Patent Description:
Plankton forms the base of the oceanic food chain, and thus, it is an important component of the whole marine ecosystem. Phytoplankton is responsible for approximately half of the photoautotrophic primary production on our planet. High-resolution mapping of the composition of phytoplankton over extended periods is very important, and yet rather challenging because the composition and relative population of different species rapidly change as a function of space and time. Furthermore, the factors governing the phytoplankton concentration and composition are not fully understood, and its population dynamics is chaotic. The changes in the seasonal bloom cycle can also have major environmental and economic effects. The vast majority of the phytoplankton species are not harmful, but some species produce neurotoxins that can enter the food chain, accumulate, and poison fish, mammals, and ultimately humans. Notable examples include Karenia brevis producing brevetoxin and causing neurotoxic shellfish poisoning, Alexandrium fundyense generating saxitoxin and causing paralytic shellfish poisoning, Dynophysis acuminata producing okadaic acid resulting in diarrhetic shellfish poisoning, and Pseudo-nitzschia forming domoic acid responsible for amnesiac shellfish poisoning, which can even lead to deaths. Currently, the monitoring of the concentrations of these species in coastal regions, including in California (USA), is usually performed by manual sample collection from coastal waters using plankton nets, followed by transportation of the sample to a central laboratory for light microscopy-based analysis, which is very tedious, slow and expensive, requiring several manual steps performed by professionals.

As an alternative to light microscopy-based analysis, flow cytometry has been used to analyze phytoplankton samples for over <NUM> years. The technique relies on using a sheath flow to confine the plankton sample to the focal point of an illuminating laser beam and measuring the forward and side scattering intensities of each individual object/particle inside the sample volume. To aid classification, it is usually coupled with a fluorescence readout to detect the autofluorescence of chlorophyll, phycocyanin, and phycoerythrin, found in algae and cyanobacteria. Several field-portable devices based on flow cytometry have been successfully used for analyzing nano- and picophytoplankton distributions in natural water samples. However, taxonomic identification based solely on scattering and fluorescence data is usually not feasible in flow cytometry, and thus, these devices are coupled with additional microscopic image analysis or they need to be enhanced with some form of imaging.

Consequently, imaging flow cytometry has become a widely used technique in which a microscope objective is used to image the sample (e.g., algae) within a fluidic flow. The image capture is triggered by a fluorescence detector, and thus, objects with a detectable autofluorescence are imaged. Some of the widely utilized and commercially available imaging flow cytometers include the Flowcam (Fluid Imaging Technologies), Imaging Flowcytobot (McLane Research Laboratories), and CytoSense (Cytobouy b. Although these systems are able to perform imaging of the plankton in a flow, they still have some important limitations. The use of a microscope objective lens provides a strong trade-off mechanism between the image resolution and the volumetric throughput of these systems; therefore, for obtaining high-quality images, the measured sample volume is limited to a few mL per hour (e.g., <NUM>-<NUM>/h). Using lower magnification objective lenses can scale up this low throughput by ~<NUM> fold at the expense of the image quality. In addition, the shallow depth-of-field of the microscope objective necessitates hydrodynamic focusing of the liquid sample into a few µm-thick-layer using a stable sheath flow. This also restricts the size of the objects that can be imaged (e.g., to < <NUM>) as well as the flow velocity and thereby the throughput of the system, which requires the use of additional expensive techniques such as acoustic focusing. As a result of these factors, currently existing imaging flow cytometers used in environmental microbiology field are fairly bulky (weighing e.g., <NUM>-<NUM>) and costly (> $<NUM>,<NUM> - $<NUM>,<NUM>), limiting their wide-spread use.

In contrast to some of these existing fluorescence-based approaches, holographic imaging of plankton samples provides a label-free alternative; in fact its use in environmental microbiology started over <NUM> years ago using photographic films and subsequently continued via digital cameras and reconstruction techniques. Holography provides a volumetric imaging technique that uses coherent or partially-coherent light to record the interference intensity pattern of an object. This hologram can subsequently be reconstructed to digitally bring the object into focus. The hologram contains information on the complex refractive index distribution of the object, and as such, not only the absorption but also the phase distribution of the sample can be retrieved. There are several implementations of digital holography for imaging a fluidic flow.

One can classify these digital holographic microscopy systems in terms of the presence of an external reference wave (in-line or off-axis), magnification of the imaged volume, and utilization of a lens or spherical wavefront for illumination. Off-axis systems can directly retrieve the phase information from the captured hologram; however, their space-bandwidth product and image quality are generally worse than those of in-line systems. Commercially-available on-line holographic imaging flow cytometer systems also exist, such as the LISST-Holo2 (Sequoia Scientific, Inc. , Bellevue, WA). This platform is a monochrome system (i.e., does not provide color information) and offers a relatively poor image quality compared to traditional imaging flow cytometers. The throughput and spatial resolution are coupled in this device, and therefore it can achieve high throughput volumetric imaging at the cost of limited resolution (~<NUM>-<NUM> equivalent spherical diameter with <NUM> feature resolution) which makes it useful for detecting and identifying only larger organisms.

<CIT> discloses a device and a corresponding method for cell recognition, comprising an illumination source; a microfluidic channel and fluidically coupled to a source of fluid containing objects therein that is configured to flow through the microfluidic channel; a monochromatic image sensor disposed adjacent to the microfluidic channel and disposed within an optical path that receives light from the illumination source that passes through the microfluidic channel, the image sensor configured to capture a plurality of image frames containing raw hologram images of the objects passing through the microfluidic channel; a computing device configured to receive the plurality of image frames generated by the image sensor, the computing device executing image processing software thereon configured to perform background subtraction.

Higher resolution and better image quality systems using microscope objectives in the optical path have also been described, however, the use of microscope objective lenses not only makes these systems more expensive, but also limits the achievable field-of-view (FOV) and depth-of-field, and therefore drastically reduces the throughput of the system e.g., ~<NUM>/h.

The invention is directed to a portable imaging flow cytometer device according to claim <NUM>. Further developments of the invention are according to dependent claims. The invention is also directed to a method of imaging objects, according to claim <NUM>.

A powerful and yet mobile and inexpensive imaging flow cytometer device is provided for environmental microbiology and related uses. An in-line holographic imaging flow cytometer is provided that is able to automatically detect and in real-time or near real-time provide color images of label-free objects inside a flowing water sample at a throughput of ~<NUM>/h or higher. In one embodiment, the high-throughput imaging flow cytometer weighs approximately <NUM> with a size of around <NUM> × <NUM> × <NUM>. The imaging flow cytometer obtains images of objects in the flowing water sample based on a deep learning-enabled phase recovery and holographic reconstruction framework running on a computing device such as a laptop or the like that, in some embodiments, is also used to control the imaging flow cytometer device. Compared to other imaging flow cytometers, the imaging flow cytometer device is significantly more compact, lighter weight and extremely cost-effective, with parts costing less than $<NUM>,<NUM> in total, which is only a fraction of the cost of existing imaging flow cytometers. This imaging flow cytometer device can continuously examine the liquid pumped through a <NUM>-mm thick microfluidic chip without any fluorescence triggering or hydrodynamic focusing of the sample, thereby also making the device robust and very simple to operate, covering a very large dynamic range in terms of the object size, from microns to several hundreds of microns.

