Dr. Ali Zarafshani Profile

Dr. Ali Zarafshani

Instructor, ECE and BME

SPIE Involvement:

Author

Area of Expertise:

Electrical Impedance Tomography-EIT , Electric field-induced Acoustic Tomography (Electroacoustic Tomography)-ECT

Publications (10)

Proceedings Article | 15 February 2021 Poster + Presentation + Paper

Electroacoustic tomography (EAT): 2D electric field reconstruction for electroporation treatment monitoring

Siqi Wang, Ali Zarafshani, Liangzhong Xiang

Proceedings Volume 11598, 1159820 (2021) https://doi.org/10.1117/12.2580691

KEYWORDS: Tomography, Ultrasonography, Transducers, Reconstruction algorithms, Oncology, Electrodes, Cancer, Acoustics

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Proceedings Article | 15 March 2019 Paper

Electroacoustic tomography system with nanosecond electric pulse excitation source

Ali Zarafshani, John Merrill, Siqi Wang, Bin Zheng, Liangzhong Xiang

Proceedings Volume 10955, 109551B (2019) https://doi.org/10.1117/12.2513051

KEYWORDS: Transducers, Acoustics, Tomography, Tissues, Ultrasonics, Electrodes, Imaging systems, Real time imaging, Signal detection, Signal processing, Acoustic emission

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The new technique for the imaging guidance to real-time monitoring of electroporation-based medical interventions could be based on the electroacoustic tomography (EAT), where the electric field applied for the electroporation process leads to induced acoustic signals based on the flow of electrical current inside the target conductive tissue. A microsecond to nanosecond electric-pulse (𝜇𝑠 − 𝑛𝑠𝐸𝑃) excitation source is an essential part of this new imaging guided to real-time monitoring electroporation process. This paper presents the design, configuration, and measurement of a compact, low-cost high voltage MOSFET-based pulsed excitation source and the simple structure of the EAT system with the single channel ultrasonic transducer to acquire acoustic signals and complemented by experimentation of its function based on agar-conductive phantom studies. The high-voltage pulsed excitation source has variable pulse widths ranging from 100 𝑛𝑠 𝑡𝑜 10 𝜇𝑠 electric pulse with a variable pulse potential magnitude of up to 1200 Volts (V). The high-voltage 𝜇𝑠 − 𝑛𝑠𝐸𝑃 is powered from a variable input source of 11.3 to 16 V in direct current (DC) and a power controller using a 0 V to 5 V in DC power line so that it is able to provide 0 to full output in potential magnitude. The high-intensity, ultra-short pulsed electric field is then connected to two electrodes separated by a distance (d) where 𝑑 = 1500𝜇𝑚 𝑡𝑜 3400 𝜇 mounted into the conductive media. An unfocused ultrasound transducer with central frequency of 500 kHz is used to acquire real-time acoustic signals. Various conductive media, including two agar-based phantoms with conductivity of 1 𝜇𝑆/𝑐𝑚 , 34 𝑚𝑆/𝑐𝑚 were studied using this pulsed excitation source to induce corresponding acoustic signals. Results indicate feasibility of the enhancing the EAT system that used up to 8 kV/cm, 𝜇𝑠 𝑡𝑜 𝑛𝑠 pulsed excitation source used in the electroporation-based clinical processes enhancing the EAT system as an imaging guidance to real-time, in-situ monitoring for the electroporation-based techniques.

Proceedings Article | 15 March 2019 Presentation + Paper

Design, fabrication and evaluation of non-imaging, label-free pre-screening tool using quantified bio-electrical tissue profile

Ali Zarafshani, Yunzhi Wang, Seyedeh-Nafiseh Mirniaharikandehei, Morteza Heidari, Faranak Aghaei, Siqi Wang, Liangzhong Xiang, Bin Zheng

Proceedings Volume 10953, 1095304 (2019) https://doi.org/10.1117/12.2513052

KEYWORDS: Tissues, Breast, Breast cancer, Mammography, Cancer, Computer aided design, Tissue optics, Machine learning, Feature extraction, Mathematics

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The design, fabrication, and performance of pre-screening tool using bioelectrical tissue profile is described. A probe of 8 electrodes using active-probe-sensing (AP-sensing) module was linearity distortion in the frequency response bonded to the 8 sub-regions of the breast surface forming the Bioimpedance transfer measurement system. Each measurement channel acquired the data from 1/8 of breast according to each breast defined in 8 sub-regions with split current injection channel and response behavioural bioelectricity detection. For this tissue bioelectrical measurement system, the electrodes were placed on the breast surface and before the bioelectrical profile were measured, we investigated a closed loop technique to compensate for the effects and measure the channel-skin-contact impedance. The AP-sensing module and connecting pads could be placed in the measurement electrode-Bras according to different breast size, and all measurement sub-regions should be equivalent for each case, but could be different in scale of 1- 2cm in related to different breast size. Bilateral structure was applied to compare each breast tissue bioelectrical properties in related to tissue behavioural in different frequency. Bioelectrical measurement efficiency was evaluated by the use of bioelectrical plots equivalent to a theoretical plot of the pure tissue profile versus average intracellular and extracellular and admittance behavioural of breast tissue able to flow electric field using Cole-Cole structure tissue modelling as a well-known bioelectrical tissue profile. The bioelectrical tissue profile as a pre-screening tool using theoretical pure tissue profiles and experimental measurements were evaluated in related a conventional Bioimpedance spectroscopy instruments. Breast bioelectrical profile of different breast density categories and average measurement values were significant, according to exist of fibro-glandular tissues in the total breast volume. The different of the theoretical values corresponding to pure fatty and fibro-glandular breast tissue behaviour was slightly different with the experimental measurements. We demonstrated the bioelectrical profile of breast tissue and extracting bioelectrical features that comparing in a bilateral structure to apply bioelectrical features as a supplementary data in the machine learning algorithms and present correspond risk factor for susceptibility of breast cancer in future studies. The preliminary equivalent theoretical and experimental results, evaluate the possibility of this new, non-imaging and label-free quantitative technique.

