Holographic image processing and data extraction

Abstract

A system, method, and computer program product for analyzing a sample volume (18). One or more holographs (32) of the sample volume (18) are generated. Each holograph (32) contains multiple pixels, each pixel having an intensity. Information is extracted from the holograph (32) by analyzing the pixels to determine the characteristics of the sample volume (18), without first reconstructing a photograph (62) from the holograph (32). The method for extracting information includes determining the variance factor of the pixel intensity and extracting holographic features (34) from the multiple pixels that belong to a class of shapes including one or more diffraction patterns (26).

Description

[Technical Field]

[0001] This invention relates, in general, to holographic imaging, and more specifically to holographic imaging of biological specimens and the extraction of data from holographic images. [Background technology]

[0002] Microbial infections should be treated as early and best as possible to provide the greatest opportunity for patient recovery and to reduce morbidity and mortality. Approximately 85% of patients who show symptoms of an infection do not have sufficient microbial concentrations in their blood at the time of initial onset to allow detection of the pathogen. Corresponding blood samples may appear negative for microorganisms until many doubling events occur, at which point sufficient microbial cells are present to reach the lower limit threshold of standard detection tests.

[0003] Conventional automated microscopy systems for detecting microbial cells in patient specimens include various configurations of specimen containers, reaction reservoirs, reagents, and optical detection systems. These optical detection systems are configured to obtain dark-field and fluorescence micrographs of microorganisms contained in the reaction reservoir, such as flow cells, chambers, and microfluidic channels. Such systems typically include a controller configured to direct the system's operation and process the microbial information derived from the micrographs. However, these systems generally cannot directly detect low concentrations of microorganisms in patient specimens. Furthermore, these systems require a culture period to ensure that, if viable microbial cells are present, they reach detectable levels in order to statistically guarantee that a negative interpretation is truly negative.

[0004] Phenotypic methods for detecting viable microbial populations within a sample include in vitro monitoring of microbial growth. While many methods have been proposed to achieve this, solutions based on direct optical interrogation remain difficult to implement. Optical methods are typically constrained by factors such as optical resolution and the need to obtain timely data on microbial growth over time. (For example, 10 5 The detection of viable bacteria at low concentrations (less than colony-forming units / milliliter (CFU / mL)) presents further challenges, as it requires the interrogation of large amounts of patient samples to ensure high-probability detection.

[0005] High-resolution optical interrogation generally relies on very long multiple-pass scanning methods employing high-precision 3D stages, high-quality objective lenses, and fine-focusing techniques. Furthermore, label-free bacteria require the use of less common imaging techniques such as phase-contrast microscopy or differential-contrast interference microscopy due to slight differences in refractive index with the suspension medium. Consequently, the hardware and software requirements for such applications do not scale well with the sample volume being investigated. [Overview of the project] [Problems that the invention aims to solve]

[0006] Therefore, there is a need for improved systems, methods, and computer programs to rapidly detect and characterize microbial cells in patient specimens during the early stages of infection. [Means for solving the problem]

[0007] In an embodiment of the present invention, a sample analysis system is provided. The system includes a holographic imager configured to generate a holograph of a sample volume, one or more processors operatively coupled to the holographic imager, and a memory operatively coupled to the one or more processors for storing program code. When the program code is executed by the one or more processors, the program code causes the system to generate a first holograph of a sample volume including a first plurality of pixels each having an intensity at a first time, determine a first dispersion factor of the intensity of at least a first portion of the first plurality of pixels, and determine a characteristic of the sample volume based on the value of the first dispersion factor.

[0008] In an aspect of the system, the program code may cause the system to further determine a characteristic of the sample volume based on the value of the first dispersion factor by comparing the value of the first dispersion factor with a predetermined threshold.

[0009] In another aspect of the system, the program code causes the system to further generate a second holograph of the sample volume including a second plurality of pixels each having an intensity at a second time, determine a second dispersion factor of the intensity of at least a second portion of the second plurality of pixels, and determine a characteristic of the sample volume based on the value of the first dispersion factor by comparing the value of the first dispersion factor with the value of the second dispersion factor.

[0010] In another aspect of the system, the first portion of the first plurality of pixels may be one of a plurality of portions of the first plurality of pixels, and the program code causes the system to further determine a second dispersion factor of the intensity of a second portion of the first plurality of pixels and determine a characteristic of the sample volume based on the value of the first dispersion factor by comparing the first dispersion factor with the second dispersion factor.

[0011] In another embodiment of the system, the program code may cause the system to further identify a portion of interest among the first plurality of parts, determine the z-height of an object that generates a diffraction pattern in the portion of interest, and analyze the object.

[0012] In another embodiment of the system, the program code may cause the system to further analyze the object by reconstructing a photograph from the first hologram at a z-height.

[0013] In another embodiment of the system, program code may cause the system to determine the intensity variance factor of each part of a first plurality of pixels in order to generate a plurality of variance factors, compare the value of each variance factor of the plurality of variance factors with one or more values ​​of the other variance factors of the plurality of variance factors, and identify a part of interest by identifying the variance factor of the part of interest from the plurality of variance factors as an outlier.

[0014] In another embodiment of the system, each portion of the first plurality of pixels may provide a tile from among the plurality of tiles of the first holograph.

[0015] In another embodiment of the system, the program code may cause the system to further apply one or more image correction processes to the first holograph that do not involve image reconstruction, before determining the first dispersion factor.

[0016] In another embodiment of the system, one or more image correction processes may include a flat-field correction process.

[0017] In another embodiment of the system, one or more image correction processes may include identifying one or more irrelevant portions of a first hologram that are not related to quantifying changes in the characteristics of the sample volume, generating a mask configured to remove one or more irrelevant portions of the first hologram, and applying the mask to the first hologram.

[0018] In another embodiment of the system, the sample volume may contain one or both of multiple microorganisms and multiple eukaryotic cells of animal or human origin.

[0019] In another embodiment of the system, the microorganisms may belong to a species or class of Gram-negative bacteria, Gram-positive bacteria, or fungi.

[0020] In another embodiment of the system, the first variance factor may be the variance.

[0021] In another embodiment of the present invention, a method for analyzing a sample volume is presented. The method includes the steps of: generating a first hologram of the sample volume comprising a first plurality of pixels, each having an intensity, in a first time; determining a first variance factor of the intensity of at least a first portion of the first plurality of pixels; and determining the characteristics of the sample volume based on the value of the first variance factor.

[0022] In another embodiment of the method, the step of determining the characteristics of a sample volume based on the value of a first variance factor may include comparing the value of the first variance factor with a predetermined threshold.

[0023] In another embodiment of the method, the method may further include the steps of: generating a second hologram of a sample volume comprising a second plurality of pixels, each having an intensity, at a second time; determining a second variance factor of the intensity of at least a second portion of the second plurality of pixels; and determining the characteristics of the sample volume based on the value of a first variance factor by comparing the value of a first variance factor with the value of a second variance factor.

[0024] In another embodiment of the method, the first portion of the first plurality of pixels may be one of a plurality of portions of the first plurality of pixels, and the method may further include the steps of determining a second variance factor of the intensity of the second portion of the first plurality of pixels, and determining the characteristics of the sample volume based on the value of the first variance factor by comparing the first variance factor with the second variance factor.

[0025] In another embodiment of the method, the method may further include the steps of identifying a portion of interest among a first plurality of parts, determining the z-height of an object that generates a diffraction pattern in the portion of interest, and analyzing the object.

[0026] In another embodiment of the method, the step of analyzing the object may include reconstructing a photograph from the first hologram at a z-height.

[0027] In another embodiment of the method, the step of identifying a portion of interest may include determining the intensity variance factor of each portion of a first plurality of pixels to generate a plurality of variance factors; comparing the value of each variance factor of the plurality of variance factors with one or more values ​​of the other variance factors of the plurality of variance factors; and identifying the variance factor of the portion of interest from the plurality of variance factors as an outlier.

[0028] In another embodiment of the method, each portion of the multiple portions of the first multiple pixels may provide a tile among the multiple tiles of the first holograph.

[0029] In another embodiment of the method, the method further comprises the step of applying one or more image modification processes to a first holograph that do not involve image reconstruction, before determining a first variance factor, wherein one or more image modification processes do not involve image reconstruction.

[0030] In another embodiment of the method, one or more image correction processes may include a flat-field correction process.

[0031] In another embodiment of the method, one or more image correction processes may include identifying one or more irrelevant portions of a first hologram that are not related to quantifying changes in the characteristics of the sample volume, generating a mask configured to remove one or more irrelevant portions of the first hologram, and applying the mask to the first hologram.

[0032] In another embodiment of the method, the sample volume may contain one or both of a plurality of microorganisms and a plurality of eukaryotic cells of animal or human origin.

[0033] In another embodiment of the method, the microorganisms may belong to a species or class of Gram-negative bacteria, Gram-positive bacteria, or fungi.

[0034] In another embodiment of the present invention, a computer program product is provided. The computer program product includes a non-temporary computer-readable storage medium and program code stored in the non-temporary computer-readable storage medium. The program code is configured, when executed by one or more processors, to cause one or more processors to generate a first hologram of a sample volume including a first plurality of first pixels, each having an intensity, in a holographic imager in a first time, to determine a first variance factor of the intensity of at least a first portion of the first plurality of pixels, and to determine the characteristics of the sample volume based on the value of the first variance factor.

[0035] In another embodiment of the present invention, another sample analysis system is provided. The system includes a holographic imager configured to generate a hologram of a sample volume, one or more processors operablely coupled to the holographic imager, and a memory operablely coupled to the one or more processors for storing program code. When the program code is executed by one or more processors, the program code causes the system to generate a first hologram of a sample volume comprising a first plurality of pixels, each having an intensity, in a first time, to extract a first set of holographic features belonging to a class of shapes comprising one or more diffraction patterns, each relating to the diffraction of light by an object in the sample volume, to determine a first number of holographic features in the first set of holographic features, and to determine the properties of the sample volume based on the value of the first number of holographic features.

[0036] In one embodiment of the system, the program code may cause the system to determine the characteristics of a sample volume based on the values ​​of a first number of holographic features by comparing the values ​​of a first number of holographic features with a predetermined threshold.

[0037] In another embodiment of the system, the program code may cause the system to generate a second hologram of the sample volume in a second time, comprising a second plurality of pixels, each having an intensity; to extract a second set of holographic features belonging to a class of shapes comprising one or more diffraction patterns from at least a second portion of the second plurality of pixels; and to determine a second number of holographic features in the second set of holographic features. In this embodiment of the system, the program code may cause the system to determine the characteristics of the sample volume based on the value of the first number of holographic features by comparing the value of the first number of holographic features with the value of the second number of holographic features.

[0038] In another embodiment of the system, the class of shapes may include one or more patterns having radial symmetry.

[0039] In another embodiment of the system, the program code may cause the system to further determine a phase shift related to the light passing through the object in the sample volume.

[0040] In another embodiment of the system, the program code may cause the system to determine the phase shift by fitting a mathematical formula to a first fringe pattern generated by an object within a first holograph and extracting parameters indicating the phase shift from the formula.

[0041] In another embodiment of the system, the phase shift of an object may be used to distinguish the object from one or more other objects having different phase shifts.

