3D Luminescence Imaging

Abstract

Systems, apparatus, and methods for 3D luminescence imaging are described by identifying a preferred optical pair and optimizing the scanned image using the preferred optical pair. The optimal filter pair may be selected from a list of two or more optical filters. An acceptable threshold of information may be obtained using a subset (e.g., the optimal filter pair) of the list of two or more optical filters. An imaging device may be configured with the optimal filter pair to generate a pair of luminescence images of the target sample. Furthermore, the luminescence images may be pre-processed to reduce the time cost of conventional processing techniques for the luminescence images. One or more computing devices may generate initial prior data based on the pair of luminescence images. The output may include one or more output luminescence sources that are improved and / or optimized from the initial prior data.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to nonprovisional U.S. application Ser. No. 17 / 534,166, filed Nov. 23, 2021, entitled “Three-Dimensional Luminescence Imaging.”

[0002] Aspects of the present disclosure relate generally to luminescence imaging techniques, and more specifically to identifying a preferred relationship between a pair of optical filters and one or more luminescence sources. [Background technology]

[0003] Three-dimensional ("3D") luminescence imaging of samples of interest may rely on conventional processing methods that require three or more optical filters, which can be slow and have low throughput. Furthermore, these conventional processing methods may not use optimal reconstructed sources and also require long processing times, approximately 20-30 min per sample, which unduly limits the number of samples that can be imaged in a limited time. Summary of the Invention [Means for solving the problem]

[0004] Systems, devices, and methods for luminescence imaging, such as 3D bioluminescence imaging, are described. The following provides a simplified overview of various aspects described herein. This overview is not an extensive overview and is not intended to identify key or important elements or to recite the claims. The following overview is merely intended to present some concepts in a simplified form as a prelude to the more detailed description provided below. Corresponding devices, systems, and computer-readable media are also within the scope of the present disclosure.

[0005] According to one aspect, the present disclosure relates to a method for 3D luminescence imaging. The method may include receiving, by a computing device, a list of optical filters and data indicative of a light emission source and a scattering medium, such as a biological material. A filtered signal may be generated based on the received list of optical filters and the data, and weights associated with one or more pairs of optical filters may be determined. Each of the one or more pairs of optical filters may be an optical filter pair of the list of optical filters. The list of one or more pairs of optical filters may be ranked based on the weights. The user device may be configured to receive the filtered signal through the highest ranked pair of optical filters of the ranked list.

[0006] In some implementations, each of the weights may correspond to a numerical value, and the highest-ranked optical filter pair may correspond to the largest numerical value of the weight. The optical filter pair may include a first optical filter and a second optical filter. The weight associated with the optical filter pair may be determined, in part, by determining a correlation between the first optical filter and the second optical filter, for example, a correlation between a first filtered signal of the filtered signals associated with the first optical filter and a second filtered signal of the filtered signals associated with the second optical filter. In some implementations, the weight may be determined, in part, by determining a ratio of the first filtered signal and the second filtered signal. In some implementations, the weight may be determined, in part, by comparing each of the first filtered signal and the second filtered signal to a predetermined threshold. In some implementations, the weight may be determined, in part, by determining a product of the first filtered signal and the second filtered signal. In some implementations, determining weights associated with the pair of optical filters may include determining a linear relationship between the first filtered signal and the second filtered signal. In some implementations, determining weights associated with the pair of optical filters may include determining a rank of a matrix having at least a first column including the first filtered signal and a second column including the second signal.

[0007] In some implementations, generating the filtered signal may include calculating the filtered signal for a plurality of distances. Each of the plurality of distances may correspond to a distance between a portion of the light emitting source and a surface boundary of the scattering medium. In some implementations, generating the filtered signal may be based on a source model. In some implementations, generating the filtered signal may include generating a volume of the biological material. The volume may include one or more voxels, each of which may be assigned a numerical value corresponding to a radiant intensity of the light emitting source.

[0008] In some implementations, the computing device may receive data indicative of a second light emission source and a second biological material. An additional filtered signal may be generated based on the list of optical filters, the second light emission source, and the second biological material. Additional weights associated with the one or more pairs of optical filters may be determined based on the additional filtered signal. The second list of one or more optical filters may be ranked based on the additional weights. The highest ranked optical filter pair may be selected from the ranked second list of one or more pairs of optical filters.

[0009] According to yet another aspect of the present disclosure, there is provided an optical filter pair for imaging an emission source within a volume of biological material, the optical filter pair may be selected from a list of available optical filters according to any of the methods disclosed herein.

[0010] Another aspect of the present disclosure is a method of optimizing a scanned image. The method may include receiving, from a user device, one or more images associated with one or more optical filters and a bioluminescent source located within a volume of biological material. An image dataset may be generated based on the one or more images, and one or more image segments may be determined based on the image dataset. One or more volumetric sub-regions associated with the one or more image segments may be generated. Each of the one or more volumetric sub-regions may include voxels, and each of the voxels may be assigned one or more radiation values ​​determined based on the bioluminescent source, the volume of biological material, and the one or more optical filters. An output volume including output voxels may be determined based on the one or more volumetric sub-regions. One or more output radiation values ​​may be assigned to each of the output voxels. The output volumetric region may be transmitted to a computing device for additional processing. In some implementations, the output volumetric region may be transmitted to a storage device. In some implementations, the one or more optical filters include a first optical filter and a second optical filter, corresponding to a pair of optical filters.

[0011] In some implementations, the image dataset may include a plurality of pixels, and one or more image segments of the image dataset may be determined by comparing a numerical value of the pixels to a threshold value. In some implementations, the quantity of the one or more image segments may be less than a predetermined quantity of image segments. In some implementations, a distance between a voxel associated with the volumetric subregion and a surface boundary of the volume of biological material may be determined for the volumetric subregion. In some implementations, the output volumetric region may be determined based on performing an average of the radiation values ​​assigned to the one or more voxels for one or more voxels of the one or more volumetric subregions. In some implementations, the user device is a three-dimensional bioluminescence imaging device.

[0012] In some implementations, the image segments may correspond to volumetric sub-regions determined by generating one or more volumetric test regions, each of which includes one or more voxels assigned one or more radiation test values. The one or more radiation test values ​​may be determined based on a bioluminescent source, a biological material, and one or more optical filters. One or more test images may be generated based on the one or more radiation test values ​​assigned to the one or more voxels and the one or more optical filters. An image test segment may be generated based on the one or more test images. A correlation between the image test segment and the image segment may be determined for each image test segment, and a volumetric test region may be selected as the volumetric sub-region based on a determination of a highest correlation value of the image test segment.

