Biological Stiffness Measurement

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Sponsors Dr. Bryn Martin, Dr. Mike Maughan
Team Name NeuroTouch
Duration Fall 2016 - Spring 2017
Faculty Advisors
  • Dr. Dev Schresta
Graduate Advisor
  • Richard Leathers
  • Students
    • Brooke Deans
    • Brendan Souvenir
    • Christina Gibbs
    • Mazen Aljawi
    • Matt Harlow

    The aim of the project is to design and develop a handheld device that can accurately measure the stiffness of a biological tissue, specifically a brain in vivo.


    Problem Definition[edit]

    Background[edit]

    Currently doctors have just been using their fingers to attempt to get an estimation on brain stiffness for use as a symptom of a disease. Doctors can use magnetic resonance elastography (MRE) to obtain stiffness, but there really isn't a device in place to in a matter of moments determine biological tissues stiffness. Brain stiffness has been investigated increasingly as an indicator of neurological diseases, including Alzheimer's disease.

    Specifications[edit]

    Specifications
    General Requirements Specific Requirements Target Values
    Handheld Create a device that can be held and maneuvered easily Ergonomic to a user's hand
    Ease of Use Create a device that gives accurate measure of biological tissue stiffness User can plug in device and use instantly with minimal setup
    Operate in Small Area Allow device to detect brain stiffness in small diameter hole in brain End effector tip is 1cm in diameter
    Quick Results Device electrical sensors and components can relay data and compute stiffness on external display Tissue stiffness displayed on monitor in less than 10 seconds, actual operations also less than 10 seconds

    Deliverables[edit]

    • Concept sketches/CAD models of the device with sensors in place
    • Final machined device
    • Matlab and Arduino programs used to obtain raw data from sensors and send it to computation in order to establish a best fit modulus of elasticity

    Project Learning[edit]

    Previous Methods[edit]

    While finding the elastic modulus of a metal can be done quite easily, our problem is finding that of a biological tissue which reacts very differently when an external force is introduced. There have been experiments performed to determine the elastic modulus of biological tissues, they are with devices that can't be made mobile, or devices that will either endanger the brain physically or through the introduction of foreign bacteria. Our findings on previous methods are shown below.

    Biological Tissue Stiffness Tests
    Pig Indentation
    Pigtest.png
    Pig Apparatus Detail
    Pigtest2.png
    Gun Device
    Guntest.png

    Biological Tissue[edit]

    Biological tissue stiffness operates differently than that of metals, as seen in the figure below in which a pig spleen was tested.

    Pigspleen.png

    We will be using a least-squares curve-fitting algorithm in order to map a best-fit curve to our force and indentation depth data points as seen below

    Fhcurve.png

    Design Development[edit]

    Mechanical Designs[edit]

    Device Concepts
    Concept 1
    Concept1.png
    Concept 2
    Concept2.png
    Concept 3
    Concept3.png
    Final Concept
    Final Design.png

    Electrical Hardware Designs[edit]

    System Design: Processor(s) and Memory[edit]

    The first revision of this project involved an idea of using a dual ported memory with two processors on either side. The first process would be involved with gathering the data and placing it within the memory. The second processor would focus on taking the information from the memory and calculating the elastic modulus from this information. It was then decided that due to the complexity of syncing two different processors over the same memory, it would be a better route of action to move towards a single processor solution.

    The second revision involved consolidating this design down to a single processor as to lessen the amount of complexity of the system. The choices under consideration were Arduino (specifically the Arduino Uno) and the Raspberry Pi 3. Upon further research, it became apparent that communication with the General Purpose Input/Output (GPIO) pins of the Raspberry Pi would be best accessed via the bash terminal, and due to lack of experience with this method of interfacing, the team chose to utilize the Ardiuno Uno within their design.

    System Design: Measuring Methods[edit]

    Originally, the team looked on towards three different methods of distance measuring: photoelectric, ultrasonic, and laser.

    Photoelectric sensors can produce accurate results when they are properly calibrated, but since their response is dependent upon the time taken from the generated signal to bounce off of the reading surface and back to the optical reading unit, a change in color of the surface can affect the reading. This means that for each color change, the photoelectric sensor would need to be re-calibrated. Since the NeuroTouch team was not entirely sure of a uniformly-colored surface for the brain, the decision was made to not utilize this type of sensing.

    The second option for proximity sensing was the ultrasonic sensor. From the project learning research, it was found that ultrasonic sensing was very good for minute distances (on the order of <100mm) which is exactly the range that the device is specified to measure. Additionally, a representative from Balluff was able to provide the group with a sensor to experiment with. After some preliminary testing, it was found that over the range planned for the device (30-40 mm), there was significant noise on the output when making sub-millimeter measurements. The only solution to increase the precision of measurement would be to move the sensor closer to the tip of the unit, and even that would not guarantee an increase in accuracy. Thus, the team chose to utilize a third option of measurement reading: laser.

    In a side-by-side comparison, it was shown that laser sensing can produce a more stable, consistent, and precise reading than the ultrasonic sensor, at both close (approx 10 mm) and medium (>40 mm) distances. This is the current proximity method used by the NeuroTouch team.

    Additionally, a small force sensor (sensing 0 - 20 N, overforce of 60 N) will remain in contact with a metal rod in order to detect the amount of pressure exerted upon the soft tissue.

