Prototype of a Digital Biopsy Device

8/8/2019 Prototype of a Digital Biopsy Device

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Prototype of a Digital Biopsy Device

Introduction

Our project measures and analyzes the electromechanical properties of

tissue using a probe containing a microarray of electrodes, to aid as a tool in

predicting the health of the tissue sample.

³Despite significant efforts to develop early detection strategies for breast

cancer, the diagnostic efficiency remains poor. Approximately 1 million

breast biopsies are performed annually as directed by mass screenings, but

70-80% of these are negative for cancer at an expense of $2.6 Billion. The

locally destructive nature of these biopsies incurs a 60% risk of serious

morbidity including lymphedema and nerve damage. The spectrum of new

devices and methods that have or will reach the market are marginal

improvements to the same basic approaches. Our objective is to create a

device to diagnose tissue health nondestructively with sensitivity comparable

to excisional biopsies.´ ± Dr. Jonathan Butcher, research advisor

The foundation of our device is a complex impedance analyzer that is

tailored to operate in the desired region of normal tissue impedance. An RC

circuit similar to the following can approximate the impedance of biological

tissue:



Figure 1:

Circuit Model of Biological Tissue

The hardware of our project is responsible for producing signals that

accurately reflect the resistance between electrodes and the phase shift

caused by capacitive elements of the tissue. Our Atmega644 generates

varying sinusoidal voltage waves that are stimulated through the tissue, and

converts the analog signals from our circuitry into digital representations of

the acquired data. The data is serially sent to a remote PC, and is utilized by

Matlab for analysis.

High Level Design

Our project was inspired by current research of one of the team members,

Grant, in the Biomedical Engineering Department at Cornell. He is

investigating how mechanical stresses to tissue can provide information on

the tissue¶s health. Electrical tomography devices currently exist in the

medical community, however, they focus just on analyzing electrical signals

through the body to spot abnormalities. No major projects currently utilize a

system that manipulates both electrical and mechanical properties of tissue

at once in attempts to characterize its health, and thus a potentially

attractive, yet nondestructive device lends itself to our ECE 476 project.



Grant¶s previous work in the research group had been to develop a simple

two-electrode probe that could apply a suction pressure to tissue, and

monitor the electrical resistance between the electrodes in hopes to find a

correlation between elongation and resistance. Currently, a more

sophisticated probe was developed with a microarray of 6 pairs of

electrodes, and a more elaborate method of testing could now be used.

Figure 2: Acquired Data from Single Electrode Probe

First, the electrode array provides increased sensitivity for measurements,

because it can collect impedance data from 6 distinct points along the tissue

sample. Also, it provides a way to measure the elongation into the pipette

non-optically, and can generate a stress-strain curve for the tissue sample.A set of known distances within the pipette can be established since the

electrodes are spaced equally throughout the array at 250um apart, with a

width of 250um. When tissue first crosses an electrode pair, the resistance

between the two nodes drops from an extremely high value (since it is



equivalent to an open circuit), to a real valued resistance. This initial drop in

resistance can be equated to the tissue just reaching the electrode, and

therefore correlated to the distance the electrode is into the pipette. By

monitoring the pressure before each impedance reading, we can quickly

establish a stress-strain point from this information. Further, when tissue

completely covers an electrode, the resistance saturates to a fairly constant

value, allowing us to establish another pressure vs. length data point. We

can then generate a stress-strain curve with 12 data points; while the

resolution and accuracy of this method could be increased with more

electrodes for future models, our current electrode array provides enough

data for initial research and testing.

Impedance is a complex number that represents the resistance as its real

part and reactance as its imaginary part. The phase angle of impedance

gives the phase difference between the voltage and current in the system.

Resistors have entirely real impedances, and capacitors are entirely

imaginary. By measuring the voltage across an unknown load for a given

current, we are able to calculate the magnitude of the impedance using the

formula Z = V / I. Then, if we know the phase angle, we can find the

resistance and reactance of the load.

This is the basis of our impedance circuitry. A constant current source

generates a consistent current, which is driven across a load, in our case a

tissue between a given set of electrodes. By building circuitry that captures

the resulting voltage drop between the pair of electrodes, we have the

necessary data to form the relationship between current, voltage and

impedance. Our circuitry also utilizes a phase comparator that detects the

resulting shift between the input sine wave, and the sine wave that results

from stimulation across the tissue.



