Medical Design Briefs - April 2022 - 12

Image Cytometry
0 µs
Image Data:
* Size
* Shape
* Color
19 µs
Kinematic Data:
* Event Market
* Position
* Speed
38 µs
100 µm
Fig. 2 - A sample time series of colored polystyrene beads flowing through a microchannel. The system
can collect key image data and kinematic data in real time.
Several commercially available image
cytometers can perform such characterizations
with throughputs on the order of
thousands of cells per second. But these
technologies are unable to process data
at high speeds, eliminating the ability to
run real-time feedback loops that are
critical for future artificial intelligence
(AI) and machine learning applications.
This article illustrates how off-the-shelf
hardware - i.e., camera, frame grabbers,
processors - and software can be
integrated to process individual cells in
real time with microsecond latencies at
high framing speeds (10-100 kfps) and
ultra-short exposure times. This system
can be designed to make inferences
derived from images on the order of
microseconds, providing a basis for executing
microsecond and microscale
decisions on the individual cells.
Image Cytometry and Feedback
Experimental System. In this model,
red, blue and clear polystyrene beads were
flowed through a Cole Parmer 200-µmwide
microchannel. A Nikon SMZ18 was
set to 13.5× magnification, and an AMETEK
Vision Research Phantom S710 was
configured to record at 52,000 fps and 18
µs. A photonic 5100 LED fiber light backlit
the sample. The data was captured and
pushed through a SPICAtek DHS-RT station,
which can capture up to 16 CXP
channels on two Euresys frame grabbers.
The station can process the data in real
Fig. 3 - Sample of the cytoTracker GUI that is used to control the camera, visualize real-time results,
and process image data.
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time and store up to 45 minutes of highspeed,
uncompressed video at these
speeds. Typical image data is shown in
Figure 2, where red, blue, and clear particles
can be seen traversing the microchannel
at high speeds.
Technical Challenges. Real-time imaging
at high speeds (10-100 kfps) faces
three primary challenges: selection of the
proper camera sensor and architecture,
configuration of the correct camera settings,
and the use of a backend PC capable
of handling the high throughputs.
Camera Sensor and Architecture. The
scientific high-speed camera market is
dominated by onboard RAM-based camera
architectures with gigabit Ethernet
(GbE) communication protocols. With
this architecture, the camera records all
the image data to a fixed-size RAM buffer
that is later off-loaded to external memory
storage. The size of these buffers can
vary between a few gigabytes to fractions
of a terabyte, permitting high-speed
recordings that are generally a few seconds
in length. This strategy is highly
effective for capturing individual and relatively
short high-speed events, but
recording and processing continuously
for minutes or hours is very limited.
In addition, the fastest communication
is generally over 10 GbE, allowing save
speeds from high-speed cameras at rates
of ~400 MB*s-1. However, CoaXPress
(CXP)-based machine vision cameras are
capable of streaming off upwards of 7
GB*s-1 and were specifically developed
for high-speed image transmission and
machine vision applications. They directly
integrate with commercially available
CXP frame grabber cards, providing the
interface between the camera and the
printed circuit motherboards. The card
directly connects to a PCIe slot with the
proper size and number of lanes, which
are throughput dependent.
Camera Settings. The image sensor
must be fast enough in terms of fps and
exposure time to properly image cells
while mitigating temporal aliasing
and/or motion blur. Resolution (pixel
areal density) is selected based on the
particle size relative to the field of view.
In this case, the smallest particle had a
diameter of ~10 pixels. At elevated particle
speeds, it is not uncommon to use
ultra-short exposure times ranging from
~0.1 to 20 µs. In this case, the Phantom
S710 was configured to record at 52,000
fps with an exposure time of 18 µs. The
camera was configured with cytoTracker
software, and a list of cellular analytics
Medical Design Briefs, April 2022

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Medical Design Briefs - April 2022

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