The imaging flow cytometer device may be used, in some embodiments, to image water-borne microorganisms. Water-borne microorganisms include micro-plankton and nano-plankton as well as algae. Other microorganism including parasites and the like may also be imaged with the imaging flow cytometer device. Examples of such water-borne parasites include, for example, Giardia. Giardia is a microscopic parasite that causes the diarrheal illness known as giardiasis. In other embodiments, the imaging flow cytometer device may be used to count or quantify the numbers of water-borne microorganisms in a sample. This includes the total number of water-borne microorganisms as well as identifying particular sub-counts of particular species or classes of microorganisms. In still other embodiments, the flow cytometer device is capable of classifying identified microorganism as belonging to a particular species, class, or phenotype.

The capabilities of the field-portable holographic imaging flow cytometer were demonstrated by imaging micro-plankton and nano-plankton composition of ocean samples along the Los Angeles coastline, and also measured the concentration of potentially harmful algae Pseudo-nitzschia, achieving a good agreement with independent measurements conducted by the California Department of Public Health (CDPH). Of course, other microorganisms may also be imaged as noted herein. These field results establish the effectiveness of the high-throughput imaging flow cytometer. The imaging flow cytometer device, in other embodiments, may form the basis of a network of a plurality of imaging flow cytometers that can be deployed for large-scale, continuous monitoring and quantification of microscopic composition of water samples. For example, environmental observers in the field may use the device to monitor the status or health of various water bodies. This may include oceans, rivers, lakes, streams, ponds, potable water sources, and the like.

<FIG> illustrates photographic image of a flow cytometry imaging system <NUM> that incorporates an imaging flow cytometer device <NUM> that, in one embodiment, obtains images of moving objects <NUM> such as those seen in <FIG> moving in the direction of arrows A that pass through a microfluidic channel <NUM> of a microfluidic device <NUM>. The moving objects <NUM> are carried within a flowing fluid within the microfluidic channel <NUM>. The flowing fluid is typically an aqueous-based fluid such as water. <FIG> also illustrates (in image panel A) a schematic view of various internal components of the imaging flow cytometer device <NUM>. Image panel B shows a photographic view of the internal working components of the imaging flow cytometer device <NUM> with a housing or enclosure <NUM> in an open state. The housing or enclosure <NUM> may include a bottom portion <NUM> as best seen in <FIG> that contains the majority of the components of the imaging flow cytometer device <NUM>. The housing <NUM> may include a lid or top <NUM> that, in one embodiment, is hinged or connected to the bottom portion <NUM> and may be opened/closed to provide access to the internal portion of the housing <NUM>. The overall size of the imaging flow cytometer device <NUM> which is contained within the housing or enclosure <NUM> is small enough such that the imaging flow cytometer device <NUM> is portable and can be moved from location to location. In one embodiment, the imaging flow cytometer device <NUM> weighs around <NUM> or less and has total volume that is less than about <NUM>,<NUM><NUM>. For example, the imaging flow cytometer device <NUM> may have dimensions of around <NUM> × <NUM> × <NUM> as an example. Compared to other imaging flow cytometers, the imaging flow cytometer device <NUM> is significantly more compact, lighter weight and extremely cost-effective, with its parts costing less than $<NUM>,<NUM>, which is only a fraction of the cost of existing imaging flow cytometers.

As illustrated in <FIG> and <FIG>, the system <NUM> further includes a computing device <NUM> that is operatively connected to the imaging flow cytometer device <NUM>. The computing device <NUM>, in one embodiment, is used to control various operational aspects of the flow cytometer device <NUM> using control software <NUM> and/or image processing software <NUM>. This includes controlling the rate at which the fluid containing the objects <NUM> is pumped through the imaging flow cytometer device <NUM>, imaging parameters such as the intensity of the various colored light sources are that are described herein, and camera settings (e.g., frame rate, exposure time for the LEDs, gain, color ratios, and the like).

The control software <NUM> and/or image processing software <NUM> of the computing device <NUM> may also be used to view, control, or modify the reconstruction parameters that are used to reconstruct phase and amplitude images as described herein. The control software <NUM> and/or image processing software <NUM> may also be used to calibrate the parameters needed for reconstruction of higher-resolution images of the objects. This includes angle compensation (θ, Ψ) for the red, green, and blue LEDs. The control software <NUM> and/or image processing software <NUM> of the computing device <NUM> may also be used to view and save various images (including hologram images, reconstructed images, and phase-recovered images). For example, in one particular embodiment, the phase recovered intensity image and phase recovered phase image are combined or merged to generate a phase recovered phase-contrast image of the object(s) <NUM>. These may be displayed on a display <NUM> or the like associated with the computing device <NUM>. <FIG> and <FIG> illustrate an exemplary graphical user interface (GUI) <NUM> that be used to view data and images as well as control various operational aspects of the imaging flow cytometer device <NUM>.

The computing device <NUM>, in one embodiment, contains image processing software <NUM> that is used to perform imaging and other operations as described more fully herein. This includes, for example, image pre-processing operations such as background subtraction, image resample, object segmentation, object focusing operations. The image processing software <NUM> also performs the high-resolution color reconstruction in which hologram images are reconstructed into intensity and/or phase images. The image processing software <NUM> may also execute the trained neural network (e.g., deep neural network or DNN) used to generate phase recovered intensity and/or phase images (or phase recovered phase-contrast images that merge these two). The trained neural network may also be used to identify or classify the type of object(s) <NUM> that are imaged.

The image processing software <NUM> is also used for the acquisition and storage of the many image files that are collected during the operation of the imaging flow cytometer device <NUM>. In some modes, real time data and images may be generated by the image processing software <NUM>. In other modes, the image processing software <NUM> may be used to analyze images that have previously been captured and then transferred to the computing device or to storage media accessible by the computing device <NUM>. The transfer of image files may occur via wire or a wireless transmission. In some embodiments as noted herein, the image processing software <NUM> is able to automatically identify or classify the object <NUM>. For example, in the context of using the imaging flow cytometer device <NUM> to evaluate water bodies, the image processing software <NUM> may identify the type of plankton or other microorganism. Type may refer to particular species of microorganisms or it may refer to a particular phenotype.

The image processing software <NUM> may be integrated into the control software <NUM> that is used to control various operational aspects of the imaging flow cytometer device <NUM>. In some embodiments, however, the control aspects of the imaging flow cytometer device <NUM> may be run by control software <NUM> that is separate from the image processing software <NUM>. In this regard, the control software <NUM> may reside on a first computing device <NUM> while the image processing software <NUM> may reside on a second computing device <NUM>. For example, a local computing device <NUM> may be used to control the imaging flow cytometer device <NUM> with the control software <NUM> while a remotely located computing device <NUM> (e.g., server, cloud computer, etc.) may execute the image processing software <NUM>. Of course, as illustrated in <FIG>, a single computing device <NUM> may operate the image processing software <NUM> and the control software <NUM>.