[0013] In some implementations, one or more additional images may be received from the user device, the one or more additional images associated with one or more additional optical filters and a bioluminescent source located within the volume of biological material. A second image data set may be generated based on the one or more additional images, and one or more additional image segments may be determined based on the second image data set. One or more additional volumetric sub-regions associated with the one or more additional image segments may be generated. Each of the one or more additional volumetric sub-regions may include voxels, and each of the voxels may be assigned one or more radiation values ​​determined based on the bioluminescent source, the volume of biological material, and the one or more additional optical filters. A second output volumetric region including additional output voxels may be generated based on the one or more additional volumetric sub-regions. Each of the additional output voxels may be assigned one or more output radiation values. The second output volumetric region may be transmitted to a computing device for additional processing.

[0014] According to another aspect of the present disclosure, one or more non-transitory computer-readable media are provided having stored thereon instructions that, when executed, perform one or more of the methods disclosed herein. According to another aspect of the present disclosure, a system is provided that includes one or more processors and a memory that stores instructions that, when executed by the one or more processors, cause the system to perform one or more of the methods disclosed herein.

[0015] Additional aspects, configurations, embodiments, and examples are described in further detail below.

[0016] Certain features are illustrated by way of example, and not by way of limitation, in the accompanying drawings, in which like numerals refer to like elements and in which: [Brief description of the drawings]

[0017] [Figure 1] FIG. 1 illustrates an exemplary optical filter pair selection process. [Figure 2A] 4 is a flowchart illustrating an exemplary method for determining a pair of optical filters. [Figure 2B] 4 is a flowchart illustrating an exemplary method for determining a pair of optical filters. [Figure 2C] 4 is a flowchart illustrating an exemplary method for determining a pair of optical filters. [Diagram 3] FIG. 1 illustrates an exemplary 3D luminescent imaging process. [Figure 4] FIG. 2 illustrates an exemplary process for generating initial prior data. [Figure 5A] 4 is a flow chart illustrating an exemplary method for determining initial prior data. [Figure 5B] 4 is a flow chart illustrating an exemplary method for determining initial prior data. [Figure 5C] 4 is a flow chart illustrating an exemplary method for determining initial prior data. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0018] The inventors have recognized and understood that luminescence imaging, such as bioluminescence imaging and chemiluminescence imaging, can be time-consuming, slow, and reduce throughput when processing samples. In particular, 3D luminescence imaging may require taking multiple images using multiple filters, typically three or more, and more commonly six or more. At the same time, luminescence imaging can be time-sensitive, for example, due to time constraints of anesthesia in the case of biological samples and the movement of the luminescence source. As such, scientists and laboratory technicians often choose other methods to achieve their various end goals. As a result, the embodiments described herein provide new methods and systems that reduce the time required to perform luminescence imaging without significantly compromising accuracy or quality, thereby paving the way for increasing the use of luminescence imaging for a wider range of applications.

[0019] In some embodiments, the number of optical filters used to perform luminescence imaging can be reduced from three or more to only two optical filters without significantly compromising the accuracy or quality of the images and measurements. Reducing the number of filters used to acquire image data reduces the required data acquisition time. For example, data acquisition using a single optical filter takes 2-5 minutes, whereas data acquisition using six optical filters traditionally takes about 30 minutes. On the other hand, in some embodiments, using only two optical filters can reduce the data acquisition time to 5-10 minutes. Using two optical filters that provide complementary information with sufficient signal-to-noise ratio can reduce the time for data acquisition without compromising the accuracy or quality of the images and measurements.

[0020] In some embodiments, the number of optical filters used can be reduced to a single pair of filters by determining an optimal pair of filters to use for a particular purpose from a list of available optical filters. The optimal pair of filters can be determined without first performing luminescence imaging or performing any physical optimization of the pair of filters on the sample to be imaged. The optimal pair of filters can be selected from a list of two or more optical filters. For example, the one or more computing devices can determine, based on performing one or more electromagnetic simulations, that one or more optical filters of the list of optical filters can be discarded without reducing the amount of measured spectral information of the luminescence source below an acceptable threshold of information. The acceptable threshold of information can be obtained using a subset (e.g., the optimal pair of filters) of the list of two or more optical filters. The imaging device can be configured with the optimal pair of filters to generate a pair of luminescence images of the target sample.

[0021] The inventors have also recognized and understood that image reconstruction from acquired luminescence imaging data is a time-consuming iterative process. Conventional image reconstruction techniques use "unintelligent" initial guesses as initial prior data to begin the reconstruction process. The inventors have also recognized and understood that using a fast and simple estimation process to generate the initial prior data can significantly increase the speed of conventional reconstruction techniques, as only a small number of iterations are required to generate the final image reconstruction. Thus, in some embodiments, unlike the single-stage reconstruction of conventional methods, image reconstruction is performed using a two-stage reconstruction. In two-stage reconstruction, some embodiments perform pre-processing, which may include making a rough estimate of one or more luminescence sources during a first stage. For example, one or more computing devices may generate initial prior data based on pairs of luminescence images. The initial prior data may include data indicative of one or more luminescence sources. This rough estimate of the one or more luminescence sources may then be used as initial prior data in more conventional image reconstruction techniques. The output of the image reconstruction techniques may be one or more output luminescence sources that have been improved and / or optimized from the initial prior data. These and other features and advantages are described in more detail below.

[0022] 1 illustrates an exemplary method 100 for selecting a pair of optical filters from a list of available optical filters 110. For example, a computing device 101 may determine an optimal filter pair by performing the method described below in conjunction with FIGS. 2A-2C. The optimal filter pair may be used to image a target sample in a luminescence imaging system.

[0023] The computing device 101 may include one or more processors 103, which may execute instructions of a computer program to perform any of the functions described herein. The instructions may be stored in one or more non-transitory computer-readable media 105 (e.g., random access memory (RAM)). The computing device 101 may further include one or more data interfaces (e.g., a USB port, a CD / DVD drive, one or more network ports, a modem, etc.), one or more output devices (e.g., a display device, speakers, etc.), and / or one or more user input devices (e.g., a keyboard, a mouse, a touch screen, a microphone, etc.).

[0024] The list of available optical filters 110 may comprise optical filters capable of spectrally filtering light. For example, the list of available optical filters 110 may comprise at least one or more of an absorptive optical filter, a dichroic optical filter, a monochromatic optical filter, an infrared filter, an ultraviolet filter, an interference filter, a thin film filter, a long-pass optical filter, a band-pass optical filter, a short-pass optical filter, or a machine vision filter. Additionally, the list of available optical filters 110 may comprise optical filters comprising optical materials such as optical glass (e.g., CaF2, fused silica, S-FSL5, N-BK7, other types of optical glass) and / or absorbing coatings (e.g., anti-reflective coatings, dielectric mirror coatings, short-wave pass filter coatings, long-wave pass filter coatings, band-pass filter coatings, dichroic filter coatings, notch filter coatings, etc.). Additionally, the optical filters may correspond to geometric shapes such as a circle, a cylindrical disk, a square, a rectangular prism, or other two- or three-dimensional shapes.

