Determining blood flow using laser speckle imaging

Abstract

In some examples, a system includes processing circuitry configured to generate a laser speckle contrast signal based on a received signal indicative of detected light scattered by tissue from a coherent light source. The processing circuitry may also determine a flow value from the laser speckle contrast signal and a waveform metric from the laser speckle contrast signal. Based on the flow value and the waveform metric, the processing circuitry may determine a blood flow metric for the tissue and output a representation of the blood flow metric.

Description

[0001] This application claims the benefit of U.S. Provisional Patent Application No. 62 / 948,506, filed on December 16, 2019, and entitled “Algorithms for the Analysis of Transmission Laser Speckle Imaging Information,” which is incorporated herein by reference in its entirety. Technical Field

[0002] The present disclosure relates to blood flow monitoring. Background Art

[0003] Various technologies can be used to monitor aspects of blood flow. For example, photoplethysmography (PPG) is an optical technique used to assess changes in blood volume in arteries during the cardiac cycle. Following each systole and diastole phase of the heart, the arteries are thought to undergo subtle volume expansion and contraction, respectively, which alters the light absorption signal measured by PPG. In this way, PPG can provide a "waveform" of the cardiac cycle, which can be used to assess a subject's vital signs, such as heart rate and oxygen saturation. As another example, laser speckle imaging (LSI) is an optical technique used to measure blood flow. Summary of the Invention

[0004] This disclosure describes devices, systems, and techniques for determining blood flow metrics using laser speckle imaging (LSI). A system can generate a laser speckle imaging signal representing blood flow within a tissue region of a patient, such as a digit (e.g., a finger or toe) or a limb. The laser speckle imaging signal can change with pulsatile flow during the patient's cardiac cycle, thereby generating a blood flow waveform over time. In some examples, the system is configured to analyze individual components of one or more waveforms of the laser speckle imaging signal, such as flow values and waveform shapes, to determine a blood flow metric representing the blood flow state of the tissue. The system can output the blood flow metric and / or related diagnostic metrics for use by another device and / or for display to a user.

[0005] In one example, the system includes processing circuitry configured to: generate a laser speckle contrast signal based on a received signal indicative of detected light scattered by tissue of a subject and from a coherent light source; determine a flow value based on the laser speckle contrast signal; determine a waveform metric based on the laser speckle contrast signal; determine a blood flow metric for the tissue based on the flow value and the waveform metric; and output a representation of the blood flow metric.

[0006] In another example, a method includes generating, by processing circuitry, a laser speckle contrast signal based on a received signal indicative of detected light scattered by tissue of a subject and from a coherent light source; determining, by the processing circuitry, a flow value based on the laser speckle contrast signal; determining, by the processing circuitry, a waveform metric based on the laser speckle contrast signal; determining, by the processing circuitry, a blood flow metric of the tissue based on the flow value and the waveform metric; and outputting, by the processing circuitry, a representation of the blood flow metric.

[0007] In another example, a non-transitory computer-readable medium includes instructions that, when executed, cause a processing circuit to: generate a laser speckle contrast signal based on a received signal indicative of detected light scattered by tissue of a subject and from a coherent light source; determine a flow value from the laser speckle contrast signal; determine a waveform metric from the laser speckle contrast signal; determine a blood flow metric for the tissue based on the flow value and the waveform metric; and output a representation of the blood flow metric.

[0008] The details of one or more examples are set forth in the accompanying drawings and the description below. Other features, objects, and advantages will be apparent from the description and drawings, and from the claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 is a conceptual block diagram illustrating an exemplary blood flow detection apparatus.

[0010] Figure 2 is a conceptual block diagram illustrating an exemplary blood flow detection apparatus configured to monitor the blood flow status of at least a portion of a patient.

[0011] Figure 3 Illustrating exemplary waveforms of blood flow detected using laser speckle imaging technology.

[0012] Figure 4 is a graph illustrating exemplary waveforms and blood flow values corresponding to various blood flow states of a patient.

[0013] Figure 5 is a diagram illustrating determination of an exemplary blood flow state based on exemplary blood flow velocity and waveform metrics.

