Displacement Data Processing for ARFI Imaging
| dc.contributor.author | Onyia, Jude | |
| dc.contributor.supervisor | Rakhmatov, Daler | |
| dc.date.accessioned | 2022-04-28T07:40:20Z | |
| dc.date.available | 2022-04-28T07:40:20Z | |
| dc.date.copyright | 2022 | en_US |
| dc.date.issued | 2022-04-28 | |
| dc.degree.department | Department of Electrical and Computer Engineering | en_US |
| dc.degree.level | Master of Engineering M.Eng. | en_US |
| dc.description.abstract | Shear Wave Elastography (SWE) is an ultrasound imaging application that aims to estimate bio-mechanical properties of tissues, such as the Young’s modulus, within an imaged area of interest. The SWE process entails tracking tissue movement caused by an internal or external excitation, with the goal of determining the shear wave speed (SWS) propagating through the tissue. Acoustic radiation force impulse (ARFI) imaging is an important SWE modality that involves creating an internal push via a focused ultrasound transmission, which gives rise to shear waves, followed by tissue displacement tracking via ultrafast plane-wave transmissions. This report deals with displacement data processing for ARFI imaging that aims to provide SWS estimates. Our reported evaluation results are based on the public-domain ARFI dataset from the UltraSound ToolBox (USTB), serving as an illustrative example. This dataset contains ultrasound scans of a tissue-mimicking phantom containing an 80-KPa sphere (10 mm in diameter) embedded in the 25-KPa background. This report discusses three well-known displacement tracking techniques: Doppler frequency estimation based on auto-correlation, two-dimensional analytic minimization (2D AM), and clutter filter wave imaging (CFWI). The SWS values were then computed using the sliding-window Radon sum transform, applied to the displacement data after directional filtering and masking based on Canny edge detection. The latter highlights relevant spatiotemporal trajectories in displacement images that increases the accuracy of SWS estimates. Additionally, the SWS images themselves underwent morphological opening that enhanced their visual appearance. Overall, this report recommends using the auto-correlation technique that led to SWS estimates in good agreement with their ground-truth values. | en_US |
| dc.description.scholarlevel | Graduate | en_US |
| dc.identifier.uri | http://hdl.handle.net/1828/13889 | |
| dc.language.iso | en | en_US |
| dc.rights | Available to the World Wide Web | en_US |
| dc.subject | Ultrasound | en_US |
| dc.subject | ARFI | en_US |
| dc.subject | Elastography | en_US |
| dc.subject | Filtering | en_US |
| dc.title | Displacement Data Processing for ARFI Imaging | en_US |
| dc.type | project | en_US |