[0025] The computing device 101 may receive the optical filter data comprising the list of available optical filters 110 and the additional data via one or more of its data interfaces. For example, the optical filter data and the additional data may be received via keyboard input, a CD-ROM, a USB flash drive, or any other device suitable for data transfer. As described below in connection with FIGS. 2A-2C, the computing device 101 may determine an optical filter pair, such as the optimal filter pair 112 shown in FIG. 1, from the optical filter data. The optimal filter pair 112 may comprise a pair of optical filters (e.g., optical filter 110-j and optical filter 110-k shown in FIG. 1) selected from the list of optical filters 110 available by the computing device 101. Based on a ranked list of the optical filter pairs selected from the list of available optical filters 110, the optical filter pair (e.g., optical filter 110-j and optical filter 110-k shown in FIG. 1) may be selected as the optimal filter pair 112. The optimal filter pair may be the highest ranked optical filter pair in the ranked list. The selected optimal filter pair 112 may be transmitted to an output device of the computing device 101. For example, information indicative of the selected optimal filter pair 112 may be displayed on a computer monitor. Additionally, or alternatively, information indicative of the selected optimal filter pair 112 may be stored on a storage device (e.g., a USB flash drive, a hard drive, etc.) associated with the computing device 101 for access by one or more users. Additionally, a luminescence imaging device (not shown) may be configured to image one or more objects (e.g., tissue samples, small animals, rodents, other biological material, etc.) using the optimal filter pair 112. For example, the luminescence imaging device may be configured with the optimal filter pair 112 to generate a pair of luminescence images (e.g., a luminescence image generated for each optical filter of the optimal filter pair 112).As described below in connection with Figures 3 and 4A-4C, a computing device (e.g., computing device 101 or another computing device) may perform pre-processing of the luminescence image to generate initial prior data as input for additional image processing.

[0026] 2A-2C are flow charts illustrating an example method for determining an optimal filter pair. One, some, or all steps of the example method of FIGS. 2A-2C may be performed by computing device 101, and for convenience, FIGS. 2A-2C are described below with reference to computing device 101. Also, or alternatively, one, some, or all steps of the example method of FIGS. 2A-2C may be performed by one or more other computing devices. One or more steps of the example method of FIGS. 2A-2C may be rearranged (e.g., performed in a different order), omitted, and / or otherwise modified, and / or other steps may be added.

[0027] In step 201, optical filter data comprising a list of available optical filters (e.g., a list of available optical filters 110) is received (e.g., by computing device 101). Additionally, additional data may be received, comprising additional information associated with the list of available optical filters, the light emitting source, and / or the scattering medium. For example, the additional information may indicate a type of light emitting source (e.g., a biological light emitting source such as firefly luciferase, Renilla luciferase, bacterial luciferase, dinoflagellate luciferase, Metridia luciferase, or any other light emitting source) for use with the optimal filter pair. Additionally, the additional information may comprise emission characteristics of the light emitting source (e.g., power spectral density, kinetic spectral emission profile, or other spectral characteristics of the radiation emitted from the light emitting source) and characteristics of each of the optical filters of the list of optical filters (e.g., type of optical filter, spectral characteristics, etc.). The additional information may further comprise optical characteristics of the scattering medium (e.g., biological material) (e.g., permittivity, dielectric constant, refractive index, etc.). Upon receiving the list of available optical filters, the computing device 101 may be configured to proceed to step 203. Alternatively, the computing device 101 may be configured to wait for instructions (e.g., instructions received via user input) before proceeding to step 203.

[0028] In step 203, an optical filter may be selected from the list of available optical filters 110. Based on the selected optical filter and the additional information received in step 201, a filtered signal may be generated (e.g., by the computing device 101). For example, the computing device 101 may generate a filtered signal based on the selected optical filter, the light emitting source, and the scattering medium. The filtered signal may be generated via one or more electromagnetic signal propagation models and / or one or more signal processing techniques based on the optical signal emitted from the light emitting source, scattered and / or attenuated in the scattering medium, and filtered by the selected optical filter. Furthermore, the spectral characteristics of the filtered signal are transmitted to an output device (e.g., a display device and / or a storage device) for retrieval and use in additional steps described below in conjunction with FIG. 1 and FIG. 2A-FIG. 2C. Additional details of step 203 are described below in conjunction with FIG. 2B.

[0029] In step 205, computing device 101 may determine whether there are additional optical filters available for selection from the list of available optical filters 110. If computing device 101 determines that there are additional optical filters for selection, computing device 101 may select another optical filter to use in step 203, and step 203 may be performed. Otherwise, step 207 may be performed.

[0030] In step 207, a ranked list of optical filter pairs may be determined. For example, the computing device 101 may determine a ranking for an optical filter pair comprising a pair of optical filters selected from the available list of optical filters 110, for example in step 203, based on the filtered signals generated for the two selected optical filters. The ranking may be a value assigned based on weighting factors determined for the optical filter pair. For example, a pair of optical filters having a high weighting factor may be assigned a high rank and another pair of optical filters having a low weighting factor may be assigned a low rank. The weighting factors may be determined based on applying one or more weighting criteria to the filtered optical signals corresponding to the optical filter pair. Additional details of step 207 are described below in connection with FIG. 2C.

[0031] In step 209, from the ranked list of optical filter pairs, the highest ranked optical filter pair may be selected. The highest ranked optical filter pair may be determined to be the optimal filter pair. An indication of the optimal filter pair may be transmitted to at least one of an output device, a display device, and / or a storage device (e.g., of the computing device 101). A user device (e.g., a luminescence imaging device) may be configured according to the optimal filter pair. For example, a user may configure the user device to image one or more objects based on the optimal filter pair.

[0032] FIG. 2B illustrates additional details of step 203 of FIG. 2A, as indicated by the dashed box. In particular, FIG. 2B illustrates exemplary steps 203.1-203.6 that may be performed to generate a filtered optical signal for each of the optical filters in the list of available optical filters 110. In step 203.1, a scattering medium may be generated based on additional data received in step 201. For example, the additional data received in step 201 may comprise one or more optical properties of the tissue sample (e.g., tissue permittivity, tissue relative permittivity, tissue scattering and / or absorption properties). The one or more optical properties may be derivable via experimentally measured data. For example, the optical properties may comprise experimentally measured optical permittivity values ​​for one or more spectral components associated with the pair of optical filters. Also, or alternatively, the one or more optical properties may be derivable via a theoretical model. For example, the optical properties may comprise an attenuation coefficient and / or an electronic relaxation time. The computing device 101 may generate an optical permittivity value for the scattering medium based on, for example, a damping coefficient and a permittivity model (e.g., a bound electron oscillator model, a Debye relaxation model, etc.) The scattering medium may be generated to fill a volume bounded by one or more surface boundaries.

[0033] In step 203.2, a volumetric region comprising a plurality of voxels in the scattering medium may be selected, for example, by user input, or may be generated, for example, by computer simulation. Each of the plurality of voxels may be assigned one or more radiation values ​​corresponding to the light emitting source and the scattering medium. For example, the computing device 101 may assign a radiation intensity to voxels that have similar spectral characteristics as the light emitting source located in the scattering medium. The shape of the volumetric region may be a sphere, an elliptical region, or any other continuous spatial region.

