Dr. Farbod Tabesh Profile

Dr. Farbod Tabesh

at Stanford Univ School of Medicine

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Proceedings Article | 10 April 2023 Presentation + Paper

Quantitative methods for molecular ultrasound imaging

Negar Sadeghipour, Farbod Tabesh, Arutselvan Natarajan, Meredith Jones, Xuxin Chen, Ramasamy Paulmurugan, Bin Zheng, Ahmed El Kaffas

Proceedings Volume 12470, 124700F (2023) https://doi.org/10.1117/12.2654230

KEYWORDS: Ultrasonography, Tumors, Cancer, Biological imaging, Proteins, Bubbles, Blood vessels, Image enhancement

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Molecular ultrasound imaging is used to image the expression of specific proteins on the surface of blood vessels using the conjugated microbubbles (MBs) that can bind to the targeted proteins, which makes MBs ideal for imaging the protein expressed on blood vessels. However, how to optimally apply MBs in an ultrasound imaging system to detect and quantify the targeted protein expression needs further investigation. To address this issue, objective of this study is to investigate feasibility of developing and applying a new quantitative imaging marker to quantify the expression of protein markers on the surface of cancer cells. To obtain a numeric value proportional to the amount of MBs that bind to the target protein, a standard method for quantification of MBs is applying a destructive pulse, which bursts most of the bubbles in the region of interest. The difference between the signal intensity before and after destruction is used to measure the differential targeted enhancement (dTE). In addition, a dynamic kinetic model is applied to fit the timeintensity curves and a structural similarity model with three metrics is used to detect the differences between images. Study results show that the elevated dTE signals in images acquired from the targeted (MBTar) and isotype (MBIso) are significantly different (p<0.05). Quantitative image features are also successfully computed from the kinetic model and structural similarity model, which provide potential to identify new quantitative image markers that can more accurately differentiate the targeted microbubble status.

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