    Illustration of Current Electrical Hardware Revision[edit]

    Electrical Hardware Designs
    Main PCB
    MainPCB.png
    Accessory PCB
    AccessoryBoard.png

    System Design: Noise Reduction[edit]

    Within the design is contained two Dataforth SCM5B (further referred to as 5B) modules, officially titled as Isolated Analog Signal Conditioning Modules. These 5B modules serve two purposes within this design. The first is that these modules are guaranteed to reduce the noise of signals outside of the bandwidth by at least 60 dB. This will provide a very clean, clear signal for the Analog to Digital Converters (ADCs) to easily interpret. Secondly, these modules electrically isolate the system, so that any static shock will not be propagated through the rest of the circuit, causing damage to more sensitive components in the latter parts of the design.

    Prototypes[edit]

    Midway Design and Renders
    Render 1
    Render1.PNG
    Render 2
    Render2.PNG
    CAD Drawing
    ConceptDrawing.png

    The first design accommodated for a cylindrical ultrasonic sensor that was later eliminated.

    Second Design and Renders
    Render 1
    Second1.JPG
    Render 2
    Second2.JPG
    Second Drawing
    SecondDrawing.PNG

    This design was probably the bulkiest out of all of them due to the introduction of the large laser sensor into the design. We were constrained by this large sensor and realized later we could move it from being held with our hands.

    Final Design
    Final Prototype
    Final Design.png
    Cut in half
    Finalhalf.png
    Bottom view
    Finalbottom.png

    The final design eliminates the laser sensor being held with the hand and provides for a pen like ergonomic grip on the device. A separate machined part seen above in silver was made in order to house the accessory PCB, which provides routing for the 4 leads coming out of the force sensor as well as providing a perpendicular area for the force sensor to sit on. This design is very comfortable compared to our other ones and is easy to assemble.

    Testing Implementation[edit]

    Oscilloscope Data & Testing
    Oscilloscope View
    Oscope1.png
    Force and Laser Sensors Working in Conjunction
    Oscope2.png
    Texture Analyzer
    Textureanalyzer.png

    In the pictures above you can see the output voltages of the force sensor in green and the laser sensor in yellow. As the distance decreases, the voltage goes down, and as the force increases, the voltage goes up. The texture analyzer is a device that is able to do similar functions as our project, only it wouldn't be able to be used in a surgical setting.

    Team Information[edit]

    Picture Bio Discipline
    Deansbio.png Brooke Deans:

    My name is Brooke Deans and I am from Palmer, Alaska. I am a senior studying Mechanical Engineering. Some previous, related projects I have been involved with over the years include: creating a mechanical, conveyer belt mechanism to transport a raw egg in an obstacle course, participated in a DeVlieg Scholars Design Project by testing various saxophone cleaning procedures by creating a 3D printed mouth piece, machined and created a Sterling Engine, and created a four degrees-of-freedom robot. I have had two internships through out my collegiate career; during the summer of 2015 I worked at ENERGYneering Solutions, Inc. in Sisters, Oregon working on creating green energy from methane in landfills and during the summer of 2016 I worked at Intel in Chandler, Arizona in Supply Chain ensuring the process of creating semiconductors. I have also been actively involved in the University of Idaho with various clubs such as Engineering Scholars, Honors Ambassador Program of the Honors College, Engineering Ambassadors of all the Engineering Colleges, and with the greek organization of Kappa Kappa Gamma. Other activities I enjoy are hiking, rock climbing, skiing and snowboarding, camping, kayaking, traveling, and experiencing new things.

    Mechanical Engineering
    Souvbio2.jpg Brendan Souvenir:

    I originate from Coeur d'Alene, Idaho, and have spent all of my life in the Pacific Northwest, loving this area wholeheartedly! After finishing my undergraduate in Electrical Engineering with focuses on Microcontrollers and Signals and Systems (December 2017), I plan on attending graduate school focused on medical technology and research, preferably related to the nervous system. Aside from Electrical Engineering, I am an avid Ballroom, Latin, and Swing dancer.

    Electrical Engineering
    Gibbsbio.jpg Christina Gibbs:

    I am a senior Biological Engineering major and am the current president of the University of Idaho's Biomedical Engineer Society chapter. I am a member of the Neurophysiological Imaging and Modeling Lab and working as a CNA at a local nursing home and plan to use my education and experience by pursuing a career in the medical field. I graduated from Kellogg High School and have lived in Northern Idaho my whole life except for a semester where I lived in Hawaii as a part of the National Student Exchange program. In my free time, and depending on the season, I enjoy fishing, hiking, skiing and attending local motocross races.

    Biological Engineering
    Aljawibio.jpg Mazen Aljawi:

    As from the bride of the red sea Jeddah, Saudi Arabia I have lived my past five years in the beautiful area of Moscow Idaho the city that I consider as my second home and I will never forget. I am currently a senior in Biological Engineering who wants to emphasis more into biomedical and biomechanical engineering areas and use these valued concepts to help the next generation of medical sciences. I will finish the Engineering program in the spring of 2016 and graduate by that time as well. After graduation, I am willing to pursue my knowledge of biomedical engineering by getting into the work field to start building my experience. I enjoy playing and watching soccer, going out with friends, and variant disciplines reading.

    Biological Engineering
    Harlowbio.PNG Matt Harlow:

    I am originally from Coeur d'Alene, Idaho but came to Moscow after two years as a Nuclear Machinist's Mate in the Navy to study mechanical engineering and return to the fleet as a Submarine Officer. My brother and mom both live in Coeur d'Alene. I have held two executive board positions for the Pi Kappa Alpha fraternity and serve as the Battalion Commander for the Naval ROTC here on campus. In my free time I enjoy playing volleyball, ultimate frisbee, and working with computers. After the Navy I would like to either continue my work in the nuclear field or possibly pursue work in the renewable energy field.

    Mechanical Engineering

    Document Archive[edit]

    • References