[0034] The center of the volumetric region may be used to define a surface distance between the volumetric region and a surface boundary that separates the scattering medium and an adjacent region that has optical properties consistent with a different medium, such as air or free space. The center of the volumetric region may be the center of a spherical region, the center of symmetry of an elliptical region, the center of mass, or any other part of the volumetric region. The surface distance of the volumetric region may be the distance between the center of the volumetric region and the closest part of the surface boundary of the scattering medium. Volumetric regions near the surface boundary (e.g., where the surface distance is below a predetermined threshold) may be generated, for example, by the computing device 101 using an elliptical shape. Volumetric regions far from the surface boundary (e.g., where the surface distance is above a threshold) may be generated using a spherical shape. Also, or alternatively, the volumetric region may be generated as any shape at any surface distance. Furthermore, the volumetric region of the light emitting source may be configured to emit optical radiation according to the radiation characteristics of the additional information received in step 201. For example, the volumetric region may comprise a plurality of voxels. Each voxel may be assigned a radiation value (e.g., radiation intensity, radiation field strength) based on the radiation characteristics received in the additional information.

[0035] In step 203.3, an optical signal comprising one or more spectral components associated with the pair of optical filters may be generated using an electromagnetic model (e.g., a model derivable from Maxwell's equations) and / or one or more electromagnetic numerical solver techniques (e.g., finite difference time domain method, finite element method, method of moments, etc.). For example, the computing device 101 may simulate emission of an optical signal from an emission source in a scattering medium. Furthermore, the emitted optical signal may be attenuated and / or dispersed based on the optical properties of the scattering medium (e.g., those determined in step 203.1). The computing device 101 may simulate refraction of the optical signal at the surface boundary of the scattering medium (e.g., based on Snell's law).

[0036] In step 203.4, the optical signal may be filtered by a selected optical filter after exiting the scattering medium through the surface boundary. For example, the computing device 101 may simulate the propagation of at least a portion of the refracted optical signal through the optical filter. The filtered signal may be determined based on using signal processing techniques (e.g., Fourier transform, convolution, etc.). The filtered signal may comprise a numerical value for each of one or more spectral components of the optical signal. For example, the numerical value may correspond to a received optical power (e.g., irradiance, luminous flux density, refractive power, and / or spectral irradiance).

[0037] In step 203.5, the computing device 101 may determine whether it is necessary to generate an additional filtered optical signal using the selected optical filter. For example, the additional information received in step 201 may comprise an indication that it is necessary to generate a filtered optical signal for one or more surface distances between the volumetric region of the light emitting source and the surface boundary of the scattering medium. If the computing device 101 determines that it is necessary to generate an additional optical signal using the selected optical filter, step 203.2 may be executed. Otherwise, if the computing device 101 determines that it is not necessary to generate an additional optical signal using the selected optical filter, step 203.6 may be executed.

[0038] In step 203.6, an output filtered signal may be generated based on the filtered signals generated in steps 203.2-203.5. For example, the output filtered signal may be generated based on performing an average of the filtered signals. Also, or alternatively, the output filtered signal may be generated based on performing a weighted sum of the filtered optical signals. For example, a filtered optical signal generated for an emission source near an interface of the scattering medium may be given a higher weight than a filtered optical signal generated for an emission source far from the interface. Alternatively, a filtered signal generated for an emission source far from the interface of the scattering medium may be given a higher weight than a filtered optical signal generated for an emission source near the interface. Alternatively, the filtered signals generated for the emission sources may be given equal weight regardless of the source distance from the interface of the scattering medium. The output filtered signal of the selected optical filter may be transmitted to one or more of a display device, a storage device, or other output device of the computing device 101.

[0039] FIG. 2C shows additional details of step 207 of FIG. 2A, as indicated by the dashed box. In particular, FIG. 2C shows exemplary steps 207.1-207.6 that may be performed to generate a ranked list of optical filter pairs based on the output filtered signals generated for each of the optical filters in step 203. In step 207.1, one or more weighting criteria for the stored output filtered signals generated in step 203 may be obtained (e.g., via additional information received in step 201). The one or more weighting criteria may comprise one or more criteria for determining a performance relationship between two optical filters (i.e., a pair of optical filters) from the list of available optical filters 110. For example, the one or more weighting criteria may be associated with determining a correlation between the two optical filters of the pair of optical filters. Also, or alternatively, one or more weighting criteria may be used to determine that the output filtered signals associated with the pair of optical filters meet additional criteria, such as meeting a minimum output filtered signal power or a minimum signal-to-noise ratio per output filtered signal.

[0040] In step 207.2, the computing device 101 may determine sub-weights of the optical filter pair (e.g., the optical filter pair selected from the list of available optical filters 110) based on one of one or more weighting criteria. The sub-weights may be determined based on using the output filtered signals (e.g., power spectral density, spectral intensity, etc.) generated in step 203 as inputs to the weighting criteria. For example, the weighting criteria may comprise calculating a ratio of a sum of the first output filtered signal I1 to a sum of the second output filtered signal I2. The first output filtered signal I1 may be associated with the first optical filter of the selected optical filter pair. The second output filtered signal I2 may be associated with the second optical filter of the selected optical filter pair. The weighting criteria may be calculated based on the weights of the first output filtered signal I1 and the second output filtered signal I2. ratio =Σ i I 1,i / Σ j I 2,j where the summation is performed for each spectral component I1 and I2.

[0041] Also, or alternatively, the weighting criterion may comprise determining a total number of values ​​of the first and second output filtered signals that exceed a threshold. For example, the first output filtered signal I1 may comprise N1 spectral components, where N1 is an integer. The computing device 101 may determine that an integer number n1 of the spectral components exceeds a threshold, where n1≦N1. Similarly, the second output filtered signal I2 may comprise N2 spectral components, where N2 is an integer. The computing device 101 may determine that an integer number n2 of the spectral components exceeds a threshold, where n2≦N2. The weighting criterion may be mathematically determined as w n =(n1+n2) / (N1+N2).

[0042] Also, or alternatively, the weighting criterion may comprise determining a product, or a normalized product, of the first output filtered signal I1 and the second output filtered signal I2. product =2Σ i I 1,i Σ j I 2,j / ((Σ i I 1,i ) 2 +(Σ j I 1,j ) 2 ), where the summation is performed over each of the I1 and I2 spectral components.

[0043] Also, or alternatively, the weighting criterion may comprise determining a linear relationship between the first output filtered signal I1 and the second output filtered signal I2. For example, a numerical relationship f may be defined as I2=f(I1). A goodness of fit value R may be determined based on the numerical relationship f, where 0≦R≦1. The goodness of fit value R may indicate a correlation between the numerical relationship f and a linear function. The weighting criterion may be mathematically expressed as w R = 1-R.

