Continuous blood flow visualization using laser speckle contrast imaging

Abstract

A system and method for visualizing blood flow is disclosed. The method generally includes obtaining a laser speckle contrast imaging (LSCI) image of blood flow, obtaining a white light image of tissue, where the white light image captures an anatomical structure of interest in a region associated with the LSCI image of blood flow, spatially registering the LSCI image and the white light image with respect to one another, overlaying the spatially registered LSCI image and the white light image, and generating display data including the spatially registered LSCI and white light images and continuously depicting the blood flow overlaying the tissue.

Description

[Technical field]

[0001] STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH This invention was made with Government support under Grant No. R01 EB011556 awarded by the National Institutes of Health. The Government has certain rights in this invention.

[0002] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of and priority to U.S. Provisional Patent Application No. 63 / 335,854, filed April 28, 2022, which is incorporated by reference in its entirety. [Background technology]

[0003] Cerebral blood flow (CBF) monitoring is routine during cerebrovascular surgery to inform decision making. In cases of cerebral aneurysm clipping, a variety of techniques are routinely used to confirm vascular patency and determine the success of aneurysm occlusion. Current intraoperative tools for CBF monitoring and visualization include indocyanine green angiography (ICGA), Doppler, and transit-time ultrasound, as well as percutaneous transfemoral digital subtraction angiography (DSA). ICGA records the fluorescent washout of a bolus of indocyanine green following intravenous injection. DSA images are acquired by obtaining multiple time-controlled x-rays as contrast is injected into the artery. Summary of the Invention

[0004] One implementation of the present disclosure is a method for visualizing blood flow, the method including: acquiring a laser speckle contrast imaging (LSCI) image of the blood flow; acquiring a white light image of tissue, where the white light image captures an anatomical structure of interest in a region associated with the LSCI image of the blood flow; spatially registering the LSCI image and the white light image with respect to one another; overlaying the spatially registered LSCI image and the white light image; and generating display data that continuously depicts the blood flow overlaying the tissue, where the display data includes the spatially registered LSCI image and the white light image.

[0005] In some implementations, the display data continuously depicts blood flow overlaying tissue in real time during a surgical procedure.

[0006] In some implementations, the method further includes displaying the representation data on a user interface.

[0007] In some implementations, the tissue includes the vasculature.

[0008] In some implementations, the tissue is brain tissue.

[0009] In some implementations, spatially aligning the LSCI image and the white-light image with respect to one another includes creating a lookup table based on the spatial transformation used to align the LSCI image and the white-light image.

[0010] In some implementations, overlaying the spatially aligned LSCI image and the white-light image includes mapping each pixel from the LSCI image to each pixel from the white-light image using a look-up table.

[0011] In some implementations, overlaying the spatially aligned LSCI image and the white-light image further includes contrast stretching the LSCI image, mapping the LSCI image to an n-bit color map, and performing a weighted sum with the white-light image.

[0012] Another implementation of the present disclosure is a system for blood flow visualization, the system including: a first light source configured to illuminate blood flow in a target region of a subject; a first camera configured to record a raw laser speckle image of the blood flow; and a computing device including a processor and a memory, the memory storing instructions that, when executed by the processor, cause the computing device to obtain a raw laser speckle image of the blood flow via the first camera, derive a laser speckle contrast imaging (LSCI) image from the raw laser speckle image, obtain a white light image of tissue in the target region of the subject, where the white light image captures an anatomical structure, spatially register the LSCI image and the white light image with each other, overlay the spatially registered LSCI image and the white light image, and generate display data that continuously depicts the blood flow overlaying the tissue, where the display data includes the spatially registered LSCI image and the white light image.

[0013] In some implementations, the system is a surgical microscope, an endoscope, an exoscope, a robotic surgical platform, a standalone imaging system, or a system dedicated to blood flow imaging.

[0014] In some implementations, the display data continuously depicts blood flow overlaying tissue in real time during a surgical procedure.

[0015] In some implementations, the instructions further cause the computing device to present the display data on a user interface.

[0016] In some implementations, the tissue includes the vasculature.

[0017] In some implementations, the tissue is brain tissue.

[0018] In some implementations, spatially aligning the LSCI image and the white-light image with respect to one another includes creating a lookup table based on the spatial transformation used to align the LSCI image and the white-light image.

[0019] In some implementations, overlaying the spatially aligned LSCI image and the white-light image includes mapping each pixel from the LSCI image to each pixel from the white-light image using a look-up table.

[0020] In some implementations, overlaying the spatially aligned LSCI image and the white-light image further includes contrast stretching the LSCI image, mapping the LSCI image to an n-bit color map, and performing a weighted sum with the white-light image.

[0021] Yet another implementation of the present disclosure is a method for visualizing blood flow, the method including, sequentially, capturing a laser speckle contrast imaging (LSCI) image of blood flow in a subject using a first image capture device, capturing a white light image of tissue using a second image capture device, the white light image capturing an anatomical structure, co-registering the LSCI image and the white light image, overlaying the co-registered LSCI image and the white light image, and displaying the overlaid and co-registered LSCI image and the white light image via a user interface.

[0022] In some implementations, the tissue includes vasculature or brain tissue.

[0023] In some implementations, co-registering the LSCI image and the white-light image with respect to one another includes creating a look-up table based on the spatial transformation used to register the LSCI image and the white-light image. [Brief description of the drawings]

[0024] [Figure 1] FIG. 1 is a block diagram of a blood flow visualization system, according to some implementations. [Diagram 2] 2A-2C are diagrams of various configurations of the visualization system of FIG. 1 according to some implementations. [Diagram 3] 2 is an image of an example configuration of the visualization system of FIG. 1 according to some implementations. [Figure 4] FIG. 1 is a block diagram of a computer system for implementing the processes described herein, according to some implementations. [Diagram 5] FIG. 1 is a flow diagram of a method for visualizing blood flow, according to some implementations. [Figure 6] FIG. 1 is a flow diagram of a method for visualizing blood flow, according to some implementations. [Figure 7] 7A-7D are exemplary image processing pipelines for generating a Laser Speckle Contrast Imaging (LSCI) overlay, according to some implementations. The LCSI image, thresholded image, white light image, and LCSI overlay image are shown in FIGS. 7A-7D, respectively. [Figure 8] Included are several example images captured of a patient before aneurysm clipping (e.g., pre-clipping), immediately after aneurysm clipping (e.g., post-clipping), and during indocyanine green angiography (ICGA) according to some implementations. [Figure 9] 1 is an example graph of relative cerebral blood flow (CBF) during a clipping procedure, according to some implementations. [Figure 10-11]Figure 10 includes a number of example images captured from a patient during ICGA according to some implementations. Figure 11 includes an example image comparing an LSCI overlay (Figure 11A) with an ICGA overlay (Figure 11B) according to some implementations. [Figure 12] 12A includes a number of example images of an arteriovenous malformation (AVM) resection according to some implementations. FIG. 12B is an example graph of average speckle contrast values ​​before and after AVM resection according to some implementations. [Figure 13] 13A includes a number of example images showing fluorescence intensity changes during administration of a bolus of indocyanine green (ICG) and LSCI measurements of relative blood flow for the same region of interest according to some implementations. FIG. 13B is an example graph of fluorescence intensity changes during administration of a bolus of ICG and LSCI measurements of relative blood flow for the same region of interest according to some implementations.

[0025] Various objects, aspects, and features of the present disclosure will become more apparent and better understood by reference to the detailed description in conjunction with the accompanying drawings, in which like reference characters identify corresponding elements throughout and in which like reference numbers generally indicate identical, functionally similar, and / or structurally similar elements. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0026] Monitoring cerebral blood flow (CBF) plays a key role in a myriad of neurosurgical and neuroscience applications, such as during cerebrovascular surgery. As mentioned above, current techniques are often inadequate for active monitoring of CBF in real-time or near real-time, as many techniques provide only periodic measurements or may require significant additional effort by the physician to implement. Thus, generally, it may be more desirable for CBF to be monitored continuously, rather than measured periodically. As mentioned above, ICGA is an effective decision support, but cannot provide continuous imaging, since it requires an injected contrast agent. Doppler ultrasound provides absolute flow velocity, but is limited to measurements at a single location and requires contact with the vessel of interest. DSA has been utilized to confirm aneurysmal occlusion and patency of the underlying parent vasculature, but it is invasive, typically requiring removal of an operating microscope, fluoroscopy, and transfemoral selective arterial catheterization, for example, compared to time-ICGA or Doppler ultrasound.

[0027] Laser speckle contrast imaging (LSCI) has emerged as a promising tool for noninvasively monitoring CBF, as it generates real-time full-field blood flow maps without contrast agents. Thus, LSCI may provide a potential continuous CBF monitoring solution. Several studies have demonstrated LSCI during neurosurgical procedures in humans and shown its promise as a CBF monitoring tool, including surgical revascularization, awake functional mapping, brain tumor resection, cortical spreading depression, and infarct in ischemic stroke. However, many previous clinical implementations required external devices to be introduced into the surgical field, leading to interruption of the surgical procedure. Other implementations have incorporated the equipment into the neurosurgical operating microscope, eliminating the need for external devices and surgical disruption, but the surgical procedure still needed to be paused while LSCI images were acquired, making it impossible to record CBF images over an extended period of time or simultaneously with ICGA.

[0028] LSCI systems and related methods that address these and other limitations of existing blood flow monitoring devices are described herein. In some implementations, the LSCI systems described herein can be integrated into a surgical microscope or other medical devices to facilitate real-time continuous visualization of CBF overlaid on the surgical field during surgical procedures, including, for example, simultaneous ICGA and LSCI imaging. To demonstrate efficacy, the disclosed LSCI systems and methods were evaluated during cerebral aneurysm clipping and arteriovenous malformation (AVM) resection surgeries. The present disclosure demonstrates the feasibility of LSCI for human CBF monitoring in at least two ways: LSCI was performed continuously during cerebral aneurysm clipping and AVM resection surgeries without affecting the surgical workflow, including real-time visualization of CBF during aneurysm clip placement, and LSCI and ICGA were performed simultaneously to visualize CBF for five exemplary neurovascular cases. Taken together, these results demonstrate that LSCI can continuously monitor CBF during neurovascular procedures when the LSCI device is integrated into a surgical microscope, and that LSCI and ICGA provide different but complementary information about vascular perfusion.

[0029] LSCI system 1, a block diagram of a blood flow visualization system 100 is shown according to some implementations. The system 100 is shown to include a first light source 102 and a corresponding first optical portion 104, and a second light source 106 and a corresponding second optical portion 108. In some implementations, the first light source 102 and the second light source 106 are configured to illuminate a target 120, e.g., a target area of ​​a patient. At least a portion of the light emitted by the first light source 102 and the second light source 106 is then reflected from the target 120 to the first camera 110 and the second camera 112. Each of the first camera 110 and the second camera 112 can then capture a respective image of the target 120 based on the reflected light and transmit the image to a remote computing device 114.

[0030] As described herein, the first light source 102 is generally any suitable light source capable of generating and emitting coherent light. As described herein, coherent light is generally light in which electromagnetic waves maintain a fixed and predictable phase relationship with each other over a sufficiently long period of time so that interference effects can be recorded by a sensor. Such light may include a single wavelength or a narrow bandwidth. As one skilled in the art will appreciate, coherent light does not actually require a single wavelength, and industry standards allow for relative deviations, such as a bandwidth within ±1 nm of a target wavelength. It is also possible to have a wide bandwidth, such as when a pulsed laser is used as the coherent light source. In some implementations, the wavelength of the first light source 102 is 785 nm (±5 nm) with a maximum output power of 300 mW. In some implementations, the first light source 102 is a laser.

[0031] The second light source 106 is generally a white light source or, alternatively, is generally configured to output light at a wavelength different from that of the first light source 102. In some implementations, the second light source 106 may be configured to emit light within a wavelength range of 350 nm to 850 nm. In this regard, the light emitted by the second light source 106 may be a collection of multiple wavelengths or a single wavelength between 350 nm to 850 nm. As shown in FIG. 1, the light emitted by the second light source 106 is generally directed by the second optical portion 108 toward 120.

[0032] The first optic 104 generally includes one or more optical components for filtering, focusing, and / or otherwise modifying light emitted by the first light source 102 and / or light reflected from the first light source 102 to the target 120. In some implementations, the first optic 104 includes one or more lenses and / or mirrors for focusing and / or directing light from the first light source 102 to the target 120. In some implementations, the first optic 104 includes one or more filters for filtering light emitted by the first light source 102 and reflected from the target 120. In some implementations, the first optic 104 includes one or more filters for reducing or preventing light from the second light source 106 or other background light from entering the first camera 110. In some implementations, the first optic 104 includes a bandpass filter centered or around the wavelength of the first light source 102. In some implementations, the first optic 104 includes a polarizer to reduce specular reflections from the target 120. In some such implementations, the polarizer may be on a rotatable mount that allows for rotation of the polarizer.

