

Index Trees for Efficient Deformable Shape-based Retrieval

An improved method for deformable shape-based image indexing and retrieval is described. A pre-computed index tree is used to improve the speed of our previously reported on-line model fitting method; simple shape features are used as keys in a pre-generated index tree of model instances. In addition, a coarse to fine indexing scheme is used at different levels of the tree to further improve speed while maintaining matching accuracy. Experimental results show that the speedup is significant, while accuracy of shape-based indexing is maintained. A method for shape population-based retrieval is also described. The method allows query formulation based on the population distributions of shapes in each image. Results of population-based image queries for a database of blood cell micrographs are shown.