[0042] In another embodiment of the system, the objects may be cells, and one or more other objects may be debris.

[0043] In another embodiment of the system, an object may be a first type of cell, and one or more other objects may include a second type of cell.

[0044] In another embodiment of the system, the first part of the first plurality of pixels may be one of a plurality of parts of the first plurality of pixels, and the program code may cause the system to further extract a second set of holographic features belonging to a class of shapes including one or more diffraction patterns from the second part of the first plurality of pixels, determine a second number of holographic features in the second set of holographic features, and determine the characteristics of the sample volume based on the value of the first number of holographic features by comparing the first number of holographic features with the second number of holographic features.

[0045] In another embodiment of the system, the program code may cause the system to further identify a portion of interest among multiple portions of a first plurality of pixels, determine the z-height of an object that generates a diffraction pattern in the portion of interest, and analyze the object.

[0046] In another embodiment of the system, the program code may cause the system to analyze an object by reconstructing a photograph from a first hologram at a z-height.

[0047] In another embodiment of the system, program code may cause the system to identify a part of interest by extracting a set of holographic features from each of multiple parts of a first set of pixels, determining the number of holographic features in each set of holographic features extracted from the multiple parts, comparing the number of holographic features in each set of holographic features with the number of holographic features in other sets of holographic features, and identifying the number of holographic features extracted from the part of interest as an outlier from the number of holographic features in other sets of holographic features.

[0048] In another embodiment of the system, each portion of the first plurality of pixels may provide a tile from among the plurality of tiles of the first holograph.

[0049] In another embodiment of the system, the sample volume may contain one or both of multiple microorganisms and multiple eukaryotic cells of animal or human origin.

[0050] In another embodiment of the system, the microorganisms may belong to a species or class of Gram-negative bacteria, Gram-positive bacteria, or fungi.

[0051] In another embodiment of the present invention, another method for analyzing a sample volume is presented. The method includes the steps of: generating a first hologram of the sample volume comprising a first plurality of pixels, each having an intensity, in a first time; extracting a first set of holographic features belonging to a class of shapes, each comprising one or more diffraction patterns related to the diffraction of light by an object in the sample volume, from at least a first portion of the first plurality of pixels; determining a first number of holographic features in the first set of holographic features; and determining the characteristics of the sample volume based on the value of the first number of holographic features.

[0052] In one embodiment of the method, the step of determining the characteristics of a sample volume based on a value of a first number of holographic features may include comparing a value of the first number of holographic features with a predetermined threshold.

[0053] In another embodiment of the method, the method may further include the steps of generating a second hologram of a sample volume comprising a second plurality of pixels, each having an intensity, at a second time; extracting a second set of holographic features belonging to a class of shapes comprising one or more diffraction patterns from at least a second portion of the second plurality of pixels; and determining a second number of holographic features in the second set of holographic features. In this embodiment of the method, the step of determining the characteristics of the sample volume based on a value for the first number of holographic features may include comparing the value for the first number of holographic features with the value for the second number of holographic features.

[0054] In another embodiment of the method, the class of shapes may include one or more patterns having radial symmetry.

[0055] In another embodiment of the method, the method may further include the step of determining a phase shift related to light passing through an object in a sample volume.

[0056] In another embodiment of the method, the step of determining the phase shift may include fitting a mathematical formula to a first fringe pattern generated by an object within a first holograph and extracting parameters from the formula that indicate the phase shift.

[0057] In another embodiment of the method, the phase shift of an object may be used to distinguish the object from one or more other objects having different phase shifts.

[0058] In another embodiment of the method, the object may be a cell, and one or more other objects may be waste.

[0059] In another embodiment of the method, the object may be a first type of cell, and one or more other objects may include a second type of cell.

[0060] In another embodiment of the method, the first portion of the first plurality of pixels may be one of a plurality of portions of the first plurality of pixels, and the method may further include the steps of: extracting a second set of holographic features belonging to a class of shapes including one or more diffraction patterns from the second portion of the first plurality of pixels; determining a second number of holographic features in the second set of holographic features; and determining the characteristics of a sample volume based on the value of the first number of holographic features by comparing the first number of holographic features with the second number of holographic features.

[0061] In another embodiment of the method, the method may further include the steps of identifying a portion of interest among a plurality of portions of a first plurality of pixels, determining the z-height of an object that generates a diffraction pattern in the portion of interest, and analyzing the object.

[0062] In another embodiment of the method, the step of analyzing the object may include reconstructing a photograph from the first hologram at a z-height.

[0063] In another embodiment of the method, the step of identifying a portion of interest may include extracting a set of holographic features from each portion of a plurality of first pixels, determining the number of holographic features in each set of holographic features extracted from the plurality of portions, comparing the number of holographic features in each set of holographic features with the number of holographic features in other sets of holographic features, and identifying the number of holographic features extracted from the portion of interest as an outlier from the number of holographic features in other sets of holographic features.

[0064] In another embodiment of the method, each portion of the multiple portions of the first multiple pixels may provide a tile among the multiple tiles of the first holograph.

[0065] In another embodiment of the method, the sample volume may contain one or both of a plurality of microorganisms and a plurality of eukaryotic cells of animal or human origin.

[0066] In another embodiment of the method, the microorganisms may belong to a species or class of Gram-negative bacteria, Gram-positive bacteria, or fungi.

[0067] In another embodiment of the present invention, another computer program product is provided. The computer program product includes a non-temporary computer-readable storage medium and program code stored in the non-temporary computer-readable storage medium. The program code is configured, when executed by one or more processors, to cause one or more processors to generate a first hologram of a sample volume in a holographic imager, each containing a first plurality of pixels having an intensity; to extract a first set of holographic features belonging to a class of shapes, each containing one or more diffraction patterns related to the diffraction of light by an object in the sample volume, from at least a first portion of the first plurality of pixels; to determine a first number of holographic features in the first set of holographic features; and to determine the properties of the sample volume based on the value of the first number of holographic features.

[0068] The above summary presents a simplified outline of several embodiments of the invention to provide a basic understanding of the specific aspects of the invention discussed herein. The summary is not intended to provide a comprehensive outline of the invention, nor to identify any important or definitive elements, nor to delineate the scope of the invention. Its sole purpose is to present some concepts in a simplified form as an introduction to the detailed description presented below.

[0069] The accompanying drawings incorporated herein and forming part thereof illustrate various embodiments of the present invention and, together with the overview of the invention given above and the detailed description of embodiments given below, serve to illustrate embodiments of the present invention. [Brief explanation of the drawing]

[0070] [Figure 1] This is a diagram illustrating an exemplary holographic imager, including a light source and an image sensor. [Figure 2]Figure 1 shows the diffraction pattern generated by an object on the light-receiving surface of the imaging sensor. [Figure 3] Figure 1 or Figure 2 shows an example holographic image that may be generated by the imaging sensor. [Figure 4] This is a perspective view of an exemplary sample analysis system, including a light source assembly and a sensor assembly. [Figure 5] Figure 4 is a front view of the sensor assembly. [Figure 6] Figure 4 is a front view of the light source assembly. [Figure 7] Figure 5 is a front view of the sensor assembly showing the insertion of a microfluidic card configured to hold the sample volume. [Figure 8] Figure 5 is a front view of the sensor assembly showing the insertion of a microfluidic card configured to hold the sample volume. [Figure 9] This diagram shows the process of reconstructing a photograph from a hologram. [Figure 10] This is a diagram illustrating an exemplary image flattening process for flattening a hologram. [Figure 11] This is a diagram illustrating an exemplary image masking process for masking a hologram. [Figure 12] This figure shows a sequence of holograms taken over a certain period of time, and a sequence of photographs reconstructed from the holograms showing cell proliferation within a sample volume. [Figure 13] Figure 12 is a graph showing a plot of the variance of pixel intensity in the hologram and a plot of the average pixel intensity in the photograph. [Figure 14] This figure contains a graph with plots comparing the use of holographic intensity variance in a time series of holograms to characterize sample volume with the use of complete image reconstruction from a time series of holograms. [Figure 15]This figure contains a graph with plots comparing the use of holographic intensity variance in a time series of holograms to characterize sample volume with the use of complete image reconstruction from a time series of holograms. [Figure 16] This figure shows the diffraction patterns of individual objects captured by holography, and a corresponding graph illustrating a theoretical curve that fits these patterns to experimental diffraction patterns. [Figure 17] This is a diagram of the object detection process for analyzing holograms. [Figure 18A] This figure shows tiled photographs of sample volumes that have experienced cell proliferation. [Figure 18B] This figure shows tiled photographs of sample volumes that have experienced cell proliferation. [Figure 18C] This figure shows tiled photographs of sample volumes that have experienced cell proliferation. [Figure 18D] This figure shows tiled photographs of sample volumes that have experienced cell proliferation. [Figure 18E] This figure shows tiled photographs of sample volumes that have experienced cell proliferation. [Figure 19] Figures 18A–18E are graphs showing early event detection based on growth indicators extracted from a single tile, including plots of cell growth indicators extracted from either the entire hologram or a single tile of the hologram of the sample volume hologram sequence. [Figure 20] This flowchart illustrates the process for preparing a holographic sequence for data extraction. [Figure 21] This is a holographic diagram illustrating the process shown in Figure 20. [Figure 22] This is a holographic diagram illustrating the process shown in Figure 20. [Figure 23] Figures 1 through 22 are diagrams of a computer that may be used to implement one or more of the components or processes shown. [Modes for carrying out the invention]

[0071] It should be understood that the accompanying drawings are not necessarily to the correct scale and may present somewhat simplified representations of various features illustrating the fundamental principles of the present invention. For example, certain design features of the sequence of operations disclosed herein, including specific dimensions, orientations, locations, and shapes of various illustrated components, may be determined in part by the specific intended use and environment. Certain features of illustrated embodiments may be enlarged or distorted compared to other embodiments to facilitate visualization and clear understanding. In particular, thin features may be thickened, for example, for clarity or illustration.

[0072] Embodiments of the present invention relate to systems and methods for detecting the presence of objects, such as microbial cells, suspended in a sample volume using inline holography. Inline holography refers to a process that involves irradiating light through a sample volume to generate a diffraction pattern and capturing an image of the diffraction pattern, referred to herein as a “holograph.” The diffraction pattern is generated by the diffraction of light by the objects, which defines the three-dimensional suspension in the medium of the sample volume. An inline holographic imaging system may include a light source for illuminating the sample volume, a sample holder configured to receive a consumable in the form of a sample container containing the sample volume, and an image sensor for capturing the holograph.

[0073] Conventional imaging techniques generate non-holographic images (referred to herein as "photographs") by focusing light (for example, using lenses) to form a focused image on the photosensitive surface of an image sensor. Photographic systems generally rely on capturing photographs of proliferating cells at each of multiple focal planes located within a sample volume. This requires repeated focusing and capture of multiple photographs (e.g., one for each focal plane) at each of multiple selected sample times. In contrast, holographic imaging systems only require capturing a single hologram of the sample volume at each of multiple selected sample times.