[0033] The second optics 108 also generally includes one or more optical components for filtering, focusing, and / or otherwise modifying light emitted by the second light source 106 and / or light reflected from the second light source 106 to the target 120. In some implementations, the second optics 108 includes one or more lenses and / or mirrors for focusing and / or directing light from the second light source 106 to the target 120. In some implementations, the second optics 108 includes one or more filters for filtering light emitted by the second light source 106 and / or light reflected from the target 120. In some implementations, the second optics 108 includes one or more filters for reducing or preventing light from the first light source 102 or other background light from entering the second camera 112. In some implementations, the second optics 108 includes a bandpass filter centered or around the wavelength of the second light source 106. In some implementations, the second optic 108 includes a polarizer to reduce specular reflections from the target 120. In some such implementations, the polarizer can be on a rotatable mount that allows for rotation of the polarizer.

[0034] Although not shown in FIG. 1 , it should be understood that the first optical section 104 and / or the second optical section 108 may include one or more components disposed between the respective first light source 102 and second light source 106 and the target 120, and / or may include one or more components positioned between the target 120 and the respective first camera 110 and second camera 112. In some implementations, the first light source 102 and / or the second light source 106 are fixedly coupled to each other to move in coordination with one another, for example, in response to manipulation by a user. For example, a user may move a surgical microscope, which would move the first light source 102 and / or the second light source 106 simultaneously with each other while maintaining the focus of both the first camera 110 and the second camera 112 on the target 120. In some implementations, the first camera 110 and the second camera 112 are focused simultaneously. In some implementations, when a first camera 110 is added to the microscope, the optics for the first camera 110 co-align it with the second camera 112 so that the two cameras are both in focus.

[0035] The first camera 110 and the second camera 112 are generally any suitable image capture devices. In some implementations, the first camera 110 is specifically configured to capture an image of the target 120 within a wavelength range emitted by the first light source 102. Similarly, in some implementations, the second camera 112 can be specifically configured to capture an image of the target 120 within a wavelength range emitted by the second light source 106. In some implementations, the second camera 112 is an imaging device coupled to, embedded in, or otherwise integrated into a medical imaging device. For example, the second camera 112 can be associated with a surgical microscope, an endoscope, an endoscope, a robotic surgery platform, or the like.

[0036] In some implementations, the first camera 110 is configured to capture raw LSCI. In some implementations, the first camera 110 is or includes a 10-bit or higher resolution charge-coupled device (CCD) camera with an exposure time ranging from about 1 ms to about 20 ms. In other implementations, the first camera 110 may be or include a near-infrared (NIR) enhanced complementary metal-oxide semiconductor (CMOS) camera. In some implementations, the first camera 110 and / or the second camera 112 may operate at a frame rate on the order of 10 to 160 frames per second, although higher frame rates may be used depending on the application. As described herein, the images captured by both the first camera 110 and the second camera 112 may include still images or may be recorded continuously to create a video.

[0037] In some implementations, although not shown in the figures, the system 100 may further include a third camera for capturing ICGA images. In some such implementations, the ICGA images are represented as raw fluorescence intensity images collected by an integrated fluorescence camera. In some implementations, the third camera may include a NIR camera. Such fluorescence imaging includes, for example, ICGA. In some implementations, including performing laser speckle contrast imaging and additional imaging simultaneously, a single camera may be used to record fluorescence images and raw laser speckle images by interleaving image acquisition of laser speckle and fluorescence. Additionally or alternatively, in some implementations, a single camera may be used to perform laser speckle contrast imaging, fluorescence imaging, and white light imaging, as described above.

[0038] In general, the remote computing device 114 is any computing device that is external to or remote from the first camera 110 and / or the second camera 112. The computing device 114 may be coupled to the first camera 110 and / or the second camera 112 via one or more communication links. This disclosure contemplates that the communication link is any suitable communication link. For example, the communication link may be implemented by any medium that facilitates data exchange between network elements, including, but not limited to, wired, wireless, and optical links. The remote computing device 114 may generally include processing circuitry including a processor and memory, where the memory stores instructions for performing various methods and processes described herein. Details of the remote computing device 114 are provided below with respect to FIG. 4. As shown, the remote computing device 114 is generally configured to receive images captured by the first camera 110 and the second camera 112, such as LSCI and white light images of the target 120, for processing and / or storage. Additionally, in some implementations, the remote computing device 114 may instruct various other components of the system 100 (e.g., the first light source 102, the second light source 106, the first camera 110, and / or the second camera 112) to capture an image. For example, the remote computing device 114 may be configured to activate one or both of the first light source 102 and the second light source 106 and can operate the first camera 110 and the second camera 112 simultaneously to capture an image.

[0039] As discussed herein, it is generally difficult for a surgeon to understand anatomical structures in an LSCI image showing blood flow. However, the system 100 addresses this issue by overlaying a LSCI blood flow image captured by the first camera 110 onto a second image showing anatomical structures captured by the second camera 112. To overlay the LSCI image, the LSCI image captured by the first camera 110 may be "thresholded" or subject to thresholding to show only flow between set values ​​(e.g., "minimum" and "maximum"). Specifically, the remote computing device 114 may apply a thresholding technique to the image captured by the first camera 110. The remote computing device 114 then spatially aligns the thresholded LSCI image with a second image (e.g., a white light image) captured by the second camera 112, and then overlays the thresholded and aligned LSCI image onto the second image.

[0040] As mentioned above, the second camera 112 may be an image capture device coupled to or integrated into a medical imaging device. Thus, in various implementations, the remote computing device 114 can be configured to overlay the LSCI image captured by the first camera 110 onto an image captured by the second camera 112 from a surgical microscope having white light illumination or any wavelength including 350 nm to 850 nm white light, to overlay the LSCI image captured by the first camera 110 onto an image captured by the second camera 112 from an endoscope having white light illumination or any wavelength including 350 nm to 850 nm white light, to overlay the LSCI image captured by the first camera 110 onto an image captured by the second camera 112 from an exoscope having white light illumination or any wavelength including 350 nm to 850 nm white light, or to overlay the LSCI image captured by the first camera 110 onto an image captured by the second camera 112 from a robotic surgical platform having white light illumination or any wavelength including 350 nm to 850 nm white light.

[0041] Exemplary Configurations 2A, 2B, and 3, diagrams of various configurations of the system 100 are shown according to some implementations. Specifically, FIGS. 2A and 2B show an exemplary configuration of the system 100 attached to a microscope. FIG. 3 shows an exemplary implementation of the system 100 described herein. It should be understood that during testing, the system 100 was found not to interfere with the sterile draping or normal operation of the microscope. In this example, a λ=785 nm laser diode 212 (e.g., the first light source 102) with a maximum output power of 300 mW was attached to an add-on laser adapter 210 (e.g., MM6 Micromanipulator, Carl Zeiss Meditec Inc., Oberkochen, Germany). The nm laser diode 212 can be mounted externally as in this example, or integrated inside the microscope. The laser adapter 210 was attached to the bottom of the microscope such that a steering mirror (not shown) directs the light downward toward the surgeon's field of view. The beam size was approximately 2 cm at a working distance of 35 cm. The maximum emitted irradiance was 0.10 W / cm. 2 and the American National Standards Institute (ANSI) limit for skin at 785 nm is 0.3 W / cm 2 was much lower.

[0042] The backscattered laser light was directed to a NIR-enhanced CMOS camera 202 (e.g., Basler AG, Ahrensburg, Germany) mounted on the lateral observer port on the same side as the craniotomy via a camera adapter 208. This allowed an observer to participate during the study. The pixel area was slightly cropped during acquisition to capture only pixels on the brain tissue. A filter wheel 206 (e.g., CFW6, Thorlabs Inc.) and a polarizer 204 (e.g., LPNIR100, Thorlabs Inc.) were positioned between the camera adapter 208 and the camera 202. The filter wheel 206 held various neutral density filters to control the power output of the nm laser diode 212. The polarizer 204 was mounted on a motorized rotation mount (e.g., RSC-100, Pacific Laser Equipment Inc., Santa Ana, California, USA) to reduce specular reflections. A bandpass filter (FF01-788 / 3-25, Semrock Inc., Rochester, New York, USA) was added in front of the NIR-enhanced CMOS camera 202 (not shown) to enable simultaneous LSCI acquisition during indocyanine green illumination, block non-laser light, and avoid interference with regular white light illumination throughout each procedure.

[0043] It should be understood that in the embodiment of Figures 2A and 2B, the laser diode 212 may be the same as or equivalent to the first light source 102, the polarizer 204 and the filter wheel 206 may together form the first optics 104, and the camera 202 may be the same as or equivalent to the first camera 110. In some implementations, one or more of the camera 202, the polarizer 204, and the filter wheel 206 may be coupled to the microscope via a C-mount adapter or a cage rod. In some implementations, the second light source 106, the second optics 108, and the second camera 112 are integrated into the surgical microscope and therefore are not shown. Also, although not shown in Figures 2A and 2B, the camera 202 and the second camera 112 may be connected to an external computer.

[0044] Computing Devices Referring now to Figure 4, a computing device 400 for implementing the image analysis techniques described herein according to some implementations is shown. In some implementations, the remote computing device 114 is the same as or functionally equivalent to the computing device 400. It will therefore be understood that Figure 4 may be considered a detailed block diagram of the remote computing device 114. More generally, the computing device 400 is a computing device configured to obtain laser speckle images and / or white light images from the first camera 110 and / or the second camera 112 to generate and display blood flow visualizations.

[0045] Computing device 400 is shown to include processing circuitry 402, including a processor 404 and memory 410. Processor 404 may be a general-purpose processor, an application specific integrated circuit (ASIC), one or more field programmable gate arrays (FPGAs), a group of processing components, or other suitable electronic processing structures. In some embodiments, processor 404 is configured to execute program code stored in memory 410 to cause computing device 400 to perform one or more operations, as described in more detail below. In embodiments in which computing device 400 is part of another computing device, it will be understood that components of computing device 400 may be shared with or the same as a host device. For example, if computing device 400 is implemented via a server, computing device 400 may utilize processing circuitry, processor, and / or memory of the server to perform the functions described herein.

[0046] The memory 410 may include one or more devices (e.g., memory units, memory devices, storage devices, etc.) for storing data and / or computer code for completing and / or facilitating various processes described in this disclosure. In some embodiments, the memory 410 includes a tangible (e.g., non-transitory) computer-readable medium that stores code or instructions executable by the processor 404. A tangible computer-readable medium refers to any physical medium that can provide data that causes the computing device 400 to operate in a particular manner. Examples of tangible computer-readable media may include, but are not limited to, volatile, non-volatile, removable and non-removable media implemented in any manner or technology for storing information such as computer-readable instructions, data structures, program modules or other data. Thus, the memory 410 may include random access memory (RAM), read-only memory (ROM), hard drive storage, temporary storage, non-volatile memory, flash memory, optical memory, or other suitable memory for storing any software objects and / or computer instructions. The memory 410 may include database components, object code components, script components, or any other type of information structures for supporting the various activities and information structures described in this disclosure. The memory 410 may be communicatively coupled to the processor 404, such as via the processing circuitry 402, and may include computer code for executing (e.g., by the processor 404) one or more processes described herein.

[0047] Although depicted as individual components, it will be appreciated that the processor 404 and / or memory 410 may be implemented using a variety of different types and amounts of processors and memories. For example, the processor 404 may represent a single processing device or multiple processing devices. Similarly, the memory 410 may represent a single memory device or multiple memory devices. Additionally, in some embodiments, the computing device 400 may be implemented within a single computing device (e.g., one server, one enclosure, etc.). In other embodiments, the computing device 400 may be distributed across multiple servers or computers (e.g., may be in distributed locations). For example, the computing device 400 may include multiple distributed computing devices (e.g., multiple processors and / or memory devices) that communicate with each other and cooperate to perform operations. For example, without limitation, an application may be partitioned in a manner that allows for simultaneous and / or parallel processing of the application's instructions. Alternatively, data processed by the application may be partitioned in a manner that allows for simultaneous and / or parallel processing of different portions of the data set by two or more computers.