[0014] Figure 6 is a flow chart illustrating an exemplary technique for determining a patient's blood flow status using waveforms determined using laser speckle imaging techniques. DETAILED DESCRIPTION

[0015] The present disclosure describes devices, systems, and techniques for determining a subject's blood flow status using multiple aspects of laser speckle imaging waveforms. Photoplethysmography (PPG) is an optical technique used to assess changes in blood volume in arteries during the cardiac cycle. After each systolic and diastolic phase of the heart, the arteries are thought to undergo subtle volume expansion and contraction, respectively, which alters the light absorption signal measured by the PPG. In this way, PPG can provide a "waveform" of the cardiac cycle that can be used to assess a subject's vital signs, such as heart rate and oxygen saturation. PPG is a useful technique, but may have some disadvantages due to its reliance on volume changes alone. Some conditions that may cause errors in the PPG signal include one or more of low blood perfusion, low cardiac output, hard / sclerotic arteries, vasoconstriction, hypoperfusion, vascular disease, arterial occlusion, or a combination thereof.

[0016] Laser speckle imaging (LSI) is an optical technique for measuring blood flow and can also provide cardiac waveforms for vital sign monitoring. LSI differs from PPG in several key ways and has the potential to improve medical diagnostics and monitoring beyond what is possible with PPG. Unlike PPG, LSI is sensitive to the motion of light scatterers, such as red blood cells, rather than changes in light absorption. Therefore, processing of LSI data quantifies not only the movement of blood but also the absorption changes associated with volume fluctuations of blood within the tissue of interest, as performed by PPG. For this reason at least, LSI does not share many of the drawbacks of PPG and can potentially provide reliable measurements across all perfusion and vascular tone states of a subject. Furthermore, LSI measurements inherently require the collection of PPG data, as PPG information can be inferred from LSI data, but LSI information cannot be obtained from a setup specifically designed for PPG measurements. Consequently, LSI devices can provide more reliable patient data than using a PPG sensor alone.

[0017] In this way, data obtained through PPG and LSI can appear somewhat similar in terms of extractable information (e.g., heart rate). However, the fact that LSI measures different parameters than PPG potentially enables more accurate diagnosis and monitoring of a subject's physiological state, where blood flow measurements can prove advantageous. Thus, systems that utilize LSI to quantify blood flow and hemodynamics can provide valuable information on a wide range of medical conditions and scenarios, from disease diagnosis to surgical procedures to training and rehabilitation.

[0018] The disclosed devices, systems, and techniques enable the determination of blood flow metrics in tissue of a subject (also referred to herein as a patient) based on multiple features of laser speckle analysis (LSI) waveforms. Laser speckle analysis performed using transmitted, coherent light offers significant advantages over coherent interference techniques using reflected light or reflection-based LSI, such as laser Doppler. When LSI is performed on a subject's digit (e.g., finger or toe), the detected light is captured as coherent illumination on opposite sides of the digit, and thus interacts with the entire tissue volume (e.g., the digit volume). This can provide an improved measure of overall blood flow and a stronger signal (e.g., greater amplitude and / or enhanced signal-to-noise ratio) compared to using reflected light. This contrasts with reflectance techniques, which typically interrogate the top 1-2 millimeters (mm) of tissue and are therefore only sensitive to skin perfusion. The highly diffuse nature of the transmitted light also ensures measurement repeatability, as each value is averaged across the entire digit volume and is less susceptible to variation based on the specific location of the illumination source and detection elements. Instead, the light analyzed by the reflectivity technique has been shown to be significantly less diffuse, and there is significant variability in the measured blood flow velocity, depending on where the probe has been placed. This variability reduces clinical utility.