[0074] The sample volume may be analyzed using methods that avoid the need to focus on any single event within the sample volume. For example, an algorithm may be used to extract information from one or more holograms by reconstructing an image of the object at each of one or more focal planes using a Fourier transform. The focal planes from which the reconstructed images are generated may be selected based on a non-reconstructive analysis of the holograms. Three-dimensional or four-dimensional holographic methods may be used to extract data from one or more holograms of the sample volume. For example, reconstructed images (three-dimensional) across multiple focal planes of the sample volume may be acquired over time (four-dimensional) using a video frame rate.

[0075] Several strategies may be employed to extract information from holograms that is useful for determining the characteristics of the sample volume from which the hologram was generated. Reconstruction-based strategies involve using Fourier transforms to reconstruct microscopic images at arbitrary heights (i.e., arbitrary z-planes) within the sample volume. However, the image reconstruction process tends to be computationally intensive, requiring complex algorithms not only to perform the transform but also to find the appropriate data-rich z-plane from which to reconstruct the image. This can become an iterative process and therefore a serious bottleneck when multiple holograms and cell cultures need to be analyzed simultaneously.

[0076] The direct-from-holograph strategy extracts data about the characteristics of the sample volume from the hologram without first reconstructing a photograph from the hologram. Characteristics of the sample volume that may be determined include, for example, the number and / or characteristics of one or more objects suspended in the sample volume.

[0077] The holographic imaging systems and associated methods for capturing and analyzing holograms disclosed herein enable the analysis of objects within a hologram without first reconstructing a “real-space” representation (e.g., a photograph) of the sample volume using angular spectra or similar image reconstruction techniques. Conventional understanding of holograms suggests that there is no net signal or difference (null) within each hologram due to the sum of waves emanating from the diffraction patterns of objects within the hologram, which cancel each other out. However, the holographic-direct methods for analysis disclosed herein overcome this theoretical limitation imposed by the wave-cancelling null hypothesis. Advantageously, the holographic-direct methods for analysis disclosed herein may also consume fewer computational resources compared to conventional holographic analysis techniques based on image reconstruction.

[0078] In some cases, the direct-from-holograph method may provide sufficient information about the specimen, eliminating the need for image reconstruction. However, the information extracted from the holograph using the direct-from-holograph method may also be used to determine when and where (e.g., at what time and with respect to which z-plane) the photograph should be reconstructed. For example, object detection in one or more holographs may enable targeted reconstruction of a photograph in a specific area of ​​interest within the specimen volume, thereby eliminating the need to reconstruct multiple photographs in different z-planes, or even a complete photograph in a single z-plane. An exemplary area of ​​interest might include, for example, an object-rich region in the specimen volume where significant cell proliferation or morphological changes are occurring due to exposure to an effector. The information extracted from the holograph may also be used to know the precise geometry of the holographic setup so that more advanced reconstruction techniques may be used. Specific direct-from-holograph methods, which may be applied directly to the holograph either alone or in combination, are described in more detail below.

[0079] Figure 1 shows an exemplary holographic imager 10, including a light source 12, a sample container 14, and an image sensor 16. The light source 12 may include, for example, a laser diode or a point light source that illuminates a sample volume 18 contained in the sample container 14 with light 20, such as coherent light. The light source 12 may include lenses, filters, apertures, or other optical components (not shown) configured to correct the coherence of the light 20 received by the sample volume 18. The sample volume 18 may contain an object 22 (e.g., a bacterial colony) suspended in a culture medium (e.g., culture medium and / or gelling agent). As the light 20 passes through the sample volume 18, a portion of the light 20 may be diffracted by the object 22. The diffracted portion of the light 20 may interact with the rest of the light 20 to produce a diffraction pattern 26 in the image sensor 16. The light source 12 and the image sensor 16 may be coupled to a computer 28 for operational use. The computer 28 may be configured to illuminate the sample volume 18 with the light source 12 and to have the image sensor 16 capture an image of the diffraction pattern 26 in holographic form. The computer 28 may store and / or analyze the hologram and the data extracted from the hologram.

[0080] Figure 2 shows the generation of a diffraction pattern on the photosensitive surface of the image sensor 16 that defines the sensor plane 30, and Figure 3 shows an exemplary holograph 32 that may be generated by the image sensor 16. The holograph 32 shows a diffraction pattern that may be generated by multiple objects 22 suspended in a sample volume 18, and is essentially a superposition of individual diffraction patterns 26 generated by each of the objects 22. Each of the circular patterns in the holograph 32 related to the diffraction pattern 26 generated by a single object 22 may be referred to herein as a holographic feature 34 of the holograph 32. One or more holographs 32 may be generated by multiple light sources 12, for example, three different wavelengths λ l , λ m , λ nThe image sensor 16 may use three laser diodes, each emitting light 20 (e.g., coherent light). In embodiments where the image sensor 16 can distinguish between different colors of light (e.g., a red, green, and blue color image sensor), different wavelengths of light may be used to facilitate the analysis of different diffraction patterns produced by the interaction of the object 22 with each individual light source 12. In an alternative embodiment, each light source 12 may emit light having the same spectral content but be activated at different non-overlapping times so that a separate hologram 32 can be captured by the image sensor 16 for each light source 12.

[0081] The position of a holographic feature 34 within the holograph 32 may be determined with respect to a fixed reference coordinate system 36 defined by a set of direction vectors of unit length. The unit length vectors of the reference coordinate system 36 may include an x-axis and a y-axis perpendicular to the x-axis, where each of the x and y axes is coplanar with the sensor plane 30 and, therefore, the holograph 32 produced by the image sensor 16. For example, the x-axis may be parallel to the height dimension of the sensor plane 30, and the y-axis may be parallel to the width dimension of the sensor plane 30. The z-axis of the reference coordinate system 36 may be perpendicular to both the x and y axes and, therefore, perpendicular to the sensor plane 30. Thereafter, the x, y, and z axes may form a right-handed coordinate system for defining the positions of the object 22 in the sample volume 18 and the holographic feature 34 on the holograph 32. The origin of the reference coordinate system 36 may define a point at coordinate (0, 0, 0) and may be located on the sensor plane 30. Therefore, all coordinates on the sensor plane 30 have a z-coordinate value z = 0, and thus every point on the holograph 32 has a known z-coordinate value z = 0.

[0082] Object 22 may be positioned between the light source 12 and the image sensor 16 such that object 22 generates a diffraction pattern 26 on the light-receiving surface of the image sensor 16. Each light source 12 has a different azimuth angle relative to object 22 compared to the other light sources 12.

[0083]

Number

[0084] and / or elevation angle θ l , θ m , θ n may be arranged relative to the image sensor 16 so as to propagate at an angle having θ. The position of each holographic feature 34 may correspond to the position of the diffraction pattern 26 on the light-receiving surface of the image sensor 16 that defines the holographic feature 34. Thus, each holographic feature 34 may have different positions within the hologram 32 that can be defined by coordinates (x l , y l , 0), (x m , y m , 0), (x n , y n , 0) when its center 38 is on the sensor plane 30. The coordinates of the holographic features 34 on the hologram 32 may be used to determine the position of the object 22 that generated the diffraction pattern 26 associated with the holographic feature 34. The light source 12 may be configured such that the light 20 has substantially plane waves at the object 22. Thus, each light source 12 may be treated as being at an infinite distance d from the object 22 for the purposes of diffraction pattern analysis.

[0085] Each light source 12 may cause the object 22 to generate a diffraction pattern 26 having a unique shape and position (x, y, 0) at the image sensor 16, and each of these diffraction patterns 26 may be analyzed as a separate hologram 32. The number and arrangement of the light sources 12 may vary in different holographic imagers 10. In response to the diffraction pattern 26 changing over time, the specimen analysis system may determine that the object 22 associated with the diffraction pattern 26 is changing, e.g., growing, shrinking, becoming more transparent / more opaque, changing its shape, etc.

[0086] To increase throughput, the sample analysis system may have multiple image sensors 16, each configured to capture a hologram 32 from a different sample volume 18. Multiple image sensors 16 may facilitate the detection of changing object behavior over short periods of time. For example, a hologram of each sample volume 18 may be acquired every 10, 20, or 30 minutes over a period of 1 to 3 hours. With respect to sample volumes 18 containing microorganisms, a period of 1 to 3 hours may provide sufficient time for two to three (or more) doublings of the object 22 in the growth chamber or flow cell.

[0087] Figures 4 to 8 show an exemplary sample analysis system 40 including a light source assembly 42 and an image sensor assembly 44. The image sensor assembly 44 may include a microfluidic card 46 or a plurality of sample containers 14, or may be configured to receive a plurality of sample containers 14. When the microfluidic card 46 is inserted into the image sensor assembly 44, each sample container 14 may be selectively positioned between the light source assembly 42 and the image sensor assembly 44, depending on the position of the microfluidic card 46 within the image sensor assembly 44.

[0088] As best illustrated in Figure 5, the image sensor assembly 44 may include one or more sensor arrays 50, each containing one or more image sensors 16, for example, eight sensor arrays 50, each containing four image sensors 16. As best illustrated in Figure 6, the light source assembly 42 may include a plurality of light source subassemblies 48, each containing one or more light sources 12, for example, eight light source subassemblies 48, each containing three light sources 12.

[0089] The light source assemblies 42 and image sensor assemblies 44 may be configured such that each light source subassembly 48 is positioned opposite the associated sensor array 50 to collectively define the holographic imager 10. The light source assemblies 42 may be positioned relative to the image sensor assemblies 44 by a support bracket 52 which is operably coupled to the image sensor assemblies 44 via a sensor assembly base 54. Each holographic imager 10 may be configured to generate a hologram 32 for each of multiple sample volumes 18 at once, for example, one hologram 32 for each sensor in the sensor array 50.

[0090] The microfluidic card 46 may include a plurality of pods 56, each having a plurality of sample containers 14 in the form of wells 58, for example, 12 pods 56, each having 8 wells 58. Each sample container 14 may be configured to receive a sample volume 18. As best shown by Figures 7 and 8, the image sensor assembly 44 may be configured to receive the microfluidic card 46 at a plurality of predetermined positions, for example, each of three positions. Each predetermined position may align each portion of the pods 56 (for example, four pods 56) with the sensor array 50.

[0091] When light 20 emitted by light source 12 encounters object 22, the light waves may be distorted from their original paths. The diffraction pattern 26 generated by the diffracted light may then be recorded as a hologram 32 by image sensor 16. Each holographic imager 10 may include one or more image sensors 16 for monitoring and capturing events occurring in multiple areas of one or more sample containers 14, e.g., chambers or flow cells. The exemplary sample analysis system 40 shown by Figures 4–8 includes 32 image sensors 16 arranged in eight sensor arrays 50, each having four image sensors 16, but embodiments are not limited to any particular number of image sensors or arrays. This configuration of image sensors 16 may be organized to accommodate assay consumables, such as the multi-well microfluidic card 46 shown, which provides sample containers 14 whose chambers or flow cells are positioned above the sensor arrays 50. In this example, the consumables are shown as a 96-well microfluidic card 46 positioned directly above the sensor arrays 50. However, it should be understood that the embodiments are not limited to consumables including any particular number or type of sample containers 14. In any case, the sensor array 50 may record events occurring in the sample containers 14 when the light source 12 above the card emits a flash.