[0048] The memory 410 is shown to include an image analyzer 412 that processes image data obtained from one or both of the first camera 110 and the second camera 112. In some implementations, image data is received from the first camera 110 and / or the second camera 112 and stored in a database 414 for later evaluation. "Image data," as described herein, generally includes one or both of a laser speckle image captured by the first camera 110 and a white light image captured by the second camera 112. In some implementations, the computing device 400 communicates directly with the first camera 110 and thus receives a laser speckle image of a target area of ​​interest (e.g., target 120) directly from the first camera 110. Similarly, the computing device 400 may communicate directly with the second camera 112 to receive a white light image. In some implementations, one or both of the laser speckle image captured by the first camera 110 and the white light image captured by the second camera 112 are received indirectly, for example through another computing device.

[0049] The image analyzer 412 is generally configured to implement image analysis techniques described herein, for example, with respect to FIG. 5 and FIG. 6 below. Generally, the image analyzer 412 obtains both a laser speckle image and a white light image of a target region of the object and performs various post-processing techniques. As described above, the laser speckle image generally captures blood flow within the object, and the white light image generally captures the anatomical structures of the object within the region associated with the laser speckle image of blood flow. After receiving the raw laser speckle image as a still image, a series of still images, or as a video feed, the image analyzer 412 may derive a laser speckle contrast image or "LSCI image." In some implementations, the image analyzer 412 derived the laser speckle contrast image using a sliding pixel window. In some implementations, the image analyzer 412 applies thresholding to the laser speckle contrast image, as described in detail below. In some implementations, the image analyzer 412 applies a color map to the laser speckle contrast image.

[0050] In some implementations, the image analyzer 412 is configured to register the laser speckle contrast image with the white light image and / or vice versa. In some such implementations, the laser speckle contrast image and the white light image are spatially registered with one another, for example, using an affine transformation. In some implementations, the laser speckle contrast image and the white light image are spatially registered using a look-up table that maps the pixels of the laser speckle contrast image and the white light image to one another. The co-registered laser speckle contrast image can then be overlaid on the white light image. In some implementations, the co-registered and overlaid laser speckle contrast image and the white light image are used to generate display data that sequentially depicts the blood flow overlaying the tissue. In some implementations, the image analyzer 412 generates and then presents the display data via the user interface 422, as described below.

[0051] Computing device 400 is also shown to include a communication interface 420 that facilitates communication between computing device 400 and any external components or devices. For example, communication interface 420 can provide a means for transmitting data to or receiving data from first camera 110 and / or second camera 112. Thus, communication interface 420 can be or include a wired or wireless communication interface (e.g., a jack, antenna, transmitter, receiver, transceiver, wire terminal, etc.) for conducting data communication, or a combination of wired and / or wireless communication interfaces. In some embodiments, communication via communication interface 420 is direct (e.g., local wired or wireless communication) or occurs over a network (e.g., a WAN, the Internet, a cellular network, etc.). For example, communication interface 420 can include one or more Ethernet ports for communicatively coupling computing device 400 to a network (e.g., the Internet). In another example, communication interface 420 can include a Wi-Fi transceiver for communicating over a wireless communication network. In yet another embodiment, communication interface 420 may include a cellular or mobile phone communication transceiver.

[0052] In some implementations, the computing device 400 is communicatively coupled to a user interface 422 via the communication interface 420 (e.g., via a wired connection). Generally, the user interface 422 is an electronic device that allows a user to interact with the computing device 400, for example, by presenting data and / or receiving user input. In this regard, the user interface 422 generally includes at least a display (e.g., a screen) for displaying data. For example, the user interface 422 may include an LED or LCD screen for displaying data (e.g., graphics, text, video, etc.). The user interface 422 may be specifically configured to display at least the blood flow visualization data (e.g., video) generated by the image analyzer 412. In some implementations, the user interface 422 also includes at least one user input device for receiving user input. For example, the user interface 422 may include one or more of a keyboard, a mouse, a number pad, arrow keys, buttons, etc. In some implementations, the user interface 422 is a separate computing device from the computing device 400, including its own processor and / or memory. The user interface 422 may include, for example, a mobile phone, an electronic tablet, a laptop or desktop computer, etc. In some implementations, the user interface 422 may be connected wirelessly (e.g., via the Internet) rather than directly connected to the computing device 400.

[0053] Image analysis As described above, the raw laser speckle image collected by the first light source 102 / camera 202 can be expressed as follows: K=σ s / is processed by the remote computing device 114 to calculate a spatial speckle contrast value (K) within a sliding moving window (e.g., a 7×7 pixel window) according to s is the spatial standard deviation, is the average intensity in the region. In some implementations, the image captured by the first camera 110 / camera 202 (e.g., "Image 1") can be spatially transformed to the image captured by the second camera 112 (e.g., "Image 2"). In other implementations, the image captured by the second camera 112 (e.g., "Image 2") can be spatially transformed to the image captured by the first light source 102 / camera 202 (e.g., "Image 1"). In still other implementations, no spatial transformation is applied. One example of a spatial transformation that may be applied is an affine transformation.

[0054] For relative blood flow measurements, speckle contrast is calculated by evaluating the average decay time of the speckle electric field autocorrelation function, assuming unity for the measurement factor, as the correlation time (τ c ) was converted to the speckle correlation time τ c is a more quantitative measure of blood flow and is the inverse correlation time (ICT = 1 / τ c ) is commonly used as a metric of blood flow in blood vessels or perfusion in the parenchyma. To display ICT time course data, the first 5 seconds of data were used as a normalization factor to more easily visualize changes in flow rate relative to baseline values. To overlay the LSCI with images from the built-in microscope white light camera, the video output of the surgical microscope system was continuously recorded. The LSCI, white light reflectance, and ICGA images were spatially coregistered by applying an affine transformation to all corresponding images.

[0055] 5, a flow diagram of a process 500 for visualizing blood flow according to some implementations is shown. In some implementations, the process 500 is implemented by a remote computing device 114, e.g., computing device 400, as described herein. It will be understood that certain steps of the process 500 may be optional, and in some implementations, the process 500 may be implemented using fewer than all of the steps. It will also be understood that the order of steps shown in FIG. 5 is not intended to be limiting.

[0056] In step 502, a laser speckle contrast image (e.g., an LSCI image) of blood flow is obtained. In some implementations, the laser speckle contrast image is derived from a raw laser speckle image captured by the first camera 110. In some such implementations, the laser speckle contrast image is derived from the raw laser speckle image using a sliding pixel window. As mentioned above, in some implementations, the spatial speckle contrast value (K) is calculated according to the formula K=σ s / is calculated from the raw laser speckle image within a sliding moving window (e.g., a 7 × 7 pixel window) according to s is the spatial standard deviation, is the average intensity in the region. In some implementations, thresholding is applied to the laser speckle contrast image, as described below with respect to Figure 6. In some implementations, a color map is applied to the laser speckle contrast image, as also described below with respect to Figure 6.

[0057] At step 504, a white light image of tissue in the region of interest associated with blood flow is obtained. In some implementations, the white light image is captured by the second camera 112. As described herein, a "white light" image refers to an image of a target region (e.g., tissue) illuminated with substantially white light. Generally, the white light image captures the anatomical structures of the subject in the region associated with the laser speckle contrast image of blood flow. In some implementations, the "tissue" refers to the vasculature or brain tissue.

[0058] In step 506, the laser speckle contrast image and the white light image are spatially aligned (e.g., co-registered) with respect to one another. In some implementations, the laser speckle contrast image and the white light image are spatially aligned using an affine transformation. In some implementations, the laser speckle contrast image and the white light image are spatially aligned using a lookup table that maps pixels of the laser speckle contrast image and the white light image to one another. In some implementations, spatially aligning the laser speckle contrast image and the white light image includes creating a lookup table based on the spatial transformation used to align the laser speckle contrast image and the white light image.

[0059] In step 508, the spatially registered laser speckle contrast image is overlaid on the white light image. In some implementations, overlaying the spatially registered laser speckle contrast image and the white light image includes mapping each pixel from the laser speckle contrast image to each pixel from the white light image using the lookup table created in step 506. In some implementations, overlaying the spatially registered laser speckle contrast image and the white light image includes contrast stretching the laser speckle contrast image, mapping the laser speckle contrast image to an n-bit color map, and / or performing a weighted sum with the white light image.

[0060] At step 510, display data is generated that continuously depicts blood flow (e.g., laser speckle contrast images) overlaid on the tissue. Typically, the display data includes spatially registered and overlaid laser speckle contrast images and white light images. In some implementations, the display data includes a still image of blood flow (e.g., from the laser speckle contrast images) overlaid on the tissue (e.g., from the white light images). In some implementations, the display data includes a series of images and / or videos showing blood flow through the tissue. An example of the display data is shown in FIG. 7D.

[0061] In step 512, the display data is presented via a user interface. In particular, the display data may be presented to a user (e.g., a physician) via the user interface in real-time or near real-time. In this manner, the user may view ("visualize") blood flow through / under tissue in real-time or near real-time. An example of the display data is shown in FIG. 7D.

[0062] Referring now to Figure 6, a flow diagram of a process 600 for visualizing blood flow according to some implementations is shown. In some implementations, the process 600 is implemented by a remote computing device 114, e.g., computing device 400, as described herein. It will be understood that certain steps of the process 600 may be optional, and in some implementations, the process 600 may be implemented using fewer than all of the steps. It will also be understood that the order of steps shown in Figure 6 is not intended to be limiting.

[0063] In step 602, the first camera 110 captures a raw laser speckle image of a target area of ​​the object. In some implementations, the target area of ​​the object is illuminated via a first light source 102, which may be a laser or another device that generates coherent light, as discussed above. In some implementations, the light from the first light source 102 passes through various filters and / or other optics, such as the first optic 104. Generally, the first light source 102 is configured to illuminate the target area to visualize blood flow. In some implementations, the light is reflected from the target and the backscattered light is filtered or reduced using a first filter configured to pass the wavelength or wavelength range output by the first light source 102 while blocking the wavelengths output by the second light source 106. In some implementations, the filter is configured to enable simultaneous LSCI acquisition during indocyanine green illumination and block non-laser light. In some implementations, the backscattered light is polarized using a polarizing filter. In some implementations, the first camera 110 captures the raw laser speckle image after filtering and polarizing to reduce backscattering.

[0064] In step 604, the raw laser speckle image is transmitted to the remote computing device 114 or another computing device for post-processing.

[0065] In step 606, the computing device 400 (e.g., the remote computing device 114) derives a laser speckle contrast image or "LSCI image" from the raw laser speckle image. In some implementations, the laser speckle contrast image is based on a sliding pixel window. However, in other implementations, the image may not be acquired or processed based on a sliding pixel window. In some implementations, the laser speckle contrast image is a spatial laser speckle contrast image. Thus, various implementations may be based on various neighborhood schemes. For example, in some implementations, the laser contrast speckle image may be derived based on a temporal speckle contrast analysis or a spatiotemporal laser speckle analysis. For non-spatial speckle contrast analysis, in some implementations, pseudocolor is applied to the LSCI image.

[0066] In step 608, the computing device 400 (e.g., the remote computing device 114) thresholds the LSCI image between a minimum speckle contrast value and a maximum speckle contrast value. In some implementations, the laser speckle contrast image has a laser speckle contrast value between a predetermined minimum speckle contrast value and a predetermined maximum speckle contrast value that is greater than zero. In some implementations, if the minimum speckle contrast value for thresholding is zero, no values ​​are discarded (for a low threshold) because the theoretical minimum of the speckle contrast value is zero. However, in other implementations, the minimum value is greater than zero, in which case the system may discard values ​​below the selected minimum value. This is different from transparency. In some implementations, transparency applies only to values ​​between the selected minimum and maximum speckle contrast values, and may refer to the amount of transparency to apply when the speckle contrast values ​​are overlaid on a white light image of tissue. For example, 0% transparency means that the speckle contrast values ​​are not visible, only the tissue is visible, and 100% transparency means that the speckle contrast values ​​are visible, but the tissue is not visible.

[0067] In step 610, the computing device 400 (e.g., the remote computing device 114) applies a color map to the LSCI image. In some implementations, the color map is an 8-bit color map, but the color map can be any n-bit color map.

[0068] In step 612, the thresholded and / or color mapped LSCI image is aligned with a second image (e.g., a white light image) captured by the second camera 112, as described below. In some implementations, the LSCI image and the second image are spatially aligned using an affine transformation. In some implementations, the LSCI image and the second image are spatially aligned using a lookup table that maps pixels of the laser speckle contrast image and the white light image to each other.

[0069] In conjunction with processing the first image (e.g., the raw laser speckle image), in step 614, the second camera 112 captures a second white light image of the target area of ​​the subject. As described above, the white light may be emitted by the second light source 106 through the second optics 108 onto the target area of ​​the subject. In some implementations, the white light reflected from the tissue is filtered using a filter configured to pass one or more wavelengths between 350 nm and 850 nm. In some implementations, the reflected white light is polarized using a polarizing filter.

