1. Introduction
In recent years, breast cancer is the most common type of cancer in women and one of the leading causes of female mortality worldwide [1] and the majority of patients having breast cancer diagnosis have the sub-type invasive ductal carcinoma [2]. There is a need to develop newer and better diagnostic tools for the detection of breast cancer and bioimpedance is hypothesized as one such approach in this work. Bioimpedance is defined as the opposition of a biological tissue to current flow. Bioimpedance analysis can be broadly defined as the impedance of biological specimens that includes a wide spectrum of applications ranging from the whole human body measurements to those conducted at the level of DNA. Since the middle of the 20th century, electrical impedance measurement technique has become a standard analytic tool used in the early-recognition of disease onset, 3D monitoring of the cancer progression, detection of pathogenic bacteria in food, and dynamic identification and quantification of cellular changes [3–6]. The utility of this technology originates from the fact that impedance can capture and reflect the rich biological and electrical characteristics exhibited at the whole body, organ, tissue, cell and organelle level [7]. The recent development of micro/nanotechnology has provided additional advantages and introduces new paradigms into the investigation of traditional electrical impedance concepts. The biosensors fabricated using MEMS technology can be used for real-time monitoring of the underlying mechanisms of the onset and progression of cancer [8]. With the advent of micro and nanofabrication technologies, conventional bench-top instruments can be miniaturized [8]. Similarly, microfluidic embedded sensor systems for achieving integrated systems, such as micro total analysis system (µTAS) is also possible [9]. Miniaturized sensors have significant advantages such as high throughput, small characteristic dimensions, low power consumption, and portability [10]. Drug screening [11], cellular adhesion [12], and cellular kinetics [13] have all been successfully monitored using bioimpedance techniques. Inter-digitated electrodes (IDEs) are implemented in various sensing devices such as surface acoustic wave (SAW) sensors, chemical sensors as well as MEMS based biosensors [14]. MEMS-based bioimpedance measurement are primarily limited to cells [11,12,14]. Different techniques such as microfilters [15–18], microwells [19–21] and dam structure [22] have been implemented for separation of cells. Automated characterization of cancerous cells using micro-cavity array and micromanipulator for exact positioning of cellular spheroids has been studied by other groups [11,23,24]. Ozkan et al. [25] and Umehara et al. [26] showed optical tweezers for manipulation of cells in micro-fluidic devices, including parallel manipulation of cells. Microdevices with modified interior surface by reactive coating and microwells using SU-8 were studied to dock the cells within predefined location [19, 27–29]. Quartz crystal microbalance (QCM) sensor has been used to study the cell-surface interactions and different cell-adhesion behaviors between the healthy and ill-behaved cells [5, 30–31]. Electrode Cell substrate Impedance Sensing (ECIS) is a non-invasive method used to the study the cells based on their attachment to the substrate (electrodes) [30]. This method has been recently used to study cell properties such as attachment, spreading, growth and proliferation [30, 32–38]. While IDEs have been optimized for a variety of single cell-based sensing applications, the impedance characterization at the tissue level, in particular, benign and cancerous breast tissue has not been adequately investigated. The advantage of characterizing micro-scale tissues over cells is that the degree of malignancy as well as the architectural changes that occur during the progression of cancer can be quantified on a larger micro-scale sample to make a deterministic assessment compared to studying a single cell.
An automated measurement system for bio-medical detection has advantages such as higher efficiency, excellent repeatability and statistical reliability [39]. Yamamoto et al. developed an automated system for measuring the real part (electrical resistive component) and imaginary part (reactive component) of the impedance [40]. Our goal is to build an automated bioimpedance measurement system using MEMS-based devices capable of measuring bioimpedance of tissue array simultaneously thereby increasing the throughput. To achieve this we have made an array of microdevices (IDE’s) [5 × 6] glass wafer. The semiautomated way of impedance measurement is as shown in Fig. 1. In this work, we have fabricated a microchip having interdigital electrodes inside a SU-8 well to measure the impedance of benign and cancerous breast tissues. To our knowledge, this is the first such study for characterizing the benign and cancerous breast tissues using IDEs and bioimpedance method. It is known that the output signal strength of microchips having IDEs can be controlled through careful design of the active area, width, and spacing of the electrode fingers [41, 42]. In this paper, we investigated breast tissue specimen that originated from a total of ten cases of high-grade invasive ductal carcinoma. The “benign breast tissues” are sampled from normal appearing breast lobules or terminal ducts, and “cancerous breast tissues” are high grade invasive ductal carcinoma. The rest of this paper is organized as follows. In section 2, we provide the experimental details for sensor fabrication, tissue microarray preparation and lumped circuit modeling of the microchip-tissue interface. In section 3, we provide the experimental results and discussion. Finally, in section 4, we make some concluding remarks. The schematics of (a) the MEMS-based bioimpedance sensor for tissue microarray and (b) the semi-automated bioimpedance measurement system is shown in Fig.1.