[0092] Inline holography relies on a coherent light source that emits light with a well-defined, predictable wavefront. One way to actually achieve this is by placing the coherent light source behind a screen containing a single pinhole. The sample volume and image sensor, placed on the opposite side of the screen, then experience the illumination as a point source of coherent light, greatly simplifying downstream interpretation and analysis. To further simplify the interpretation of the resulting hologram, the sample volume is usually placed far enough away from the pinhole so that the incident light approximates a plane wave. Pinhole filters are a convenient way to produce a homogeneous source of coherent light. However, pinhole filters also have significant drawbacks. For example, pinhole light sources are energetically inefficient because only a small fraction (e.g., less than 1%) of the generated light passes through the pinhole to reach the sample volume. Therefore, most of the generated light is wasted. The pinhole filter itself adds complexity to the hardware and overall setup, representing manufacturing and design constraints and becoming a potential point of failure (for example, due to pinhole blockage by dust).

[0093] Advantageously, the use of pinholes can be avoided by applying algorithmic flat-field correction to the hologram. The flat-field correction process may avoid the need for a pinhole light source by reducing the need for a homogeneous source of coherent light, thereby allowing the use of a coherent light source (such as a laser diode) to directly illuminate the sample volume. In practice, direct illumination typically produces a non-uniform illumination pattern, which hinders the interpretation and analysis of the resulting hologram. One way to flatten these non-uniformities is by using a calibrated hologram. A calibrated hologram may be generated experimentally by capturing the hologram in the absence of the sample, or it may be determined based on the laws of diffraction and the physical properties of the light source. Flat-field correction may produce an extremely flat profile across the entire corrected hologram, thereby increasing the signal-to-noise ratio of data extracted from the hologram, such as a reconstructed z-plane photograph of the object. The improved signal-to-noise ratio may be important for the analysis of both the hologram and the reconstructed photograph. Field flattening may also improve the signal-to-noise ratio of cell proliferation indicators extracted from flattened holograms.

[0094] Figure 9 shows an exemplary image reconstruction process 60 for extracting a photograph 62 from a holograph 32 using a holographic image transformation process such as a Fourier transform 64. When a lens (not shown) is placed between the image sensor 16 and the sample volume 18, different planes within the sample volume 18 located along the z-axis may be focused by adjusting the position of the lens, thereby generating a photographic image on the image sensor 16. The distance u between the z-plane and the lens, the distance v between the image sensor 16 and the lens, and the focal length f of the lens for focusing a photographic image of a particular z-plane onto the image sensor 16 are:

[0095]

number

[0096] The relationship is established by applying the Fourier transform 64 to the holograph 32, which provides a focusing function similar to that of a lens, thereby transforming the diffraction pattern represented by the holograph 32 into one or more photographs 62 of one or more planes along the z-axis.

[0097] One method that may be used to track cell proliferation within a sample volume 18 is to monitor variations in the brightness of the holograph 32 over time. These variations in brightness may provide a cell proliferation indicator that can be used to detect cell proliferation without reconstructing a photograph 62 from the holograph 32. A variance factor refers to a factor that has a value of zero when the values ​​of each pixel are the same and increases as the values ​​of the pixels become more diverse. Examples of variance factors include, but are not limited to, variance, standard deviation, variance-to-mean ratio, range, interquartile range, mean absolute difference, median absolute deviation, and mean absolute deviation. Variance factors such as standard deviation may be affected by variations in all length scales within the holograph 32, including length scales that are too long to originate from cells, e.g., length scales exceeding 500 μm. Therefore, variations in brightness on long length scales within the holograph 32 may add noise to variance-based cell proliferation indicators, reducing their sensitivity. Noise on long length scales may be generated by uneven illumination of the sample volume, variations across the entire sample container, etc.

[0098] Flattening the holograph 32 before extracting cell proliferation indicators can reduce or eliminate the aforementioned causes of long-length scale noise. Image flattening may be achieved, for example, by subtracting an nth-order two-dimensional polynomial fit of the image from the image itself using the flat-field correction techniques described above, or by applying a high-pass filter to the image. Any of these image flattening techniques can dramatically reduce long-length scale noise. Reducing long-length scale noise may allow for the detection of cell proliferation several hours earlier than would have been possible without it.

[0099] Figure 10 shows an exemplary image flattening process 70 for flattening a hologram (e.g., an unprocessed or “raw” hologram 72) using a calibration hologram 74. The calibration hologram 74 may be generated by having the holographic imager 10 capture one or more “blank” holograms with no sample container 14 and / or sample volume 18 placed between the light source 12 and the image sensor 16. One of these blank holograms may then be used as the calibration hologram 74. In an alternative embodiment where multiple blank holograms are captured, the multiple blank holograms may be stacked to generate the calibration hologram 74. The subsequent raw hologram 72 of the sample volume 18 captured by the holographic imager 10 may then be divided by the calibration hologram 74 to generate a flattened hologram 76. As can be seen from the exemplary image in Figure 10, flattening the raw hologram 72 in this manner removes visible illumination artifacts in the unprocessed hologram 72 that would otherwise have interfered with its analysis.

[0100] Figure 11 shows an exemplary image masking process 80. Masking is another type of image processing that may be used to remove artifacts related to the experimental setup before extracting sample information from the hologram. Sources of artifacts that may be removed by masking include (1) geometric features of any other part of the sample container 14 and any consumables between the light source 12 and the image sensor 16, (2) debris objects in the sample holder, on the image sensor 16, or otherwise in the line of sight between the light source 12 and the image sensor 16, and / or (3) air bubbles in the sample volume 18.

[0101] Given the geometric predictability of these objects over time as they appear in the hologram, the identification of these objects may be performed based on their time-invariant properties. The hologram 82 to be processed may first be analyzed to identify areas of the hologram 82 that contain undesirable artifacts. Once these areas are identified, a mask 84 to cover the undesirable artifacts may be determined. The mask 84 may then be applied to the hologram 82 to remove the undesirable areas (for example, by applying the mask 84 to the hologram 82), and the resulting masked hologram 86 may be used for sample analysis. Masking may be performed before or after other pretreatment steps such as flattening.

[0102] Geometric features of a sample container are one type of artifact that can typically be removed from a hologram by masking. For example, the rim of a sample container (or other features related to consumables) may appear in the raw hologram. These artifacts may be detected as areas of the hologram with a pixel intensity distribution that is significantly different from the pixel intensity distribution of the area of ​​the hologram containing the diffraction pattern. This information may be used together with the known geometry of the sample container to mask the container's geometric features from downstream analysis. In this way, downstream analysis may exclude these interfering features, thereby concentrating the extraction of cell proliferation indicators on the holographic region containing information related to the sample volume. This concentration may increase the signal-to-noise ratio of the extracted cell proliferation indicators, thereby improving the speed and reliability of cell proliferation detection.

[0103] Image subtraction may be performed to remove immutable objects that may interfere with downstream analysis. A complementary technique for systematically rejecting undesirable holographic features 34 from the holograph 32 may be by comparing holographs 32 of the same specimen at multiple time points. Biological activity of interest tends to generate holographic features 34 that change over time, while holographic features 34 generated by debris and chamber sidewalls tend to remain static. Static holographic features 34 may be systematically excluded from the analysis by subtracting holographs 32 collected at an earlier time point from holographs 32 collected at a later time point. Thus, subtraction may be beneficial for direct analysis of holographs without reconstruction. This technique may be effective in removing sets of holographic features 34 associated with consumables and static objects that appear in the holograph 32.

[0104] Another form of interfering holographic feature 34 is one caused by an object of interest but which also changes over time. Objects that may produce interfering holographic features 34 that change over time within the holograph 32 may include, but are not limited to, fibers, macroscopic or microscopic bubbles in the sample containing water, microscopic particles, and irregularities in consumables that scatter light differently over time. Unlike confocal microscopy, these undesirable features do not need to be "in focus" to negatively affect the analysis of the holograph. Interfering objects 22 that change over time may occur within the sample container 14, above or below the sample container 14, or even directly on the image sensor 16. They can usually be subtracted from the holograph 32, but not necessarily from the photograph 62 reconstructed from the holograph 32.

[0105] Macroscopic debris generally causes the areas of holograph 32 affected by them to have a significantly different pixel intensity distribution than the areas unaffected by holograph 32. The affected areas can usually be identified as contributing very significantly to the tail of what would otherwise be a Gaussian distribution of pixel intensity across the entire holograph 32. Comparing these local pixel intensity distributions is one method for detecting and rejecting debris.

[0106] A common and disruptive object is air bubbles in water-containing samples. In addition to generating particularly dark or bright pixel intensity areas on the holograph 32, bubbles may also be characterized by their round shape. The detection strategies discussed above have often been successful in detecting bubbles. Blob detection based on Hough transform and OpenCV also offers a complementary detection mechanism with high sensitivity to the round shape of bubbles. Embodiments of the processes disclosed herein may employ one or more combinations of the above-described methods to detect bubbles and exclude them from downstream analysis.

[0107] Microscopic, tiny debris can produce diffraction patterns similar to those of individual cells, making it difficult to distinguish them from cells. One way debris objects 22 may be distinguished from cellular objects 22 is by their refractive index. The refractive index of object 22 may be determined from its diffraction pattern 26, for example, by mapping holographic features 34 associated with object 22 to patterns produced by objects having known properties and / or formulas, or by measuring the phase offset of object 22 in a reconstructed photograph 62.

[0108] In confocal microscopy, cells may be identified in the image as "blobs" of varying intensity that stand out from the background. Therefore, the overall aggregated pixel intensity (or average pixel intensity) scales to the number of cells. Thus, average pixel intensity provides a computationally efficient metric for tracking cell proliferation in confocal microscopy. In inline holography, objects 22 within the sample volume 18 produce holographic features 34 in the holograph 32, such that the intensity fluctuations have sinusoidal and cosine functions. The property of these holographic features 34 is that their aggregated intensity (and therefore average intensity) is zero. This is because for every peak in brightness, there is also a corresponding trough that cancels out the peak. Therefore, metrics that respond to changes in global pixel intensity may have low sensitivity to the presence or absence of objects 22 and thus have limited usefulness in tracking cell proliferation.

[0109] A more suitable index for responding to the presence or absence of sine and cosine is an index that responds to the variation in intensity rather than the aggregated intensity. Therefore, the standard deviation σ of the pixel intensity of the holograph 32 may provide an effective index for detecting cell proliferation. Another index that may be used to detect cell proliferation within the holograph 32 is the variance σ of the pixel intensity of the holograph 32, which is referred to herein as holographic intensity variance. 2Holographic intensity dispersion also has the desirable property of being able to scale linearly with increasing cell numbers. As a result, biological characteristics such as cell division rates can be conveniently extracted from growth curves based on holographic intensity dispersion of holograms captured over a period of time.

[0110] The following is an example of how holographic intensity dispersion should be used to determine the concentration of an object. To extract the division rate and other relevant parameters, it may be desirable to have an index that scales to the cell concentration, preferably linearly scaled to the cell concentration. Since object 22 adds a sine wave to the holograph 32, it makes sense to use an index to capture its variability in a way that scales linearly to the cell concentration. The dispersion is,

[0111]

number

[0112] It can be defined as follows, I i This represents the intensity of pixel i,

[0113]

number

[0114] This represents the average intensity of the set of pixels for which the variance has been determined.