[0019] In the examples described herein, the system's processing circuitry is configured to obtain a raw signal generated by a photodetector that detects light scattered from a light source (e.g., a coherent light source) through tissue of a subject. The processing circuitry can then generate an LSI signal from this raw signal to provide a representation of blood flow in the tissue over time. For example, the LSI signal can include a waveform that at least partially reflects changes in blood flow due to the pulsatile nature (or lack thereof) of the cardiac cycle. The processing circuitry can determine multiple characteristics of the LSI signal, such as flow (e.g., blood flow) and waveform metrics for one or more waveforms of the LSI signal. For example, flow can be a measured flow rate (e.g., a flow value that can be an average or median flow rate determined over a period of time and represented by the amplitude of the waveform) for some or all of the one or more waveforms. The waveform metrics can characterize the shape of the one or more waveforms, such as one or more of the following: the number of peaks present within a waveform representing a single cardiac cycle, one or more slopes of the waveform, a power spectrum of the waveform's frequency, a shape identified by one of multiple waveform templates, or any other such characteristics or combinations thereof.

[0020] Based on multiple features of the waveform, the processing circuitry can determine a blood flow metric. Because this blood flow metric considers features of the waveform in addition to just the overall flow rate, the blood flow metric can indicate different blood flow states in tissue that can distinguish between healthy and impaired blood flow. This blood flow metric can provide more accurate information about the patient's impaired circulation in at least the sampled tissue. The system can transmit the blood flow metric to another device as feedback for a treatment or surgical procedure, or the system can display the blood flow metric for use by a clinician in diagnosing or treating the patient. In some examples, the system can provide a diagnostic metric that indicates whether the patient is likely to have a blood flow abnormality, such as peripheral vascular disease (PVD), affecting the sampled tissue.

[0021] Processing circuitry implementing the devices, systems, and techniques of the present disclosure can offer advantages over other systems. For example, LSI signals can reflect light through the entire volume of tissue, not just the surface tissue used to reflect the signal. This increased volume can provide an improved, comprehensive view of blood flow in tissue, the toe, and the patient as a whole. Furthermore, using waveform metrics of LSI signals to determine blood flow metrics enables blood flow metrics to be sensitive to diseased or damaged vascular structures or other issues that manifest as improper blood flow distribution. For example, blood flow metrics based on LSI signals can distinguish between normal vasoconstriction (e.g., a patient simply suffering from a cold) and pathologically reduced tissue perfusion, where both conditions result in reduced blood flow but have distinct blood flow waveforms. For example, a healthy patient with vasoconstriction may exhibit a multiphasic waveform with a reduced mean blood flow velocity, while a patient with peripheral arterial disease may exhibit a decaying, monophasic waveform with a similarly reduced mean blood flow velocity. In this way, blood flow metrics generated as described herein can enable more accurate detection of abnormalities in a patient's circulation or other conditions using non-invasive techniques.

[0022] Figure 1 is a conceptual block diagram illustrating an example blood flow detection device 100. The blood flow detection device 100 includes processing circuitry 110, memory 120, a user interface 130 including a display 132, lighting circuitry 140, a light source 142, light detection circuitry 150, and a light sensor 152. In some examples, the blood flow detection device 100 is configured to determine and display a blood flow metric of a patient or a patient's tissue, for example, for diagnosis, during a medical procedure, or for longer-term monitoring. A clinician can receive information about the patient's blood flow metric via the display 132 (or another output, such as an audio circuit configured to generate sound) and, based on the blood flow metric, diagnose or adjust treatment or therapy for the patient.

[0023] The processing circuitry 110 and other processors, processing circuits, controllers, control circuits, and the like described herein may include one or more processors. The processing circuitry 110 may include any combination of integrated circuits, discrete logic circuits, analog circuits, such as one or more microprocessors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), or field-programmable gate arrays (FPGAs). In some examples, the processing circuitry 110 may include multiple components (e.g., any combination of one or more microprocessors, one or more DSPs, one or more ASICs, or one or more FPGAs) as well as other discrete or integrated logic circuits, and / or analog circuits.

[0024] For example, the memory 120 can be configured to store data related to blood flow metrics, such as raw signals from the light detection circuit 150, LSI signals, or other information. In some examples, the memory 120 can be configured to store information regarding other sensed information from other sensors or devices, which in some examples can be displayed along with the blood flow metrics. The memory 120 can also be configured to store information used to determine blood flow metrics, diagnostic metrics, characteristics of LSI signals (e.g., flow rate and waveform metrics), control the lighting circuit 140 and the light detection circuit 150, control the user interface 130, or any other such information related to the operation of the blood flow detection device 100.