[0044] Also, or alternatively, the weighting criterion may comprise determining the rank of a matrix comprising a first output filtered signal I1 in a first column and a second output filtered signal I2 in a second column. For example, the matrix

number

number

number

number

number

number

[0045] In step 207.3, the computing device may determine whether to apply an additional weighting criterion to the first and second output filtered signals. If it is determined to apply an additional weighting criterion, step 207.2 may be performed. Otherwise, step 207.4 may be performed.

[0046] In step 207.4, output weights for the selected pair of optical filters may be generated based on one or more sub-weights calculated in step 207.2. For example, the computing device 101 may calculate a product of one or more sub-weights (e.g., w final =w ratio ×w n ×w product ×w R ×w rank Alternatively, the computing device 101 may calculate the output weight by taking a weighted sum of one or more sub-weights. For example, the output weight is mathematically denoted by w final =ρ ratio w ratio +ρ n w n +ρ product w product +ρ R w R +ρ rank wrank where the weighting coefficients are real numbers and ρ ratio +ρ n +ρ product +ρ R +ρ rank = 1. Alternatively, the computing device 101 may calculate the output weights based on using the sub-weights as inputs to any other algorithms and / or mathematical relationships indicated by the additional information received in step 201.

[0047] In step 207.5, the computing device 101 may determine whether to calculate output weights for additional optical filter pairs. The information received in step 201 may comprise instructions for calculating output weights for all possible pairings of optical filters in the list of available optical filters 110. For example, the computing device 101 may determine a list of optical filter pairs comprising all possible optical filter pairings from the list of available optical filters 110. The computing device 101 may calculate output weights for each optical filter pair from the list of optical filter pairs. If it is determined to calculate output weights for additional optical filter pairs, step 207.1 may be performed. Otherwise, step 207.6 may be performed.

[0048] In step 207.6, a ranked list of optical filter pairs may be generated. For example, the computing device may sort the list of optical filter pairs based on the output weights. High output weight values ​​may correspond to higher ranked optical filter pairs and low output weight values ​​may correspond to lower ranked optical filter pairs.

[0049] FIG. 3 illustrates an exemplary 3D luminescence imaging process. An imaging device 301 (e.g., an IVIS Spectrum series imaging device, or any other 3D luminescence imaging device) may be configured with an optimal filter pair (e.g., optical filters 110-j and 110-k shown in FIG. 3) and a photodetector 302. The optical filter pair may correspond to a highest-ranked optical filter pair selected from a ranked list of optical filter pairs (e.g., a ranked list of optical filter pairs determined based on execution of steps 201-209 of FIG. 2A, etc.). The photodetector 302 may receive an optical signal filtered through one optical filter of the optimal filter pair.

[0050] For example, the imaging device 301 may be configured to generate an image based on an optical signal emitted by the light emission source 311. The light emission source 311 may emit an optical signal (e.g., visible light corresponding to a wavelength between 400-700 nm) into a scattering medium 310 (e.g., a biological tissue sample). The emitted optical signal may propagate through the scattering medium, exit the scattering medium, pass through a first optical filter (e.g., optical filter 110-j or 110-k), and be filtered through the first optical filter. The filtered optical signal may be received by the photodetector 302 and used by the imaging device 301 to generate a first image.

[0051] After generating the first image based on the first optical filter, the imaging device 301 may also be configured to image the light emission source 311 based on a second optical filter (e.g., either optical filter 110-k or 110-j that was not used to generate the initial image). The light emission source 311 may emit another optical signal that may propagate through the scattering medium, exit the scattering medium, and pass through the second optical filter. Upon exiting the second optical filter, the filtered optical radiation may be received by a photodetector and used by the imaging device 301 to generate a second image.

[0052] After imaging the light emission source 311 based on the first and second optical filters, the imaging device 301 may output a first image based on the first optical filter (e.g., optical filter 110-j) and a second image based on the second optical filter (e.g., optical filter 110-k). The first and second images may be input to a computing device (e.g., imaging device 301, computing device 101, or computing device 303) for image pre-processing and generation of initial prior data 330 (e.g., initial source estimates that may be used as input to conventional luminescence imaging processing methods). For example, as described below in connection with FIGS. 5A-5C, upon receiving the first and second images, the computing device 303 may generate an initial representation of the light emission source 311. The initial representation of the light emission source 311 may correspond to a first “pass” computational reconstruction of the light emission source 311 and may serve as an initial input to subsequent conventional reconstruction techniques. For example, the initial representation of the light emitting source 311 may comprise one or more light emitting volumetric sub-sources for one or more volumetric regions of the scattering medium 310. The initial prior data may be used as input to the computing device 303 or another computing device (e.g., computing device 101, imaging device 301, etc.) for further processing 340 (e.g., conventional reconstruction techniques of a light emitting image).

[0053] FIG. 4 illustrates an exemplary process 400 for generating initial prior data. For example, a threshold criterion (further described below in connection with FIG. 5B) may be applied to a pair of luminescence images (e.g., images 411 and 412). Image data (e.g., image data 421 and 422) generated based on application of the threshold criterion may be combined into a single image data set. The single image data set may be further processed to divide into one or more image segments 430 (see description below in connection with FIG. 5B). The image segments may be further processed to generate one or more volumetric sub-regions 441 and 442 as reconstructed sources located within the scattering medium. For example, surface data 445 received from the additional information in step 201 may indicate a surface boundary of the scattering medium. The output initial prior data 440 may comprise one or more volumetric sub-regions 441 and 442 and surface data 445. An exemplary method for implementing the exemplary process illustrated in FIG. 4 is described below with reference to FIGS. 5A-C.

[0054] 5A-5C are flow charts illustrating an exemplary method for determining initial prior data. One, some, or all steps of the exemplary methods of FIGS. 5A-5C may be performed by one or more computing devices, and for convenience, FIGS. 5A-5C are described below with reference to computing device 303. Also, or alternatively, one, some, or all steps of the exemplary methods of FIGS. 5A-5C may be performed by one or more other computing devices (e.g., computing device 101, computing device 303, imaging device 301, etc.). One or more steps of the exemplary methods of FIGS. 5A-5C may be rearranged (e.g., performed, transmitted, or received in a different order), omitted, and / or otherwise modified, and / or other steps and / or communications may be added.

[0055] In step 501, computing 303 may receive a luminescence image from imaging device 301. For example, the luminescence image may comprise a first luminescence image based on a first optical filter (e.g., one of two optical filters of a pair of optical filters) and a second luminescence image based on a second optical filter (e.g., another of two optical filters of a pair of optical filters). The luminescence image may have been generated based on the luminescence imaging process described above in conjunction with FIG. 3. Furthermore, the first optical filter and the second optical filter may have been selected based on the optical filter pair selection process described above in conjunction with FIG. 1 and FIG. 2A-2C.