[0115] When an object 22 (for example, a cell) is added to a sample volume 18, the average pixel intensity of the holograph 32 of the sample volume 18 may not change due to the sinusoidal nature of the diffraction pattern 26 produced by the object 22. However, the intensity of individual pixels will change, usually due to the additional sinusoidal diffraction pattern 26 added by the object 22. The effect of a single cell may be modeled one-dimensionally for pixels, which are infinitesimally small to allow the use of integrals and continuous functions rather than summation. In this model, the variance is,

[0116]

number

[0117] It may be defined as follows: When an object is added, I(x) picks up a sine term such as I1(x) ≈ I0(x) + sin(x), where I0(x) is the intensity before adding object 22 and I1(x) is the intensity after adding object 22. Therefore, the variance σ 2 teeth,

[0118]

number

[0119] Therefore, this equation can be expanded as follows.

[0120]

number

[0121]

number

[0122] In equation 6, the first integral

[0123]

number

[0124] × Number of peaks (this is a constant), and the second integral is due to the sine function.

[0125]

number

[0126] And the third integral

[0127]

number

[0128] This is simply the variance σ before the object is added. 2 Therefore,

[0129]

number

[0130] Equation 7 is the variance σ of the pixel intensity of the holograph 32. 2 However, we can determine—at least approximately—how it changes when object 22 is added, and show that the holographic intensity dispersion increases by a constant. Mathematically speaking,

[0131]

number

[0132] That is the case.

[0133] Therefore, the holographic intensity dispersion increases linearly as cells are added. Conveniently, the holographic intensity dispersion can be directly extracted from a series of holograms 32 of sample volume 18 containing proliferating and dividing cells.

[0134] Figure 12 shows the sequences of holographs 90–92 and photographs 96–98. Holographs 90–92 were generated using light with a wavelength λ = 405 nm 30 minutes (holograph 90), 2 hours (holograph 91), and 4 hours (holograph 92) after the sample volume 18 was prepared. Each of photographs 96–98 was reconstructed from one of each of holographs 90–92 with respect to z = 266 μm. As can be seen from each sequence of images, there was significant cell proliferation within the sample volume 18 over the period. Holographic intensity dispersion was measured directly from the holographic images (not shown) at regular intervals over a 5-hour period and compared to the mean pixel intensity μ of the reconstructed images at a specified z height in the sample container 14.

[0135] Figure 13 shows a graph including a plot 102 of the holographic intensity variance of the holographs described above, a plot 103 of the average pixel intensity μ of the reconstructed images generated from those holographs, and a plot 104 showing the slope representing the number of microorganisms growing at a growth rate of 1.7 divisions per hour. As can be seen, both the growth curves for the holographic intensity variance of the holographs and the average pixel intensity μ of the reconstructed images are similar in slope and magnitude. Therefore, using the measured values ​​of the pixel intensity variance in the time series of holographs 32 is comparable to using the average pixel intensity of the reconstructed photographs 62 from the same holographs 32.

[0136] Figure 14 shows plots of growth curves for *E. coli* microorganisms at various cell seeding concentrations. Plots 110–114 show the mean pixel intensity μ of the reconstructed photographs extracted from the hologram, and plots 120–124 show the standard deviation of intensity divided by the mean intensity of the hologram of the sample. The cell seeding concentrations are 10 for plots 110–112 and 120–122. 5 CFU / mL, for plots 113 and 123, 10 6 CFU / mL, for plots 114 and 124, 10 4The value was CFU / mL. The normalized standard deviation of holographic intensity closely matches the number of microorganisms as they grow and divide, and is comparable to the average intensity of photographs reconstructed from the hologram at the same time point. The curve shapes are nearly identical, indicating that tracking the normalized standard deviation of holographic intensity is a good indicator of microbial growth. Therefore, the normalized standard deviation of holographic intensity can provide valuable behavioral data without the need to perform a complete image reconstruction from the hologram.

[0137] Figure 15 shows the holographic intensity dispersion σ for each measurement scenario in Figure 14. 2 Plots 130–135 of the image mean intensity μ are shown. The plots shown illustrate a linear relationship between tracking object proliferation by holographic intensity variance compared to the image mean intensity.

[0138] A more granular method for monitoring cell proliferation may be provided by individually detecting each cell in the holograph 32. Detecting individual objects 22 may allow the number of objects 22 to be measured directly rather than inferred from coarser indicators such as holographic intensity dispersion. In confocal microscopy, individual objects 22 can be detected by identifying localized blobs of intensity in the photograph. This method may not be effective in the holograph 32 because individual objects 22 yield sinusoidal holographic features 34 instead of blobs. However, individual objects 22 may be detected in the holograph 32 based on the radiative symmetry of the diffraction patterns they generate.

[0139] Figure 16 shows a holograph 140 containing overlapping holographic features 34 and a graph 142 of the average pixel intensity as a function of distance from the center 38 of one of the holographic features 34. Holographic features 34 associated with a particular object 22 may be analyzed by plotting the average pixel intensity of the holographic feature 34 as a function of distance from its center 38, as indicated by the data point 144. To facilitate the analysis of the holographic features 34, a suitable formula (e.g., a polynomial or sinc function) may be fitted to the data point 144. An exemplary plot 146 shows a formula fitted to the data point 144 in graph 142, which has a decaying sinusoidal shape including a peak 148. Analysis of the holographic features 34 may be used to track, as a function of time, the specific behavior of the object 22 that generates the diffraction pattern 26 that produced the holographic features 34. One type of analysis involves examining the amplitude of the peaks 148 of the fitted function and the distance d between the peaks 148. The fit constant for generating plot 146 is the distance z from the image sensor to the object at 249 μm. c , signal strength β of 658 V / μm c , including a length l of 12.6 μm and a light propagation delay ψ through an object at 5.1 or 292 degrees.

[0140] Electric field E PLANE A plane light wave with a certain electric field E propagates along the z-axis and can be considered to be incident on object 22 at some point in space. If object 22 is infinitesimally small, the light 20 is diffracted around object 22 but does not pass through object 22. This is because of the electric field E POINT A spherical wavefront is created. If these two wave sources can interfere on a plane perpendicular to the z-axis (for example, the light-receiving surface of the image sensor 16), then they are superimposed. E TOTAL = E PLANE + E POINT formula 9 It interferes according to this.

[0141] To derive the precise interference patterns formed on a plane, the functional forms of those waves in space must be established. PLANE It propagates along the z-axis, E PLANE = E1e ikz Formula 10 It can be written as follows: E POINT It extends spherically from a point in space, E POINT = E2e ikδ-ψ Formula 11 It can be written as follows, where δ is the distance between the scattering object 22 and another arbitrary point, and ψ is the phase shift given to the light passing through the object. Then the total electric field at any point in space is, E TOTAL = E1e ikz + E2e ikδ-ψ Formula 12 It can be written like this.

[0142] Focusing on the shape of diffraction pattern 26 and ignoring its size, E0= E1= E2 formula 13 By setting it this way, the amplitude component can be ignored. Therefore, the total electric field can be derived as follows. E TOTAL = E0(e ikz + e ikδ-ψ ) Equation 14

[0143] What is recorded on the photosensitive surface of the image sensor 16 is not the electric field E, but rather the intensity I of the electric field E, which is the square of the electric field.

[0144]

number

[0145] hyperbolic trigonometric identity cosh(a) = (e a + e -a Using ) / 2, equation 15 is,

[0146]

number

[0147] It can be simplified to cosh(0) = (e 0 + e 0 ) / 2 = 1, and sinh(0) = (e 0 - e 0 Note that ) / 2 = 0. Applying these to the intensity formula,

[0148]

number

[0149] This is the result.

[0150] From this general formula, a coordinate system can be defined for determining the two-dimensional diffraction pattern 26 recorded by the image sensor 16 as the holographic feature 34 of the holograph 140. The coordinate system may be defined such that the sensor plane 30 is orthogonal to the z-axis, intersects the z-axis at z = 0, and the object 22 is located at (0, 0, z), i.e., directly above the origin (0, 0, 0). Then, every location on the sensor plane 30 can be expressed as (x, y, 0), and δ is,

[0151]

number

[0152] It is possible to express it as follows. Substituting equation 18 into equation 17 means that

[0153]

number

[0154] This equation yields the final form of the diffraction pattern 26 of a point scatterer on the sensor plane 30 of the image sensor 16. For practical purposes, it is sometimes convenient to express the wavenumber k in terms of wavelength λ. k = 2π / λ Equation 20 Due to the rotational symmetry of diffraction pattern 26, it may also be convenient to use the radial distance from the center of the pattern. The radial distance is, r 2 = x 2 + y 2 formula 21 It is given by Substituting equations 20 and 21 into equation 19,

[0155]

number

[0156] This equation yields a maximum and minimum value whenever the argument of the cosine = nπ, where n = 0, 1, 2, ...

[0157] The location of these extreme values ​​r EXT However, it is possible to determine this for n = 0, 1, 2, ... and the formula

[0158]

number

[0159] This helps to quickly determine the parameters Z and ψ from only the locations of the peaks and valleys of the fringes. Equation 23 is used for r EXT Solving this means,

[0160]

number

[0161] It brings about.

[0162] The diffraction pattern 26 generated by the small object 22 generally contains multiple concentric circles around a central point. Therefore, circle detection techniques can be an effective way to quickly detect the object 22 and their individual coordinates from the holograph 32. For example, Canny edge detection processing of the holograph 32 followed by a Hough transform may be used to detect circles within a narrow radius range. One strategy for quickly identifying cellular objects is to select a good candidate radius for the search using equation 24. This technique is computationally efficient but can also produce false positives. A slower but more robust strategy is to iterate the Hough transform over a wide radius to identify the location of (x, y) in relation to many circles of various sizes that are characteristic of concentric circles centered at (x, y). The presence of concentric circles in the holograph 32 is a reliable indicator of cellular objects 22 within the sample volume 18.

[0163] Figure 17 shows an exemplary object detection process 150 in which the holograph 152 first undergoes an edge detection process 154, for example, Canny edge detection. The resulting edge-enhanced image 156 may then undergo a Hough transform 158 to generate an accumulated score image 160. The accumulated score image 160 may then be superimposed on the holograph 152 to generate a composite image 164 (162). Holographic features 34 may then be identified on the composite image 164 based on the accumulated score of the accumulated score image 160 to generate a final image 168 (166), and the identified holographic features 34 in the final image 168 may be counted.

[0164] Given that the (x, y) coordinates of object 22 are known, the z position of each object 22 can be determined based on the shape of the holographic feature 34, which is essentially the same as the shape of the diffraction pattern 26 that generated the holographic feature 34. The z position of each object 22 may be determined by performing a two-dimensional fitting of equation 22 to each individual holographic feature 34, where z is determined as a free parameter for optimizing the fit. A similar potentially faster process may involve first averaging the holographic features 34 over the azimuth angle φ of the polar coordinates to generate a one-dimensional function of r. Then, a one-dimensional fitting may be performed on equation 22 to determine the z position of object 22. Another potentially faster process is to average the holographic features 34 over the azimuth angle φ of the polar coordinates to generate a one-dimensional function of r, and then use peak detection to determine the radial positions r of the peaks and troughs of the fitted function. EXT This may involve determining the z position using equation 24, which can be expressed as various r values. EXT It can be calculated directly from this. Advantageously, this method avoids the curve fitting step.