We used a hardware resistor ladder combined with a sinusoidally varying 8 bit variable togenerate our sine wave. We started by creating a sine look up table with 256 values spaced

evenly throughout the period of a sine wave. We would assign one of these values to the output based on a memory look up. The index of the memory input was an 8-bit char that was actually

the highest 8 bits of a 32 bit phase accumulator. The accumulator is updated at 125 kHz by

adding a predetermined increment to it. The increment is determined by what frequency outputis desired. A small increment may cause the 8 most significant bits to remain unchanged for many accumulator updates. One of the early trade offs we were forced to make was how fast to

have these updates occur. Higher frequency updates allow greater resolution of the sine wave,while lower frequency improves the performance of the rest of the program by minimizing the

number of hardware interrupts that pause execution to allow the ISR to execute.

We only used the 7 most significant bits of the MCU output to drive our resistor ladder. Addingthe 8

thbit would not have increased the peak voltage much and would not have had any effect on

the resolution of the wave. These bits enter the voltage ladder, which acts as a voltage summer,with each bit weighted differently.

Figure 3: Flowchart of C Program

The major hardware/software tradeoff we encountered entailed the

generation of our input voltage sinewaves. We chose to utilize a resistive

ladder for digital to analog conversion, and this limited our ability to produce

smooth input waves above 10kHz. This was not too much of a concern for

us, however, since most biological tissue testing uses frequencies ranging

only up to 100kHz. We realized the higher order of magnitude would be very

difficult to generate and is often less important in analysis, and decided only

to pursue frequencies below 10kHz.

Another tradeoff occurred in the implementation of syncing Matlab with theMCU. In order to ensure the first values that Matlab received were from the

first data points taken, we had to first run the Matlab script files, followed by

turning on the MCU afterwards. Given more time for project implementation,



we could have implemented this feature, but it was not a priority for us

during development.

Medical devices require attention to the details of IEEE standards, such as

isolation of the user/patient from 120V sources. Since our device is currently

only going to be used on hydrogels and excised animal tissue samples, this

isolation is not necessary. Also, IEEE Standard 1073 regulates medical

device communication, and although it did not impact our project directly, it

might be a Standard to abide by for long-term implementation of a device

similar to ours.

Program Details

Our software is made up of two parts: the C program that controls the MCU and the Matlabscript that receives data from the MCU and analyses it. The primary task of the MCU is to

control the electrodes, collect data from the probe and pressure sensor, and transmit that data tothe computer. The Matlab script first receives this data and then performs some calculations on

it, eventually graphing the impedance measure by each set of probes against the pressure on thetissue at that point.

C Program

The C program consists of a single ISR that handles basic timing and sine wave generation, amain function that schedules the probe readings and 6 nearly identical functions that each takes

readings from a pair of electrodes. The ISR is set up so that it executes on the timer0 comparematch at 127. This means the routine is executed at 125kHz. It¶s primary purpose is to generate

the sine wave. In software, this is done similarly to the DDS from lab 2, where a 32 bitaccumulator is incremented by a different amount depending on the desired frequency and the

top 8 bits are used to generate a sine wave via lookup table. However, unlike lab 2, we don¶toutput this wave through the PWM. Instead, we take the result of the sine table look up and

output it through a resistor ladder as a manual digital to analog converter. The second functionof the ISR is to decrement the function timers every millisecond to establish the time base.

The main function performs a couple basic actions. First, it initializes the sine table that is used

for the sine wave generation. Next it calls the initialize function which performs the standardMCU set up. It sets up the uart so we can use the serial connection to communicate with the PC,

sets up timer0 as was already described, sets up PORTA as an input and PORTB, C and D asoutputs, initializes the time counters and various flags, sets up the ADC for its first input and

turns on the interrupts. Finally, the main function enters a timing loop that executes forever andcalls the various tasks whenever they are due.