[0025] In some examples, memory 120 stores program instructions that may include one or more program modules that can be executed by processing circuit 110. When executed by processing circuit 110, such program instructions cause processing circuit 110 to provide the functionality attributed thereto herein. The program instructions may be embodied in software, firmware, and / or RAMware. Memory 120 may include any volatile, non-volatile, magnetic, optical, or electronic media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), electrically erasable programmable ROM (EEPROM), flash memory, or any other digital media.

[0026] User interface 130 and / or display 132 may be configured to present information to a user (e.g., a clinician). For example, user interface 130 and / or display 132 may be configured to present a graphical user interface to the user, wherein each graphical user interface may include an indication of the values of one or more physiological parameters of the subject. For example, processing circuitry 110 may be configured to present, via display 132, blood pressure values, physiological parameter values, and an indication of a blood flow metric over time or the patient's disease state. In some examples, if processing circuitry 110 determines that the patient's blood flow metric indicates a reduced condition, processing circuitry 110 may present, via display 132, a notification (e.g., an alarm) indicating reduced blood flow indicative of the patient's condition.

[0027] The user interface 130 and / or display 132 may include a monitor, a cathode ray tube display, a flat-panel display such as a liquid crystal (LCD) display, a plasma display, or a light-emitting diode (LED) display, a personal digital assistant, a mobile phone, a tablet computer, a laptop computer, any other suitable display device, or any combination thereof. The user interface 130 may also include means for projecting audio to the user, such as audio generation circuitry and one or more speakers. The processing circuit 110 may be configured to present a visual, auditory, or somatosensory notification (e.g., an alarm signal) indicating a patient's blood flow metric via the user interface 130. The user interface 130 may include or be part of any suitable device for conveying such information, including a computer workstation, server, desktop computer, notebook computer, laptop computer, handheld computer, mobile device, and the like. In some examples, the processing circuit 110 and the user interface 130 may be part of the same device or supported within a single housing (e.g., a computer or monitor). In other examples, a user interface separate from the blood flow detection device 100 may be configured to present information regarding the blood flow metric to the user, where such information may be provided to the user interface via the processing circuit 110.

[0028] Light emitting circuit 140 is configured to control light source 142 to generate light. For example, light source 142 may include a coherent light source (e.g., a laser source) that emits light having substantially the same frequency. Light sensor 152 includes one or more photosensitive structures (e.g., an optoelectronic device such as a charge-coupled device (CCD) or some other sensor) configured to convert light energy into an electrical signal. Light detection circuit 150 may be powered and receive an electrical signal from light sensor 152. Light detection circuit 150 may then generate a raw signal representing the light detected by light sensor 152. In some examples, light detection circuit 150 may perform initial processing and / or analog-to-digital conversion of the electrical signal from light sensor 152 to generate a raw signal that can be used by processing circuit 110 to generate an LSI signal. In other examples, light detection circuit 150 may include processing circuitry configured to generate the LSI signal.

[0029] In operation, light source 142 and light sensor 152 are each positioned at different locations on a portion of a patient's body, such that light sensor 152 can detect light scattered from light emitted by light source 142. For example, light source 142 and light sensor 152 can be positioned on opposing surfaces of a digit (e.g., a finger or toe). Light source 142 and light sensor 152 can be physically separated from each other and can be positioned separately on the patient. As another example, light source 142 and light sensor 152 can be part of the same sensor or supported by a single sensor housing. For example, light source 142 and light sensor 152 can be part of an integrated sensor system configured to non-invasively measure blood flow in tissue between light source 142 and light sensor 152.

[0030] Despite Figure 1 An example blood flow detection device 100 is shown in FIG. Figure 1 The components shown in the are not intended to be limiting. In other examples, additional or alternative components and / or implementations may be used.