[0165] In an alternative embodiment, the z-position of the object 22 may be determined using geometric triangulation between different light sources 12. For example, by using multiple light sources 12, each light source 12 located above a different (x, y) position of the image sensor 16, as shown in Figure 2. The positions of the light sources 12 may be known in advance or may be determined using a calibration step. Having multiple light sources 12 allows the difference in the coordinates of the centers 38 of the holographic features 34 between the various holographs 32 to be used to determine the height of the object 22. The mathematics behind this is the elevation angle (θ l ,θ m ,θ n The z-position of object 22 is determined using ).

[0166] As described above, in point objects, diffraction is the sole cause of scattering, and none of the light 20 is phase-shifted by passing through object 22. However, many objects of interest (e.g., cells) have a finite size, allowing light 20 to pass through them. Light 20 passing through object 22 may experience a velocity v modified according to the refractive index n of object 22, where v = c / n. This change in velocity compared to propagation in a suspension medium may ultimately manifest as a phase shift ψ of some amount of light 20. This phase shift ψ can be modeled within the framework of the above derivation using equation 22, in which the phase shift ψ appears as a cosine argument. Thus, it is possible to determine the phase shift ψ by fitting the formula of equation 22 to the holographic feature 34. With the phase shift ψ determined, it is also possible to estimate the refractive index n of object 22, which provides information about the material composition and volume of object 22. Knowing the refractive index n of each object 22 may also facilitate distinguishing cells from non-cellular debris.

[0167] The processes described above may be applied to the hologram individually, in any combination, or to parts of the hologram. These parts of the hologram may contain subsets of pixels, each subset defining a contiguous portion of the hologram. Subsets of pixels that form regular shapes (e.g., triangles, squares, or hexagons) within an image are sometimes called “tiles.” The process of tiling a hologram is sometimes called holographic tessellation.

[0168] Applying these processes separately to one or more parts of a hologram may facilitate the detection of localized phenomena, which can provide an early indicator of subsequent bulk activity. For example, some organisms exhibit heterogeneous responses to effectors such as antimicrobial agents, with most of the population dying, while a small subset of the population develops resistance. Bulk measurements, such as broth microdilution using turbidimetric readings over long periods, correctly identify the organism as resistant because the resistant subpopulation eventually grows to a quantity large enough to be macroscopically detectable. However, measurements on a fast timescale may mischaracterize the organism as susceptible to antimicrobial agents after the majority of the organism has been observed to die.

[0169] Extracting cell proliferation indicators from individual parts of a hologram may increase the likelihood of identifying localized resistance. This is due to the increased impact of small areas of resistance on the cell proliferation indicator in the hologram portion where resistance is present. This increased sensitivity may allow for the evaluation of sample volume on a shorter timescale than is possible by other methods.

[0170] Figures 18A–18E show a sequence of photographs A–E of sample volume 18, with Figure 18A showing the earliest photograph and Figure 18E showing the latest photograph. Photographs A–E show cell proliferation of Klebsiella oxytoca (CDCJI 380) cultured with 0.5 mg / mL of the antibiotic meropenem over approximately 8 hours. Photographs A–E provide an example of the complex response of microorganisms to antibiotics. Holographs 32 of sample volume 18 were captured every 10 minutes during the culture period. Figure 19 shows a graph including plot 170 of cell proliferation indicators (e.g., holographic intensity dispersion) extracted from the complete holograph 32 and plot 172 of cell proliferation indicators extracted from a set of pixels containing tile 6 of the holograph 32. The times corresponding to photographs A–E in Figures 18A–18E are indicated by arrows labeled to correspond to those in the graph in Figure 19. Based on a complete holographic plot 170, the cell proliferation indicator decreases after 3.5 hours of culture, followed by a slight increase after 7 hours of culture, suggesting the microorganisms are susceptible to antibiotic doses. The slight increase in the cell proliferation indicator after 7 hours is the earliest indication of a potential resurgence of cell proliferation, which could be due to the microorganisms becoming resistant, for example.

[0171] In contrast, tile plot 172 of the cell proliferation indicator extracted from tile 6 provides indication of a resurgence of growth, which is due to developing antibiotic resistance, at approximately 4.5 hours. The divergence of tile plot 172 from the complete holograph plot 170 begins at approximately 3.5 hours. This is due to the microbial response to the antibiotic in a localized area of ​​the sample volume corresponding to tile 6. This area of ​​the sample volume contains a subpopulation of bacterial cells that appear to have become resistant to the antibiotic and begun to grow within clusters of colonies. This example of delayed antibiotic resistance is clinically significant, and early recognition of resistance in testing can lead to timely administration of the appropriate antibiotic in the correct dose to treat the patient. From an algorithmic perspective, systematically dividing a holograph into parts (e.g., tiles) and calculating the cell proliferation indicator for each part could be used to identify events or hotspots in microbial growth or death.

[0172] Figure 20 shows a flowchart illustrating an exemplary process 178 for performing an analytical procedure (e.g., an assay) on a target specimen and / or facilitating the performance of the analytical procedure. Figures 21 and 22 show exemplary images that may be captured, generated, and analyzed by process 178. Process 178 may be performed by, for example, a specimen analysis system 40, or any other suitable system for capturing and analyzing a hologram of a target specimen, such as a specimen container 14 containing a specimen volume 18.

[0173] Referring here to Figures 20 and 21, in block 180, process 178 captures a blank hologram 184. The blank hologram 184 may be a hologram captured when there is no target sample between the image sensor 16 and the light source 12, for example, before the target sample is loaded into the sample analysis system 40. In block 186, process 178 places the target sample (e.g., a sample container 14 containing sample volume 18) between the light source 12 and the image sensor 16. The placement of the target sample may be performed manually or automatically, for example, by a laboratory technician or a robotic sample loading device. After the target sample is placed in position, process 178 captures a hologram 188 of the target sample (e.g., sample hologram N = 0).

[0174] In block 190, process 178 generates a flattened hologram 192 from the sample hologram 188 by, for example, dividing the sample hologram 188 by the blank hologram 182, using the blank hologram 182. The blank hologram 182 may have been captured by the same holographic imager 10 as the sample hologram 188, except that the sample volume 18 or consumables are not in place. Flattening the sample hologram 188 may remove distortions and non-uniform illumination patterns introduced by the sample volume 18 and / or consumables. Compared to the sample hologram 188, the flattened hologram 192 may be free from or have reduced background illumination anomalies, reflectivity anomalies, and other anomalies that could interfere with image analysis. A hologram taken early in the analysis procedure (for example, before any changes occur in the target sample) may be used as a reference hologram to reduce noise in later holograms, as described in more detail below.

[0175] In block 194, process 178 may wait for a sufficient period of time to allow the target sample to be cultured, incrementing N to, for example, N = N + 1. A typical culture period is between 10 minutes and 1 hour and may vary depending on the characteristics of the test sample. In block 196, process 178 proceeds to block 200 and captures another sample hologram 198 (for example, sample hologram N = 1) before generating a flattened hologram 202. The flattened hologram 202 may be generated by dividing the sample hologram 198 with the same blank hologram 182 used to flatten the previous sample hologram 188.

[0176] In block 204, process 178 generates a noise-corrected / aligned hologram 206 using a previously generated flattened hologram (for example, flattened hologram 192 generated at N = 0) as a reference flattened hologram. Process 178 may generate the noise-corrected / aligned hologram 206 by subtracting the reference flattened hologram from the current flattened hologram 202 and / or by dividing the current flattened hologram 202 by the reference flattened hologram.

[0177] Referring here to Figure 22, and continuing to refer to Figure 20, process 178 proceeds to block 208, where a mask 210 is generated from the denoised / aligned holograph 206. The mask 210 may be generated by analyzing the denoised / aligned holograph 206 using an automated algorithm that detects holographic features in the denoised / aligned holograph 206 generated by undesirable objects. Each mask 210 may be unique to the denoised / aligned holograph 206 from which it was generated. Thus, the mask 210 may be configured to remove artifacts in the denoised / aligned holograph 206 caused by impurities, debris, and other objects that are not part of the target sample but may change from sample period to sample period. In block 212, process 178 generates a masked holograph 214 by applying the mask 210 to the denoised / aligned holograph 206. It may be desirable to mask all undesirable appearances of the holograph. These undesirable appearances may include, but are not limited to, holographic features within the hologram caused by bubbles and chamber boundaries, as well as other holographic features that are not useful for data analysis.

[0178] In block 216, process 178 may extract information (e.g., variance factors, holographic features relating to objects within the sample volume, etc.) from the masked holograph 214 and / or one or more parts thereof, as described above. The extracted information may then be used to identify and / or quantify changes in the test sample during the analysis procedure. Methods of data extraction may include, but are not limited to, object proliferation tracking, edge detection and object counting, and image tiling for hotspot detection. Photographs 62 may be reconstructed from the raw and / or processed holograph 32. These reconstructed photographs 62 may target a specific z-plane within the sample volume 18 and / or a part thereof, based on the information extracted from the holograph 32.

[0179] The masked holograph 214 may be used to determine a number of parameters such as the dispersion of objects, the number of objects, and / or the concentration of objects. Tiling may be performed to detect specific events within the masked holograph 214. In the case of microorganisms, the detected events may indicate cell proliferation, cell death, and / or other significant changes in objects that can be derived. In an alternative embodiment of process 178, the mask 210 may be applied to a flattened holograph 202 and / or a noise-corrected / aligned holograph 206, and information may be extracted from one or more of these masked holographs. It should also be understood that each holographic analysis process and each step of each holographic analysis process described herein may be applied individually, in any combination, and / or in any order to analyze the holographs.

[0180] Referring here to Figure 24, the embodiments or parts thereof of the present invention described above may be implemented using one or more computer devices or systems, such as an exemplary computer 220. The computer 220 may include a processor 222, memory 224, an input / output (I / O) interface 226, and a human-machine interface (HMI) 228. The computer 220 may also be coupled to one or more external resources 230 via a network 232 or the I / O interface 226. The external resources may include, but are not limited to, servers, databases, mass storage devices, peripheral devices, cloud-based network services, or any other resources that may be used by the computer 220.

[0181] The processor 222 may operate under the control of an operating system 234 residing in memory 224. The operating system 234 may manage computing resources such that computer program code, which may be embodied as one or more computer software applications such as application 236 residing in memory 224, may have instructions executed by the processor 222. In an alternative embodiment, the processor 222 may execute application 236 directly, in which case the operating system 234 may be omitted. One or more data structures 238 may also reside in memory 224 and may be used by the processor 222, the operating system 234, or application 236 to store or manipulate data.

[0182] The I / O interface 226 may provide a machine interface that connects the processor 222 to other devices and systems, such as external resources 230 or a network 232, in an operable manner. Thereafter, application 236 may work in cooperation with the external resources 230 or network 232 by communicating via the I / O interface 226 to provide various features, functions, applications, processes, or modules constituting embodiments of the present invention. Furthermore, application 236 may have program code executed by one or more external resources 230, or otherwise rely on functions or signals provided by other systems or network components outside the computer 220. Moreover, given the almost limitless possible hardware and software configurations, those skilled in the art will understand that embodiments of the present invention may include applications provided by computing resources (hardware and software) located outside the computer 220, distributed across multiple computers or other external resources 230, or provided as a service via the network 232, such as a cloud computing service.