Each task is designed to perform the same functions, just to a different set of electrodes in the probe. First each task outputs the number of the probe that is about to take the readings. This is

so the Matlab script can organize the data by probe to develop an elongation distance estimate.After this, the MCU measures the pressure in the tube. This requires setting the ADC to read

PORTA.0 and then taking a reading. While the conversion is happening, the program simply

waits idly for it to finish before outputting the pressure to the serial port. Next, it will measurethe resistance (IMPEDANCE???) and phase shift of the sine wave through the tissue at 100Hz,500Hz and 1000Hz. To do this, we change the input to the ADC to PORTA.1 for the resistance

(IMPEDANCE???) and PORTA.2 for the phase shift and start a conversion. The increment ischanged before every different frequency measurement and the sine wave is given a short period

of time to settle into a steady state before measurements are taken. To minimize the disturbancefrom system noise, we actually perform 20 conversions (10???) for each reading and average the

values together. These values are output to the serial port.

At the start, only the first electrode (on the tip) is tested in this way. When it encounters a non-infinte impedance (indicating the presence of tissue), it triggers a flag, which allows the second

electrode to start taking measurements as well. When the second electrode starts to encounter tissue, the third electrode is activated, and on down the line. This scheme is more efficient than

scanning all six electrode in series every time because if the second electrode hasn¶t encounteredtissue, then the third or fourth obviously would not have encountered any either.

Matlab Script

The Matlab script sets up a serial port object to read data from the MCU serial connection, and

then enters a loop. In this loop, it reads raw data from the MCU in a certain order (discussedlater ) and then processes that data into real electrical values. We decided to do this on a PC

instead of on the MCU both because of the PC¶s better capacity to handle large numbers and

complex calculations and because Matlab is far simpler to use for the calculations than C programming.

The data retrieval loop uses fscanf to get data from the serial port and performs calculations and

sorting operations after it has gathered each piece of data from a given run of electrodes. At firstwe were concerned with the timing of performing these calculations between scans of the serial

port, but doing so after all the data was collected was especially tedious. We decided to move itinto the data retrieval loop after we reaized that the PC runs more than 100x faster than the

MCU, and that it would be able to perform these extra with plenty of spare run time.

After all the data has been collected, the script writes it to a Excel file and outputs some plots of

the data. It plots the impedance against the pressure for each of the six electrode pairs. Thesecurves are interesting individually, and plotting them all on the same axis also allows us to infer some information about the elongation of the tissue into the pipette, which can also be useful in

future diagnostics run on the device.

C-Matlab Interface



To minimize programming complexity, we decided not to worry about error checking the inputto the Matlab script. We didn¶t do anything in software to allow the PC and MCU to sync up on

their own. Instead, we rely on a very specific data output from the microcontroller. Data isalways output in the following order: electrode number, pressure, voltage at 100Hz, phase

difference at 100Hz, voltage at 500Hz, phase difference at 500Hz, voltage at 1kHz and phase

difference at 1kHz. If the Matlab script is activated when the microcontroller is in the middle of a continuous output, there is no guarantee that the correct data sets get stored in the correctvariables in Matlab. To handle this, we always begin running the Matlab script while the MCU

is powered off, and only activate it when Matlab is ready to receive input. One way we couldhave handled this is a simple activate button on the MCU that would allow the MCU to start

taking measurements after a restart. Another more complicated method, if we had more time,would be to output a syncing character at the beginning of every function, just before the

electrode number. The Matlab script could enter a while loop that waits for this character toshow up, and once it does, it then procedes to read next values to their respective variables.

Software Hurdles and Solutions

For the most part, the software for this project came together fairly easily. The MCU¶s primary

function is to read an analog data signal and relay that data to the computer. Development of theMatlab software was fairly straightforward as well, taking data from a serial port, scaling it the

appropriate amount and graphing it. One snag we did encounter was related to the length of timeit took for our probe to take the impedance measurements. We were planning on running testing

every pair of electrodes each time through the main loop to determine both the impedance andthe elongation. Because it takes longer for us to take the measurement than we had anticipated,

we had to come up with a more efficient way to do this and still produce the desired curves. Weended up with the scheme we have now, where electrodes are activated one by one, starting at

the tip, only after the previous electrode has encountered tissue.

Another example of a problem we overcame was with our sine wave generator. Originally, our plan was to implement this sinewave using the PWM, similar to how we implemented the DTMF

dialer in lab 2. However, after some preliminary testing, we discovered that this method did not produce smooth sine waves for frequencies greater than 3 kHz. At this point, we were planning

on measuring impedances at10 kHz, so we changed our plan and implemented the DAC withresistor ladder that we currently have. While this was an improvement over the PWM, it was

also unable to produce sufficiently smooth curves at 10 kHz, so we were forced to scale back our project.