[0070] As described herein, a "target region" of an object is generally a region of tissue. "Tissue" may include any part of the body and may be internal (e.g., the brain surface) or external (e.g., the skin or eye). It should be understood that in some implementations, a single camera may be used to record the laser speckle contrast image and the white light image. This can be achieved by interleaving the acquisition of the laser speckle image and the white light image. For example, the laser speckle image may be acquired with a first optic 104 in front of a single camera, and then the first optic 104 is switched with the second optic 108, and the white light image is recorded on the single camera. During this interleaving process, the light source 102 and the second light source 106 may illuminate the tissue continuously or in a controlled manner, such that the first light source 102 illuminates during the capture of the laser speckle image and the second light source 106 illuminates during the capture of the white light image.

[0071] The second image may then be transmitted to the remote computing device 114 for additional processing. In step 616, the remote computing device 114 spatially aligns the second image with the first LSCI image. In some implementations, the LSCI image and the second image are spatially aligned using an affine transformation. In some implementations, the LSCI image and the second image are spatially aligned using a look-up table that maps pixels of the laser speckle contrast image and the white light image to each other.

[0072] In step 618, the spatially registered, thresholded, and color-mapped LSCI image is overlaid on the second image. In this manner, the remote computing device 114 can generate a continuous stream of red, green, and blue (RGB) images depicting blood flow overlaying the tissue. In some implementations, a "continuous stream of images" is defined as at least one frame / image per second. In some implementations, a "continuous stream of images" is a video that is longer than 0.5 seconds in duration and / or continuous. However, in other implementations, the duration may be 10 seconds, 30 seconds, 60 seconds, or more.

[0073] In this regard, it should be appreciated that in some implementations, the process 600 may be repeated continuously to generate a real-time or near real-time video feed overlaying the LSCI image on the white light image to visualize blood flow in the context of the subject's anatomy. In some implementations, the overlaid LSCI and white light images / videos are presented, for example, in real-time or near real-time, via a user interface, as described above. As used herein, "real-time" is defined in a clinical sense in that a healthcare provider using the visualization system may visualize blood flow (e.g., pulsing) in the video stream simultaneously with the blood flow as it progresses within the patient. In some implementations, real-time is at least 5 frames / second, although either the LSCI image or the white light image may be acquired slower than 5 frames / second, even though the overlay image is displayed at 5 frames / second or more. In some implementations, the video stream includes a field of view larger than 5 mm x 5 mm. In some implementations, the video stream includes RBG images recording the cessation of blood flow due to a surgical intervention. For example, LSCI images can be acquired continuously so the surgeon can immediately see whether their actions have resulted in the desired interruption (e.g., clipping) of blood flow.

[0074] In some implementations, in response to the laser speckle contrast image having a laser speckle contrast value between a predetermined minimum speckle contrast value and a predetermined maximum speckle contrast value, the video stream includes blood flow greater than a minimum blood flow equal to or greater than zero and less than a maximum blood flow. In some implementations, in response to the laser speckle contrast image having a laser speckle contrast value between a predetermined minimum speckle contrast value and a predetermined maximum speckle contrast value, the video stream does not include blood flow less than a minimum blood flow or greater than a maximum blood flow. In some implementations, in response to the laser speckle contrast image having a laser speckle contrast value between a predetermined minimum speckle contrast value and a predetermined maximum speckle contrast value, the video stream includes blood flow greater than a minimum blood flow greater than zero and less than a maximum blood flow. In some implementations, in response to the laser speckle contrast image having a laser speckle contrast value between a predetermined minimum speckle contrast value and a predetermined maximum speckle contrast value, the video stream does not include blood flow less than a minimum blood flow or greater than a maximum blood flow.

[0075] In some implementations, a third camera is used to capture a fluorescence image, for example, at the same time that the laser speckle and white light images are captured. In some implementations, the third camera is a NIR camera. The fluorescence imaging includes, for example, ICGA. In some implementations, the laser speckle contrast imaging and the additional imaging are performed simultaneously, the laser speckle contrast imaging is based on the laser speckle contrast imaging, and the additional imaging includes at least one of Doppler imaging, ultrasound imaging, percutaneous transfemoral DSA, or a combination thereof. In some implementations, a single camera can be used to record a fluorescence image and a raw laser speckle image by interleaving image acquisition for the laser speckle and the fluorescence. In some implementations, a single camera can be used to perform laser speckle contrast imaging, fluorescence imaging, and white light imaging, as described above.

[0076] In some implementations, although not shown in FIG. 6 , the process 600 may further include spatially aligning the laser speckle contrast image, the white light image, and the fluorescence image with each other. In some implementations, the process 600 may further include simultaneously performing multispectral reflectance imaging (MSRI) and laser speckle contrast imaging, where the laser speckle contrast image is based on the laser speckle contrast imaging. In some implementations, the process 600 may further include simultaneously performing hyperspectral imaging and laser speckle contrast imaging, where the laser speckle contrast image is based on the laser speckle contrast imaging. In some implementations, prior to capturing the laser speckle and / or white light image, the first field of view of the first camera 110 is aligned with the second field of view of the second camera 112. In some implementations, the first trajectory of the first light source 102 is aligned with the second field of view.

[0077] 7A-7D show an exemplary application of process 600 in which an exemplary LSCI image is thresholded and overlaid on an exemplary visible white light reflectance image. First, an LSCI image is acquired. An exemplary LCSI image is shown in FIG. 7A. A threshold is then applied to the LSCI image such that only speckle contrast values ​​corresponding to flow values ​​within a certain range remain (i.e., high flow in blood vessels). The threshold is applied to both the LSCI image and the raw camera intensity pixels. By thresholding the raw camera intensity pixels to include only regions with adequate illumination, the LSCI image has few artifacts due to insufficient illumination. By thresholding the LSCI image between a minimum and maximum speckle contrast value, the contrast of the LSCI image is enhanced for visualization such that regions of specific blood flow are optimized for mapping to a color lookup table. In addition to thresholding, a 2D Gaussian blur filter is applied to smooth the LSCI image. A representative thresholded image is shown in FIG. 7B.

[0078] Next, the laser speckle contrast values ​​within the threshold boundaries are mapped onto a color map. In some implementations, the color map is configured / set by the user. Exemplary color maps can be grayscale, inverse grayscale, jet or inverse jet, perura or inverse perura, etc. During this step, the LSCI image is also spatially aligned with the white light image so that the pixels are aligned in the final overlay image. An exemplary white light image is shown in FIG. 7C. In general, spatially aligning the LSCI image and the white light image is referred to herein as "co-registration." The spatial alignment is performed by a geometric transformation that maps the LSCI to the white light image, such as an affine transformation. After applying the threshold and color map, the LSCI image is merged with the white light image. In this step, the transparency of the thresholded and aligned LSCI image can be adjusted to generate different visualization effects. In some implementations, the thresholded and aligned LSCI image is fully visible on top of the white light image. Alternatively, the thresholded and registered LSCI image may be partially transparent to show the anatomical structures underneath the thresholded and registered LSCI image. An example LCSI overlay image is shown in FIG. 7D.

[0079] As discussed above, processing of the LSCI frames by thresholding the raw intensity and speckle contrast values, aligning the thresholded LSCI image with the white-light image, and overlaying the thresholded and aligned LSCI image on the white-light image to create an overlay image can be performed by the remote computing device 114 or any other suitable computing device. In some implementations, the LSCI and white-light images can be captured and / or processed continuously (e.g., at video rates) and continuously displayed to a neurosurgeon or other user, e.g., in real time, on a monitor.

[0080] The raw laser speckle image (I), the speckle contrast image (K), the white light image (W), the low light intensity threshold (t1), the Gaussian blur standard deviation (σ), a look-up table (LUT) that maps pixels from K(x,y) to W(x,y) based on the spatial transformation calculated during image registration, and the minimum and maximum speckle contrast values ​​(K min and K max ), the minimum and maximum transparency of the speckle contrast overlay (α min and α max ), a 256×3 matrix that defines a color map (CMAP) where each row defines an RGB color. Pseudocode for computer-implemented image analysis (e.g., process 500 and / or process 600) is provided below. The output of the method includes an overlay image (O) that merges the speckle contrast and white light images into a single RGB image. 1. If I(x,y)≦t1 2.Let K(x,y)=0 3.Let K=blur(K,σ), where blur() is a two-dimensional Gaussian blur filter. 4. For(x,y)inK 5.Let(x',y')=LUT(x,y) 6.Let K(x',y') = K(x,y) 7. For(x,y) in K' 8.Let scale=(K'(x,y)-Kmin) / (Kmax-Kmin) 9.Let index=int(255×scale+0.5) such that {index|0≦index≦255} 10.Let α=K^' (x,y)×( α_max-α_min )+α_max-K_α×(α_max-α_min) / (K_α-K_max 11.Let O(x,y,:)=(1-α)×W(x,y,:)+α×CMAP(index,:)

[0081] As shown, a threshold is applied to K based on the value of I, and then a two-dimensional (2D) Gaussian blur filter is applied to smooth the LSCI image. The laser speckle contrast values ​​K(x,y) are then mapped onto a coordinate space W(x,y) that is aligned with the white-light image calculated by a look-up table. The laser speckle values ​​are then thresholded, scaled, and mapped to an 8-bit color map to assign transparency relative to the white-light image.

[0082] Experimental Results To test the system 100 and corresponding methods, as described herein, Experimental surgery was performed on five different subjects. A summary of patient details is provided in Table 1 below. [Table 1]

[0083] Prior to surgery, the field of view of the camera used for LSCI (e.g., first camera 110) was co-aligned and centered with the built-in microscope camera (e.g., second camera 112). Additionally, the laser beam was centered in the field of view of the built-in microscope camera. After the craniotomy was performed, the microscope was positioned over the patient at the neurosurgeon's discretion. LSCI could be performed at any time when the microscope was positioned over the patient by turning on the laser illumination. LSCI did not interfere with the neurosurgeon's workflow and was performed at multiple key time points throughout the surgery. The neurosurgical collaborator performed the majority of the surgery using the eyepieces of the surgical microscope and was able to observe the LSCI data in real time on a monitor mounted next to the surgical microscope displaying overlaid blood flow images.

[0084] Referring now to Figure 8, images are shown before and after clipping of the aneurysm, as well as from a single time point during ICGA for "Patient 2." Specifically, Figure 8 shows images taken before aneurysm clipping (pre-clipping), immediately after aneurysm clipping (post-clipping), and at dye wash-in (ICGA wash-in ) and maximum fluorescence signal (ICGA max ) images acquired from "Patient 2" during ICGA. Visible light images were acquired from the built-in microscope white light camera (white light), laser speckle contrast imaging (LSCI) images were acquired by a NIR-enhanced CMOS camera adapted to the microscope, and ICGA images were acquired by the built-in microscope NIR camera. LSCI overlay images were created by thresholding the LSCI images and overlaying them in pseudocolor onto the white light images. Scale bar is 1 cm. FIG. 8 shows a montage of white light and LSCI overlay images before, during, and after the clipping procedure. FIG. 8 also shows a montage of white light, LSCI overlay, and ICGA images during the injection of ICG dye.

[0085] Specifically, white light and LSCI images were acquired throughout the period of aneurysm clipping, with ICGA images only available during injection of indocyanine green dye after the aneurysm clip was placed. In this surgical procedure, a temporary clip was placed on the carotid artery in the neck after the pre-clipping images but before aneurysm clipping. The pre-clipping LSCI image in FIG. 8 shows that there was high flow within the aneurysm prior to clipping, and that LSCI has the spatial resolution to visualize flow on small vessels above the optic nerve (identified by the arrows on the pre-clipping white light image in FIG. 8). LSCI was used to visualize the filling of the aneurysm during the cardiac cycle and the pulsatile motion of flow within the aneurysm.

[0086] This was especially evident when a temporary clip was placed on the carotid artery in the patient's neck, causing a reduction in flow within the aneurysm. The advantage of having an LSCI instrument integrated into the microscope, as opposed to a stand-alone device, is that images can be acquired during operation of the microscope without interrupting the surgical workflow. This advantage is illustrated when a clip is placed over the aneurysm and the LSCI blood flow map immediately indicates cessation of flow within the aneurysm. This is similarly shown in the LSCI image after clipping in Figure 8. Approximately 5 minutes after clipping, ICGA is used to confirm successful occlusion of the aneurysm and to confirm patency of the surrounding vessels. In Figure 8, the maximum fluorescent signal (ICGA max ) ICGA images during the procedure reveal flow cessation in the aneurysm.