2. Experimental Work
2.1 Sensor Fabrication
To make the sensor biocompatible, the sensor was fabricated on 1.0 mm-thick glass substrate (Pyrex 7740). The microchip was fabricated using a two-mask process. The fabrication process is shown in Fig. 2. After cleaning glass using a 70:30 solution of H2SO4 and H2O2 [Fig. 2(a)], Cr/Au (0.1 µm/0.5 µm) was deposited using thermal evaporation on glass substrates [Fig. 2(b)]. Using a standard photolithography technique interdigital electrodes (10 µm width with 10 µm spacing and 30 µm spacing) were patterned [Fig. 2(c)]. SU-8 2007 was used to form reservoir. SU-8 photoresist was spin coated at 2000 rpm for 1 minute on glass substrate to obtain 7 µm thickness [Fig. 2(d)] and was patterned using photolithography to form reservoir [Fig. 2(e)]. For using SU-8 on glass substrates, adhesion promoters are not required. The reservoir is made to hold the tissue and medium inside the desired area. The schematic of the microchip is shown in Fig. 3(a) and the reservoir dimensions are shown in Fig. 3(b). Figure 4(a) and 4(b) shows the optical photographs of the fabricated microchips with 10 µm and 30 µm spacing, respectively. The width in both cases is kept at 10 µm.
2.2 Tissue Microarray Preparation
Benign and cancerous breast tissue blocks were carefully selected from archival tissue resources at the Histopathology and Imaging Core Facility at Rutgers Cancer Institute of New Jersey. Conventional histology specimen preparation protocol was used in this experiment to fix, slice, and deparaffinize the tissue.
Briefly, fresh tissue is first fixed in formalin – this process preserves tissue from degradation and maintains cellular structure and sub-cellular structure. The fixed specimen is then dehydrated by going through a series of progressively more concentrated ethanol, followed by hydrophobic clearing agent (xylene), infiltration agent (molten paraffin wax), and finally embedded in liquid wax. After cooling to room temperature, the paraffin-embedded specimen can be stored for a very long period of time (a tissue bank in this case), and is hard enough to be sliced at 4 µm for histological purposes. The deparaffinization protocol is also routine histological process, in which the wax is first softened in a baking oven and then gradually replaced with water through the series of ethanol baths. The microchips were deparaffinized as follows: Xylene (5 min, 3 times); 100% Alcohol (5 min, 2 times); 95% Alcohol (5 min, 1 time); 75% Alcohol (5 min, 1 time); rinse in PBS (1–2 times) and immersed in PBS holding solution until impedance analysis. Following the fixation and preparation process described above, the Histology and Imaging Core Facility at Rutgers Cancer Institute of New Jersey archives them and hosts a cancer related tissue bank by storing the formalin-fixed paraffin-embedded (FFPE) specimens at room temperature. Our archive, along with similar tissue banks at other institutes, supported countless experiments in explorative cancer research.
The conventional FFPE method to preserve tissue had been proven effective in reproducing cell morphology and tissue architecture after decades of storage. It is also considered effective in preserving molecular antigenicity for immunohistochemical studies in majority of the cases. Protocols also exist to extract DNA and RNA sample from FFPE specimens for investigative use.
Uniform-sized tissue cores of 0.6 mm in diameter were extracted from each donor tissue block, and inserted into recipient paraffin blocks using Manual Tissue Arrayer (Beecher Instruments) [43]. These single-cored TMAs were rigorously quality-controlled via Hematoxylin & Eosin (H&E) stained sections and the above process was repeated until two characteristic tissue cores were identified from each case. The individual tissue cores were sectioned at 4 µm in thickness and carefully positioned in the center of the interdigited electrodes. Fig. 5 shows the optical photograph of cancerous tissue placed on the interdigited electrodes having 30 µm spacing.
Optical photograph of cancerous tissue placed on interdigited electrodes: (a) 4X and (b) 20X magnification.