The computing device(s) <NUM> that may be used with the flow cytometry imaging system <NUM> may include any number of computing devices such as personal computers (PCs), laptops, tablet computers, mobile phones (e.g., Smartphones), servers, and the like. As noted herein, the image processing software <NUM> is preferably executed on a computing device <NUM> that has one or more graphics processing unit (GPU) which increases the speed at which images or other output are generated by the image processing software <NUM>. The computing device(s) <NUM> may interface with the imaging flow cytometer device <NUM> via a wired (e.g., USB or the like) and/or wireless connection (Wi-Fi, Bluetooth, or the like). The imaging flow cytometer device <NUM> may be powered by power supply that can be connected to an AC outlet (and converted by power supply to 5V DC). Alternatively, the imaging flow cytometer device <NUM> may be powered by one or more batteries (e.g., 5V battery pack) that may be internal or external to the housing or enclosure <NUM>.

Still referring to <FIG>, the imaging flow cytometer device <NUM> is used in connection with source of fluid that contains object(s) <NUM> therein. The source of fluid may be contained in receptacle <NUM> like a test tube, cuvette, vial, or the like. Two such receptacles <NUM> are provided as illustrated in <FIG>. Each receptable <NUM> is connected via tubing <NUM> to an inlet <NUM> and outlet <NUM> (best seen in <FIG>), respectively of the microfluidic device <NUM> that contains the microfluidic channel <NUM> as described herein. A first receptacle <NUM> is used to draw fluid into the imaging flow cytometer device <NUM> while a second receptacle <NUM> is used to receive fluid that has passed through the imaging flow cytometer device <NUM>. Thus, one receptacle <NUM> is used for fluid input while the other receptacle <NUM> is used for fluid output. The source of fluid <NUM> that is contained in the receptacle <NUM> that is run through the imaging flow cytometer device <NUM> is one embodiment, an aqueous or water-based fluid. The water-based fluid may contain a sample of water obtained at a natural or artificial water body. Examples includes oceans, rivers, lakes, streams, ponds, potable water sources, and the like.

Referring to <FIG>, the imaging flow cytometer device <NUM> includes a microfluidic device <NUM> that has a microfluidic channel <NUM> formed therein that communicates with an inlet <NUM> and outlet <NUM>. The microfluidic device <NUM> may be formed as a laminate or as a monolithic structure and is held within a holder <NUM> within the housing or enclosure <NUM>. The microfluidic device <NUM> may take the form of a chip or flow cell, for example. The microfluidic device <NUM> may be inserted and removed from this holder <NUM> as needed (e.g., the microfluidic device <NUM> may be a disposable component that is replaced after each use). The microfluidic device <NUM> is formed from an optically transparent material (e.g., optically transparent polymer or glass) so that light from the light source is able to pass through the microfluidic channel <NUM> such that holographic images of object(s) <NUM> contained in the fluid can be captured by an image sensor as explained herein. The dimensions of the microfluidic channel <NUM> may vary from tens or hundreds of micrometers up to more than <NUM>. The size of the microfluidic channel <NUM> should be large enough such that the microfluidic channel <NUM> does not clog in response to fluid flow. The tested microfluidic channel <NUM> described herein had a height of <NUM> and a width of around <NUM>). By increasing the cross-sectional dimensions (e.g., height or width) higher throughput rates can be achieved.

A pump <NUM> is disposed in the housing or enclosure <NUM> and is used to pump the fluid containing the object(s) <NUM> from the receptacle <NUM> and into the microfluidic channel <NUM> of the microfluidic device <NUM>. Fluid leaves the receptacle <NUM> and is pumped via the pump <NUM> into the inlet <NUM> where the fluid continues down the microfluidic channel <NUM> and then exits via outlet <NUM>. The fluid leaving the microfluidic device <NUM> is emptied into the receiving receptacle <NUM> via tubing <NUM>. The pump <NUM> may include a peristaltic pump (e.g., Instech p625) such as described herein. Other types of pumps <NUM> include microfluidic pumps or any other pump that can pump fluid through the microfluidic channel <NUM>. The flow rate of the pump <NUM> may be varied using the control software <NUM>. In some embodiments, the presence of the pump <NUM> in the imaging flow cytometer device <NUM> is optional. For example, the pump <NUM> may be external to the imaging flow cytometer device <NUM>. In another embodiment, the imaging flow cytometer device <NUM> may be placed in-line with another system or process and that pumped flow may be used to push or pull fluid through the microfluidic channel <NUM>.

An image sensor <NUM> is disposed adjacent to the microfluidic device <NUM> and microfluidic channel <NUM> such that the active area of the image sensor <NUM> encompasses the area of the microfluidic channel <NUM>. The active area of the image sensor <NUM> may be centered on the center of the microfluidic channel <NUM> as described herein. A small air gap of several microns or the like may be present the bottom surface of the microfluidic channel the active area of the image sensor <NUM>, although the active area could be in contact with the surface of the microfluidic channel <NUM> in other embodiments. The image sensor <NUM> that is used is a color image sensor <NUM>. An example of such a color image sensor includes a camera <NUM> that has a CMOS color image sensor <NUM> (e.g., Basler aca4600-10uc (Basler AG, Germany) with a pixel size of <NUM>. The color image sensor <NUM> may be powered via a cable (e.g., USB cable) that also is used to transfer images (i.e., image frames) that are captured by the color image sensor <NUM>.

A light source <NUM> is disposed in the housing or enclosure <NUM> and is used to provide the illumination that is used to illuminate the object(s) <NUM> contained in the fluid that flows through the microfluidic channel <NUM>. In one embodiment, the light source <NUM> is a multi-colored light source that emits light at a plurality of discrete wavelength ranges or bands. For example, a multi-colored LED may be used to emit red, green, and blue light. An example includes a surface mountable RGBW LED that has individually addressable red, blue, and green LED dies that are used to create the multi-color light that are driven simultaneously to illuminate the microfluidic channel <NUM> containing the flowing fluid. An example of such a multi-colored light source <NUM> is LZ4-04MDPB emitter made by LED Engin (Osram). Triple-output LED driver controller circuitry <NUM> (LT3797, Linear technologies Driver) is provided to drive the light source <NUM>.

Referring to <FIG> and <FIG>, in one embodiment, a plurality of filters <NUM> are provided to adjust the coherence of the light that illuminates the microfluidic channel <NUM>. <FIG> illustrates two such filters <NUM> that are triple bandpass optical filters (Edmund Optics #<NUM>-<NUM>, Chroma Inc. <NUM>) to increase the temporal coherence of the illumination. The filters are spaced apart with a spacer <NUM> and held in a holder <NUM> and retained by a cap <NUM> (seen in <FIG>). It should be understood, however, that in other embodiments only a single filter <NUM> may be needed. In still other embodiments, where the coherence of the light source <NUM> is sufficiently narrow, a filter <NUM> may not even be needed.