[0087] 9A and 9B, exemplary graphs of relative CBF during a clipping procedure are shown according to some implementations. Specifically, FIGS. 9A and 9B show the time course of relative CBF within the aneurysm from "Patient 2" during a 110 second clipping procedure (FIG. 9A), and zoomed in to the first 20 seconds (FIG. 9B). The relative CBF is normalized to the first 5 seconds of data. The pulsatility of the flow within the aneurysm is clearly visible before and after the temporary clip is placed on the carotid artery. The reduction in CBF after the temporary clipping of the carotid artery is immediately evident. The cessation of flow after clipping is also evident. All transients after aneurysm clipping (t=45 seconds) are motion artifacts.

[0088] In this regard, flow dynamics within the aneurysm were quantified in Figures 9A and 9B. Pulsatile flow within the aneurysm is visible before and after a temporary clip is placed on the carotid artery in the neck. After the temporary clip is placed on the carotid artery, there is approximately a 70% reduction in mean CBF, and pulsatile flow within the aneurysm is clearly visible with a null below 5% of baseline and variable peaks between 50-80%. After the clip is placed over the aneurysm, flow is significantly reduced and pulsatile flow disappears completely. The transient spike after clip placement is due to motion artifact from the surgeon mechanically pushing the aneurysm. After the microscope is repositioned, it is clear that CBF within the aneurysm is absent and within the lower limit of single exposure LSCI measurements (approximately 4% of initial CBF).

[0089] 10, a number of example images captured from a patient during ICGA are shown, according to some implementations. Specifically, FIG. 10 shows a series of images captured during ICGA, starting at the start of an injection (ICGA start ), during dye wash-in (ICG wash-in ), and at maximum fluorescence signal (ICGA max ) includes images acquired from "Patient 4". Figure 10 shows a montage of white light, ICGA, and LSCI perfusion images during the entire ICGA procedure, where green pseudocolor is used for the LSCI overlay. Visible light images were acquired from the built-in microscope white light camera (white light), laser speckle contrast imaging (LSCI) images were acquired by a NIR-enhanced CMOS camera adapted to the microscope, and ICGA images were acquired by the built-in microscope NIR camera. The LSCI overlay images were created by thresholding the LSCI images and overlaying them on the white light images. Scale bar is 1 cm.

[0090] 11A and 11B, exemplary images comparing LSCI overlays with ICGA overlays according to some implementations are shown. Specifically, FIGS. 11A and 11B provide a comparison of a laser speckle contrast imaging (LSCI) overlay (FIG. 11A) and an indocyanine green angiography (ICGA) overlay (FIG. 11B) from "Patient 4." The images were created by overlaying the LSCI and ICGA data, respectively, onto an onboard microscope white light camera and applying green pseudocolor. The arrows highlight the ability of LSCI to detect blood flow in sidewall vessels. The scale bar is 1 cm.

[0091] 12A and 12B, images captured during an arteriovenous malformation (AVM) resection from "Patient 5" are shown according to various implementations. Specifically, FIG. 12A includes a number of example images of an arteriovenous malformation (AVM) resection according to some implementations. FIG. 12B is an example graph of average speckle contrast values ​​before and after AVM resection according to some implementations. In FIG. 12A, a white light image showing the draining vein (whiteout) of the AVM before the AVM is resected is shown. In addition, FIG. 12A includes an LSCI visualization in grayscale taken simultaneously with the white light image. The box on the vein represents the region of interest plotted in FIG. 12B. FIG. 12A also includes a light image showing the draining vein after the AVM is resected, and a grayscale LSCI visualization taken simultaneously with the white light image. The boxes in these images define the region of interest plotted in FIG. 12B. Thus, FIG. 12B is a plot of the mean speckle contrast values ​​from the orange highlighted box before and after AVM resection.

[0092] 13A and 13B, exemplary graphs of fluorescence intensity change during administration of a bolus of ICG and LSCI measurements of relative blood flow for the same region of interest over time are shown, according to various implementations. Specifically, FIG. 13A includes a number of exemplary images showing fluorescence intensity change during administration of a bolus of indocyanine green (ICG) and LSCI measurements of relative blood flow for the same region of interest over time, according to some implementations. FIG. 13B is an exemplary graph of fluorescence intensity change during administration of a bolus of ICG and LSCI measurements of relative blood flow for the same region of interest, according to some implementations. Three regions of interest whose fluorescence intensity over time is shown in FIG. 13B are highlighted in FIG. 13A. FIG. 13A includes an LSCI overlay image at the bottom from the same time as the ICGA image at the top of FIG. 13A. The three regions of interest selected from the LSCI overlay displayed in FIG. 13B are the same regions highlighted in FIG. 13A. FIG. 13B quantifies the time course of changes in ICG fluorescence intensity (top) and relative blood flow (bottom) measured by LSCI during bolus administration of ICG.

[0093] Discussion of results 12A and 12B demonstrate the capability of LSCI for longitudinal monitoring of flow in the AVM (white in FIG. 12A ) draining vein for “Patient 5.” FIG. 12A shows white light and grayscale speckle contrast images, respectively, taken at the same time points before AVM resection, where the draining vessels are arterialized prior to resection with the AVM. In addition, FIG. 12A shows white light and grayscale speckle contrast images, respectively, taken at the same time points at 1 hour and 35 minutes after resection. FIG. 12B plots the averaged speckle contrast values ​​in the orange highlighted region of interest in FIG. 12A , demonstrating that flow in the draining vein after AVM resection is lower than before resection, suggesting that flow is no longer bypassing the capillary network through the AVM.

[0094] The results of this study show that LSCI can be used to continuously monitor CBF in real time. Figure 8, as discussed above, demonstrates the ability of LSCI to continuously monitor CBF during a critical portion of a neurosurgical procedure when the implementation addressed herein is integrated into a surgical microscope and overlaid on the surgeon's view of the surgical field. LSCI reveals high flow within the aneurysm prior to aneurysm clipping, and immediately after clipping, the aneurysm flow ceases while the surrounding vessels maintain perfusion. ICGA is then used to confirm the patency of the surrounding vessels, but requires administration of a dye. This demonstrates that LSCI is complementary to ICGA, and both can be used to determine perfusion in vessels within the surgical field.

[0095] The results shown in Figures 9A and 9B demonstrate that LSCI allows for quantification of flow within the aneurysm relative to baseline values. LSCI is able to detect a pulsatile flow profile within the aneurysm before the aneurysm is clipped. After clipping, LSCI indicates that there is a >96% reduction in flow within the aneurysm relative to the initial flow rate and no pulsatile flow. Because LSCI is sensitive to any form of motion within the tissue, the relative flow uncertainty of the LSCI measurements may be roughly 5%, and thus, the aneurysm may be completely occluded but the LSCI signal will not be reduced 100% from baseline. It is expected that the LSCI signal will still be sensitive to Brownian motion of scattering particles within the aneurysm, but this signal is significantly smaller than the flow from red blood cells into the aneurysm. Although further work is required to determine the percentage reduction in flow rate in order for the surgeon to be confident that the aneurysm is completely occluded, Figure 10 provides preliminary evidence that a 96% reduction in flow rate within the aneurysm as measured by LSCI indicates successful aneurysm occlusion.

[0096] The waveforms in Figures 9A and 9B depicting CBF within the aneurysm are consistent with those measured by Doppler ultrasonography and Doppler velocity wire. Figures 9A and 9B highlight that LSCI allows for continuous CBF measurements within the aneurysm throughout the entire neurosurgical procedure, and thus may be useful for improving understanding of hemodynamics in aneurysms and validating computational fluid dynamics models of aneurysm growth and rupture.

[0097] Figures 12A and 12B demonstrate the capability of LSCI for long-term monitoring of flow in superficial vessels during AVM resection. Figure 12A specifically shows that superficial vessels adjacent to the AVM are first arterialized and then blood flow is reduced after AVM resection. This demonstration indicates that LSCI has the potential to quantify flow in feeding and outflow vessels in real time over the course of AVM resection, which can take several hours, thus providing surgeons with important and actionable information regarding the success of the surgery. Future studies will aim to establish the reproducibility of such flow measurements as the surgical environment changes.

[0098] Figures 13A and 13B further illustrate the complementary nature of the information obtained with LSCI and ICGA when incorporated into a surgical microscope. While ICGA intensity is a more direct measure of cerebral blood volume (CBV), LSCI is directly sensitive to motion and therefore a more direct measure of CBF. ICGA wash-in can also be used to distinguish feeding versus draining vessels in some surgical procedures, and the temporal dynamics of fluorescence intensity can be used to estimate CBF. Figures 13A and 13B show the time course of fluorescence intensity change during bolus administration of ICG along with LSCI measurements of relative blood flow for the same region of interest. In this particular example, the ICG fluorescence signal saturates in the largest vessel, but it is still clear that the rise time of the ICG signal is more rapid in this vessel than the two smaller vessels, indicating a higher flow rate. The LSCI signal from the same region reveals similar information, but in a different way. The LSCI signal is steady-state, since it is a direct measurement of flow that is relatively constant over the measurement period. However, the relative flow across each region of interest is evident from the steady-state value of the speckle decorrelation time. Thus, although LSCI cannot quantify absolute CBF, it can be used to estimate relative CBF over time or across spatial regions.

[0099] Despite the differences between LSCI and ICGA, LSCI can also be used to create images that look very similar to ICGA images. The results of comparing simultaneous LSCI and ICGA images shown in Figures 11A and 11B, and the implementations addressed herein, demonstrate the complementary nature of the information provided by LSCI during ICGA. The LSCI overlay image (Figure 11A) and the ICGA overlay image (Figure 11B) are rendered with a similar green color map and show similar spatial information when overlaid on the surgeon's field of view.

[0100] ICGA allows better visualization of flow in larger vessels with ICGA using a fluorescent dye as contrast, while LSCI uses the inherent properties of blood flow to scatter the laser light. Conversely, LSCI has an advantage in visualizing flow in small vessels, as shown on the vessel in the sidewall marked by the blue arrow in Figure 11, and in Figure 8, LSCI shows CBF in a small vessel supplying the optic nerve. ICGA also has the advantage of providing directionality of flow during the wash-in of the dye.

[0101] One limitation of LSCI is that it is sensitive to motion from both blood flow and the surgeon's mechanical force on the brain tissue. Thus, the LSCI overlay video cannot distinguish between the two motion sources. However, surgeon-induced motion can be avoided by the surgeon pausing for a moment while not pressing on the tissue and viewing the LSCI overlay.

[0102] The implementations addressed herein demonstrate the potential of LSCI to continuously monitor CBF during neurovascular surgery. The integration of the LSCI device into the surgical microscope enabled continuous CBF visualization and allowed for simultaneous acquisition of LSCI and ICGA. The time course of relative blood flow shown in Figures 9A, 9B, and 11, as well as the LSCI overlays addressed herein, demonstrate the ability of LSCI to continuously and in real time monitor CBF. The sequence of simultaneous LSCI and ICGA images shown in Figures 8, 10, 11, and 13A suggests that LSCI and ICGA provide complementary information on CBF, and both can be used to determine perfusion in blood vessels.

[0103] The implementation suggests that LSCI can provide continuous and real-time CBF visualization without affecting the surgeon's workflow or requiring contrast agents, and is therefore a promising tool for continuous CBF monitoring during surgery. By integrating a LSCI device into a surgical microscope, the implementation performs LSCI at key parts of neurovascular surgery, providing the surgeon with immediate, actionable information on the success of the procedure. The implementation enables simultaneous acquisition of LSCI and ICGA, demonstrating that LSCI and ICGA are complementary tools for visualizing CBF to aid in surgical decision making.

[0104] Configuration of a specific implementation The configurations and arrangements of the systems and methods shown in the various implementations are merely exemplary. Although only a few implementations have been described in detail in this disclosure, many modifications are possible (e.g., changes in the size, dimensions, structure, shape and proportions of various elements, parameter values, mounting arrangements, use of materials, colors, orientations, etc.). For example, the positions of elements may be reversed or otherwise changed, and the nature or number of individual elements or positions may be changed or modified. Accordingly, all such modifications are intended to be included within the scope of this disclosure. The order or sequence of any process or method steps may be changed or rearranged according to alternative implementations. Other substitutions, modifications, changes, and omissions may be made in the design, operating conditions, and arrangements of the implementations without departing from the scope of this disclosure.

