1. A software-based autocorrelation system using a Raspberry Pi mini computer for diffuse correlation spectroscopy
Matthew Tivnan, Rajan Gurjar, David Wolf, Karthik Vishwanath
ABSTRACT:
SPIE Photonics West
Diffuse Correlation Spectroscopy (DCS) is well established sensing technology for non-invasive measurement of blood flow. In DCS, light from a coherent source is directed to the tissue surface and the reflected (or transmitted) intensity signal is detected
using a fast photodetector. Temporal fluctuations in the light intensity are used to determine a time-autocorrelation of the detected intensity signal, which traditionally is achieved through a hardware autocorrelation board. Accurate computation of the time-
autocorrelation is critical for a DCS system. Here, we present a novel device based on the Raspberry Pi (RPi) mini-computer to compute the temporal autocorrelation function ranging from hundreds of nanoseconds through a few seconds. We present
preliminary results from experiments made on tissue phantoms and human volunteers. In the tissue phantom experiments, a fluid was pumped at varying flow rates through a subsurface channel in a scattering tissue phantom. DCS measurements were
acquired using the RPi and a traditional hardware correlation board (purchased from correlator.com(New Brunswick, NJ)). Both devices were able to accurately detect changes in flow rates. In the human experiments DCS measurements were made at the
thenar eminence of a volunteer using both devices while a blood pressure cuff was used to occlude flow. Again, both devices were clearly able differentiate flow rates at baseline from those during occlusion, and post-occlusion. The RPi device had nearly
identical performance relative to the standard hardware correlation board, but had the advantage of being smaller, programmable, and cost less than one tenth of the price of the hardware correlation boards.
ACKNOWLEDGEMENTS
MOTIVATION AND BACKGROUND RASPBERRY PI
PUMP-CONTROLLED FLOW
DATA ACQUISITION ALGORITHM CUFF-OCCLUSION
 Low cost computing device with dimensions spanning about the same area as
a credit card (~ 3.4” x 2.2” )
 700 MHz ARM processor, 512 MB of memory on chip, General; Purpose Input
Output (GPIO) pins allow for custom designs
 Capable of storing up to a minute of the time-varying intensity signal in
memory with a 5 MHz sampling frequency which is sufficiently high for DCS
 Operating Systems include Linux and Customizable “bare-metal” OS
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Intensity
Time
Time
Intensity
DCS: PRINCIPLE OF OPERATION
Auto
correlation
 In DCS, one monitors fluctuations of the reflected/transmitted speckle pattern from
a coherent source
 The detected speckle intensity at one instant is correlated with itself after a time-
delay, for a range of time-delays
 The measured intensity autocorrelation is fit using a diffusion-theory based model to
extract the decay-rate which gives a measure of the flow velocity
 Since these measurements are made from 100 ns – 10 s, they can be used to identify
pulsatile and laminar flow in tissue
EXPERIMENTAL STUDIES
 The Raspberry Pi system
consists of one device
running Linux and one
running a custom OS
 The Custom OS device
collects the time varying
intensity signal and transfers
the data to the Linux device
 The Linux device stores the
signal, and can be used to
calculate the autocorrelation
or to send the data to
another device for signal
processing.
 The autocorrelation is
calculated using the FFT
convolution method, and the
data is fit using a model from
diffuse optics to estimate the
flow rate.
 In this experiment, blood flow is occluded
using a blood pressure cuff on the upper arm
and a DCS probe is placed on the thumb
 First, a baseline measurement is taken, then
during occlusion the flow is expected to be
slower, and post-occlusion it should be faster
 Both the Raspberry Pi system and the
hardware correlator can be used to make
real world measurements of biological tissue.
 A solution of polystyrene scatterers in water
is pumped at controlled flow rates and
measured with a DCS probe.
 The autocorrelation is calculated by both the
Raspberry Pi system and the hardware
correlator.
 Both devices are capable of distinguishing
relatively fast or slow flow rates