As any geometrical variations of tissue samples can affect the impedance measurement, the following quality assurance steps and checks were implemented. The microtome (Reichert-Jung 2030) used in the experiment is regularly maintained by professionals to ensure accuracy. Additional slices of tissue were fixed onto coverglass and examined by SEM imaging. Figure 6 shows the size (diameter) of the single tissue core as being 0.6 mm. A cross-sectional SEM image of the tissue shown in Fig. 7 shows that the thickness of the tissue is 4.0 ± 0.17 µm. All specimens in one experimental batch were cut and transferred to devices in one session and hence the sample preparation condition was constant in terms of microtome thickness setting and water bath temperature. During the course of preparing and conducting the experiment, the tissue cores were quality assured multiple times under the microscope to ensure completeness of the circular shape and flat attachment to the device.
2.3 Circuit Modeling
The microchip interface is modeled using a lumped circuit model as shown in Fig. 8. The equivalent circuit consists of the PBS solution resistance RPBS, the double layer capacitance at the electrode-solution interface Cdl and the tissue impedance Zt in a series connection [44]. The tissue impedance Zt is modeled as an ohmic resistance Rt and a capacitance Ct element in parallel [45]. The net series impedance is given by:
Znet = Xdt + RPBS‖Zt + Xdt
(1)
where,
Z
=
Rt
1+j2πf
Rt
C
and
X
=
1
j2πf
C
Where, f is the frequency, Xdl is the capacitive reactance due to the electrical double layer and
j=
−1−−−
Z
=
2
j2πf
C
+(
R
PBS
1+j2πf
Rt
C
)
(2)
Simplifying Eq. 2, we get:
Z
=
RtR
PBS
(
Rt
+
R
PBS
)
(
Rt
+
R
PBS
)
2
4
π
f
C
PBS
+1
−j[
1
πf
C
+
2πf
C
PBS
(
Rt
+
R
PBS
)
2
4
π
f
C
PBS
+1
]
(3)
The circuit parameters are estimated by fitting Eq. 3 to the experimental data. Optimal parameter estimates should describe both impedance and phase data; hence residuals from both impedance and phase need to be accounted in the fitting approach. We solve the following non-linear regression problem to estimate the circuit parameters:
Minimize=log[
∑ni=1
(
Z
−|
Z
|)
2
]
+log[(
∑ni=1
(
ϕexpi
−arg|
Z
|)
2
)]
(4)
Where,
Z
3. Results and Discussion
Breast lesions include many different classes of pathologies, among which invasive ductal carcinoma is the most common form of breast cancer. In this work, we investigated differences between high grade invasive ductal carcinoma with tumor adjacent areas and benign morphology. Impedance measurements were performed for the uniform-sized benign and cancerous breast tissue cores placed on IDEs using an Agilent E4980A impedance analyzer. The impedance analyzer was calibrated and operated using the following steps: (a) Connect the probes and turn the power ON, (b) press “Measurement Setup”, (c) press “Correction” and test for open measurement and short measurement, (d) once the open and short measurements are done, press “Measurement Setup” (e) press function key and select “More” option. Select: z → z-θd, frequency → 2 MHz, Level DC = 10 mV, Trigger → manual, measurement time → Medium, average = 16, (f) press “Display Format”. Select “List Sweep” and press “Trigger”.
A voltage of 10 mV was applied to the contact pads using gold probe tips. No DC bias was applied. The impedance magnitude and phase were collected over a frequency range of 100 Hz to 2 MHz.
The impedance and the phase shift was measured for benign and cancerous breast tissue on microchip having IDE with 10 µm spacing and 30 µm spacing and are shown in Fig. 9 and Fig. 10 respectively. It was observed that the impedance and phase shift for both benign as well as cancerous breast tissue placed on IDEs with 30 µm spacing were higher than those of tissue placed on IDEs with 10 µm spacing. Furthermore, the differences between benign and cancerous tissues were more prominent when examined with the 10 µm spacing devices than on 30 µm spacing devices (3110 Ω vs 568.0 Ω at 200 KHz respectively).
Plots of impedance measurement for (a) benign breast tissue and (b) cancerous breast tissue placed on microchips with IDEs having 10 µm and 30 µm spacing, respectively.
Plots of phase measurement for (a) benign breast tissue and (b) cancerous breast tissue placed on microchips with IDEs having 10 µm and 30 µm spacing, respectively.