[0105] The present disclosure contemplates methods, systems, and program products on any machine-readable medium for accomplishing various operations. Implementations of the present disclosure may be implemented using existing computer processors, or by dedicated computer processors for suitable systems incorporated for this or another purpose, or by hardwired systems. Implementations within the scope of the present disclosure include program products that include machine-readable media for carrying or storing machine-executable instructions or data structures. Such machine-readable media can be any available medium that can be accessed by a general purpose or special purpose computer or other machine with a processor. By way of example, such machine-readable media include RAM, ROM, EPROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or executable instructions or data structures that can be accessed by a general purpose or special purpose computer or other machine with a processor, which may contain desired program code in machine-executable instructions or data structures.

[0106] When information is transferred or provided to a machine over a network or another communications connection (either hardwired, wireless, or a combination of hardwired or wireless), the machine properly views the connection as a machine-readable medium. Thus, any such connection is properly referred to as a machine-readable medium. Combinations of the above are also included within the scope of machine-readable media. Machine-executable instructions include, for example, instructions and data that cause a general purpose computer, special purpose computer, or special purpose processing machines to perform a certain function or group of functions.

[0107] Although the figures show a specific order of method steps, the order of steps may differ from that depicted. Also, two or more steps may be performed simultaneously or with partial concurrence. Such variations depend on the software and hardware systems selected, as well as designer choice. All such variations are within the scope of this disclosure. Similarly, software implementations may be accomplished using standard programming techniques with rule-based logic and other logic to accomplish the various connecting, processing, comparing, and deciding steps.

[0108] It is to be understood that the methods and systems are not limited to specific synthetic methods, specific components, or particular compositions. It is also to be understood that the terminology used herein is for the purpose of describing particular implementations only, and is not intended to be limiting.

[0109] As used in the specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. As used herein, ranges may be expressed as "about" one particular value, and / or to "about" another particular value. When such a range is expressed, another implementation includes from the one particular value and / or to the other particular value. Similarly, when values ​​are expressed as approximations, by use of the antecedent "about," it will be understood that the particular value forms another implementation. It will be further understood that the endpoints of each of the ranges are significant both in relation to the other endpoint, and independently of the other endpoint.

[0110] "Optional" or "optionally" means that the subsequently described event or circumstance may or may not occur, and the description includes cases where the event or circumstance occurs and cases where the event or circumstance does not occur.

[0111] Throughout the description and claims of this specification, the word "comprise" and variations of words such as "comprising" and "comprises" mean "including but not limited to" and are not intended to exclude, for example, other additives, components, integers or steps. "Exemplary" means "an example of" and is not intended to convey an indication of a preferred or ideal implementation. "Such as" is used for descriptive purposes rather than in a limiting sense.

[0112] Components are disclosed that can be used to perform the disclosed methods and systems. These and other components are disclosed herein, and when combinations, subsets, interactions, groups, etc. of these components are disclosed, it is understood that specific references to the various individual and collective combinations and permutations thereof may not be expressly disclosed, and that for all methods and systems, each is specifically contemplated and described herein. This applies to all aspects of this application, including but not limited to the steps of the disclosed methods. Thus, when there are various additional steps that can be performed, it is understood that each of these additional steps can be performed by any specific implementation or combination of implementations of the disclosed methods.

