Mr. Daniel Sforza Profile

Daniel Sforza

at Johns Hopkins Univ School of Medicine

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Proceedings Article | 10 April 2023 Presentation + Paper

Real-time element position tracking of flexible array transducer for ultrasound beamforming

D. China, Z. Feng, H. Hooshangnejad, D. Sforza, P. Vagdargi, M. A. L. Bell, A. Uneri, K. Ding

Proceedings Volume 12470, 124700A (2023) https://doi.org/10.1117/12.2654435

KEYWORDS: Ultrasound real time imaging, Image guided radiation therapy, Ultrasound transducers, Optical tracking

Read Abstract +

Unlike traditional ultrasound (US) transducers with rigid casing, flexible array transducers can be deformed to patientspecific geometries, thus potentially removing user dependence during real-time monitoring in radiotherapy. Proper transducer geometry estimation is required for the transducer's delay-and-sum (DAS) beamforming algorithm to reconstruct B-mode US images. The main contribution of this work is to track each element's position of the transducer to improve the quality of reconstructed images. An NDI Polaris Spectra infrared tracker was used to localize the custom design optical markers and interfaced using the Plus toolkit to estimate the transducer geometry in real-time. Each marker was localized with respect to a reference marker. Each element's coordinate position and azimuth angle were estimated using a polygon fitting algorithm. Finally, DAS was used to reconstruct the US image from radio-frequency channel data. Various transducer curvatures were emulated using gel padding placed on a CIRS phantom. The geometric accuracy of localizing the optical markers attached to the transducer surface was evaluated using 3D Cone-Beam Computed Tomography (CBCT). The tracked element positions' deviations compared to the CBCT images were measured to be 0.50±0.29 mm. The Dice score for the segmented target structure from reconstructed US images was 95.1±3.3% for above mentioned error in element position. We have obtained a high accuracy (<1mm error) when tracking the element positions with different random curvatures. The proposed method can be used for reconstructing US images to assist in real-time monitoring of radiotherapy, with minimal user dependence.

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