Image Analysis for Contrast Enhanced Ultrasound Carotid Plaque Imaging

__Abstract__ Intraplaque neovascularization (IPN) has been presented as an important biomarker for progressive atherosclerotic disease and plaque vulnerability in several pathological studies. Therefore, quantification of IPN may allow early prediction of plaque at risk of rupture and thus prevention of future cardiovascular events such as stroke. Contrast enhanced ultrasound (CEUS) enables us to detect and visualize IPN by use of ultrasound contrast agents. So, the degree of IPN can potentially be measured by quantitative imaging biomarkers derived from CEUS. Since quantification tools for IPN are scarce, so far mainly visual IPN scoring on CEUS clips has been used to assess IPN, which is subjective and tedious. Currently available commercial tools for contrast quantification, e.g. QLAB region of interest (ROI) quantification tool (Philips Medical Systems, Bothell, USA) and VueBox (Bracco Suisse SA, Geneva, Switzerland), are not suitable for quantitative analysis of IPN. These commercial quantification tools have been developed mainly for time intensity curve analysis (TIC) of large organs such as heart, liver and prostate, not for plaques. Plaques are very small and intermittently perfused. Therefore, the perfusion characteristics of plaques are quite different from those of large organs and TIC analysis as applied in large well-perfused organs is not applicable. Some IPN quantification approaches have been reported but they suffer from a number of limitations such as imaging artifacts and no or imperfect motion compensation. In this thesis work, we avoided the known limitations of IPN quantification methods reported in previous studies and developed and evaluated specialized IPN analysis tools for carotid CEUS image sequences

Quantification of Bound Microbubbles in Ultrasound Molecular Imaging

Molecular markers associated with diseases can be visualized and quantified noninvasively with targeted ultrasound contrast agent (t-UCA) consisting of microbubbles (MBs) that can bind to specific molecular targets. Techniques used for quantifying t-UCA assume that all unbound MBs are taken out of the blood pool few minutes after injection and only MBs bound to the molecular markers remain. However, differences in physiology, diseases, and experimental conditions can increase the longevity of unbound MBs. In such conditions, unbound MBs will falsely be quantified as bound MBs. We have developed a novel technique to distinguish and classify bound from unbound MBs. In the post-processing steps, first, tissue motion was compensated using block-matching (BM) techniques. To preserve only stationary contrast signals, a minimum intensity projection (MinIP) or 20th-percentile intensity projection (PerIP) was applied. The after-flash MinIP or PerIP was subtracted from the before-flash MinIP or PerIP. In this way, tissue artifacts in contrast images were suppressed. In the next step, bound MB candidates were detected. Finally, detected objects were tracked to classify the candidates as unbound or bound MBs based on their displacement. This technique was validated in vitro, followed by two in vivo experiments in mice. Tumors (n = 2) and salivary glands of hypercholesterolemic mice (n = 8) were imaged using a commercially available scanner. Boluses of 100 mu L of a commercially available t-UCA targeted to angiogenesis markers and untargeted control UCA were injected separately. Our results show considerable reduction in misclassification of unbound MBs as bound ones. Using our method, the ratio of bound MBs in salivary gland for images with targeted UCA versus control UCA was improved by up to two times compared with unprocessed images

Quantification of bound microbubbles in ultrasound molecular imaging

Molecular markers associated with diseases can be visualized and quantified noninvasively with targeted ultrasound contrast agent (t-UCA) consisting of microbubbles (MBs) that can bind to specific molecular targets. Techniques used for quantifying t-UCA assume that all unbound MBs are taken out of the blood pool few minutes after injection and only MBs bound to the molecular markers remain. However, differences in physiology, diseases, and experimental conditions can increase the longevity of unbound MBs. In such conditions, unbound MBs will falsely be quantified as bound MBs. We have developed a novel technique to distinguish and classify bound from unbound MBs. In the post-processing steps, first, tissue motion was compensated using block-matching (BM) techniques. To preserve only stationary contrast signals, a minimum intensity projection (MinIP) or 20th-percentile intensity projection (PerIP) was applied. The after-flash MinIP or PerIP was subtracted from the before-flash MinIP or PerIP. In this way, tissue artifacts in contrast images were suppressed. In the next step, bound MB candidates were detected. Finally, detected objects were tracked to classify the candidates as unbound or bound MBs based on their displacement. This technique was validated in vitro, followed by two in vivo experiments in mice. Tumors (n = 2) and salivary glands of hypercholesterolemic mice (n = 8) were imaged using a commercially available scanner. Boluses of 100 μL of a commercially available t-UCA targeted to angiogenesis markers and untargeted control UCA were injected separately. Our results show considerable reduction in misclassification of unbound MBs as bound ones. Using our method, the ratio of bound MBs in salivary gland for images with targeted UCA versus control UCA was improved by up to two times compared with unprocessed images