[0113] Example Implementations Example 1. A method for visualizing blood flow, comprising: [A] simultaneously illuminating (a) a blood flow with a first light from a first light source and (b) a tissue with a second light from a second light source, wherein (c)(i) the first light source is a coherent light source, the first light having a first wavelength, and (c)(ii) the second light source is a white light source, the second light having one or more wavelengths between 350 nm and 850 nm; and [B] illuminating the blood flow with the first light by simultaneously illuminating the tissue with the first light by simultaneously illuminating the tissue with the second light. [c] filtering backscattered light from the blood flow with a first filter configured to filter the backscattered light with a first polarizer; (b) polarizing the backscattered light with a first polarizer; [C] recording a raw laser speckle image with a first camera in response to filtering and polarizing the backscattered light; [D] recording a white light image with a second camera in response to illuminating the tissue with a second light; and [E] deriving a laser speckle contrast image from the raw laser speckle image, comprising: (a) filtering backscattered light from the blood flow with a first filter configured to filter the backscattered light with a first polarizer; [F] spatially aligning the laser speckle contrast image and the white light image with respect to one another; [G] in response to spatially aligning the laser speckle contrast image and the white light image, overlaying the laser speckle contrast image with the white light image to generate a respective continuous stream of red, green, and blue (RGB) images depicting the blood flow overlaying the tissue; and [H] outputting the continuous stream of RGB images depicting the blood flow overlaying the tissue as a video stream, wherein the video stream has a duration greater than 0.5 seconds and is continuous. Example 2. A method of visualizing blood flow, comprising: [A] (a) illuminating the blood flow with a first light from a first light source and (b) illuminating tissue with a second light from a second light source, where (c)(i) the first light comprises a first wavelength and (c)(ii) the second light has one or more wavelengths between 350 nm and 850 nm; [B] in response to illuminating the blood flow with the first light, (a) filtering backscattered light from the blood flow with a first filter configured to pass the first wavelength and (b) polarizing the backscattered light with a first polarizer; [C] in response to filtering and polarizing the backscattered light, recording a raw laser speckle image with a first camera; [D] in response to illuminating the tissue with the second light, recording an image with a second camera; and [E] deriving a laser speckle contrast image from the raw laser speckle image, where (a) the laser speckle contrast image is a sliding filter. [F] spatially aligning the laser speckle contrast image and the second camera image with respect to one another; [G] in response to spatially aligning the laser speckle contrast image and the second camera image, overlaying the laser speckle contrast image with the second camera image to generate a respective continuous stream of red, green, and blue (RGB) images depicting the blood flow overlaying the tissue; and [H] outputting as a video stream the continuous stream of RGB images depicting the blood flow overlaying the tissue, wherein the video stream has a duration greater than 0.5 seconds and is continuous. Example 3. A method for visualizing blood flow, comprising: [A] simultaneously illuminating (a) a blood flow with a first light from a first light source and (b) a tissue with a second light from a second light source, wherein (c)(i) the first light source is a coherent light source, the first light having a first wavelength, and (c)(ii) the second light source is a white light source, the second light having one or more wavelengths between 350 nm and 850 nm; and [B] in response to illuminating the blood flow with the first light, (a) filtering light having the first wavelength. (a) filtering backscattered light from the blood flow with a first filter configured to filter the backscattered light with a first polarizer; (b) polarizing the backscattered light with a first polarizer; [C] recording a raw laser speckle image with a first camera in response to filtering and polarizing the backscattered light; [D] recording a white light image with a second camera in response to illuminating the tissue with a second light; and [E] deriving a laser speckle contrast image from the raw laser speckle image, comprising: (a) filtering backscattered light from the blood flow with a first filter configured to filter the backscattered light with a first polarizer; [F] spatially aligning the laser speckle contrast image and the white light image with respect to one another; [G] in response to spatially aligning the laser speckle contrast image and the white light image, overlaying the laser speckle contrast image with the white light image to generate a respective continuous stream of red, green, and blue (RGB) images depicting blood flow overlaying the tissue; and [H] outputting as a video stream the continuous stream of RGB images depicting blood flow overlaying the tissue, wherein the video stream has a duration greater than 0.5 seconds and is continuous. Example 4. A method of visualizing blood flow, comprising: [A] (a) illuminating blood flow with a first light from a first light source and (b) illuminating tissue with a second light from a second light source, wherein (c)(i) the first light source is a coherent light source, the first light having a first wavelength, and (c)(ii) the second light source is a white light source, the second light having one or more wavelengths between 350 nm and 850 nm; and [B] in response to illuminating the blood flow with the first light, (a) passing the first wavelength. [c] filtering backscattered light from the blood flow with a first filter configured to filter the backscattered light from the blood flow with a first filter configured to filter the backscattered light from the blood flow with a first polarizer; [d] recording a raw laser speckle image with a first camera in response to filtering and polarizing the backscattered light; [e] recording a white light image with a second camera in response to illuminating the tissue with a second light; and [f] deriving a laser speckle contrast image from the raw laser speckle image, comprising: [F] spatially aligning the laser speckle contrast image and the white light image with respect to one another; [G] in response to spatially aligning the laser speckle contrast image and the white light image, overlaying the laser speckle contrast image with the white light image to generate a respective continuous stream of red, green, and blue (RGB) images depicting blood flow overlaying the tissue; and [H] outputting as a video stream the continuous stream of RGB images depicting blood flow overlaying the tissue, wherein the video stream has a duration greater than 0.5 seconds and is continuous. Example 5. A method for visualizing blood flow, comprising: [A] (a) illuminating the blood flow with a first light from a first light source and (b) illuminating tissue with a second light from a second light source, where (c)(i) the first light comprises a first wavelength and (c)(ii) the second light has one or more wavelengths between 350 nm and 850 nm; [B] in response to illuminating the blood flow with the first light, (a) filtering backscattered light from the blood flow with a first filter configured to pass the first wavelength and (b) polarizing the backscattered light with a first polarizer; [C] in response to filtering and polarizing the backscattered light, recording a raw laser speckle image with a camera; [D] in response to illuminating the tissue with the second light, recording a non-raw laser speckle image with the camera; and [E] deriving a laser speckle contrast image from the raw laser speckle image, where (a) the laser speckle contrast image is a sliding pixel window. [F] spatially aligning the laser speckle contrast image and the non-raw laser speckle image with respect to one another; [G] in response to spatially aligning the laser speckle contrast image and the non-raw laser speckle image, overlaying the laser speckle contrast image with the non-raw laser speckle image to generate a respective continuous stream of red, green, and blue (RGB) images depicting the blood flow overlaying the tissue; and [H] outputting the continuous stream of RGB images depicting the blood flow overlaying the tissue as a video stream, wherein the video stream has a duration greater than 0.5 seconds and is continuous. Example 6. A method for visualizing blood flow, comprising: [A] (a) illuminating the blood flow with a first light from a first light source and (b) illuminating tissue with a second light from a second light source, where (c)(i) the first light comprises a first wavelength and (c)(ii) the second light has one or more wavelengths between 350 nm and 850 nm; [B] in response to illuminating the blood flow with the first light, (a) filtering backscattered light from the blood flow with a first filter configured to pass the first wavelength and (b) polarizing the backscattered light with a first polarizer; [C] in response to filtering and polarizing the backscattered light, recording a raw laser speckle image with a first camera; [D] in response to illuminating the tissue with the second light, recording an image with a second camera; and [E] deriving a laser speckle contrast image from the raw laser speckle image, [F] spatially aligning the laser speckle contrast image and the second camera image with respect to one another; [G] in response to spatially aligning the laser speckle contrast image and the second camera image, overlaying the laser speckle contrast image with the second camera image to generate a respective continuous stream of red, green, and blue (RGB) images depicting the blood flow overlaying the tissue; and [H] outputting as a video stream the continuous stream of RGB images depicting the blood flow overlaying the tissue, the video stream having a duration greater than 0.5 seconds and being continuous. Example 7. A method of visualizing blood flow, comprising: [A] (a) illuminating the blood flow with a first light from a first light source and (b) illuminating tissue with a second light from a second light source, where (c)(i) the first light comprises a first wavelength and (c)(ii) the second light has one or more wavelengths between 350 nm and 850 nm; [B] in response to illuminating the blood flow with the first light, (a) filtering backscattered light from the blood flow with a first filter configured to pass the first wavelength and (b) polarizing the backscattered light with a first polarizer; [C] in response to filtering and polarizing the backscattered light, recording a raw laser speckle image with a first camera; [D] in response to illuminating the tissue with the second light, recording an image with a second camera; and [E] deriving a laser speckle contrast image from the raw laser speckle image. [F] spatially aligning the laser speckle contrast image and the second camera image with respect to one another; [G] in response to spatially aligning the laser speckle contrast image and the second camera image, overlaying the laser speckle contrast image with the second camera image to generate a stream of images depicting blood flow overlaying tissue; and [H] outputting the stream of images depicting blood flow overlaying tissue as a video stream. Example 8. A method for visualizing blood flow, comprising: [A] (a) illuminating the blood flow with a first light from a first light source and (b) illuminating tissue with a second light from a second light source, where (c)(i) the first light comprises a first wavelength and (c)(ii) the second light has one or more wavelengths between 350 nm and 850 nm; [B] in response to illuminating the blood flow with the first light, (a) filtering backscattered light from the blood flow with a first filter configured to pass the first wavelength and (b) polarizing the backscattered light with a first polarizer; [C] in response to filtering and polarizing the backscattered light, recording a raw laser speckle image with a first camera; [D] in response to illuminating the tissue with the second light, recording an image with a second camera; and [E] determining laser speckle contrast from the raw laser speckle image. 1. A method for visualizing blood flow comprising: deriving an image, wherein (a) a laser speckle contrast image is based on a sliding pixel window, and (b) the laser speckle contrast image has a laser speckle contrast value between a predetermined minimum speckle contrast value greater than or equal to zero and a predetermined maximum speckle contrast value; [F] aligning the laser speckle contrast image and a second camera image with respect to each other; [G] in response to aligning the laser speckle contrast image and the second camera image, overlaying the laser speckle contrast image with the second camera image to generate a red, green, blue (RGB) image depicting blood flow overlaying the tissue; and [H] outputting the RGB image depicting blood flow overlaying the tissue. Example 9. The method of any one of Examples 1 to 8, comprising outputting a video stream in real time while simultaneously illuminating blood flow and tissue. Example 10. The method of any one of Examples 1 to 9, wherein the spatially registered laser speckle contrast image and white light image are based on an affine transformation. Example 11. The method of any one of Examples 1 to 10, wherein the second camera is included in one of a surgical microscope, an endoscope, an exoscope, and a robotic surgical platform. Example 12. The method of any one of Examples 1 to 11, wherein the video stream comprises a field of view larger than 5 mm x 5 mm. Example 13. A method according to any one of Examples 1 to 12, wherein: [A] in response to the laser speckle contrast image having a laser speckle contrast value between a predetermined minimum speckle contrast value and a predetermined maximum speckle contrast value, the video stream includes blood flow greater than a minimum blood flow equal to or greater than zero and less than a maximum blood flow; and [B] in response to the laser speckle contrast image having a laser speckle contrast value between a predetermined minimum speckle contrast value and a predetermined maximum speckle contrast value, the video stream does not include blood flow less than the minimum blood flow or blood flow greater than the maximum blood flow. Example 14. A method according to any one of Examples 1 to 12, wherein: [A] in response to the laser speckle contrast image having a laser speckle contrast value between a predetermined minimum speckle contrast value and a predetermined maximum speckle contrast value, the video stream includes blood flow greater than a minimum blood flow greater than zero and less than a maximum blood flow; and [B] in response to the laser speckle contrast image having a laser speckle contrast value between a predetermined minimum speckle contrast value and a predetermined maximum speckle contrast value, the video stream does not include blood flow less than the minimum blood flow or blood flow greater than the maximum blood flow. Example 15. The method of any one of Examples 1 to 14, comprising, in response to illuminating the tissue with a second light, (a) filtering the white light reflected from the tissue with a filter configured to pass one or more wavelengths between 350 nm and 850 nm; and (b) polarizing the reflected white light reflected from the tissue with a second polarizer. Example 16. The method of any one of Examples 1-15, wherein the first filter is configured to block one or more wavelengths of the second light between 350 nm and 850 nm. However, in other implementations, the filter is configured to enable simultaneous LSCI acquisition during indocyanine green illumination and block non-laser light. Example 17. The method of any one of Examples 1 to 16, wherein the first and second light sources are fixedly coupled to one another so as to move in conjunction with one another in response to user manipulation of the first and second light sources. Example 18. The method of any one of Examples 1 to 17, comprising applying a color map to the laser speckle contrast image. Example 19. A method according to any one of Examples 1 to 18, comprising spatially aligning the laser speckle contrast image and the white light image with respect to one another based on a lookup table that maps pixels of the laser speckle contrast image and the white light image to one another. Example 20. [A] Separating a laser speckle contrast image having a laser speckle contrast value between a predetermined minimum speckle contrast value and a predetermined maximum speckle contrast value from a laser speckle contrast image not having a laser speckle contrast value between the predetermined minimum speckle contrast value and the predetermined maximum speckle contrast value; [B] spatially aligning the laser speckle contrast image and the white light image with respect to each other in response to separating the laser speckle contrast image. Example 21. A method according to any one of Examples 1 to 20, comprising separating laser speckle contrast images having laser speckle contrast values ​​between a predetermined minimum speckle contrast value and a predetermined maximum speckle contrast value from laser speckle contrast images not having laser speckle contrast values ​​between the predetermined minimum speckle contrast value and the predetermined maximum speckle contrast value in response to spatially aligning the laser speckle contrast image and the white light image with respect to one another. Example 22. The method according to any one of Examples 1 to 21, comprising simultaneously performing fluorescence imaging and laser speckle contrast imaging, wherein the laser speckle contrast image is based on laser speckle contrast imaging. Example 23. The method of example 22, further comprising simultaneously recording a raw laser speckle image with a first camera, a white light image with a second camera, and a fluorescent image with a third camera. In some implementations, the third camera may include a NIR camera. Example 24. The method of any one of Examples 1 to 23, comprising spatially registering the laser speckle contrast image, the white light image, and the fluorescence image with respect to one another. Example 25. A method according to any one of Examples 1 to 24, comprising simultaneously performing multispectral reflectance imaging (MSRI) and laser speckle contrast imaging, wherein the laser speckle contrast image is based on laser speckle contrast imaging. Example 26. A method according to any one of Examples 1 to 25, comprising simultaneously performing hyperspectral imaging and laser speckle contrast imaging, wherein the laser speckle contrast image is based on laser speckle contrast imaging. Example 27. A method according to any one of Examples 1 to 26, comprising: [A] aligning a first field of view of a first camera with a second field of view of a second camera; and [B] aligning a first trajectory of a first light source with the second field of view. Example 28. The method of any one of Examples 1 to 27, wherein the video stream includes RBG images recording the cessation of blood flow due to a surgical intervention. For example, LSCI images can be acquired continuously, so that the surgeon can immediately see whether his / her actions resulted in the desired interruption (e.g., clipping) of blood flow. Example 29. A method according to any one of Examples 1 to 28, comprising determining a transparency level for a laser speckle contrast image having a laser speckle contrast value between a predetermined minimum speckle contrast value and a predetermined maximum speckle contrast value. Example 30. The method of any one of Examples 1 to 29, wherein the laser speckle contrast image is a spatial laser speckle contrast image. Example 31. A method for visualizing blood flow comprising: [A] determining a laser speckle contrast imaging (LSCI) image of blood flow; [B] applying a threshold to the LSCI image to separate a first LSCI image from a second LSCI image, the first LSCI image corresponding to a higher level of blood flow than the second LSCI image; [C] applying a pseudocolor to the first LSCI image; and [D] overlaying the first LSCI image with a white light image acquired simultaneously with a speckle image corresponding to the first LSCI image. Example 32. At least one non-transitory machine-readable medium comprising a plurality of instructions that, in response to being executed on a computing device, cause the computing device to perform the method of any one of Examples 1 to 31. Example 33. A processor-based system configured to carry out the method according to any one of Examples 1 to 31. Example 34. An apparatus comprising means for carrying out any one of Examples 1 to 31. Example 35. A blood flow visualization system comprising: [A] first and second light sources; [B] a first filter and a first polarizer; [C] a first and second camera; and [D] at least one machine-readable medium storing data, which when used by the at least one machine, is configured to: [D] [i] simultaneously illuminate (a) the blood flow with a first light from a first light source and (b) tissue with a second light from a second light source, (c) (i) the first light source is a coherent light source, and the first light has a first wavelength. (c)(ii) illuminating the tissue with a second light source, the second light having one or more wavelengths between 350 nm and 850 nm; and, [D][ii] in response to illuminating the blood flow with the first light, (a) filtering backscattered light from the blood flow with a first filter configured to pass the first wavelength and (b) polarizing the backscattered light with a first polarizer; [D][iii] in response to the filtering and polarizing the backscattered light, recording a raw laser speckle image with a first camera; and, [D][iv] illuminating the tissue with the second light. [D][v] deriving a laser speckle contrast image from the raw laser speckle image, (a) the laser speckle contrast image based on a sliding pixel window, and (b) the laser speckle contrast image having a laser speckle contrast value between a predetermined minimum speckle contrast value greater than zero and a predetermined maximum speckle contrast value; [D][vi] spatially registering the laser speckle contrast image and the white light image with each other; [D][vii] in response to spatially registering the laser speckle contrast image and the white light image, overlaying the laser speckle contrast image with the white light image to generate a respective continuous stream of red, green, and blue (RGB) images depicting blood flow overlaying the tissue; and [D][viii] outputting as a video stream the continuous stream of RGB images depicting blood flow overlaying the tissue, the video stream having a duration of 0.and outputting the blood flow visualization system for more than 5 seconds and continuously. Example 36. A blood flow visualization system, comprising: [A] a first light source and a second light source, (a) the first light source being a coherent light source that emits a first light having a first wavelength, and (b) the second light source being a white light source that emits a second light having one or more wavelengths between 350 nm and 850 nm; [B] a first filter and a first polarizer; [C] first and second cameras; and [D] at least one machine-readable medium storing data, which when used by the at least one machine, comprises: [D][i] recording a raw laser speckle image with a first camera and recording a white light image with a second camera in response to illuminating the blood flow with a first light and illuminating the tissue with a second light; and [D][ii] deriving a laser speckle contrast image from the raw laser speckle image, wherein (a) the laser speckle contrast image is based on a sliding pixel window; and (b) the laser speckle contrast image has a predetermined minimum speckle contrast value greater than zero and a predetermined maximum speckle contrast value greater than zero. [D][iii] spatially aligning the laser speckle contrast image and the white light image with respect to one another; [D][iv] in response to spatially aligning the laser speckle contrast image and the white light image, overlaying the laser speckle contrast image with the white light image to generate a respective continuous stream of red, green, and blue (RGB) images depicting the blood flow overlaying the tissue; and [D][v] outputting as a video stream the continuous stream of RGB images depicting the blood flow overlaying the tissue, wherein the video stream has a duration greater than 0.5 seconds and is continuous. Example 37. A system as described in Example 35 or 36, wherein at least one machine-readable medium stores data that, when used by at least one machine, causes the at least one machine to perform a method including outputting a video stream in real time while simultaneously illuminating blood flow and tissue. Example 38. A system described in any one of Examples 35 to 37, wherein the spatially aligned laser speckle contrast image and white light image are based on an affine transformation. Example 39. A system described in any one of Examples 35 to 38, wherein the second camera is included in one of a surgical microscope, an endoscope, an exoscope, and a robotic surgical platform. Example 40. A system described in any one of Examples 35 to 39, wherein the video stream includes a field of view larger than 5 mm x 5 mm. Example 41. A system described in any one of Examples 35 to 40, wherein: [A] in response to the laser speckle contrast image having a laser speckle contrast value between a predetermined minimum speckle contrast value and a predetermined maximum speckle contrast value, the video stream includes blood flow greater than a minimum blood flow greater than zero and less than a maximum blood flow; and [B] in response to the laser speckle contrast image having a laser speckle contrast value between a predetermined minimum speckle contrast value and a predetermined maximum speckle contrast value, the video stream does not include blood flow less than the minimum blood flow or blood flow greater than the maximum blood flow. Example 42. A system described in any one of Examples 35 to 41, comprising a second filter and a second polarizer, the second filter configured to pass one or more wavelengths between 350 nm and 850 nm, and the second polarizer configured to polarize reflected white light reflected from the tissue with the second polarizer. Example 43. A system described in any one of Examples 35 to 42, wherein the first filter is configured to block one or more wavelengths of the second light between 350 nm and 850 nm. Example 44. A system described in any one of Examples 35 to 43, wherein the first and second light sources are fixedly coupled to each other so as to move in coordination with one another in response to user manipulation of the first and second light sources. Example 45. A system described in any one of Examples 35 to 44, wherein at least one machine-readable medium stores data that, when used by the at least one machine, causes the at least one machine to perform a method including applying a color map to a laser speckle contrast image. Example 46: A system described in any one of Examples 35 to 45, wherein at least one machine-readable medium stores data that, when used by at least one machine, causes at least one machine to perform a method including spatially aligning the laser speckle contrast image and the white light image with respect to each other based on a lookup table that maps pixels of the laser speckle contrast image and the white light image to each other. Example 47. At least one machine-readable medium, when used by at least one machine, includes: [A] separating laser speckle contrast images having laser speckle contrast values ​​between a predetermined minimum speckle contrast value and a predetermined maximum speckle contrast value from laser speckle contrast images not having laser speckle contrast values ​​between the predetermined minimum speckle contrast value and the predetermined maximum speckle contrast value; [B] A system described in any one of Examples 35 to 46, storing data that causes at least one machine to execute a method including: spatially aligning the laser speckle contrast image and the white light image with respect to each other in response to separating the laser speckle contrast image. Example 48. A system described in any one of Examples 35 to 47, wherein at least one machine-readable medium stores data that, when used by at least one machine, causes at least one machine to execute a method including separating laser speckle contrast images having laser speckle contrast values ​​between a predetermined minimum speckle contrast value and a predetermined maximum speckle contrast value from laser speckle contrast images that do not have laser speckle contrast values ​​between a predetermined minimum speckle contrast value and a predetermined maximum speckle contrast value in response to spatially aligning the laser speckle contrast image and the white light image with respect to one another. Example 49. A system described in any one of Examples 35 to 48, wherein at least one machine-readable medium stores data that, when used by at least one machine, causes at least one machine to execute a method including simultaneously performing fluorescence imaging and laser speckle contrast imaging, wherein the laser speckle contrast image is based on the laser speckle contrast imaging. Example 50. The system described in Example 49, wherein at least one machine-readable medium stores data that, when used by the at least one machine, causes the at least one machine to perform a method including simultaneously recording a raw laser speckle image with a first camera, a white light image with a second camera, and a fluorescence image with a third camera. Example 51. A system described in Example 49 or 50, wherein at least one machine-readable medium stores data that, when used by the at least one machine, causes the at least one machine to perform a method including spatially aligning a laser speckle contrast image, a white light image, and a fluorescence image with respect to one another. Example 52. A system described in any one of Examples 35 to 51, wherein at least one machine-readable medium stores data that, when used by at least one machine, causes at least one machine to perform a method including simultaneously performing multispectral reflectance imaging (MSRI) and laser speckle contrast imaging, wherein the laser speckle contrast image is based on the laser speckle contrast imaging. Example 53. A system described in any one of Examples 35 to 52, wherein at least one machine-readable medium stores data that, when used by at least one machine, causes at least one machine to perform a method including simultaneously performing hyperspectral imaging and laser speckle contrast imaging, wherein the laser speckle contrast image is based on the laser speckle contrast imaging. Example 54. A system described in any one of Examples 35 to 53, wherein the video stream includes RBG images recording the cessation of blood flow due to surgical intervention. Example 55. A system described in any one of Examples 35 to 54, wherein at least one machine-readable medium stores data that, when used by the at least one machine, causes the at least one machine to perform a method including determining a transparency level for a laser speckle contrast image having a laser speckle contrast value between a predetermined minimum speckle contrast value and a predetermined maximum speckle contrast value. Example 56. A system described in any one of Examples 35 to 55, wherein the laser speckle contrast image is a spatial laser speckle contrast image. Example 57. At least one machine-readable medium storing data which, when used by the at least one machine, causes the at least one machine to perform a method including: [A] determining a laser speckle contrast imaging (LSCI) image of blood flow; [B] applying a threshold to the LSCI image to separate a first LSCI image from a second LSCI image, the first LSCI image corresponding to a higher level of blood flow than the second LSCI image; [C] applying a pseudocolor to the first LSCI image; and [D] overlaying the first LSCI image with a white light image acquired simultaneously with a speckle image corresponding to the first LSCI image. Example 58. A method for visualizing blood flow comprising: [A] receiving a laser speckle contrast imaging (LSCI) image of blood flow; [B] receiving a white light image of tissue, the white light image capturing anatomical structures; [C] registering the LSCI image and the white light image with respect to one another; [D] overlaying the registered LSCI image and the white light image; and [E] generating display data continuously depicting blood flow overlaying the tissue, the display data including the registered LSCI image and the white light image. Example 59. The method described in Example 57 or 58, wherein the display data continuously depicts blood flow overlaying tissue in real time during a surgical procedure. Example 60. The method of any one of Examples 57 to 59, further comprising displaying the display data on a user interface. Example 61. The method of any one of Examples 57 to 60, wherein the tissue comprises vasculature. Example 62. The method of any one of Examples 57 to 61, wherein the tissue is brain tissue. Example 63. A method described in any one of Examples 57 to 62, wherein the step of aligning the LSCI image and the white-light image with respect to each other includes creating a lookup table based on the spatial transformation used to align the LSCI image and the white-light image. Example 64. A method described in any one of Examples 57 to 63, wherein the step of overlaying the aligned LSCI image and white-light image includes mapping each pixel from the LSCI image to each pixel from the white-light image using a lookup table. Example 65. A method described in any one of Examples 57 to 64, wherein the step of overlaying the aligned LSCI image and white-light image further includes contrast stretching the LSCI image, mapping the LSCI image to an n-bit color map, and performing a weighted sum with the white-light image. Example 66. At least one non-transitory machine-readable medium comprising a plurality of instructions that, in response to being executed on a computing device, cause the computing device to perform a method according to any one of Examples 57 to 65. Example 67. A system for blood flow visualization, comprising: [A] first and second light sources, the first light source configured to illuminate the blood flow and the second light source configured to illuminate tissue with white light; [B] first and second cameras, the first camera configured to record a raw laser speckle image of the blood flow and the second camera configured to record a white light image of the tissue; and [C] a computing device comprising a processor and a memory, the memory, when executed by the processor, configured to provide the processor with: [C][i] a raw laser speckle image of the blood flow; [C][ii] receive a laser speckle contrast imaging (LSCI) image; [C][iii] receive a white light image of tissue, the white light image capturing anatomical structures; [C][iv] register the LSCI image and the white light image with each other; [C][v] overlay the registered LSCI image and the white light image; and [C][vi] generate display data that continuously depicts blood flow overlaying the tissue, the display data including the registered LSCI image and the white light image. Example 68. The system described in Example 67, wherein the system is included in one of a surgical microscope, an endoscope, an exoscope, or a robotic surgical platform. Example 69. A method, comprising carrying out the blood flow visualization method according to any one of Examples 57 to 65; and continuously monitoring blood flow overlying the tissue during the surgical procedure. Example 70. The method of Example 69, wherein the surgical procedure is brain surgery. Example 71 The method of Example 69, wherein the surgical procedure is cerebral aneurysm clipping. Example 72. The method of Example 69, wherein the surgical procedure is an arteriovenous malformation (AVM) resection. Example 73. A blood flow visualization system comprising: a first light source; a first filter and a first polarizer; a first camera; and at least one machine-readable medium storing data, the data, when used by the at least one machine, providing to the at least one machine: illuminating the blood flow with a first light from the first light source, the first light being a coherent light source, the first light having a first wavelength; and in response to illuminating the blood flow with the first light, (a) filtering backscattered light from the blood flow with a first filter configured to pass the first wavelength; (b) polarizing the backscattered light with a first polarizer; and in response to filtering and polarizing the backscattered light, recording a raw laser speckle image with the first camera; receiving a derived white light image from a second camera; and deriving a laser speckle contrast image from the raw laser speckle image, the (a) filtering backscattered light from the blood flow with a first filter configured to pass the first wavelength; and .... deriving (a) a laser speckle contrast image based on a sliding pixel window, and (b) a laser speckle contrast image having a laser speckle contrast value between a predetermined minimum speckle contrast value greater than zero and a predetermined maximum speckle contrast value; spatially aligning the laser speckle contrast image and the white light image with respect to one another; and in response to spatially aligning the laser speckle contrast image and the white light image, overlaying the laser speckle contrast image with the white light image to generate a respective continuous stream of red, green, and blue (RGB) images depicting the blood flow overlaying the tissue; and outputting the continuous stream of RGB images depicting the blood flow overlaying the tissue as a video stream, wherein the video stream has a duration greater than 0.5 seconds and is continuous. Example 74. A blood flow visualization system comprising: a first light source for illuminating a blood flow with a first light, the first light source being a coherent light source, the first light having a first wavelength; a first filter and a first polarizer, respectively, for (a) filtering backscattered light from the blood flow with a first filter configured to pass the first wavelength, and (b) polarizing the backscattered light with a first polarizer; a first camera; and at least one machine-readable medium storing data, the data, when used by the at least one machine, for recording a raw laser speckle image with the first camera in response to filtering and polarizing the backscattered light, receiving a derived white light image from the second camera, and deriving a laser speckle contrast image from the raw laser speckle image, wherein (a) the laser speckle contrast image is a sliding pixel image. based on the cell window, (b) a laser speckle contrast image having a laser speckle contrast value between a predetermined minimum speckle contrast value greater than zero and a predetermined maximum speckle contrast value; spatially aligning the laser speckle contrast image and the white light image with respect to one another; and in response to spatially aligning the laser speckle contrast image and the white light image, overlaying the laser speckle contrast image with the white light image to generate a respective continuous stream of red, green, and blue (RGB) images depicting the blood flow overlaying the tissue; and outputting the continuous stream of RGB images depicting the blood flow overlaying the tissue as a video stream, wherein the video stream has a duration greater than 0.5 seconds and is continuous.

