Time-domain ultrasound as prior information for frequency-domain compressive ultrasound for intravascular cell detection: A 2-cell numerical model

Ashkan Ghanbarzadeh-Dagheyan; Vahid Amin Nili; Mehdi Ejtehadi; Reza Savabi; Zahra Kavehvash; Mohammad Taghi Ahmadian; Bijan Voshoughi Vahdat

doi:10.1016/j.ultras.2022.106791

Time-domain ultrasound as prior information for frequency-domain compressive ultrasound for intravascular cell detection: A 2-cell numerical model

Ultrasonics. 2022 Sep:125:106791. doi: 10.1016/j.ultras.2022.106791. Epub 2022 Jun 20.

Authors

Ashkan Ghanbarzadeh-Dagheyan¹, Vahid Amin Nili², Mehdi Ejtehadi³, Reza Savabi⁴, Zahra Kavehvash², Mohammad Taghi Ahmadian⁵, Bijan Voshoughi Vahdat²

Affiliations

¹ Department of Mechanical Engineering, Sharif University of Technology, Tehran, Iran; Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran. Electronic address: ashkan.ghanbarzade@gmail.com.
² Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran.
³ Department of Mechanical Engineering, Sharif University of Technology, Tehran, Iran.
⁴ School of Mechanical Engineering, University of Tehran, Tehran, Iran.
⁵ Department of Mechanical Engineering, Sharif University of Technology, Tehran, Iran. Electronic address: ahmadian@sharif.ir.

PMID: 35809517
DOI: 10.1016/j.ultras.2022.106791

Abstract

This study proposes a new method for the detection of a weak scatterer among strong scatterers using prior-information ultrasound (US) imaging. A perfect application of this approach is in vivo cell detection in the bloodstream, where red blood cells (RBCs) serve as identifiable strong scatterers. In vivo cell detection can help diagnose cancer at its earliest stages, increasing the chances of survival for patients. This work combines time-domain US with frequency-domain compressive US imaging to detect a 20-μ MCF-7 circulating tumor cell (CTC) among a number of RBCs within a simulated venule inside the mouth. The 2D image reconstructed from the time-domain US is employed to simulate the reflected and scattered pressure field from the RBCs, which is then measured at the location of the receivers. The RBCs are tagged one time by a human operator and another time, automatically, by template-based computer vision. Next, the resulting signal from the RBCs is subtracted from the measured total signal in frequency domain to generate the scattered-field data, coming from the CTC alone. Feeding that signal and the background pressure field into a norm-one-based compressive sensing code enables detecting the CTC at various locations. As errors could arise in determining the location of the RBCs and their acoustic properties in the real world, small errors (up to 10% in the former and 5% in the latter) are purposefully introduced to the model, to which the proposed method is shown to be resilient. Localization errors are smaller than 12 μ when a human tags the RBCs and smaller than 25 μ when computer vision is applied. Despite its limitations, this study, for the first time, reports the results of combining two US modalities aimed at cell detection and introduces a unique and useful application for ultrahigh-frequency US imaging. It should be noted that this method can be used in detecting weak scatterers with ultrasound waves in other applications as well.

Keywords: Cancer; Cell detection; Compressive sensing; Computer vision; Ultrasound.

MeSH terms

Acoustics
Data Compression*
Humans
Ultrasonography / methods