[0056] In step 503, one or more image segments may be generated for the luminescent image. For example, an image dataset may be generated based on performing one or more of a sum, an average, or a weighted sum of the luminescent images. The computing device 303 may determine that one or more portions of the image dataset may be divided into one or more image segments. The boundaries of the image segments may comprise any geometric and / or polygonal shape associated with the luminescent image. The one or more image segments may be contiguous or non-contiguous. Also, or alternatively, one or more of the one or more image segments may fully overlap or partially overlap each other. Also, or alternatively, the one or more image segments may correspond to a portion of a 3D surface associated with the luminescent image. One or more image segments may be determined for each of the luminescent images of the pair of luminescent images. Additional details of step 503 are described below in conjunction with FIG. 5B.

[0057] In step 505, one or more volumetric sub-regions may be generated based on the one or more image segments determined in step 503. The volumetric sub-regions may comprise one or more voxels. Each of the voxels of the volumetric sub-regions may be assigned a radiation value based on an illumination source and one or more optical filters (e.g., a pair of optical filters). The one or more volumetric sub-regions may be contiguous or non-contiguous with one another. Also, or alternatively, a volumetric sub-region may fully overlap or partially overlap one or more other volumetric sub-regions. Additional details of step 505 are described below in conjunction with FIG. 5C.

[0058] In step 507, the computing device 303 may determine whether there are additional image segments to process. The additional image segments may be associated with either of the two luminescent images of the luminescent image pair. If the computing device 303 determines that there are additional segments to process, step 505 may be performed. Otherwise, step 509 may be performed.

[0059] In step 509, initial prior data may be generated based on one or more volumetric sub-regions. The initial prior data may comprise a reconstruction of an emission source. The reconstruction of the emission source may comprise a volumetric region comprising a plurality of voxels. Each of the plurality of voxels may be assigned one or more radiation values ​​(e.g., intensity or electric field). For example, the reconstructed emission source may be generated based on performing an average of radiation values ​​on each voxel for the one or more volumetric sub-regions. Also, or alternatively, the reconstructed emission source may be generated based on adding, for each voxel, radiation values ​​of the one or more volumetric sub-regions. Also, or alternatively, the reconstructed emission source may be generated based on performing, for each voxel, a weighted sum of radiation values ​​of the one or more volumetric sub-regions.

[0060] FIG. 5B shows additional details of step 503 of FIG. 5A, as indicated by the dashed box. In particular, FIG. 5B shows exemplary steps 503.1-503.4 that may be performed to generate one or more image segments from a luminescent image. In step 503.1, a luminescent image (e.g., one of the luminescent images received in step 501) may be selected. One or more threshold criteria may be applied to the luminescent image. For example, applying the one or more threshold criteria may comprise determining a numerical value associated with each pixel of the luminescent image. The image segments may be generated based on pixels with numerical values ​​that exceed a threshold. Also, or alternatively, the criteria may comprise fitting a Gaussian function to a histogram of numerical values ​​associated with each pixel of the luminescent image. Numeric values ​​that exceed a threshold of the Gaussian function (e.g., exceed the center of the Gaussian distribution plus the full width at half maximum) may be associated with the image segment, and numerical values ​​that fall below the threshold may be associated with the background. A Gaussian mixture model may also be used.

[0061] In step 503.2, a filtered image may be generated based on the applied threshold criterion or criteria of step 503.1. For example, a filtered image may be generated comprising background and image segment pixels. The pixels of the filtered image may be mapped to sibling pixels of the luminescent image. Thus, if the sibling pixels of the luminescent image do not satisfy one or more of the threshold criteria, the pixels of the filtered image may be assigned a background numerical value (e.g., 0). Furthermore, if the sibling pixels of the luminescent image satisfy one or more of the threshold criteria, the pixels of the filtered image may be assigned an image segment numerical value (e.g., 255). Also, or alternatively, the background and image segment pixels of the filtered image may comprise any other two numerical values ​​that may be used to distinguish between pixels associated with an image segment and pixels associated with a background.

[0062] In step 503.3, one or more image segments may be generated from the filtered image. For example, the computing device 303 may determine that one or more image segment pixels of the filtered image can be enclosed within a single closed polygon or other geometric shape. The closed polygon or other geometric shape may indicate a boundary region of the image segment. Also, or alternatively, the computing device 303 may determine that a portion of one or more image segment pixels of the filtered image can be enclosed in a first closed polygon shape and a second portion of one or more image segment pixels of the filtered image can be enclosed in a second closed polygon shape. The first and second closed polygon shapes may correspond to the boundary regions of the first and second image segments. Also, or alternatively, the computing device 303 may determine that multiple portions of one or more image segment pixels of the filtered image can be enclosed in multiple closed polygon shapes. The multiple closed polygon shapes may correspond to the boundary regions of the multiple image segments.

[0063] In step 503.4, the computing device 303 may determine whether additional luminescent images are available for image segmentation. If additional luminescent images are available for image segmentation, step 503.1 may be performed. Otherwise, step 505 of FIG. 5A may be performed.

[0064] FIG. 5C illustrates additional details of step 505 of FIG. 5A, as indicated by the dashed box. In particular, FIG. 5C illustrates exemplary steps 505.1-505.7 that may be performed to generate light emitting sub-sources based on an image segment. In step 505.1, the image segment may be projected by the computing device 303 onto a surface boundary of a scattering medium. For example, the scattering medium may comprise a plurality of voxels. A portion of the plurality of voxels may comprise surface voxels that correspond to the surface boundary of the scattering medium. The computing device 303 may associate one or more of the surface voxels with the image segment.

[0065] In step 505.2, a volumetric sub-region may be generated. For example, the volumetric sub-region may comprise one or more voxels. The one or more voxels may be contiguous or non-contiguous. Furthermore, the location of each of the one or more voxels may be generated by the computing device 303 using a pseudo-random generator, and the volumetric sub-region may correspond to a pseudo-randomly generated 3D shape. Alternatively, the one or more voxels may be generated based on one or more predefined shapes of the volumetric sub-region (e.g., a spherical or elliptical spatial region). For example, the volumetric sub-region may be elliptical in shape based on being located within a predefined surface distance from a surface boundary of the scattering medium. Alternatively, the volumetric sub-region may be spherical in shape based on being located outside a predefined surface distance from a surface boundary of the scattering medium. Also, or alternatively, the volumetric sub-region may be elliptical in shape regardless of the surface distance from the surface boundary of the scattering medium. Also, or alternatively, the volumetric sub-region may be spherical in shape regardless of the surface distance from the surface boundary of the scattering medium.

[0066] Additionally, each of one or more voxels of the volumetric subregion may be assigned a radiation value (e.g., radiant intensity or radiant field strength) for one or more spectral values ​​(e.g., 560 nm and 620 nm, or any other optical wavelengths associated with one or more optical filters). Additionally, or alternatively, each of one or more voxels of the volumetric subregion may be assigned a radiation type (e.g., electric monopole, electric dipole, directional plane wave, cylindrical wave, spherical wave, etc.).