[0031] Processing circuitry 110 is configured to generate an LSI signal using the raw signal from light detection circuitry 150. The LSI system provides laser speckle contrast measurements over time. LSI is an imaging method used to create an image-based representation of blood flow in tissue of interest. LSI uses coherent light to illuminate a portion of a patient's body (e.g., a finger or toe). The presence and movement of blood within the illuminated body part interacts with the light traveling through the tissue. Consequently, the coherent laser light is scattered within the sample of interest. These scattering events result in varying path lengths between photons. The result is a speckle pattern, typically imaged using a light sensor 152 (e.g., a photodetector), such as a camera, with a finite exposure time. If the scattering objects (e.g., blood cells) are in motion, the speckle pattern fluctuates over time and blurs during the camera exposure. The amount of blur is related to flow and is quantified using a parameter known as speckle contrast.

[0032] Various characteristics of the changes can be used to extract information about the presence and flow of blood. For example, such characteristics can include changes in detected light intensity and contrast within the observed light pattern, both of which are related to the movement of red blood cells. Analysis of the changes in intensity and contrast within the observed light pattern over time then provides dynamic and quantitative feedback about changes in the patient's peripheral blood flow and tissue perfusion, from which informed inferences about the patient's physical state can be drawn. Because this information has been demonstrated to be accessible even when a pulse oximeter (which can be used to analyze a patient's hemodynamics) is no longer fully functional, LSI provides clinicians with information that can be used to proactively treat critically ill patients.

[0033] In some examples, the position of coherent light source 142 and light sensor 152 is coupled to the movement of the tissue sample. Thus, patient movement causes light sensor 152 and coherent light source 142 to move similarly. Consequently, the field of view of the tissue sample does not change as the tissue sample moves. Coupling of the light sensor, coherent light source, and tissue sample can be facilitated by shortening the distance between light sensor 152 and coherent light source 142. The distance between light sensor 152 and coherent light source 142 can be shortened by reducing the field of view of light sensor 152 and forgoing the formation of a focused image, thereby eliminating the need for a focal length. In other examples, an unfocused image can be preferably received by light sensor 152. In some examples, coherent light source 142 and light sensor 152 are contained in a compact housing, such as a clamshell. The clamshell can be configured to accommodate a patient's tissue sample, such as a finger or toe.

[0034] In some examples, light source 142 is configured to emit coherent light via an optical fiber coupled to a laser source. In these examples, the optical fiber can emit a portion of the coherent light emitted by coherent light source 142. The position of coherent light source 142 can be coupled to patient movement by fixing the position of coherent light source 142 and / or the optical fiber relative to the tissue sample. As used herein, "coherent light source" can include coherent light emitted via an optical fiber and / or the coherent light source itself.

[0035] In some examples, the blood flow detection device 100 does not require a focused image to be formed on the light sensor 152 of the light sensor. Therefore, some examples of the light source 142 and / or light sensor 152 can forgo lenses, thereby reducing cost and size compared to other LSI systems. Additionally, while other LSI systems require a light sensor with image-forming optics, some example blood flow detection devices 100 described herein can use an inexpensive light sensor without image-forming optics, such as a photodiode.

[0036] In some examples, light sensor 152 may include an opaque sheet with an aperture near light sensor 152. The opaque sheet increases the size of the speckle incident on the light sensor, thereby avoiding undersampling issues traditionally associated with unfocused images and small spot sizes. Furthermore, in some examples, processing circuitry 110 determines perfusion using the values of all pixels in the image rather than a small sliding window. This eliminates artifacts from streaking images. Furthermore, the fact that the perfusion direction of the fingertip is random further aids in artifact removal.

[0037] As generally described herein, light source 142 and light sensor 152 are used in a transmission geometry. In transmission geometry, light source 142 and light sensor 152 are positioned on opposite sides of the tissue of interest. Thus, the light sensor is configured to receive transmitted light that has propagated through the entire thickness of the tissue. As mentioned, because blood has a very high forward scattering capacity, using transmitted light rather than backscattered light can provide a higher signal-to-noise ratio than a reflection geometry. Furthermore, the transmission geometry enables acquisition of signals from all or most blood vessels within the tissue of interest.