[0183] The HMI 228 may be coupled to the processor 222 of the computer 220 in an operable manner to enable the user to interact directly with the computer 220. The HMI 228 may include a video or alphanumeric display, a touchscreen, a speaker, and any other suitable audio and visual indicators that can provide data to the user. The HMI 228 may also include input and control devices such as an alphanumeric keyboard, a pointing device, a keypad, push buttons, control knobs, and a microphone that can receive commands or inputs from the user and transmit the inputs received to the processor 222.

[0184] A database 240 may reside in memory 224 and may be used to collect and organize data used by the various systems and modules described herein. The database 240 may include data and supporting data structures for storing and organizing the data. In particular, the database 240 may be arranged in any database organization or structure, including but not limited to relational databases, hierarchical databases, network databases, or combinations thereof. A database management system in the form of a computer software application executed as instructions on processor 222 may be used to access information or data stored in the records of the database 240 in response to queries that may be dynamically determined and executed by the operating system 234, other applications 236, or one or more modules.

[0185] In general, routines performed to carry out embodiments of the present invention may be referred to herein as “program code,” whether implemented as part of an operating system or as a specific application, component, program, object, module, or sequence of instructions, or a subset thereof. Program code typically resides at various points in time in the computer’s various memory and storage devices and, when read and executed by one or more processors of the computer, includes computer-readable instructions that cause the computer to perform the actions necessary to perform actions or elements that embody various aspects of embodiments of the present invention. Computer-readable program instructions for performing actions of embodiments of the present invention may be, for example, assembly language, source code, or object code written in any combination of one or more programming languages.

[0186] The program code embodied in any of the applications / modules described herein may be distributed individually or as a collection of various different forms of computer program products. In particular, the program code may be distributed using a computer-readable storage medium having computer-readable program instructions thereon for causing a processor to execute embodiments of the present invention.

[0187] Computer-readable storage media, which are inherently non-temporary, may include volatile and non-volatile removable and non-removable tangible media implemented in any way or technology for storing data, such as computer-readable instructions, data structures, program modules, or other data. Computer-readable storage media may further include RAM, ROM, erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory, or other solid-state memory technologies, portable compact disk read-only memory (CD-ROM), or other optical storage, magnetic cassettes, magnetic tapes, magnetic disk storage, or other magnetic storage devices, or any other media that can be used to store data and are readable by a computer. Computer-readable storage media should not be interpreted as temporary signals themselves (e.g., radio waves or other propagating electromagnetic waves, electromagnetic waves propagating through a transmitting medium such as a waveguide, or electrical signals transmitted through wires). Computer-readable program instructions may be downloaded from computer-readable storage media to a computer, another type of programmable data processing device, or another device, or to an external computer or external storage device via a network.

[0188] Computer-readable program instructions stored on a computer-readable medium may be used to instruct a computer, other type of programmable data processing device, or other device to function in a particular way so as to produce a product in which the instructions stored on the computer-readable medium perform functions, actions, or operations defined in the text, flowcharts, sequence diagrams, or block diagrams of this specification. Computer program instructions may be provided to one or more processors of a multipurpose computer, a dedicated computer, or other programmable data processing device to produce a machine in which instructions executed by one or more processors perform a series of calculations to perform functions, actions, or operations defined in the text, flowcharts, sequence diagrams, or block diagrams of this specification.

[0189] The flowcharts and block diagrams shown in the drawings illustrate the architecture, functionality, or operation of possible implementations of systems, methods, or computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction set containing one or more executable instructions for implementing one or more defined logical functions.

[0190] In certain alternative embodiments, the functions, actions, or operations defined in the text, flowcharts, sequence diagrams, or block diagrams herein may be reordered, processed sequentially, or processed simultaneously in accordance with embodiments of the present invention. Furthermore, any flowchart, sequence diagram, or block diagram may contain more or fewer blocks than those shown, in accordance with embodiments of the present invention. It should also be understood that each block in a block diagram or flowchart, or any combination of blocks in a block diagram or flowchart, may be performed by a system based on dedicated hardware configured to perform the defined function or action, or by a combination of dedicated hardware and computer instructions.

[0191] The terms used herein are intended solely to describe specific embodiments and are not intended to be limitations on embodiments of the present invention. Where used herein, the singular forms “a,” “an,” and “the” are intended to include both singular and plural forms, and the terms “and” and “or” are intended to include both alternative and conjunctive combinations, respectively, unless the context explicitly indicates otherwise. Where used herein, the terms “comprises” or “comprising” specify the presence of a mentioned feature, integer, action, step, operation, element, or component, but do not exclude the presence or addition of one or more other features, integers, actions, steps, operations, elements, components, or groups thereof. Furthermore, to the extent that the terms “includes,” “having,” “has,” “with,” “comprised of,” or variations thereof are used in either a form for carrying out the invention or in the claims, such terms are intended to be inclusive, in the same way as the term “comprising.”

[0192] While the entirety of the present invention is shown by the description of various embodiments, and these embodiments are described in considerable detail, it is not the applicant's intention to limit or restrict the scope of the appended claims in any way to such detail. Further advantages and modifications will be readily apparent to those skilled in the art. Thus, broader aspects of the present invention are not limited to specific details, representative apparatus and methods, or the exemplary examples shown and described. Accordingly, deviations from such details may be made without departing from the spirit or scope of the applicant's overall inventive concept. [Explanation of symbols]

[0193] 10 Holographic Imager 12 light source 14 specimen containers 16 Image Sensors 18 Sample Volume 20 light 22 Object 26 Diffraction Patterns 28 Computer 30 Sensor Plane 32 Holographs 34 Holographic Features 36 Reference Coordinate System 38 center 40 Sample Analysis System 42 Light source assembly 44 Image sensor assembly 46 Microfluidic Cards 48 Light source subassembly 50 Sensor Arrays 52 Support bracket 54 Sensor assembly base 56 pods 58 wells 60 Image Reconstruction Process 62 photos 64. Fourier Transform 70 Image Flattening Process 72 Live Holograph 74 Calibration Holograph 76 Flattened Hologram 80 Image Masking Process 82 Holograph 84 masks 86 Masked Hologram 90-92 Holograph 96-98 Photos 102 Plots 103 Plot 104 Plot 110-114 Plot 120-124 plot 130-135 Plot 140 Holographs 142 Graphs 144 data points 146 plots 148 peaks 154 Edge detection process 150 Object Detection Processes 156 Edge-enhanced images 158 Hough Transform 160 Cumulative Score Images 164 Composite Images 168 Final Image 170 plots 172 plots 178 processes 184 Blank Hologram 188 Holograph 192 Flattened Hologram 198 Another sample hologram 202 Flattened Hologram 206 Noise-corrected / aligned holograms 210 masks 214 Masked Hologram 220 Computers 222 processors 224 memory 226 I / O interfaces 228 HMI 230 External Resources 232 Network 234 Operating Systems 236 applications 238 Data Structures 240 databases

Claims

1. A sample analysis system, A holographic imager configured to generate a hologram of the sample volume, One or more processors coupled to the holographic imager in an operable manner, Coupled to operate on one or more of the aforementioned processors, and when executed by the one or more of the aforementioned processors, to the system, The method involves generating a first hologram of the sample volume in a first time, wherein the first hologram includes a first plurality of pixels, each having an intensity. Determining a first dispersion factor of the intensity of at least a first portion of the first plurality of pixels, and The characteristics of the sample volume are determined based on the value of the first variance factor. Memory to store the program code that will perform the action and A sample analysis system, including the following:

2. The sample analysis system according to claim 1, wherein the program code causes the system to determine the characteristics of the sample volume based on the value of the first variance factor by comparing the value of the first variance factor with a predetermined threshold.

3. The aforementioned program code is further installed in the system, The process involves generating a second hologram of the sample volume in a second time, wherein the second hologram includes a second plurality of pixels, each having an intensity. Determining a second dispersion factor of the intensity of at least a second portion of the second plurality of pixels, and The characteristics of the sample volume are determined based on the value of the first variance factor by comparing the value of the first variance factor with the value of the second variance factor. A sample analysis system according to claim 1 or 2, which causes the following to be performed.

4. The first portion of the first plurality of pixels is one of the plurality of portions of the first plurality of pixels, and the program code further to the system Determine the second dispersion factor of the intensity of the second portion of the first plurality of pixels. The system according to any one of claims 1 to 3, wherein the characteristics of the sample volume are determined based on the value of the first variance factor by comparing the first variance factor with the second variance factor.

5. The aforementioned program code is further installed in the system, To identify the part of interest among the aforementioned first parts, Determine the z-height of the object that generates the diffraction pattern in the aforementioned region of interest. The system according to claim 4, which allows the aforementioned object to be analyzed.

6. The system according to claim 5, wherein the program code causes the system to analyze the object by reconstructing a photograph from the first hologram at the z-height.

7. The aforementioned program code is used in the system. To generate multiple dispersion factors, determine the dispersion factor of the intensity of each portion of the first plurality of pixels, The value of each of the multiple variance factors is compared with one or more values ​​of the other variance factors of the multiple variance factors, and From the aforementioned multiple variance factors, identify the variance factor of the portion of interest as an outlier. The system according to claim 5 or 6, wherein the portion of interest is identified by the system.

8. The system according to any one of claims 4 to 7, wherein each portion of the plurality of first pixels includes a tile from the plurality of tiles of the first holograph.

9. The aforementioned program code is further installed in the system, Applying one or more image modification processes to the first holograph before determining the first variance factor, wherein the one or more image modification processes do not involve image reconstruction. A system according to any one of claims 1 to 8, which causes the following to be performed.

10. The system according to claim 9, wherein the one or more image correction processes include a flat-field correction process.

11. The one or more image correction processes described above Identifying one or more irrelevant portions of the first holograph that are not related to quantifying the change in the aforementioned characteristics of the sample volume, To generate a mask configured to remove one or more irrelevant portions of the first holograph, Applying the mask to the first holograph The system according to claim 9 or 10, including the system described in claim 9 or 10.

12. The system according to any one of claims 1 to 11, wherein the sample volume comprises one or both of a plurality of microorganisms and a plurality of eukaryotic cells of animal or human origin.

13. The system according to claim 12, wherein the plurality of microorganisms belong to a species or class of Gram-negative bacteria, Gram-positive bacteria, or fungi.

14. The system according to any one of claims 1 to 13, wherein the first variance factor is variance.

15. A method for analyzing sample volume, A step of generating a first hologram of the sample volume in a first time, wherein the first hologram includes a first plurality of pixels, each having an intensity; A step of determining a first dispersion factor of the intensity of at least a first portion of the first plurality of pixels, A step of determining the characteristics of the sample volume based on the value of the first variance factor, Methods that include...

16. The method according to claim 15, wherein the step of determining the characteristics of the sample volume based on the value of the first variance factor includes comparing the value of the first variance factor with a predetermined threshold.

