Servo-Controlled Blood Vessel Occluder by rVG08Fol


									Servo-Controlled Blood
      Vessel Occluder
   Ahmed El-Gawish, Alan Chen,
   Hugo Loo, & Imad Mohammad

               Advisor: Ki Chon

Goal: measure effect of drugs and
 hypertension on MYO and TGF in
 mediation of renal autoregulation

Needed: a device that will automatically
 occlude and release blood vessels based
 on the user’s settings
 Pressure control intervals: 20 mmHg - simulating
  rapid step change in renal arterial pressure

 Control over period of 200 secs – stabilizes
  pressure effects

 Mean controlled pressure range 80 – 120 mmHg
  – range over which the MYO and TGF
  mechanisms hypothetically take effect in
 Occluder

 Sensor

 Pump

 Motor

 Software

Made from 100% silicon material
  Proven to be physiology compatibility

Size used is depends on lumen diameter
  Range is between 2mm to 24 mm

Inject air or liquid into occluder to adjust
 the diaphragm/lumen diameter
 Advantages
  Ease of pressure control
  Doesn’t damage occluder

 Disadvantage

 Used for either short or long-term occlusion
Water and saline solution
 Advantages
  Ease of pressure control

 Disadvantage
  Transpires/Evaporates through silicone rubber
  Causes damage of silicone rubber

Used for short-term occlusion (up to one hour)
 Advantages
  No transpiration
  No evaporation
  Doesn’t cause damage occluder

Use for long-term occlusion (excess of one hour)

 Blood pressure sensor is an invasive or non-
  invasive sensor
  Invasive - used or implanted directly at the
   measurement site (e.g., intra cardiac, blood vessel)
  Non-Invasive - measure systolic and diastolic blood
   pressure utilizing the oscillometric technique

 Designed to measure human blood pressure
Invasive Sensors
 Internally placed catheter-tip sensor

 Catheter fluid is coupled directly to an external

 Blood pressure is measured by observing the cavity’s
  changes in length with an optical signal conditioner

 Measuring scheme is based on white light interferometry

 Can be used on rats as well as humans
Fiber Optic Pressure Sensors (Invasive)
Non-Invasive Sensors

For humans: the sensor is attached to the
 cuff on the wrist.

Oscillometric technique is used to
 measure the blood pressure

For rats: high cost
 Relatively inaccurate

 Imprecisely measures systolic blood pressure

 Over-saturation of the blood pressure signal by ambient

 Difficulty in obtaining adequate blood pressure signals in
  dark skinned rodents

 Correlate poorly with direct blood pressure
 Similar clinical limitations to

 Utilizes piezoelectric ceramic crystals to record
  blood pressure readings

 More accurate than light-based/LED sensors

 Correlate poorly with direct blood pressure
Volume Pressure Recording-VPR
 Independent clinical validation study in 2003
  conducted at Yale University
   VPR correlated almost 100% with direct blood pressure

 Utilizes a specially designed differential pressure

 Measure six (6) blood pressure parameters
  simultaneously: systolic blood pressure, diastolic
  blood pressure, mean blood pressure, heart
  pulse rate, tail blood volume, and tail blood flow
Non-Invasive Sensors

Drives the medium based on control

Two Types
  Stepper Motor
  Servo Controlled Motor
Stepper Motors
 Resolution is set at steps per revolution

 Inexpensive

 No need for feedback
   Remembers current position and knows number of
    steps to reach another position

 Uses current even when stationary
   Generates heat (50C - 90C)
Stepper Motors (cont’d)

Digitally controlled
  Signals cause it to settle in positions based on
   coil states

  Speed determined by controller

  Maintains position without signal changes
     Higher holding torque
  Stepper Motors in Action

Animation source:
Servo-Controlled Motors
 Higher resolution

 Smoother motion

 Less heat generated

 More expensive than stepper motors

 Lower Holding Torque
Servo-Controlled Motors (cont’d)

Faster than stepper motor

Feedback determines correct positioning
  More complex than stepper motor

Oscillates when close to the desired
 position due to feedback
 Servo-Controlled Motors (cont’d)

Diagram Source:
On-Off Controller

Logic control with only two states
  e.g. temperature control with a boiler that can
   only be turned on or off

Determines whether the measurement is
 below a threshold
  If below threshold, take action
  Otherwise, no action is required
PID Controller

Proportional Integral Differential Controller

Alternative to on-off control

(error) = (set point) – (measurement)
  Set point is what you would like the value to
  Error would be the difference between the set
   point and actual value
PID Controller (cont’d)

Image Source:
P (proportional)
 If the proportional gain is well chosen, the time it takes to
  reach a new set point will be as short as possible, with
  overshoot (or undershoot) and oscillation minimized.

 Large proportional gain needed for quick response

 Small proportional gain needed to minimize overshoot
  and oscillation

 Achieving both at the same time may not be possible in
  all systems.
D (differential)
If the output is changing rapidly, there is a
 high chance of overshoot or undershoot

The derivative is the change in value from
 the previous sample to the current sample

Adding this to the proportional controller
 slows down the response time, but also
 decreases overshoot and ripple effects
I (integral)

If the is no change in value over time, the
 output may settle at the wrong value.

Integral value is small in general

Persistent error will cause sum to become
 large enough to cause a change
PID Controller Summary

Derivative and/or integral terms are added
 to proportional controllers to improve
 qualitative properties of a particular

When all three terms are used together,
 the acronym used to describe the
 controller is PID.
Schematic Presentation of PID

Used to configure the PID controller

Manages data acquisition from sensor

DataLab 2000
 Wang, H. Siu, K., Ju, K., Moore L. C., and Chon,
  K. H., Identification of Transient Renal
  Autoregulatory Mechanism Using Time-
  Frequency Spectral Techniques. IEEE
  Transactions on Biomedical Engineering, June
  2005 (52) 6:1033-1039


Brainstorming . . .

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