Quantification of targeted microbubbles in contrast enhanced ultrasound

Molecular markers associated with diseases can be visualized and quantified noninvasively with targeted ultrasound contrast agent. Techniques used for quantifying t-UCA assume that all unbound microbubbles (MB) are taken out of the blood pool few minutes after injection, and only MB bound to the molecular markers remain. However, differences in physiology, diseases, and experimental conditions can increase longevity of unbound MB. In such conditions, unbound MB will falsely be quantified as bound UCA. We have developed a novel technique to distinguish and classify bound from unbound MB. Salivary glands of diseased mice were imaged using a Vevo 2100 scanner. Boluses of 100 μL of MicroMarker MB targeted to angiogenesis markers and control untargeted MB were injected separately. 10 minutes after injecting MB, side-by-side B-mode and contrast images were recorded for 10 seconds with a flash pulse in the middle to destroy MB in the imaging plane. In the post-processing steps, first tissue motion was compensated using block matching (BM) and multidimensional dynamic programing (MDP) techniques. To preserve only stationary contrast signals a minimum intensity projection (MinIP) was applied on frames before and after the flash pulse. The MinIP after-flash was subtracted from MinIP before-flash. This way, tissue artifacts in contrast images were suppressed. In the next step, bound MB candidates were detected by matching with artificial bubble templates. Finally templates of 0.1×0.1 mm around detected objects were tracked in subgroups of 20 frames using BM and MDP to classify the candidates as unbound or bound MB based on their displacement. Our results showed significant reduction in misclassification of unbound MB as targeted ones. Using our method, the ratio of intensities in salivary gland for images with targeted MB vs. control MB was improved significantly compared to unprocessed images. Using our method ratio of intensities in salivary gland for images with targeted MB vs. untargeted MB was improved 7 times compared to unprocessed images.