17. A step of generating a second hologram of the sample volume in a second time, wherein the second hologram includes a second plurality of pixels, each having an intensity; The steps include determining a second dispersion factor of the intensity of at least a second portion of the second plurality of pixels, A step of determining the characteristics of the sample volume based on the value of the first variance factor by comparing the value of the first variance factor with the value of the second variance factor. The method according to claim 15 or 16, further comprising:

18. The first portion of the first plurality of pixels is one of the plurality of portions of the first plurality of pixels, and the method is The steps include determining a second dispersion factor of the intensity of the second portion of the first plurality of pixels, A step of determining the characteristics of the sample volume based on the value of the first variance factor by comparing the first variance factor with the second variance factor. The method according to any one of claims 15 to 17, further comprising:

19. The steps include identifying a portion of interest among the aforementioned first multiple parts, The steps include determining the z-height of the object that generates the diffraction pattern in the aforementioned region of interest, The step of analyzing the object The method according to claim 18, further comprising:

20. The method according to claim 18 or 19, wherein analyzing the object includes reconstructing a photograph from the first holograph at the z-height.

21. The step of identifying the part of interest is, To generate multiple dispersion factors, determine the dispersion factor of the intensity of each portion of the first plurality of pixels, The value of each of the aforementioned plurality of variance factors is compared with one or more values ​​of the other variance factors of the aforementioned plurality of variance factors, From the aforementioned multiple variance factors, the variance factor of the portion of interest is identified as an outlier. The method according to claim 19 or 20, including the method described in claim 19 or 20.

22. The method according to any one of claims 18 to 21, wherein each portion of the plurality of first pixels includes a tile from the plurality of tiles of the first holograph.

23. A step of applying one or more image modification processes to the first holograph before determining the first variance factor, wherein the one or more image modification processes do not include image reconstruction. The method according to any one of claims 15 to 22, further comprising:

24. The method according to claim 23, wherein the one or more image correction processes include a flat-field correction process.

25. The one or more image correction processes described above Identifying one or more irrelevant portions of the first holograph that are not related to quantifying the change in the aforementioned characteristics of the sample volume, To generate a mask configured to remove one or more irrelevant portions of the first holograph, Applying the mask to the first holograph The method according to claim 23 or 24, including the method according to claim 23 or 24.

26. The method according to any one of claims 15 to 25, wherein the sample volume comprises one or both of a plurality of microorganisms and a plurality of eukaryotic cells of animal or human origin.

27. The method according to claim 26, wherein the plurality of microorganisms belong to a species or class of Gram-negative bacteria, Gram-positive bacteria, or fungi.

28. The method according to any one of claims 15 to 27, wherein the first variance factor is the variance.

29. Non-temporary computer-readable storage media and Program code stored in the non-temporary computer-readable storage medium, which, when executed by one or more processors, is directed to the one or more processors The method involves causing a holographic imager to generate a first hologram of the sample volume in a first time, wherein the first hologram includes a first plurality of pixels, each having an intensity. Determining a first dispersion factor of the intensity of at least a first portion of the first plurality of pixels, and The characteristics of the sample volume are determined based on the value of the first variance factor. The program code to make it do this and Computer program products, including [this].

30. A sample analysis system, A holographic imager configured to generate a hologram of the sample volume, One or more processors coupled to the holographic imager in an operable manner, Coupled to operate on one or more of the aforementioned processors, and when executed by the one or more of the aforementioned processors, to the system, The method involves generating a first hologram of the sample volume in a first time, wherein the first hologram includes a first plurality of pixels, each having an intensity. Extracting a first set of holographic features belonging to a class of shapes, each containing one or more diffraction patterns related to the diffraction of light by an object in the sample volume, from at least a first portion of the first plurality of pixels. Determining a first number of holographic features in the first set of holographic features, and The characteristics of the sample volume are determined based on the first number of holographic features. Memory to store the program code that will perform the action and A sample analysis system, including the following:

31. The system according to claim 30, wherein the program code causes the system to determine the characteristics of the sample volume based on the value of the first number of holographic features by comparing the value of the first number of holographic features with a predetermined threshold.

32. The aforementioned program code is further installed in the system, The process involves generating a second hologram of the sample volume in a second time, wherein the second hologram includes a second plurality of pixels, each having an intensity. Extracting a second set of holographic features belonging to the class of shapes including one or more diffraction patterns from at least a second portion of the second plurality of pixels, and The task is to determine the second number of holographic features in the aforementioned second set of holographic features. The system according to claim 30 or 31, wherein the program code causes the system to determine the characteristics of the sample volume based on the value of the first number of holographic features by comparing the value of the first number of holographic features with the value of the second number of holographic features.

33. The system according to any one of claims 30 to 32, wherein the class of shapes includes one or more patterns having radial symmetry.

34. The aforementioned program code is further installed in the system, The system according to any one of claims 30 to 33, which determines the phase shift related to light passing through the object in the sample volume.

35. The system according to claim 34, wherein the program code causes the system to determine the phase shift by applying a mathematical formula to the first stripe pattern generated by the object in the first holograph and extracting parameters indicating the phase shift from the mathematical formula.

36. The system according to claim 34 or 35, wherein the phase shift of the object is used to distinguish the object from one or more other objects having different phase shifts.

37. The system according to claim 36, wherein the object is a cell and the one or more other objects are waste.

38. The system according to claim 36, wherein the object is a first type of cell, and the one or more other objects include a second type of cell.

39. The first portion of the first plurality of pixels is one of the plurality of portions of the first plurality of pixels, and the program code further to the system A second set of holographic features belonging to the class of shapes including one or more diffraction patterns is extracted from a second portion of the first plurality of pixels. Determine the second number of holographic features in the second set of holographic features. The system according to any one of claims 30 to 38, wherein the properties of the sample volume are determined based on the value of the first number of holographic features by comparing the first number of holographic features with the second number of holographic features.

40. The aforementioned program code is further installed in the system, To identify a portion of interest among the multiple portions of the first multiple pixels, Determine the z-height of the object that generates the diffraction pattern in the portion of interest. The system according to claim 39, which causes the aforementioned object to be analyzed.

41. The system according to claim 40, wherein the program code causes the system to analyze the object by reconstructing a photograph from the first hologram at the z-height.

42. The aforementioned program code is used in the system. Extracting a set of holographic features from each of the multiple parts of the first multiple pixels, Determining the number of holographic features in each set of holographic features extracted from the aforementioned multiple parts, Comparing the number of holographic features in each set of holographic features with the number of holographic features in other sets of holographic features, Identifying the number of holographic features extracted from the portion of interest as an outlier from the number of holographic features in the other set of holographic features. The system according to claim 40 or 41, wherein the portion of interest is identified by the system.

43. The system according to any one of claims 39 to 42, wherein each portion of the first plurality of pixels includes a tile from the plurality of tiles of the first holograph.

44. The system according to any one of claims 30 to 43, wherein the sample volume comprises one or both of a plurality of microorganisms and a plurality of eukaryotic cells of animal or human origin.

45. The system according to claim 44, wherein the plurality of microorganisms belong to a species or class of Gram-negative bacteria, Gram-positive bacteria, or fungi.

46. A method that involves analyzing the volume of the sample, A step of generating a first hologram of the sample volume in a first time, wherein the first hologram includes a first plurality of pixels, each having an intensity; A step of extracting from at least a first portion of the first plurality of pixels a first set of holographic features belonging to a class of shapes that each includes one or more diffraction patterns related to the diffraction of light by an object in the sample volume, A step of determining a first number of holographic features in the first set of holographic features, A step of determining the characteristics of the sample volume based on the first number of holographic features and Methods that include...

47. The method according to claim 46, wherein the step of determining the characteristics of the sample volume based on the value of the first number of holographic features includes comparing the value of the first number of holographic features with a predetermined threshold.

48. A step of generating a second hologram of the sample volume in a second time, wherein the second hologram includes a second plurality of pixels, each having an intensity; A step of extracting a second set of holographic features belonging to the class of shapes including one or more diffraction patterns from at least a second portion of the second plurality of pixels, A step of determining a second number of holographic features in the second set of holographic features, It further includes, The method according to claim 46 or 47, wherein the step of determining the characteristics of the sample volume based on the value of the first number of holographic features includes comparing the value of the first number of holographic features with the value of the second number of holographic features.

49. The method according to any one of claims 46 to 48, wherein the class of shapes includes one or more patterns having radial symmetry.

50. The method according to any one of claims 46 to 49, further comprising the step of determining a phase shift related to light passing through the object in the sample volume.

51. The method according to claim 50, wherein the step of determining the phase shift includes fitting a mathematical formula to a first stripe pattern generated by the object in the first holograph, and extracting parameters indicating the phase shift from the mathematical formula.

52. The method according to claim 50 or 51, wherein the phase shift of the object is used to distinguish the object from one or more other objects having different phase shifts.

53. The method according to claim 52, wherein the object is a cell and the one or more other objects are waste.

54. The method according to claim 52, wherein the object is a first type of cell, and the one or more other objects include a second type of cell.

55. The first portion of the first plurality of pixels is one of the plurality of portions of the first plurality of pixels, and the method is A step of extracting a second set of holographic features belonging to the class of shapes including one or more diffraction patterns from a second portion of the first plurality of pixels, A step of determining a second number of holographic features in the second set of holographic features, A step of determining the characteristics of the sample volume based on the value of the first number of holographic features by comparing the first number of holographic features with the second number of holographic features. The method according to any one of claims 46 to 54, further comprising:

56. The steps include identifying a portion of interest among the multiple portions of the first multiple pixels, The steps include determining the z-height of the object that generates a diffraction pattern in the portion of interest, The step of analyzing the object The method according to claim 55, further comprising:

57. The method according to claim 56, wherein the step of analyzing the object includes reconstructing a photograph from the first holograph at the z-height.

58. The step of identifying the part of interest is, Extracting a set of holographic features from each of the multiple parts of the first multiple pixels, Determining the number of holographic features in each set of holographic features extracted from the aforementioned multiple parts, Comparing the number of holographic features in each set of holographic features with the number of holographic features in other sets of holographic features, Identifying the number of holographic features extracted from the portion of interest as an outlier from the number of holographic features in the other set of holographic features. The method according to claim 56 or 57, including the method according to claim 56 or 57.

59. The method according to any one of claims 56 to 58, wherein each portion of the plurality of first pixels includes a tile from the plurality of tiles of the first holograph.

60. The method according to any one of claims 46 to 59, wherein the sample volume comprises one or both of a plurality of microorganisms and a plurality of eukaryotic cells of animal or human origin.

61. The method according to claim 60, wherein the plurality of microorganisms belong to a species or class of Gram-negative bacteria, Gram-positive bacteria, or fungi.

62. Non-temporary computer-readable storage media and Program code stored in the non-temporary computer-readable storage medium, which, when executed by one or more processors, is directed to the one or more processors The method involves causing a holographic imager to generate a first hologram of the sample volume in a first time, wherein the first hologram includes a first plurality of pixels, each having an intensity. Extracting a first set of holographic features belonging to a class of shapes, each containing one or more diffraction patterns related to the diffraction of light by an object in the sample volume, from at least a first portion of the first plurality of pixels. Determining a first number of holographic features in the first set of holographic features, and The characteristics of the sample volume are determined based on the first number of holographic features. The program code to make it do this and Computer program products, including [this].

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