[0067] In step 505.3, a filtered signal may be generated based on the assigned radiation values ​​of one or more voxels of the volumetric sub-region. For example, the computing device 303 may simulate the transmission of electromagnetic radiation based on the assigned radiation values ​​of each of the voxels of the volumetric region. One or more voxels may be configured to radiate as electric monopoles. Also, or alternatively, one or more voxels may be configured to radiate as electric dipoles. Also, or alternatively, the radiation emitted from the voxels may be a plane wave, a spherical wave, a cylindrical wave, or any other type of electromagnetic wave. The filtered signal may comprise a composite sum of the electric fields emitted by each of the one or more voxels. Alternatively, the filtered signal may comprise a composite sum of the radiation intensities emitted by each of the one or more voxels. The filtered signal may be scattered and / or absorbed by the scattering medium and may therefore be attenuated as it propagates through the scattering medium. Upon exiting the scattering medium, the computing device 303 may apply an optical filter (e.g., one optical filter of a pair of optimal filters) to the optical signal. For example, one or more of the spectral components of the optical signal may be altered based on applying an optical filter.

[0068] In step 505.4, the filtered signal may be compared to the measured data (e.g., a luminescence image or an image dataset). For example, the computing device may generate a reconstruction of the image dataset ("reconstructed image") based on the filtered signal. For a given spectral component, a correlation between the reconstructed image and the image dataset may be determined. For example, a root mean square deviation between the reconstructed image and the image dataset may be determined. A root mean square deviation value below a predetermined deviation threshold may correspond to a high correlation between the reconstructed image and the image dataset. An output correlation between the filtered signal and the measured data may be determined based on the determined correlation between the reconstructed image and the image dataset. For example, the output correlation may be determined based on at least one or more of averaging the correlations, summing the correlations, taking a product of the correlations, or performing a weighted sum of the correlations.

[0069] In step 505.5, the computing device 303 may determine whether to generate another volumetric sub-region. For example, the computing device 303 may generate a predetermined number of volumetric sub-regions (e.g., generate a volumetric sub-region a predetermined number of distances below the surface of the scattering medium). Also, or alternatively, the computing device 303 may continue generating volumetric sub-regions until no further improvement in the signal fit of step 505.4 is detected. If the computing device 303 determines that another volumetric sub-region needs to be generated, step 505.2 may be performed. Otherwise, step 505.6 may be performed.

[0070] At step 505.6, the best fitting volumetric sub-region may be selected. For example, the computing device 303 may select the volumetric sub-region associated with the highest value of output correlation (e.g., calculated at step 505.4). At step 505.7, the selected volumetric sub-region may be transmitted to a memory or storage device associated with the computing device 303.

[0071] Some embodiments may include one or more non-transitory computer-readable media (e.g., computer-readable medium 105 or 307) that store instructions that, when executed by a processor (e.g., processor 103 or 305) of a computing device (e.g., imaging device 301, computing device 101, or computing device 303), perform the methods described herein.

[0072] In the above description of various embodiments, reference is made to the accompanying drawings, which form a part of this specification, in which are shown by way of illustration various embodiments in which aspects of the present disclosure may be practiced. It is to be understood that other embodiments may be utilized and structural and functional modifications may be made without departing from the scope of the present disclosure. Aspects of the present disclosure are capable of other embodiments and can be practiced or carried out in various ways. It is also to be understood that the words and terms used herein are for the purpose of description and should not be regarded as limiting. Rather, the words and terms used herein should be given their broadest interpretation and meaning.

[0073] The use of ordinal numbers such as "first," "second," "third," etc. in a claim to modify a claim element does not, in and of itself, imply that a claim element has priority, precedence, or order over other claim elements, but is used merely as a label to distinguish a claim element having a particular name from another element having the same name (excluding the use of ordinal numbers) to distinguish among the claim elements.

[0074] The indefinite articles "a" and "an," as used in the specification and claims, unless clearly indicated to the contrary, should be understood to mean "at least one."

[0075] The term "and / or" as used in the specification and claims should be understood to mean "either or both" of the elements so conjoined, i.e., elements that are conjunctive in some cases and disjunctive in other cases.

[0076] Although examples have been described above, the features and / or steps of those examples may be combined, divided, omitted, rearranged, revised, and / or extended in any desired manner. Various changes, modifications, and improvements will be readily apparent to those skilled in the art. Such changes, modifications, and improvements, although not expressly described herein, are intended to be part of this specification and are intended to be within the spirit and scope of the present disclosure. Accordingly, the foregoing description is illustrative only and not limiting. [Explanation of symbols]

[0077] 100 ways 101 Computing Devices 103 Processors 105 Non-transitory computer-readable medium 110 Optical Filters 110-j Optical Filter 110-k Optical Filter 112 Optimal Filter Pairs 301 Imaging Devices 302 Photodetector 303 Computing Devices 305 Processor 307 Non-transitory computer-readable medium 310 Scattering medium 311 Light Source 330 Initial Preliminary Data 340 Further Processing 400 processes 411 images 412 images 421 Image data 422 Image data 430 Image Segments 440 Initial Preliminary Data 441 Volumetric Sub-Domains 442 Volumetric Sub-domains 445 Surface Data

Claims

1. receiving, by a computing device, a list of optical filters and data indicative of a light emission source and a biological material; generating a filtered signal, said generating being based on said list of optical filters, said light source, and said biological material; determining weights associated with one or more pairs of optical filters based on the filtered signal, each of the one or more pairs of optical filters comprising an optical filter pair of the list of optical filters; ranking the list of one or more optical filter pairs based on the weights; selecting a highest ranked optical filter pair from said ranked list of said one or more optical filter pairs; configuring a user device to receive a signal filtered through the selected pair of highest ranked optical filters; The method comprising:

2. The method of claim 1 , wherein each of the weights corresponds to a numerical value, and the highest-ranked pair of optical filters corresponds to the largest numerical value of the determined weights.

3. the optical filter pair includes a first optical filter and a second optical filter; The method of claim 1 , wherein the step of determining weights associated with a pair of optical filters comprises determining a correlation between the first optical filter and the second optical filter.

4. 2. The method of claim 1 , wherein the step of determining weights associated with a pair of optical filters comprises determining a ratio of a first one of the filtered signals associated with a first optical filter to a second one of the filtered signals associated with a second optical filter.

5. 2. The method of claim 1 , wherein the step of determining weights associated with a pair of optical filters comprises comparing a first one of the filtered signals associated with a first optical filter to a predetermined threshold and comparing a second one of the filtered signals associated with a second optical filter to the predetermined threshold.

6. 2. The method of claim 1 , wherein the step of determining weights associated with a pair of optical filters comprises determining a product of a first one of the filtered signals associated with a first optical filter and a second one of the filtered signals associated with a second optical filter.

7. 2. The method of claim 1 , wherein the step of determining weights associated with a pair of optical filters comprises determining a linear relationship between a first one of the filtered signals associated with a first optical filter and a second one of the filtered signals associated with a second optical filter.