Hardware

We generated our input signals through the 7 MSB of PORT B, using a digital

to analog resistive ladder to interface the software and create a sinewave of

controllable frequency. The resistors in the ladder were 1% DIPs of 10k;



and 20k;. The resulting signal was fed through a passive high pass filter to

center the wave on zero volts.

Figure 4: 5-bit resistor ladder, digital to analog converter

To generate a constant current, our design utilized a LM358 op amp

configuration that input the initial sinewave over a resistor into the inverting

input, while the non-inverting input was tied to ground. From the golden

rules of op amps, this created a virtual ground at the inverting input as well.

The output from the op amp was fedback to the inverting input through the

load, in our case the tissue between two electrodes. This scheme was a basic

implementation of an inverting op amp, and established a constant current

over the resistive load in the feedback loop, since the current across Rin

must be equal to the current through the resistive loop, since no current can

flow into the inputs of the op amp.



Figure 5: Inverting op amp constant current source

This setup allowed us to establish the following relationship for resistance of

the tissue as the load:

ZTissue = Vout * Zin/Vin

For each frequency used, the input voltage slightly varied, but was always

around 1.25V in amplitude (2.5V peak-to-peak). Since for a given frequency

the input voltage was always the same, regardless of the load, we recorded

these values for use in our calculations.

The input resistance was selected as 1M;. This resistance value generated a

constant voltage of around 2.5QA. We desired such a small current so as not

to overload the maximum current and voltage thresholds of our electrodes,

which are around 100mV and 100QA. Based on previous testing and

research, normal tissue resistances were observed around 5 ± 20k;, which

would be small enough to not overload the electrodes with a high voltage.

In order to select the desired electrode pair on the microarray, multiplexors

were inserted on either side of the inputs to the electrodes. These

multiplexors have 8-channels that propagate analog signals between the

desired ports. They are also bi-directional, meaning a single input can be fed

to one of eight outputs, or one output can be selected from eight different

inputs. PORT C was used to control the channel selection and enable bits.



When we first implemented the multiplexors in our circuit, we noticed

significant cross-talk between channels. To alleviate this problem, we

realized we had to tie all of the outputs through a large resistor to ground, in

order for them not to float.

One of the most important features of our hardware was its ability to detect

and represent the peak voltage of the sinusoidal voltage wave resulting from

the load (tissue) being driven with a constant current. The first step in this

process was to dig out the small signal caused by a low current amplitude.

We utilized a INA121 differential amplifier to compare the difference

between the output of the current source op amp and ground. The difference

was amplified 50 times by placing a 1k; resistor across the gain pins of the

INA121, based on the gain equation, G = 1 + 50k; / RG. Since the A/D

convertors on the 644 accept a voltage range from 0 to 5 volts, a gain of 50

from the diff amp sets a theoretical upper limit for measurable resistance at

80k;; though our theoretical minimum would be 0; (i.e. a short circuit),

this is not realistic. In order to produce an accurate, differentiable result, a

practical minimum resistance would be around 1k;, since this would result

in a voltage amplitude of .0625V, which could be represented by a digital 3,

since the A/D assigns one bit for every .0196 volts input.

The amplified signal from the differential amplifier is fed into the non-

inverting port of another LM358 op amp combined with a diode connected

across the negative feedback loop. This creates an ideal diode that does not

cause voltage loss between Vin and Vout. This serves to pass only the half of

the sine wave (calues above 0V) from the differnential amplifier. By

connecting a capacitor across the output to ground, we have created a peak

voltage detector that stores the maximum voltage incident on it, in our case

the peak of the sinewave. The diode does not allow the negative half of the

sinewave to propagate and cause the peak voltage to oscillate between



peaks of the sinewave. This capacitor holds the peak voltage until making

PIN A.1 an output of 0V grounds the connection and discharges the

capacitor. During voltage detection, PIN A.1 acts as an input to the A/D

converter, and the ADMUX is set to 1. After each read at a stimulating

frequency, A.1 is grounded to discharge the capacitor, and then reset to an

input before the next read. A resistor is tied between Vout and A.1 in order to

avoid floating values for voltage.