During operation of the imaging flow cytometer device <NUM>, the different LEDs of the multi-colored light source <NUM> are simultaneously illuminated in a pulse. The image sensor <NUM> is operated in global reset release mode and the pulse width is adjusted to not allow an object <NUM> traveling at the maximum speed inside the microfluidic channel <NUM> to shift by more than the width of a single sensor pixel. For a flowrate of <NUM>/h, this corresponds to a pulse length of <NUM>.

To pulse the different LEDs, high-current pulses are stored in three <NUM>-F-capacitors <NUM>, which are charged using a capacitor charger controller <NUM> (LT3750, Linear Technologies) to <NUM> V. The capacitor charge is initiated by the image sensor flash window trigger signal, which is active during the frame capture, and its length can be controlled by the camera/image sensor <NUM> software driver. The charger controller <NUM> acquires an "on" state and keeps charging the capacitors until the pre-set voltage level of <NUM> V is reached. During the short illumination pulses, the voltage on the capacitors decreases only slightly, and they are immediately recharged as each frame capture resets the charge cycle, thereby allowing continuous operation.

The LEDs are synchronized and their constant-current operation is ensured by the drive circuitry <NUM>. The controller <NUM> uses the same flash window signal from the image sensor <NUM> to turn on the LEDs of the light source <NUM> for the exposure duration set by the software. The current of each LED is kept constant for the subsequent pulses by the circuit, thus, maintaining the same illuminating intensity for each holographic frame.

In another alternative embodiment, the light source <NUM> is operated in a continuous wave operation that does not generate pulses of light. For example, the multi-color LEDs of the light source <NUM> may be emit light simultaneously over a continuous period of time (e.g., while sample analysis is being performed) while the image sensor <NUM> is operated in a "pulsed" mode to capture a plurality of image frames. The image sensor <NUM> may be operated with, for example, very fast shutter/image capture speeds using various options well known to modern camera systems. In this regard, similar images are produced of the moving objects <NUM> but instead of pulsing the light source <NUM> the image sensor <NUM> is operated in a pulse mode. Of course, use of the continuous wave light source <NUM> obviates the need for the capacitors <NUM> and associated charge controller <NUM>.

Referring to <FIG>, <FIG>, <FIG>, the housing or enclosure <NUM> includes a mirror <NUM> that in one embodiment is a convex mirror <NUM> that is mounted in the lid or top portion <NUM> with a mirror mount <NUM>. The mirror <NUM> reflects light from the light source <NUM> before reaching the microfluidic channel <NUM> so as to increase the spatial coherence while allowing a compact and light-weight optical setup. In this regard, a folded optical path is formed whereby light that is emitted by the light source <NUM> is reflected onto the microfluidic channel <NUM> whereby holograms of object(s) <NUM> within the flowing fluid are cast upon and captured by the image sensor <NUM>. In an alternative configuration, the optical path between the light source <NUM> and the microfluidic channel <NUM> is not folded, however, this will move the light source <NUM> further away and increase the overall size of the device <NUM>. While a single reflection is used as part of the folded optical path it should be appreciated that additional reflections (i.e., folded light paths) may be used beyond the one illustrated.

As best seen in <FIG>, a frame or support <NUM> is provided and secured within the bottom portion <NUM> of the housing <NUM> that holds the various components. This includes, for example, a mount <NUM> for the camera <NUM> as well as a mount or holder <NUM> for the LED drive circuitry <NUM> and light source <NUM> (e.g., LED chip). A separate mount <NUM> is provided for the pump <NUM>. A mount <NUM> is also provided for the microfluidic device holder <NUM>. The frame <NUM> also includes a mount <NUM> for a microcontroller <NUM> which is used as an interface for i2c communications.

The imaging flow cytometer device <NUM> was tested with water samples obtained from the ocean along the Los Angeles coastline. The samples were imaged at a flow rate of <NUM>/h and the raw full FOV image information was saved on computing device <NUM> in the form of a laptop that was also used to control operation of the imaging flow cytometer device <NUM>. Plankton holograms were segmented automatically as described herein in more detail (e.g., <FIG>, <FIG> and related descriptions) and reconstructed by the computing device <NUM> using a deep convolutional network, and the phase-contrast color images of plankton were calculated and saved to the local laptop computing device <NUM> that also controlled the imaging flow cytometer through a custom-designed GUI <NUM> as illustrated in <FIG> and <FIG>. <FIG> highlights the performance of the automated deep learning-enabled reconstruction process employed by the image processing software executed by the computing device <NUM> and the image quality achieved by the imaging flow cytometer device <NUM>, showcasing several plankton species with both their initial segmented raw images (holograms) and the final-phase contrast images (which are in color). Most of these plankton types were detected by the imaging flow cytometer device <NUM> based on the reconstructed images, as detailed in <FIG> images. An additional selection of unidentified plankton imaged in the same ocean samples is also shown in <FIG>. Some part of the water sample for each measurement was also sent to CDPH for comparative microscopic analysis by their experts, and the qualitative composition of different species found in each water sample was in good agreement with the measurements obtained with the imaging flow cytometer device <NUM>. Furthermore, to perform a quantitative comparison against the routine analysis performed by CDPH, the potentially toxic Pseudo-Nitzschia algae was selected and its relative abundance was evaluated at six different measurement locations (i.e., public beaches) along the Los Angeles coastline. The imaging flow cytometer results, summarized in <FIG>, also show good agreement with the analysis performed by the CDPH.

The field portability of the imaging flow cytometer device <NUM> was demonstrated by on-site operation of the imaging flow cytometer device <NUM> at the Redondo Beach pier where experiments were performed over a duration of <NUM>. The imaging flow cytometer device <NUM> itself was powered by a <NUM> V battery pack and could run for several hours. A <NUM>-Wh <NUM>-V external battery pack was used to power the laptop computing device <NUM> for the duration of the field experiments (from <NUM>:<NUM> AM to <NUM>:<NUM> PM). In these field experiments, the time evolution of the total plankton concentration was measured in the ocean during the morning hours and found that the amount of microplankton in the top <NUM> of the water increases during the day possibly owing to vertical migration (see <FIG>). The number of Pseudo-Nitzschia found in these samples was manually counted as well and observed a peak in the morning (at ~<NUM>:<NUM> AM) and a steady decline after that (<FIG>); in general these trends are rather complicated to predict since they are influenced by various factors, such as the composition of the local microbiome, tide and upwelling/downwelling patterns. These results demonstrate the capability of the imaging flow cytometer device <NUM> to periodically measure and track the plankton composition and concentration of water samples on site for several hours without the need to be connected to a power grid.