8. 2. The method of claim 1 , wherein the step of determining weights associated with pairs of optical filters comprises determining a rank of a matrix having a first column and a second column, the first column including a first one of the filtered signals associated with a first optical filter and the second column including a second one of the filtered signals associated with a second optical filter.

9. 2. The method of claim 1, wherein the step of generating a filtered signal comprises the step of calculating a filtered signal for a plurality of distances, each of the plurality of distances corresponding to a distance between a portion of the light emission source and a surface boundary of the biological material.

10. The method of claim 1 , wherein generating a filtered signal comprises generating, by the computing device, the filtered signal based on a source model.

11. 2. The method of claim 1, wherein the step of generating a filtered signal comprises generating a volume of the biological material, the volume including one or more voxels, each of the one or more voxels being assigned a numerical value corresponding to a radiation intensity of the light source.

12. receiving, by the computing device, data indicative of a second light emission source and a second biological material; generating an additional filtered signal based on the list of optical filters, the second light source, and the second biological material; determining additional weights associated with the one or more pairs of optical filters based on the additional filtered signals; ranking the second list of pairs of the one or more optical filters based on the additional weights; selecting a highest ranked optical filter pair from the second ranked list of the one or more optical filter pairs; The method of claim 1 , further comprising:

13. One or more non-transitory computer-readable media having instructions stored thereon, The instructions, when executed, receiving, by a computing device, a list of optical filters and data indicative of a light source and a biological material; generating a filtered signal, said generating being based on said list of optical filters, said light source, and said biological material; determining weights associated with one or more optical filter pairs based on the filtered signal, each of the one or more optical filter pairs including an optical filter pair of the list of optical filters; ranking the list of pairs of one or more optical filters based on the weights; selecting a highest ranked optical filter pair from said ranked list of said one or more optical filter pairs; configuring a user device to receive a signal filtered through the selected pair of highest ranked optical filters; One or more non-transitory computer-readable media that cause

14. 14. The one or more non-transitory computer-readable media of claim 13, wherein each of the weights corresponds to a numerical value, and the highest-ranked pair of optical filters corresponds to a largest numerical value of the determined weights.

15. the optical filter pair includes a first optical filter and a second optical filter; 14. The one or more non-transitory computer-readable media of claim 13, wherein determining weights associated with a pair of optical filters includes determining a correlation between the first optical filter and the second optical filter.

16. 14. The one or more non-transitory computer-readable media of claim 13, wherein generating a filtered signal comprises generating, by the computing device, the filtered signal based on a source model.

17. The instructions, when executed, receiving, by the computing device, data indicative of a second light emission source and a second biological material; generating an additional filtered signal based on the list of optical filters, the second light source, and the second biological material; determining additional weights associated with the one or more pairs of optical filters based on the additional filtered signals; ranking the second list of pairs of the one or more optical filters based on the additional weights; and selecting a highest ranked optical filter pair from the second ranked list of the one or more optical filter pairs; 14. The one or more non-transitory computer-readable media of claim 13, further comprising:

18. one or more processors; A memory for storing instructions; Equipped with The instructions, when executed by the one or more processors, provide a system: receiving a list of optical filters and data indicative of a light source and a biological material; generating a filtered signal, said generating being based on said list of optical filters, said light source, and said biological material; determining weights associated with one or more optical filter pairs based on the filtered signal, each of the one or more optical filter pairs including an optical filter pair of the list of optical filters; ranking the list of pairs of one or more optical filters based on the weights; selecting a highest ranked optical filter pair from said ranked list of said one or more optical filter pairs; configuring a user device to receive a signal filtered through the selected pair of highest ranked optical filters; A system that allows the user to:

19. 20. The system of claim 18, wherein generating a filtered signal comprises generating a volume of the biological material, the volume including one or more voxels, each of the one or more voxels being assigned a numerical value corresponding to a radiation intensity of the light source.

20. the optical filter pair includes a first optical filter and a second optical filter; Determining weights associated with the pair of optical filters includes determining, for the first signal and the second signal, determining a ratio associated with the first signal and the second signal; comparing the first signal with a predetermined threshold and comparing the second signal with the predetermined threshold; determining a product associated with the first signal and the second signal; Determining a linear relationship between the first signal and the second signal; or determining a rank of a matrix containing numerical values ​​associated with the first signal and the second signal; 20. The system of claim 18, comprising one or more of:

21. 1. A pair of optical filters for imaging a light emission source in a biological material, the pair of optical filters comprising: receiving, by a computing device, a list of available optical filters and data indicative of the light emission source and the biological material; generating a filtered signal, said generating being based on said list of available optical filters, said light source, and said biological material; determining weights associated with one or more optical filter pairs based on the filtered signal, each of the one or more optical filter pairs comprising an optical filter pair of the list of available optical filters; ranking the list of pairs of one or more optical filters based on the weights; selecting a highest ranked optical filter pair from said ranked list of said one or more optical filter pairs; a pair of optical filters selected from said list of available optical filters based on:

22. the optical filter pair includes a first optical filter and a second optical filter; 22. The pair of optical filters of claim 21, wherein determining a weight associated with a pair of optical filters comprises determining a correlation between the first optical filter and the second optical filter.

23. 22. The optical filter pair of claim 21 , wherein determining a weight associated with a pair of optical filters comprises determining a ratio of a first one of the filtered signals associated with a first optical filter to a second one of the filtered signals associated with a second optical filter.

24. 22. The optical filter pair of claim 21, wherein determining weights associated with the pair of optical filters comprises comparing a first signal associated with the first optical filter to a predetermined threshold and comparing a second signal associated with the second optical filter to the predetermined threshold.

25. 22. The optical filter pair of claim 21, wherein determining a weight associated with the pair of optical filters comprises determining a product of a first signal associated with the first optical filter and a second signal associated with the second optical filter.

26. 22. The optical filter pair of claim 21, wherein determining weights associated with the pair of optical filters includes determining a linear relationship between a first signal associated with the first optical filter and a second signal associated with the second optical filter.

27. 22. The optical filter pair of claim 21, wherein determining the weights associated with the pair of optical filters comprises determining a rank of a matrix having a first column and a second column, the first column including a first signal associated with a first optical filter and the second column including a second signal associated with a second optical filter.

28. 22. The pair of optical filters of claim 21 , wherein generating a filtered signal comprises calculating a filtered signal for a plurality of distances, each of the plurality of distances corresponding to a distance between a portion of the light emission source and a surface boundary of the volume of biological material.

29. 22. The pair of optical filters of claim 21, wherein generating a filtered signal comprises generating, by the computing device, the filtered signal based on a source model.

30. 22. The pair of optical filters of claim 21, wherein generating a filtered signal comprises generating a volume of the biological material, the volume including one or more voxels, each of the one or more voxels being assigned a numerical value corresponding to a radiation intensity of the light source.

Images