The slope covering the majority of the frequency spectrum is characteristic of the double layer capacitance at the electrode-solution interface Cdl and the tissue impedance Zt in a series connection [32]. As the impedance is inversely proportional to the capacitance, the impedance decreases when the capacitance increases on increasing the surface area. It is consistent with our observation that the impedance increases when the spacing between the IDE fingers increase.
Impedance measurements were performed on a total of eight specimens, which included 4 benign specimens and 4 cancerous specimens. Two representative cores were extracted from each specimen. Microchip having IDEs with 10 µm spacing was used for a total of 16 measurements. As seen from Fig. 11, benign and cancerous breast tissues have clear differences in their impedance and phase, which can be identified using microchip and impedance analyzer. Note that the benign breast tissues (8023-1, 8023-2) and cancerous breast tissues (3343-7, 3343-8) were acquired from older archived tissues (over 8 years in storage), which may have affected their bioimpedance characteristics.
Plots of (a) impedance and (b) phase measurement for benign and cancerous breast tissues using microchip having IDEs with 10 µm spacing.
Prior to our first attempt to study impedance properties of FFPE specimen, we were not sure what affect the preservation method or the length of storage had in change of impedance and we had to hypothesize that it had minimal or limited effect. It can be observed from Fig. 11 that: (i) the duplicated core from each case shows similar characteristics indicating the stability of measurement, (ii) with increase in frequency, the impedance of the tissue decreases and (iii) impedance properties aggregate well within the benign and cancerous group while the distinction are clear between groups.
Our findings are consistent with observations by Halter et al. [47], where they measured the impedance of benign and cancerous prostate tissues. Gabriel et al. reported average increases in conductivity from 0.1 to 0.3 S/m over the frequency range of 1 kHz to 1 MHz for a number of different human tissue types [48].
The Bode plots along with their model fits for all 16 specimens are shown in Fig. 12 and their corresponding circuit parameters are tabulated in Table I. Our initial results indicate that increasing malignancy leads to a corresponding increase in the tissue impedance. The mean tissue resistance Rt in cancerous specimens was 7125.3 Ω compared to 434.19 Ω in benign specimens, while the mean tissue capacitance Ct was observed to be 0.016 nF in cancerous specimens compared to 1.229 nF in the benign specimens. At a frequency of 200 kHz, the mean tissue impedance increased from 236.60 Ω in benign specimens to 7052.0 Ω in cancerous specimens. Since the sample size in our present work is small (8 benign and 8 cancer specimens), we used t-test. We carried out t-test at 200 kHz for benign and cancerous tissue cores and found that the p-value was less than 0.00001667, which indicates that the difference in the impedance value is statistically significant. From Table 1, it is also evident that the fitted circuit parameters Cdl and RPBS differ in benign and cancerous breast tissue specimens. This is most likely attributed to alterations in the ionic environment in the vicinity of the breast tissue cores. The results obtained from the present work (Cdl = 0.7 nF to 10.6 nF, Ct = 0.027 nF to 2.935 nF and Rt = 86 Ω to 9734 Ω) follows similar trend as reported in literatures focusing on measuring impedance in cells [32].
Plots of (a) impedance and (b) phase measurement for benign and cancerous breast tissues using microchip having IDEs with 10 µm spacing. Also overlaid are the respective fits in red (for cancerous breast tissue) and blue (for benign breast tissue).