The throughput of any imaging flow cytometer is determined by several factors, but most importantly it is governed by the required image quality. The imaging flow cytometer device <NUM> was designed to achieve the highest resolution that is allowed by the pixel size of the image sensor <NUM>, which resulted in a tight photon budget owing to the loss of illumination intensity for achieving sufficient spatial and temporal coherence over the sample volume, and the requirement for pulsed illumination for eliminating motion blur. Because of the fast flow speed of the objects <NUM> within the microfluidic channel <NUM>, pixel super-resolution approaches could not be used to improve the resolution of the reconstructed images to sub-pixel level. Experiments were conducted at <NUM>/h; however, at the cost of some motion blur this throughput could be quadrupled without any modification to the device <NUM>. It could be increased even more by using a microfluidic channel <NUM> with greater height (e.g., ><NUM>). To demonstrate this, an ocean sample was imaged with increased throughputs of up to <NUM>/h (see <FIG>). The obtained reconstructions show that the imaged alga (Ceratium Furca) still remains easily recognizable despite the increased flow speed.

In addition to the physical volumetric throughput, the processing speed of the computing device <NUM> (e.g., laptop) can also be a limiting factor, affecting mainly the maximum density of the sample that can be processed in real time. The imaging flow cytometer device <NUM> design achieves real-time operation, i.e., the computing device <NUM> processes the information faster than the image sensor <NUM> provides it to avoid overflowing the memory. Currently, the imaging flow cytometer device <NUM> can be run in three modes depending on the sample density. In a first mode, the imaging flow cytometer device <NUM> can acquire and save the full FOV holograms and perform all the reconstruction and phase recovery steps after the measurement, which is a necessary approach for high-concentration samples (e.g., ><NUM>,<NUM>-<NUM>,<NUM> objects/mL). Even denser samples can also be analyzed by the imaging flow cytometer device <NUM> device by e.g., diluting them accordingly or by lowering the throughput. In a second mode, the image processing software of the computing device <NUM> can reconstruct the holograms but not perform phase recovery of the detected objects <NUM> during the measurement. At present, the image segmentation and reconstruction procedure takes ~<NUM> for each full FOV frame, in which seven (<NUM>) objects can be reconstructed per image with parallel computing on a GTX <NUM> GPU. The major computational operations are: (<NUM>) segmentation of the full FOV hologram for object detection (~<NUM>), (<NUM>) holographic autofocusing and reconstruction (~<NUM>/object), and (<NUM>) transfer of the final amplitude and phase images (<NUM> bit, <NUM> × <NUM> pixels × <NUM> color channels) from the device (i.e., GPU) to the host (i.e., central processing unit) and saving them on an internal solid-state drive (~<NUM>-<NUM> per object). Consequently, in case of reconstructing but not phase recovering the objects <NUM>, the imaging flow cytometer device <NUM> can image, in real-time, samples with ~<NUM> objects/mL at a flowrate of <NUM>/h.

In the third mode of operation, the imaging flow cytometer device <NUM> involves performing both the image reconstruction and phase recovery steps for all the flowing objects <NUM> during the measurement. The deep learning-based phase recovery step is currently the most intensive part of the image processing algorithm with a runtime of ~<NUM>/object. Thus, if real-time phase recovery is necessary in this third mode of operation, it restricts the sample density to ~<NUM> objects/mL at a flowrate of <NUM>/h. Since the performance of GPUs increases on average <NUM>× per year, these computational performance restrictions will be partially overcome over time as GPU performance improves with time. Furthermore, it is possible to simultaneously focus all the objects in a hologram using a convolutional neural network that extends the depth-of-field of holographic reconstruction by ><NUM>-fold compared to conventional approaches. This would allow combining the phase recovery, auto-focusing and image reconstruction steps into a single neural network, making the computation time for the full FOV independent of the density of the particles, enabling real-time imaging of highly dense fluidic samples. Indeed, this approach was tested to reconstruct objects <NUM> in the <NUM> (height) microfluidic channel <NUM> and found that it gives good results regardless of the height of the objects <NUM> inside the microfluidic channel <NUM> (see <FIG>).

Although the tested imaging flow cytometer device <NUM> is a field-portable, this particular embodiment was not fully waterproof and operated above the water surface. This prototype can operate up to <NUM> meters away from the controlling computing device <NUM> (e.g., laptop) by simply changing the USB3 camera connection to GigE, and constructing a long-range microcontroller communication setup similar to an OpenROV submersible platform. Owing to its low hardware complexity in comparison with other imaging flow cytometer technologies, the component cost of the system <NUM> is very low (<$<NUM>,<NUM>), and with large volume manufacturing, it could be built for less than $<NUM> (see Table <NUM> below). This remarkable cost-effectiveness opens up various exciting opportunities for environmental microbiology research and could allow the creation of a network of computational imaging cytometers at an affordable price point for large-scale and continuous monitoring of ocean plankton composition and ocean microbiome (or other water bodies) in general.

The imaging flow cytometer device <NUM> uses a color image sensor <NUM> with a pixel size of <NUM> (Basler aca4600-10uc). The housing of the camera <NUM> is removed, and the circuit is rearranged to allow the microfluidic device <NUM> to be directly placed in contact with the protective cover glass of the image sensor <NUM> (see <FIG> and <FIG>). There may be a small air gap (several micrometer) located between the bottom of the microfluidic device <NUM> and the image sensor <NUM>. The illumination of the imaging flow cytometer device <NUM> is provided by using the red, green, and blue emitters from an LED light source <NUM> (Ledengin LZ4-04MDPB). The spatial and temporal coherence of the emitted light from the LED-based light source <NUM> is increased to achieve the maximum resolution allowed by the sensor pixel size. The spatial coherence is adjusted by using a convex mirror <NUM> (Edmund optics #<NUM>-<NUM>) to increase the light path. The LED light is also spectrally filtered by two triple bandpass optical filters <NUM> (Edmund Optics #<NUM>-<NUM>, Chroma Inc. <NUM>) to increase the temporal coherence of the illumination. The placement of the optical components is designed to tune the bandpass of the spectral filter angle to better match the emission maximum of the LEDs. Increasing the spatial and temporal coherence of the LEDs also decreases the intensity reaching the image sensor <NUM>. In addition, the short exposure time required to avoid the motion blur when imaging objects <NUM> in a fast flow makes it necessary for our configuration to utilize a linear sensor gain of <NUM>. The additional noise generated from the gain is sufficiently low to not interfere with the image reconstruction process.