[0038] Furthermore, the transmission geometry enables light source 142 to be placed very close to the sample of interest. In some examples, light source 142 contacts the surface of the sample of interest. In LSI, light is directed away from the field of view of the tissue in order to capture an accurate image. Otherwise, the light might obscure the tissue area being imaged. However, because in some examples, light is transmitted through the sample in the transmission geometry rather than reflected back, the transmission geometry enables light source 142 to be placed very close to the sample (the tissue area being imaged).

[0039] Light source 142 can be configured to transmit light through the entire thickness of the toe. Similarly, light sensor 152 can be configured to receive the transmitted light after it has passed through the entire thickness of the finger. In some examples, light source 142 can be selected to maximize light transmission through the tissue of interest. For example, light source 142 can be a laser with a wavelength in the range of 300 nm to 1100 nm. Light source 142 can further be selected to maximize speckle contrast at light sensor 152. According to some examples, light source 142 can be a single-mode laser diode or a fiber-coupled helium-neon laser. In some examples, light source 142 includes an adjustable power output to provide an appropriate signal at light sensor 152. In some examples, light sensor 152 can be a camera with or without image-forming optics, such as a charge-coupled device (CCD) camera or a complementary metal-oxide semiconductor (CMOS) camera. Light sensor 152 can also be a camera without image-forming optics, such as a photodiode.

[0040] In some examples, the blood flow detection device 100 may include one or more polarizers configured to convert light before it reaches the light sensor 152 and after it passes through the patient's toe. Placing a polarizer before the light enters the toe to convert the light is less effective because light entering scattering tissue (e.g., the toe) becomes depolarized as it scatters. An optical filter may also be included to filter out light that does not originate from the light source 142.

[0041] Processing circuitry 110 can use the speckle pattern detected by light sensor 152 to determine blood flow (e.g., perfusion) within a tissue sample. For example, light sensor 152 generates an electrical signal representing light intensity, and light detection circuitry 150 generates a raw signal representing captured frames associated with different detected light intensity values. The light intensity values captured by light sensor 152 indicate coherent light scattered by red blood cells as they transmit the toe. The coherent light that transmits the toe renders an unfocused image captured by light sensor 152. Light sensor 152 is configured to adequately sample the speckle pattern regardless of the unfocused image. In some examples, the speckle size is increased by using an opaque sheet with an aperture that changes the numerical aperture of light sensor 152.

[0042] Based on the light intensity values associated with the unfocused image provided by the raw signal, the processing circuit 110 can calculate speckle contrast spatially, temporally, or spatiotemporally (a hybrid of spatial and temporal). To calculate speckle contrast spatially, the processing circuit 110 can utilize a set of pixels at different spatial locations within the same frame. To calculate speckle contrast temporally, the processing circuit 110 can utilize pixels from the same spatial location across a sequence of frames captured at different times. The processing circuit 110 can also calculate speckle using a spatiotemporal method, which is a hybrid of the temporal and spatial methods. In any case, the processing circuit 110 can calculate speckle contrast.

[0043] In some examples, processing circuit 110 is configured to determine speckle contrast by using at least the following equation:

[0044] K =σ / 〈 I 〉, (1)

[0045] where K is the contrast, σ is the standard deviation of a set of pixel values, and is the average of a set of pixel values.

[0046] Typically, acquisition of the LSI signal does not require a focused image (e.g., the light sensor 152 receives an unfocused image). Therefore, in some examples, the processing circuit 110 can temporally determine speckle contrast from only one pixel position. Therefore, in some examples, the blood flow detection device 100 can use a photodiode as the light sensor 152. Photodiodes are less expensive than cameras with image forming optics. It is believed that in some cases, the accuracy of perfusion measurements acquired by utilizing only one pixel position can be comparable to the accuracy of perfusion measurements acquired by utilizing multiple pixel positions. In addition, the accuracy of perfusion measurements acquired by utilizing pixels from an unfocused image is comparable to the accuracy of perfusion measurements acquired by laser Doppler. In other examples, the blood flow detection device 100 can utilize a focused image to generate the LSI signal and determine a blood flow metric, as described herein.