Claims

1. 1. A method for visualizing blood flow, the method comprising: Obtaining a laser speckle contrast imaging (LSCI) image of blood flow; obtaining a white light image of tissue, the white light image capturing an anatomical structure of interest in a region associated with the LSCI image of the blood flow; spatially registering the LSCI image and the white light image with respect to one another; overlaying the spatially registered LSCI image and the white light image; generating display data that continuously depicts the blood flow overlaying the tissue, the display data including the spatially registered LSCI image and a white light image.

2. The method of claim 1 , wherein the display data continuously depicts the blood flow overlaying the tissue in real time during a surgical procedure.

3. The method of claim 1 or 2, further comprising displaying the representation data on a user interface.

4. The method of any one of claims 1 to 3, wherein the tissue comprises a vasculature.

5. The method of any one of claims 1 to 3, wherein the tissue is brain tissue.

6. The method of any one of claims 1 to 5, wherein spatially aligning the LSCI image and the white-light image with respect to each other comprises creating a lookup table based on a spatial transformation used to align the LSCI image and the white-light image.

7. 7. The method of claim 6, wherein overlaying the spatially aligned LSCI image and white-light image includes mapping each pixel from the LSCI image to each pixel from the white-light image using the lookup table.

8. 8. The method of claim 7, wherein overlaying the spatially aligned LSCI image and white-light image further comprises contrast stretching the LSCI image, mapping the LSCI image to an n-bit color map, and performing a weighted sum with the white-light image.

9. 1. A system for blood flow visualization, the system comprising: a first light source configured to illuminate blood flow in a target area of ​​the subject; a first camera configured to record a raw laser speckle image of the blood flow; a computing device comprising a processor and a memory, the memory being configured to, when executed by the processor, cause the computing device to: obtaining the raw laser speckle image of the blood flow via the first camera; deriving a laser speckle contrast imaging (LSCI) image from the raw laser speckle image; obtaining a white light image of tissue in the target area of ​​the subject, the white light image capturing anatomical structures; spatially registering the LSCI image and the white light image with respect to one another; overlaying the spatially registered LSCI image and the white light image; The system stores instructions that cause the system to generate display data that continuously depicts the blood flow overlaying the tissue, the display data including the spatially registered LSCI image and a white light image.

10. 10. The system of claim 9, wherein the system is a surgical microscope, an endoscope, an exoscope, a robotic surgical platform, a standalone imaging system, or a system dedicated to blood flow imaging.

11. 11. The system of claim 9 or 10, wherein the display data continuously depicts the blood flow overlaying the tissue in real time during a surgical procedure.

12. The system of any one of claims 9 to 11, wherein the instructions further cause the computing device to present the display data on a user interface.

13. The system of any one of claims 9 to 12, wherein the tissue comprises a vasculature.

14. The system according to any one of claims 9 to 13, wherein the tissue is brain tissue.

15. The system of any one of claims 9 to 14, wherein spatially aligning the LSCI image and the white-light image with respect to each other includes creating a lookup table based on a spatial transformation used to align the LSCI image and the white-light image.

16. 16. The system of claim 15, wherein overlaying the spatially aligned LSCI image and white-light image includes mapping each pixel from the LSCI image to each pixel from the white-light image using the lookup table.

17. 17. The system of claim 16, wherein overlaying the spatially aligned LSCI image and white-light image further comprises contrast stretching the LSCI image, mapping the LSCI image to an n-bit color map, and performing a weighted sum with the white-light image.

18. 1. A method for visualizing blood flow, the method comprising the steps of: capturing a laser speckle contrast imaging (LSCI) image of blood flow within a subject using a first image capture device; capturing a white light image of the tissue using a second image capture device, the white light image capturing an anatomical structure; co-registering the LSCI image and the white light image; overlaying the co-registered LSCI image and the white light image; and displaying the overlaid, co-registered LSCI image and white light image via a user interface.

19. 20. The method of claim 18, wherein the tissue comprises vasculature or brain tissue.

20. 20. The method of claim 18 or 19, wherein co-registering the LSCI image and the white-light image with respect to one another comprises creating a look-up table based on a spatial transformation used to register the LSCI image and the white-light image.

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