A microfluidic channel <NUM> (Ibidi µ-Slide I) with an internal height of <NUM> is placed on the top of the image sensor <NUM>, secured using a 3D-printed holder <NUM>, and connected to a peristaltic pump <NUM> (Instech p625). The size of the active area of the image sensor <NUM> is slightly smaller than the width of the microfluidic channel <NUM> (<NUM> vs. <NUM>), and the microfluidic channel <NUM> is so positioned that the image sensor <NUM> measures the center of the liquid flow. The flow profile inside the microfluidic channel <NUM> was calculated (see <FIG>) by solving the Navier-Stokes equation for non-compressible liquids assuming a non-slip boundary condition. The results show that the image sensor measures ~<NUM>% of the total volume passing through the microfluidic channel <NUM>. The flow profile is a two-dimensional paraboloid, with the maximum flow speed located at the center of the microfluidic channel <NUM>, measuring approximately <NUM> times higher than the mean velocity of the liquid (see <FIG>). To acquire sharp, in-focus images of the objects <NUM> in the continuously flowing liquid, the image sensor <NUM> was operated in the global reset release mode and illuminated the sample by flash pulses, where the length of an illuminating pulse is adjusted to not allow an object <NUM> traveling at the maximum speed inside the microfluidic channel <NUM> to shift by more than the width of a single sensor pixel. For a flowrate of <NUM>/h, this corresponds to a pulse length of <NUM>.

Because shortening the illumination time also constrains the available photon budget, the brightness of the LED light source <NUM> was maximized by operating them at currents ranging from of <NUM>-<NUM> A depending on the LED color. The currents are set for each LED emitter to create similar brightness levels at the image sensor <NUM>, ensuring that the sample is adequately lit at each color, a requirement for obtaining color images. The green LED spectrum is inherently wider than the red and blue counterparts, and so, the spectral filters <NUM> will reduce its intensity the most. Therefore, the green LED was operated at the experimentally determined maximum possible current of <NUM> A. The red and blue LEDs require a current of ~<NUM> A for matching the intensity of the green LED on the image sensor <NUM> for correcting the white balance. Control circuitry was utilized to control the components of the imaging flow cytometer device <NUM>. The circuit is powered by either a <NUM>-V-wall-mount power supply or a cellphone charger battery pack. The control circuitry fulfills four major roles of providing power to the peristaltic pump <NUM>, charging the capacitors <NUM> for providing power to the LED-based light source <NUM>, synchronizing the LEDs to the image sensor <NUM> and creating stable, short, high current pulses, and finally, providing an interface for remote control via the computing device <NUM> using Inter-Integrated-Circuit (i2c) interface for setting various parameters. The peristaltic pump <NUM> is powered by a high-efficiency step-up DC-DC converter at <NUM> V (TPS61086, Texas instruments), and its speed is controlled by a potentiometer via i2c components (TPL0401B, Texas Instruments). The charge for the high-current pulses is stored in three <NUM>-F-capacitors <NUM>, which are charged using a capacitor charger controller <NUM> (LT3750, Linear Technologies) to <NUM> V. The capacitor charge is initiated by the image sensor flash window trigger signal, which is active during the frame capture, and its length can be controlled by the camera software driver. The charger controller <NUM> acquires an "on" state and keeps charging the capacitors <NUM> until the pre-set voltage level of <NUM> V is reached. During the short illumination pulses, the voltage on the capacitors <NUM> decreases only slightly, and they are immediately recharged as each frame capture resets the charge cycle, thereby allowing continuous operation. The LEDs are synchronized and their constant-current operation is ensured by a triple-output LED driver controller <NUM> (LT3797, Linear technologies). The controller <NUM> uses the same flash window signal from the image sensor <NUM> to turn on the LEDs of light source <NUM> for the exposure duration set by the software. The current of each LED is controlled between <NUM>-<NUM> A using digital i2c potentiometers (TPL0401B, Texas Instruments), and it is kept constant for the subsequent pulses by the circuit <NUM>, thus, maintaining the same illuminating intensity for each holographic frame. During startup, it takes ~<NUM>-<NUM> frames for the circuit <NUM> to stabilize at a constant light level. To avoid having multiple devices with the same address on the i2c line, an address translator was used (LTC4317, Linear Technologies) to interface with the potentiometers controlling the red and blue LEDs. To control the circuit, the computing device <NUM> (e.g., laptop) communicates with an Arduino microcontroller <NUM> (TinyDuino from Tinycircuits), which is used as an interface for i2c communications only.

With reference to <FIG>, for automatic detection and holographic reconstruction of the target objects <NUM> found in the continuously flowing water sample, the static objects <NUM> found in the raw full FOV image <NUM> (e.g., dust particles in the flow channel) need to be eliminated first. This is achieved by calculating a time-averaged image of the preceding ~<NUM> images <NUM>, containing only the static objects, and subtracting it from the present raw hologram. To ensure appropriate reconstruction quality, the mean of this subtracted image is added back uniformly to the current frame. This yields a background-subtracted full FOV image <NUM> as seen in <FIG>, in which only the holograms of the objects <NUM> newly introduced by the flow are present. These objects <NUM> are automatically detected and segmented from the full FOV for individual processing as seen in <FIG>. The full FOV background-subtracted hologram <NUM> is first Gaussian-filtered as seen in operation <NUM> (<FIG>) and converted into a binary image by hard-thresholding <NUM> with its statistical values (mean + <NUM> × standard deviation), which isolates the peaks of the holographic signatures created by the objects included in the FOV. The binary contours with an area of a few pixels are removed to reduce the misdetection events because of the sensor noise. A closing operation is performed in the generated binary image <NUM> to create a continuous patch for each object <NUM>. The resulting binary contours represent the shapes and locations of the objects <NUM> appearing in the FOV, and their morphological information is used to filter each contour by certain desired criteria (e.g., major axis). The center coordinate of the filtered contour is used to segment its corresponding hologram. Not only is it feasible to extract all the objects <NUM> in the FOV but it is also possible to prioritize the segmentation of the objects <NUM> of interest for a specific goal by the approach. Using this, one can better utilize the computational resources of the computing device <NUM> and maintain real-time processing for denser samples.

After segmentation, the Bayer-patterned holograms are separated into three mono-color (i.e., red, green, and blue) holograms as seen in operation <NUM> (<FIG>) corresponding to the illumination wavelengths. To fully utilize the spatial resolution of the optical system, the orientation of the Bayer-patterned green pixels is rotated by <NUM>° to regularize their sampling grid. Concurrently, the red and blue mono-color holograms are upsampled by a factor of two, and a <NUM>° rotation is applied to these upsampled holograms as seen in operation <NUM>. Note that segmentation may be performed initially on the full FOV debayered image without any rotation applied (operation <NUM>). After segmentation is complete, the original bayered, background subtracted hologram is then subject to the rotation operation <NUM>. Holographic autofocusing using Tamura of complex gradient as seen in operation <NUM> is performed for each segmented object <NUM> using only a single mono-color hologram to accurately estimate the distance of the respective object <NUM> from the imaging plane of the image sensor <NUM>. At this point, each object <NUM> within the flow is 3D localized (per FOV). The coordinates of each detected object <NUM> are then used in conjunction with the estimated flow profile from calculations, and the location of each object <NUM> is predicted at the next frame. If an object <NUM> is found at the predicted coordinates, it is flagged to be removed from the total count and processing workflow to avoid reconstructing and counting the same object <NUM> multiple times.