[0047] In some examples, in addition to or in lieu of other techniques described herein, the processing circuitry 110 is configured to determine speckle contrast using a standard deviation across the entire image generated by the light sensor 152 and the light detection circuitry 150. Doing so can reduce or even eliminate artifacts in "smeared images" that might otherwise result from using an unfocused image. If the object being imaged moves during imaging, the out-of-focus speckle pattern may shift. These artifacts can propagate into the speckle contrast image, resulting in a "smeared image." In-focus speckle does not shift, and thus, the speckle contrast image does not exhibit a streaked image. By calculating the standard deviation across the entire image, image streaking can be eliminated in the unfocused image. Furthermore, image streaking is eliminated due to the random motion direction of blood perfusion.

[0048] After calculating the speckle contrast value K, the processing circuit 110 may calculate the perfusion as:

[0049] Perfusion = 1 / K 2 (2)

[0050] Other factors including camera exposure time, camera noise, optical absorption, and the presence of static scatterers may affect this calculation.

[0051] Using equation (2), processing circuitry 110 can determine a measure of perfusion based on the calculated speckle contrast value. For example, the LSI signal can indicate a perfusion measure that changes over time. In addition, processing circuitry 110 can analyze the LSI signal to determine several characteristics, such as flow and waveform measures. Processing circuitry 110 then determines a blood flow measure based on, for example, the flow and waveform measures that provide a more complete representation of how blood moves within the tissue of interest.

[0052] As described herein, the blood flow detection device 100 may include processing circuitry 110 configured to generate a laser speckle contrast signal based on a received signal indicating detected light from the light detection circuitry 150, where the detected light is scattered by tissue from a coherent light source (e.g., light source 142). The processing circuitry 110 may then determine a flow rate value based on the laser speckle contrast signal and a waveform metric based on the laser speckle contrast signal. The processing circuitry 110 may also then determine a blood flow metric for the tissue based on the flow rate value and the waveform metric. The processing circuitry 110 may then output a representation of the blood flow metric, such as for presentation by a display 132 or another output mechanism (e.g., an audio generation circuit).

[0053] A waveform metric can represent one or more waveforms contained in an LSI signal. In one example, for example, a waveform is a pulse signal corresponding to a cardiac cycle. Because the manner in which blood pulses through the vasculature can reflect certain conditions, this waveform can indicate such conditions. In some examples, processing circuitry 110 can be configured to determine the waveform metric based at least on a laser speckle contrast signal comprising one of a triphasic waveform, a biphasic waveform, a monophasic waveform, or an aphasic waveform. In other words, the number of phases or peaks within a waveform can indicate the normality of blood flow. Generally speaking, a greater number of phases or peaks within a waveform indicates normal pulse flow. Additionally, the system can determine the amplitude of one or more peaks (e.g., relative to a baseline or valley between peaks) and determine the waveform metric based at least in part on the determined amplitude. For example, a higher amplitude can correspond to a higher waveform metric.

[0054] In another example, the processing circuit 110 is configured to determine a waveform metric by comparing at least the waveform shape of the laser speckle contrast signal to a plurality of different waveform templates and selecting a waveform metric corresponding to one of the plurality of different waveform templates that best fits the waveform shape. For example, the memory 120 may store a plurality of waveform templates corresponding to flow rates at different degradation levels, and the processing circuit 110 may select the waveform template and corresponding numerical metric that best fits the detected waveform. The processing circuit 110 may determine the flow rate of the waveform, such as the total flow rate of the waveform or the flow rate from a portion of the waveform (e.g., the area under a curve of blood flow velocity over time for the waveform), or may determine some other metric of blood flow for the waveform.