Figure 6: Ideal diode connection with peak voltage detector capacitor

A phase detector is implemented to determine the shift between input and

output voltages. The input voltage wave is directed through an inverter and

high-pass filter voltage follower, before being fed into a LM339 comparator.

The signal, which is centered around zero, is compared against ground, in

order to produce a square wave representing its 50% duty cycle. The square

wave runs from a high of 5V to a low of 0V. The output of the differential

amplifier is also fed through a high-pass voltage follower to ensure that it is

still centered around zero, and is also fed to an input of the LM339. It is

also compared against ground, and a similar square wave is produced. The

resulting square waves are input into an XOR IC to produce a final square

wave that represents the phase difference between the two. If the signals

are perfectly in phase, a signal of zero volts is produced do to the exclusive

or condition. When the signals are 90o out of phase, due to a phase shift by

a capacitor, the signal is has a 50% duty cycle. By low pas filtering this, we



extract the average DC value of the comparison, and this can be linearly

correlated to the phase shift, as mentioned.

Though we had already thought of comparing the phase difference using

square waves, we would like to attribute some of our success to a previous

ECE 476 final project done by Vincent Lee and Jeffrey Wu. When we started

our project, we had not previously noticed their project, and the similarity it

had to ours. However, as we were working on our phase comparator, we

saw they had implemented theirs in a similar way to what we were thinking.

Their report helped us pick the appropriate comparators and XOR IC, and

also gave us the idea of low pass filtering the output from the XOR to create

a linearly correlated voltage that corresponded to phase difference.

Finally, our last piece of circuitry in our design was a pressure sensor used

to determine the pressure being applied to our tissue sample inside of our

probe. We used a MPXV6115 produced by Freescale Semiconductors, which

was a differential pressure system capable of determining suction pressures

from -16 psi to 0 (-110 to 0 kPa), which were typical pressures observed in

initial testing. The sensor took rails of 5 and 0V, and output .045V/kPa .

Figure 7: MPXV6115 Pressure Sensor

Some of the main difficulties we had with the hardware was correctly

establishing the right rails to use for our comparators and XOR phase IC. We

tried a few rail combinations that did not adjust the desired output to the 0-

5V range, which was needed for A/D conversion.



Circuit Schematic:

Mega644 Connection Diagram:

R esults

One of the main limitations of our device is the speed of execution. Our

fundamental concept relied on reading multiple sources for A/D conversion,

and we figured our system would be only as slow as the time it took to



switch the ADMUX and for new frequency signals to establish and propagate

through the circuit. However, one of the limiting factors of the execution was

the time necessary to allow the peak voltage detector capacitor to discharge

and readjust to the new voltage. This took a considerable amount of time,

and slowed down the speed at which the overall device could operate. We

can overcome this by simply applying negative pressure to a sample at a

slower rate, however, this significantly increases the amount of time needed

to do a test, and reduces the feasibility of the project as an actual medical

device or product until further refinements are made. The pressure sensor

and phase comparator operate efficiently, and the output to Matlab also

happens in a timely fashion.

The accuracy of our system is fairly good at around 5% for resistance and

phase measurements, while the pressure sensor is very accurate; all

sinewaves produced remained within 1% of the expected frequency, both at

the input and across the load and through the diff amp. Though the accuracy

of our impedance measurements could be improved, this was a tradeoff for

speed. Since this is only a first generation prototype meant for research

purposes, we were more concerned with ease of use. 5% accuracy allows us

to observe general trends in data, and upon testing and research, we could

tailor a circuit to have the most accuracy in operating regions of greatest

sensitivity and importance.





Figure 8: A pair of Impedance vs. Pressure graphs generated by a single electrode probe

attached to our MCU

Our device can be easily used by almost anybody, however it is currently

tailored to people doing research of biological tissues, since it mainly

functions to output data for analysis. If this were to be implemented for

clinical use by physicians to use in diagnosis of real patients, both safety and

usability issues would need to be considered. A major safety concern would

to be to isolate the patient from the any type of 120V power supply, so that

no malfunction gets propagated to the tissue between electrodes. Otherwise,

the small current and voltage required by the electrodes would not be large

enough to significantly hurt the tissue. Also, the system applying pressure



would require regulation, so large fluxes or absolute values of pressure were

not applied to the patient. Currently, we believe the pressure ranges we are

operating at are not destructive to the tissue, besides the mild side effects

resulting from an applied pressure. Also, doctors would not be concerned

with the exact impedance and pressure data from the device, so to increase

usability, efficient software should be implemented that can analyze the

recorded data and make a recommendation to the doctor about the tissue¶s

health, such as outputting a score representing the perceived health of the

tissue.