The next step is to maximize the resolution of the reconstruction by further upsampling the resampled holograms by a factor of four as seen in operation <NUM>. Each color channel is then propagated to the obtained reconstruction distance by a wave propagation algorithm as seen in operation <NUM>, and thus, it is brought into focus. In one particular embodiment, the wave propagation algorithm is an angular-spectrum based wave propagation algorithm. Details regarding the angular-spectrum based wave propagation algorithm may be found in <NPL>). The different refractive indices of the materials present in the optical path, namely the cover glass of the image sensor <NUM>, the airgap between the image sensor <NUM> and the bottom of the microfluidic channel <NUM>, the microfluidic channel <NUM>, and the water or other fluid therein are taken into account respectively, by performing four (<NUM>) successive angular spectrum propagations each corresponding to the material and its respective thickness. The image sensor <NUM> cover glass, the airgap, and the bottom thickness of the microfluidic channel <NUM> are constant for each object <NUM>, while the object's distance from the bottom of the microfluidic channel <NUM> varies, and is given by the result of the autofocus algorithm <NUM> performed on a single color as explained above. The slight incidence angle difference between the red, green, and blue emitters of the LED chip light source <NUM> is corrected by modifying the propagation kernel accordingly. To evaluate the resolution of the imaging flow cytometer device <NUM> for the objects <NUM> located inside the microfluidic channel <NUM>, the flow channel was replaced with a <NUM> Air Force test chart (see <FIG>). Owing to the partially-coherent nature of the illumination, the resolution depends on the object-sensor distance; thus, it was measured by placing the test chart at various heights above the image sensor <NUM>. The width of the smallest resolved line varied between <NUM>-<NUM> depending on the height of the object <NUM>, with <NUM> corresponding to the smallest resolvable feature for most flowing objects <NUM> imaged by the imaging flow cytometer device <NUM> during its regular operation.

These raw reconstructed phase and intensity images <NUM>, which include both reconstructed intensity images 100i and reconstructed phase images 100p, however, are contaminated by self-interference and twin-image noise, which are characteristic of an in-line digital holographic imaging system, due to the loss of the phase information of the hologram at the plane of the image sensor <NUM>. Thus, to achieve accurate image reconstruction without these artifacts, a deep learning-based digital holographic phase recovery method was employed, using a trained deep neural network <NUM> (e.g., convolutional neural network) (see <FIG>, <FIG>) that was pre-trained with various phase-recovered reconstructions of water-borne objects <NUM> captured with the imaging flow cytometer device <NUM>. The phase-recovered ground truth or "gold standard" reconstructions may be obtained using, for example, multi-height images of the objects <NUM> in in which phase recovery is performed using multi-height phase recovery such as that disclosed in <NPL>). This enables automated and accurate acquisition of the spectral morphology of an object <NUM> without sacrificing the high-throughput operation of the imaging flow cytometer device <NUM>, which otherwise would be very challenging as other existing phase recovery methods require static repetitive measurements and/or time-consuming iterative calculations which would not work for flowing objects <NUM>.

The trained deep neural network <NUM> outputs phase recovered images <NUM> which include phase recovered intensity images 104i and phase recovered phase images 104p. The phase recovered intensity images 104i and phase recovered phase images 104p can be combined or merged to create phase recovered phase-contrast images <NUM> as seen in <FIG> also shows in the panel of the trained deep neural network <NUM> as phase recovered phase-contrast image <NUM>. For the visualization of transparent objects <NUM> such as plankton, the color phase-contrast image <NUM> based on the complex-valued reconstructions of the red, green, and blue channels assists in accurately resolving the fine features and internal structures of various water-borne microorganisms with a high color contrast (see e.g., <FIG>).

A GUI <NUM> was used to operate the device (<FIG> and <FIG>) which the user interacts with via the display <NUM> of the computing device <NUM>. Through this GUI <NUM>, all the relevant measurement parameters can be specified, such as the liquid flow speed, the driving currents, the incidence angles for the red, green, and blue LEDs, the flash pulse duration, the camera sensor gain, etc. The GUI <NUM> gives a real time, full field-of-view reconstructed image at the center of the microfluidic channel <NUM> allowing visual inspection during the flow with and without background subtraction and displays the total number of the detected objects <NUM> in the current frame. The GUI <NUM> is also capable of visualizing up to twelve (<NUM>) segmented, autofocused, and reconstructed objects <NUM> in real time (or course more or less objects <NUM> could be displayed). The user can specify whether to digitally save any combination of the raw, background subtracted holograms, or reconstructed images (e.g., images <NUM>). The GUI <NUM> can be also run in demo mode, allowing the analysis of previously captured image datasets, without the presence of the imaging flow cytometer device <NUM>.

The sampling protocol recommended by the CDPH (USA) for obtaining the ocean samples was followed. A plankton net was used with a diameter of <NUM> and vertical tows were performed with a total length of <NUM> (<NUM>×<NUM>) from the end of the pier at each sampling location where a pier is present (Malibu, Santa Monica, Venice, Manhattan, and Redondo beaches in California, USA). There was no pier at the Point Dume so a horizontal tow was performed from the shoreline. The plankton net condensed the micro- and nano-plankton found in the ocean into a sample volume of ~<NUM>, i.e., in this case a condensation ratio of ~<NUM>×. <NUM> of the condensed sample was extracted and re-diluted with <NUM> of filtered ocean water its contents were imaged using the imaging flow cytometer device <NUM>. The remaining samples were sent to the CDPH for subsequent analysis (used for comparison purposes). During the field tests, the same plankton net was used, but only performed one vertical tow was performed from a depth of <NUM> at each measurement. <NUM> of the obtained sample was re-diluted by <NUM> of filtered ocean water. To conserve the battery power of the controlling computing device <NUM> (i.e., laptop), ~<NUM> of this sample was imaged on-site. The imaging flow cytometer device <NUM> automatically detected and saved the reconstructed images <NUM> of all the detected plankton and provided the user real-time feedback on the total plankton count detected. Specific counting of Pseudo-Nitzschia was done manually by scanning through the dataset of the saved images and visually identifying Pseudo-Nitzschia.

In another embodiment and with reference to <FIG>, the flow cytometer imaging system <NUM> is used with a neural network classifier <NUM> that is configured to detect and count specific types of objects <NUM>, e.g., specific types of microorganisms. In this alternatively embodiment, the trained deep neural network <NUM> previously described is substituted with a neural network classifier <NUM> that is trained, in one embodiment, to output a binary determination (i.e., yes/no) of whether the particular microorganism is of a particular type or species. For example, the neural network classifier <NUM> was used to detect and count various concentrations of Giardia lamblia cysts. The neural network classifier <NUM> trained for this purpose is a variant of the DenseNet-<NUM> network described in <NPL>).