Table 1
Fitted model parameters of the equivalent circuit for benign and cancerous breast tissue specimens shown in Fig. 12
| Tissue Label | RPBS (Ω) | Cdl(nF) | Rt (Ω) | Ct (nF) | Zt (Ω) (at 200 kHz) |
|---|---|---|---|---|---|
| Benign 8023-1 | 222.02 | 1.777 | 314.33 | 0.211 | 313.24 |
| 8023-2 | 155.81 | 1.994 | 393.57 | 0.177 | 155.72 |
| 26945-3 | 35.349 | 8.281 | 86.564 | 1.480 | 35.272 |
| 26945-4 | 31.488 | 10.654 | 132.12 | 2.935 | 31.278 |
| 27122-5 | 29.871 | 8.329 | 284.22 | 1.114 | 264.06 |
| 27122-6 | 31.843 | 6.283 | 896.81 | 1.674 | 419.88 |
| 26106-7 | 47.029 | 5.288 | 265.48 | 0.450 | 262.54 |
| 26106 | 35.26 | 6.547 | 1100.5 | 1.796 | 410.86 |
| Mean | 73.583 | 6.144 | 434.19 | 1.229 | 236.60 |
| Cancer 25313-1 | 851.01 | 1.617 | 5821.5 | 0.023 | 5735.9 |
| 25313-2 | 971.69 | 1.781 | 5710.6 | 0.013 | 5682.4 |
| 24468-3 | 1274.4 | 0.931 | 5706.9 | 0.027 | 5596.9 |
| 24468-4 | 911.39 | 1.359 | 8456.4 | 0.010 | 8400.4 |
| 24531-5 | 866.06 | 0.869 | 9734.5 | 0.011 | 9634.5 |
| 24531-6 | 1096.2 | 0.780 | 7893.5 | 0.011 | 7843.6 |
| 3343-7 | 1166.4 | 0.705 | 6968.9 | 0.018 | 6882 |
| 3343-8 | 720.7 | 0.933 | 6710.7 | 0.017 | 6641 |
| Mean | 982.231 | 1.121 | 7125.3 | 0.016 | 7052.0 |
Many changes occur during the onset and progression of tumor. Morphologically, cancer cells tend to be larger, with increased nuclear-to-cytoplasmic ratio and often basophilic due to heightened protein and nucleic acid synthesis. Biochemists found many disturbed signaling pathways showing either enhanced or diminished activities. Another famous and important phenomenon is that cancer cells loose contact inhibition which implies the usual anchoring and signaling between cell membranes, as well as between cells and extra-cellular matrix, have significantly altered. While it is impossible to prove any direct causation relation at the current stage, we believe that the observed impedance change reflects the overall tissue change in the cancer development.
4. Conclusions
A microchip capable of detecting benign and cancerous breast tissue is successfully fabricated using MEMS technology. The bioimpedance measurement technique was used to analyze benign and cancerous breast tissue cores that are fixed on the microchips. We found that cancerous breast tissue specimens displayed significantly different bioimpedance characteristics compared to benign breast tissue specimens. It was also observed that by decreasing the electrode spacing, the effective electrode area is increased, thereby increasing the sensitivity of the device. This is our initial attempt to investigate the feasibility of using bioimpedance measurement to assess cancerous tissue based on tissue specimens obtained through tissue microarray technology, which extracts uniform sized tissue cylinders from paraffin-embedded tissue blocks with a hollow needle. Though significant efforts were made to ensure uniform preparation of specimens, we recognize that there may exist minor intraand inter-experimental differences in experimental conditions including tissue heterogeneity, microtome settings, environmental temperature, etc. Further works in this area with more cases, different geometries of the tissues and electrodes, and variety of disease types will allow us to examine these factors and investigate them in more detail to further understand the impedance measurement phenomenon more clearly. We also plan to integrate bioimpedance measurement with mechanical characterization for automated sampling of breast tissue core specimens and apply this technology to investigate a broader set of breast diseases or other organs, especially at different stages of cancer progression.
Acknowledgement
This work was supported by the National Cancer Institute of the National Institutes of Health grant R01CA161375.
Biographies
•
Hardik J. Pandya is currently in Post-Doctoral position at the Robotics, Automation, and Medical Systems (RAMS) Laboratory in the Department of Mechanical Engineering at University of Maryland, College Park, USA. He completed his Bachelor of Science in Electronics (2002) and Master of Science in Electronics with gold medal (2004) from Sardar Patel University, Vallabh Vidhyanagar, Gujarat, India. He received his Ph.D. in Microelectronics Engineering from Instrument Design Development Centre, Indian Institute of Technology Delhi, India in 2013. He worked as Project Fellow at Department of Electronics, Sardar Patel University from June 2004 to July 2006. From August 2006 to February 2009, he served as a Lecturer in Department of Electronics, Invertis Institute of Engineering and Technology, Bareilly, U.P., India. From May 2009 to August 2012, he worked as Sr. Research Fellow and Research Associate in Center for Applied Research in Electronics, Indian Institute of Technology Delhi, India. His research interests include design and fabrication of Bio-MEMS, Flexible electronics, Bio-sensors, Microfluidic devices, synthesis of metal oxide nanostructures and their applications.
•
Hyun Tae Kim received the Bachelor of Engineering degree in Mechanical Engineering from Korea University, Seoul, Korea, in 2006, and the Master of Science degree in Electrical Engineering from the University of Utah, Salt Lake City, USA, in 2012. He is currently working towards his Ph.D. degree in Mechanical Engineering at the University of Maryland, College Park, USA. His research interests include MEMS, robotics, automation, and medical systems.