Proceedings Article | 15 March 2019 Presentation + Paper

Electroacoustic tomography (EAT): linear scanning with a single element transducer

Ali Zarafshani, John Merrill, Siqi Wang, Mengxiao Wang, Bin Zheng, Liangzhong Xiang

Proceedings Volume 10955, 1095513 (2019) https://doi.org/10.1117/12.2512812

KEYWORDS: Transducers, Acoustics, Tomography, Tissues, Signal detection, Real time imaging, Ultrasonics, Ultrasonography, Electrodes, Signal processing, Acoustic emission

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Proceedings Article | 9 March 2018 Paper

Real-time in-situ monitoring of electrotherapy process using electric pulse-induced acoustic tomography (EpAT)

Ali Zarafshani, Nicklas Dang, Pratik Samant, Rowzat Faiz, Bin Zheng, Liangzhong Xiang

Proceedings Volume 10573, 105732J (2018) https://doi.org/10.1117/12.2293138

KEYWORDS: Acoustics, Signal detection, Receivers, Electrodes, Transducers, Acoustic emission, Tomography, Ultrasonics, Ultrasonography, Prototyping

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Proceedings Article | 9 March 2018 Paper

Developing a unique portable device to non-invasively detect bio-electrochemical characteristics of human tissues

Ali Zarafshani, Sreeram Dhurjaty, Seyedeh-nafiseh Mirniaharikandehei, Faranak Aghaei, Liangzhong Xiang, Bin Zheng

Proceedings Volume 10573, 105735L (2018) https://doi.org/10.1117/12.2293507

KEYWORDS: Breast, Tissues, Breast cancer, Cancer, Electrodes, Bismuth, Calibration, Mammography, Skin, Signal detection

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Proceedings Article | 27 February 2018 Paper

Applying a CAD-generated imaging marker to assess short-term breast cancer risk

Seyedehnafiseh Mirniaharikandehei, Ali Zarafshani, Morteza Heidari, Yunzhi Wang, Faranak Aghaei, Bin Zheng

Proceedings Volume 10575, 105753F (2018) https://doi.org/10.1117/12.2291527

KEYWORDS: Mammography, Computer aided diagnosis and therapy, Breast cancer, Cancer, Computer aided design, Image fusion, Breast, Machine learning, Performance modeling, Computer-aided diagnosis

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Proceedings Article | 27 February 2018 Paper

Association between mammogram density and background parenchymal enhancement of breast MRI

Faranak Aghaei, Gopichandh Danala, Yunzhi Wang, Ali Zarafshani, Wei Qian, Hong Liu, Bin Zheng

Proceedings Volume 10575, 105752O (2018) https://doi.org/10.1117/12.2293377

KEYWORDS: Breast, Magnetic resonance imaging, Mammography, Breast cancer, Cancer, Computer aided diagnosis and therapy, Image segmentation, Image enhancement, Machine learning

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Proceedings Article | 19 February 2018 Paper

Real-time, in situ monitoring of nanoporation using electric field-induced acoustic signal

Ali Zarafshani, Rowzat Faiz, Pratik Samant, Bin Zheng, Liangzhong Xiang

Proceedings Volume 10495, 104950Q (2018) https://doi.org/10.1117/12.2292052

KEYWORDS: Acoustics, Signal processing, Electrodes, Transducers, Cancer, Ultrasonography, Magnetic resonance imaging, Tissues, Tumors, Real time imaging

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Proceedings Article | 13 March 2017 Paper

Current source enhancements in Electrical Impedance Spectroscopy (EIS) to cancel unwanted capacitive effects

Ali Zarafshani, Thomas Bach, Chris Chatwin, Liangzhong Xiang, Bin Zheng

Proceedings Volume 10137, 101371X (2017) https://doi.org/10.1117/12.2254629

KEYWORDS: Capacitance, Dielectric spectroscopy, Biomedical optics, Molecular spectroscopy, Signal detection, Cancer, Resistance, Electrodes, Data acquisition, Inductance, Voltage controlled current source, Amplifiers, Resistors, Imaging systems, Breast

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Showing 5 of 10 publications

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