Changes DenseNet-<NUM> network include the omission of the batch-norm layer and use of a dropout of <NUM>. The network optimizer of choice was adaptive moment estimation (ADAM). A total of <NUM> Giardia images, and <NUM> dirt particles images were used to train the neural network classifier <NUM>. <NUM> images of each category served as the validation set. Data augmentation techniques of image rotation and flipping were also employed to increase the variety of the sample images. The neural network classifier <NUM> was trained, and the subsequent decision was made on the reconstructed, but non-phase recovered phase and amplitude images. Just as in the case for the phase recovery trained neural network <NUM>, the input of the neural network classifier <NUM> is also the reconstructed red, green, and blue intensity and phase images (i.e., images 100i, 100p in <FIG>) (<NUM> × <NUM> × <NUM> layers). Due to the pixel size of the imaging flow cytometer device <NUM>, and the small size of the Giardia cysts, the center <NUM> × <NUM> area of every image is cropped as a first step. The entire network architecture can be seen in <FIG>.

The Giardia lamblia samples were prepared according to the EPA <NUM> Method (EPA <NUM>, Section <NUM>) by Wisconsin State Laboratory of Hygiene (Madison, WI) and the irradiation of samples was done by Waterborne Inc. (New Orleans, LA). The test samples have a Giardia cyst count of <NUM>, <NUM>, <NUM>, and <NUM> respectively in the manufacturer's standard <NUM> buffer volume. These samples were re-diluted into <NUM> bottled water before being analyzed by the imaging flow cytometer device <NUM>. The training of the neural network classifier <NUM> was performed on separate, high concentration Giardia samples (<NUM> cyst/ml), which allowed to generate a high number of Giardia lamblia images. Several non-spiked water samples were imaged to provide images of the typical dirt particles found in the buffer water.

After the training process was completed, the neural network classifier <NUM> with the best validation accuracy (<NUM>% for Giardia, and ~<NUM>% for dirt) was selected. Since even the high concentration Giardia samples contain some dirt in the buffer water which results in noisy labeling of the Giardia images, <NUM>% validation accuracy for Giardia is not expected. After the neural network classifier <NUM> was trained the performance of the system <NUM> was tested using low concentration Giardia samples. The samples were imaged with a throughput of <NUM>/h, and, to allow fluorescence microscope comparison, the effluent of the imaging flow cytometer device <NUM> was collected and filtered onto a membrane. The filter membrane containing the Giardia cysts that flow through the imaging flow cytometer device <NUM> was treated with fluorescein labelled Giardia specific antibodies (product no. A300FLR-20X, Waterborne Inc. ), and incubated overnight at <NUM>. The fluorescently labeled cysts were manually counted using a benchtop microscope. The results show good agreement and are summarized in Table <NUM> below.

Table <NUM> shows the performance of the imaging flow cytometer device <NUM> in detecting and automatically classifying Giardia Lamblia cysts. The <NUM> water samples were imaged at a <NUM>/h flow rate for ~<NUM> minutes. In order to account for the cysts adhering to the manufacturer's sample container and subsequently lost during sample preparation, the sample was collected and filtered after it left the cytometer. The Giardia captured by the filters were fluorescently stained using Giardia-specific dye, and manually counted using a fluorescence microscope. The results show good agreement.

In another embodiment and with reference to <FIG>, the flow cytometer imaging system <NUM> is used to compute the thickness of the object <NUM> or, alternatively, the refractive index distribution within an object <NUM>. Computing the refractive index distribution within an object <NUM> such as a microorganism may be used to infer, for example specific biochemical states or properties that exist within the microorganism. As one specific example, the refractive index distribution may be computed for a microorganism <NUM> and used as a proxy to determine the chemical content of the organism. For example, the chemical content may include the lipid content of the organism. This may have significant potential for the identification and screening of microorganisms such as algae for biofuel applications.

The optical path difference is a measure of the distance travelled by the light inside the object <NUM> of interest (i.e., plankton) multiplied by the refractive index difference between the object <NUM> of interest and the surrounding medium. If the optical path length difference is defined as ΔL(x,y), then the phase distribution at the object plane at each wavelength can be written as ϕk(x,y) = <NUM>π·ΔL(x,y)/λk. The phase of the wavefront is a 2π periodic measure, thus, in case of thicker objects and larger optical path lengths, phase wrapping can occur. This wrapped phase is ϕk,wrapped(x,y) = ϕk(x,y) ± <NUM>Nπ where -π < ϕk,wrapped ≤ π and N is an integer. These resulting wrapped phase maps {ϕk,wrapped} that are generated by the three phase-retrieved reconstructions at the three wavelengths can be processed, i.e. by an optimization algorithm, such as that disclosed in <NPL>, which is incorporated herein by reference, finds an optimum path length ΔLopt(x,y) at each spatial point on the image (x,y) by minimizing a cost function that is defined as: <MAT>.

In one implementation, in order to reduce the computation cost/time, one can define a search range of [ΔL<NUM>-min{λk}/<NUM>, ΔL<NUM>+ min{λk}/<NUM>], where ΔL<NUM> is the initial guess of the optical path length: <MAT>.

where the total number of wavelengths (K=<NUM>). Within this search interval, one can scan the values to find the optical path length ΔLopt(x,y) that minimizes the cost function, resulting in the optical path difference. <FIG> shows an example for this process. The optical path difference is a measure which couples the refractive index distribution of the object <NUM> and the object's thickness together. If one knows the refractive index distribution of the object <NUM> the correct thickness can be calculated. Conversely, if one knows the thickness of the object <NUM> and the refractive index of the surrounding medium, it is possible to compute the refractive index distribution inside the object <NUM>. In one possible application, obtaining the refractive index distribution inside a microorganism such as algae can be used to infer its lipid (or other chemical) content.

Claim 1:
A portable imaging flow cytometer device (<NUM>) comprising:
a housing (<NUM>);
at least one illumination source (<NUM>) disposed in the housing (<NUM>) and configured for pulsed or continuous wave operation at a plurality of colors;
a microfluidic channel (<NUM>) disposed in the housing and fluidically coupled to a source of fluid (<NUM>) containing objects (<NUM>) therein that is configured to flow through the microfluidic channel (<NUM>);
a color image sensor (<NUM>) disposed adjacent to the microfluidic channel (<NUM>) and disposed within an optical path that receives light from the at least one illumination source (<NUM>) that passes through the microfluidic channel (<NUM>), the color image sensor (<NUM>) configured to capture a plurality of image frames containing raw hologram images of the objects (<NUM>) passing through the microfluidic channel (<NUM>); and
a computing device (<NUM>) configured to receive the plurality of image frames generated by the color image sensor (<NUM>), the computing device (<NUM>) executing image processing software (<NUM>) thereon configured to perform background subtraction and automatically detect moving objects (<NUM>) in the plurality of image frames and configured to reconstruct phase and/or intensity images of the moving objects (<NUM>) for each of the plurality of colors, wherein the phase and/or intensity image reconstruction is performed using a wave propagation-based algorithm and optionally a trained neural network (<NUM>) that receives the reconstructed phase and/or intensity images and outputs phase recovered intensity and/or phase images or phase recovered phase-contrast images of the moving objects (<NUM>).