•
Rajarshi Roy is currently a working as a Postdoctoral Research Scholar in the Department of Mechanical Engineering at Vanderbilt University, Nashville, Tennessee. He completed his undergraduate studies from Jadavpur University, Kolkata, India in 2008 with a B. Eng degree. He received his M.S. and Ph.D. in Mechanical Engineering from the University of Maryland, College Park in 2013 and 2014 respectively. His research interests include image-guided micromanipulation, AFM based micro-scale tissue characterization and soft tissue biomechanics.
•
Wenjin Chen received her Ph.D. degree from joint program in Molecular Biosciences at University of Medicine and Dentistry of New Jersey and Rutgers, the State University of New Jersey in 2005. She is currently working as Associate Director, Computational Imaging at Center for Biomedical Imaging & Informatics, Rutgers Cancer Institute of New Jersey, and oversees the imaging services at Histopathology and Imaging Core Facility. Her research interest focuses on utilizing image processing and computer vision methods, robotic and virtual microscopy to facilitate new technology development in cancer research.
•
Lei Cong received the MBBS degree from China. She has both HTL and QIHC certifications from ASCP. She is the Supervisor of Histopathology and Imaging Shared Resources, and the Biospecimen Repository Services in Rutgers Cancer Institute of New Jersey, New Brunswick, New Jersey, USA. She oversees both the Histopathology and Imaging Services, and the Biospecimen Repository Services. She is responsible for quality control of tissue specimens, tissue microarrays, specimen storage and acquisition, histology and IHC, and biospecimen database managements, etc. She is engaged in many research projects involving tissue microarrays, multispectral imaging analysis, study design, clinical trials, and biomarkers.
•
Hua Zhong studied medicine in China where he also had his initial clinical and research training. In 2001, following a 3-year fellowship at Johns Hopkins Oncology Center, he became a research faculty member at Emory University School of Medicine. Currently, he is a boardcertified surgical pathologist at Rutgers Robert Wood Johnson Medical Group, teaches medical students at Rutgers University, co-leads biorepository service and provides pathology support for research projects conducting at Rutgers Cancer Institute of New Jersey.
•
David J. Foran earned a bachelor’s degree in Zoology and Physics from Rutgers University in 1983 and received a Ph.D. in Biomedical Engineering jointly from Robert Wood Johnson Medical School and Rutgers University in 1992. He was recruited to the Faculty and currently serves as Professor of Pathology, Laboratory Medicine & Radiology and Chief of the Division of Medical Informatics at Rutgers -Robert Wood Johnson Medical School and as Executive Director of Biomedical Informatics & Computational Imaging and CIO at Rutgers Cancer Institute of New Jersey. A major concentration for Foran’s laboratory has been the development of a family of data-mining, imaging and computational tools for characterizing a wide range of malignancies and elucidating the role that protein and molecular expression plays in disease onset and progression. This work has resulted in numerous publications, invited book chapters and several pending and issued patents. This work has received competitive extramural funding from the Whitaker Foundation, the NJ Commission on Science & Technology, the federal Defense Advanced Research Projects Agency (DARPA), the Department of Defense (DoD), the Radiological Society of North America, the National Institutes of Health (NIH) and the private sector.
•
Jaydev P. Desai is currently a Professor in the Department of Mechanical Engineering at University of Maryland, College Park (UMCP) and the Director of the Robotics, Automation, and Medical Systems (RAMS) Laboratory. He completed his undergraduate studies from the Indian Institute of Technology, Bombay, India, in 1993 with a B.Tech degree. He received his M.A. in Mathematics in 1997, M.S. and Ph.D. in Mechanical Engineering and Applied Mechanics in 1995 and 1998 respectively, all from the University of Pennsylvania. He is a recipient of the NSF CAREER award and the Ralph R. Teetor Educational Award. He was an invited speaker at the 2011 National Academy of Sciences Distinctive Voices at The Beckman Center and was also invited to attend the National Academy of Engineering’s (NAE) 2011 U.S. Frontiers of Engineering Symposium. His research interests include image-guided surgical robotics, rehabilitation robotics, haptics, reality-based soft-tissue modeling for surgical simulation, model-based teleoperation in robot-assisted surgery, and micro-scale cell and tissue characterization. He is a member of the ASME and IEEE.
Footnotes
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