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Multimodal Imaging Using RF and Ultrasound

Our work in imaging spans a variety of media and reconstruction algorithms.

We work on detection and imaging using the thermoacoustic (TA) effect, in which the target being imaged generates acoustic waves due to thermal expansion caused by the absorption of microwave energy. Microwave-induced TA combines the contrast of microwave imaging, which is based on the dielectric properties of different materials, with the high resolution of ultrasound detection. Unlike photoacoustic imaging, which is suitable only for superficial applications, TA has the potential to achieve a penetration depth of more than 5 cm, even in dispersive tissue.

Conventional microwave-induced TA uses a high-power RF source to generate a short pulse, which leads to bulky and expensive equipment and possible safety issues. Our research instead focuses on continuous-wave (CW) approaches, which can make TA imaging more cost-effective by reducing device size and power requirements.

We have demonstrated coherent frequency domain signaling in the form of SFCW and FMCW [1-3] approaches. The FMCW method reduces the requirement for peak power by increasing pulse duration and takes advantage of a matched-filtering receiver to achieve significant SNR improvement. In addition, we have extended our system to be able to image the first few millimeters of tissue (with applications in imaging superficial veins) by using electrical structures that concentrate pulsed microwave excitation near the dermis, combined with conventional antennas that radiate CW deeper into the tissue. With this approach, we have demonstrated proof-of-concept TA imaging of microcapillaries and plant vasculature using substantially reduced pulsed-excitation power compared to the state of the art [4-5].

Our other projects in this area include: using microwave-induced TA to non-invasively extract spectroscopic information from human tissue [6]; using a fast iterative reconstruction algorithm to speed up TA imaging [7]; using multi-element beamforming to improve TA signal generation [8]; using an interferogram-based machine learning algorithm to improve beast tumor classification using TA signals [9]; and using the magnetoacoustic effect, in which electric currents interact with magnetic fields to produce acoustic waves via the Lorentz force, to demonstrate detection and imaging [10].

Block diagram of CW TA setup used for spectroscopy.

Results of CW TA spectroscopy experiment show that this method can be used to extract the effective conductivity of target materials.

(a) Imaging sample; (b) matched filtering image; (c) measured 1D signal after matched filtering; (d) reconstructed image using FD-SAR algorithm.

TA imaging of (a) scallion plant with xylem comparable to a 260 μm diameter wire with log scaling, and (b) earthworm showing inherent contrast between its two blood vessels and other tissue with log scaling.

[1] H. Nan and A. Arbabian, "Stepped-Frequency Continuous-Wave Microwave-Induced Thermoacoustic Imaging," Appl. Phys. Lett., vol. 104, no. 22, 224104, 2014.

[2] H. Nan and A. Arbabian, "Coherent Frequency-Domain Microwave-Induced Thermoacoustic Imaging," Proc. IEEE Int. Microw. Symp., Tampa, FL, 2014.

[3] H. Nan and A. Arbabian, "Peak-Power Limited Frequency-Domain Microwave-Induced Thermoacoustic Imaging for Handheld Diagnostic and Screening Tools," IEEE Trans. Microw. Theory Tech., vol. 65, pp. 2607-2616, July 2017.

[4] M. Aliroteh and A. Arbabian, "Microwave-Induced Thermoacoustic Imaging of Subcutaneous Vasculature with Near-Field RF Excitation,” accepted to IEEE Trans. Microw. Theory Tech.

[5] M. S. Aliroteh, H. Nan, and A. Arbabian, "Microwave-Induced Thermoacoustic Tomography for Subcutaneous Vascular Imaging," Proc. IEEE Ultrason. Symp., Tours, 2016.

[6] S. Liu, H. Nan, N. Dolatsha, and A. Arbabian, "Extracting Dielectric Spectroscopic Properties from Microwave-Induced Thermoacoustic Signals," Proc. 2016 Int. Conf. IEEE Eng. Med. Biol. Soc., Orlando, FL, 2016, pp. 3618-3621.

[7] H. Nan, B. A. Haghi, M. S. Aliroteh, M. Fallahpour, and A. Arbabian, “Fast Iterative Reconstruction Algorithm for Microwave-Induced Thermoacoustic Imaging,” Proc. Biomed. Circuits Syst. Conf., Shanghai, 2016.

[8] H. Nan, S. Liu, N. Dolatsha and A. Arbabian, "A 16-Element Wideband Microwave Applicator for Breast Cancer Detection Using Thermoacoustic Imaging," Progress in Electromagnetics Research Symposium (PIERS) Proceedings, 243-247, July 6-9, Prague, 2015.

[9] H. Nan, S. Liu, N. Dolatsha, and A. Arbabian, "A 16-Element Wideband Microwave Applicator for Breast Cancer Detection Using Thermoacoustic Imaging," Proc. Progress Electromag. Res. Symp., Prague, 2015, pp. 243-247. (Best Student Paper Award)

[10] M. S. Aliroteh, G. C. Scott, and A. Arbabian, "Frequency-Modulated Magneto-Acoustic Detection and Imaging: Challenges, Experimental Procedures, and B-Scan Images," arXiv preprint arXiv:1602.06931, 2016.