Conclusion

Overall, our design met our basic expectations, however, we were not able

to achieve everything that we wanted due to time constraints. We were abe

to successfully measure the agar gel with a single electrode probe, but we

were unable to interface the MCU with our 6-electrode probe. The research

group delivered the 6-electrode to us later in the development cycle than we

were expecting. On first testing, the probe did not work with the gels. This

could be the result of a manufacturing error related to the small size of the

micro-array or exceeding the probe's threshold voltages, despite our best

efforts to remain within the constraints. On the other hand, our MCU could

accurately calculate impedance values and pressure values, and

communicate with Matlab through serial communication in order for data

analysis. If we had more time, we would have liked to implement a smart

algorithm in the MCU to interpret some of the basic results with the help of

Matlab, and to hone in its testing on specific electrodes of interest in order to

provide more accurate data. For example, if allowed to test this system and

generate some data sets, we could have programmed the microcontroller to

pick up on differences from the expected data ranges, and tailor its testing

to specific electrodes and frequencies where abnormalities occurred.



No major standard currently applied to our project, however, we began to

look into Standard 1073 that details how medical devices in clinical use must

have ease of use similar to Plug-and-Play, where a device can be hooked up

to a data terminal and easily be connected with the software. Also, it states

that the data must be easily transferable and compatible between such

layers as the front-end diagnostic system to the hospital¶s database and

patient records.

Upon starting this project, we were building our circuitry and software from

the ground up with little help from external resources, other than from input

from Bruce. About halfway through the design, we discovered a similar

project to ours had been done in the Spring of 2007, by Vincent Lee and

Jeffrey Wu, where they implemented a complex impedance analyzer. At this

point we already had the peak voltage detector working, and were in the

process of implementing the phase comparator. Though our initial design

plans were to utilize comparators to determine phase shift, Vincent and

Jeffrey¶s project pointed us in the right direction as far as parts to use, and

more importantly gave us the idea to implement a low pass filter to convert

the duty cycles to a constant DC representation. We developed most of our

code ourselves, except for a Matlab communication file supplied to us by

Bruce.

We did not reverse engineer any parts of our project, and did not have to

sign non-disclosures for sample parts.

Patent opportunities may exist for the a device similar to this in the long run.

However, a more powerful and sophisticated device than our current project

would need to be developed, and extensive testing on tissue would need to

be done in order characterize exact implementation standards and to

determine software schemes to be used. As noted before, our device is not

as fast as we had hoped, and has a somewhat limited range of impedance



measuring capabilities, meaning more professional hardware will have to be

used for upcoming testing.

Once again, publishing opportunities are not apparent for our current device.

Once the research side of the project progresses, publishing may become an

option.

As engineers progress in their studies and begin applying learned skills to

actual projects, following the IEEE Code of Ethics becomes very important.

During the development of our project, we abided by the Code of Ethics, and

our behavior can be highlighted by some of the actions we took. Abiding by

Code 2, we avoided conflicts of interest by actively sharing the resources of

the lab, whether it was tools, soldering stations, or lab benches. According to

Code 7, we actively sought out input and critiques about our project from

the TA¶s and from Bruce while working on it in lab. We recognize the

contributions made to our project by the previous work of former 476

students, and also credit Bruce with supplying us with some sample code to

get our MCU communicating with Matlab. Acting by Code 9, we avoided

harming others¶ property and projects by using care when working around

others, and being observant and respectful when others were demonstrating

or showing us their work. Also, according to Code 10, we often collaborated

with other colleagues by actively discussing different parts of our various

projects, whether it was to give advice on hardware, offer input for software

implementation, or provide feedback on design choices. And most

importantly, this lab allowed us to improve our technical competency by

embedding ourselves in a complex project design that consisted of extensive

hardware and software implementation, thus complying with Code 6.

Since no wireless signals are generated, the only legal considerations

applicable to our project may include medical device malpractice



documentation; however, since our specific project is not intended for

clinical use, no legal considerations are relevant to our project.

Documents

Prototype of a Digital Biopsy Device