[0055] In some examples, processing circuitry 110 may perform waveform decomposition (e.g., using one or more waveforms from the LSI signal) using any one or more different methods to classify the waveforms into waveform metrics. Exemplary classification techniques for determining waveform metrics may include one or more waveform quality metrics (e.g., based on template matching), diastolic flow values, systolic flow values, peak-to-valley amplitudes (e.g., the amplitude between adjacent peaks and valleys or between systolic and diastolic portions), mean flow, standard deviation of flow, slope (e.g., difference or variation) of flow (e.g., the maximum value of the derivative of the LSI signal), the ratio of the maximum slope of the LSI signal (e.g., the systolic jet slope) to one of the systolic flow, diastolic flow, or mean flow, and / or frequency domain values. The examiner frequency domain value may comprise one or more coefficients of a Fourier or wavelet transform. Processing circuitry 110 may determine a waveform metric or blood flow metric based on any one or a combination of these values. In one example, processing circuitry 110 may determine a waveform metric as the ratio of the maximum slope of one or more waveforms (e.g., the maximum derivative of the LSI signal) to the systolic flow value of one or more waveforms.

[0056] Processing circuitry 110 may then determine an overflow metric based on several features from the LSI signal, such as flow values and waveform metrics. In some examples, processing circuitry 110 is configured to determine a blood flow metric based on a total score of the flow values and waveform metrics. Lower flow values and lower waveform metrics may indicate decreased blood flow and vascular function, whereas a higher total score of flow and waveform metrics may indicate better overall vascular function and perfusion. In some examples, processing circuitry 110 may determine that the blood flow metric is insufficient if the total score falls below a predetermined threshold. In other examples, processing circuitry 110 is configured to determine a blood flow metric from a lookup table that defines a relationship between flow values and waveform metrics. For example, the lookup table may identify a combination of flow values and waveform metrics that yields a specific blood flow metric. As another example, processing circuitry 110 may determine a blood flow metric based on a formula that may or may not weight the flow values and / or waveform metrics.

[0057] In some examples, the processing circuit 110 can determine both the flow value and the waveform metric based on the same waveform or the same plurality of waveforms. For example, the processing circuit 110 can be configured to determine the flow value by at least determining an average flow rate across a plurality of waveforms of the laser speckle contrast signal, and to determine the waveform metric by at least determining an average waveform metric across the same plurality of waveforms of the laser speckle contrast signal. Alternatively, the processing circuit 110 can determine the flow value and the waveform metric based on different waveforms or at least partially different sets of waveforms.

[0058] Blood flow metrics can represent a patient's perfusion state and vascular function state. In some examples, processing circuitry 110 can determine diagnostic metrics for one or more conditions based on the blood flow metrics and / or changes in the blood flow metrics over time. For example, processing circuitry 110 can be configured to determine a peripheral vascular disease (PVD) metric based on a tissue blood flow metric, wherein the PVD metric indicates a quantitative or qualitative indication of the patient's peripheral vascular disease state. Processing circuitry 110 can control user interface 130 and / or display 132 to present representations of blood flow metrics and / or diagnostic metrics. These metrics can be displayed as a single value, a graph of the metric over time, or any other graphical, numerical, or textual representation. User interface 130 can present these metrics via display 132 in real time or near real time (e.g., with a delay of less than one second).

[0059] Figure 2 is a conceptual block diagram illustrating an exemplary laser speckle imaging (LSI) apparatus 200 configured to monitor blood flow status of at least a portion of a patient. Figure 2 In the example shown, the LSI device 200 is coupled to the sensing device 250, and the LSI device and the sensing device can be collectively referred to as a blood flow detection system that generates and processes physiological signals of a subject. In some examples, the sensing device 250 and the LSI device 200 are part of a patient monitoring device. Figure 2 As shown, the LSI device 200 includes a back-end processing circuit 214, a user interface 230, an optical drive circuit 240, a front-end processing circuit 216, a control circuit 245, and a communication interface 290. The LSI device 200 is coupled to the sensing device 250 in a communication manner. Figure 1 An example of a blood flow detection device 100 is shown. In some examples, the LSI device 200 may also include other physiological sensors.

[0060] exist Figure 2 In the example shown, sensing device 250 includes at least one light source 260 and at least one detector 262 (e.g., a light sensor). In some examples, sensing device 250 may include more than two detectors. Light source 260 may be configured to emit a photon signal comprising coherent light (e.g., light of one wavelength) into tissue of a subject. For example, light source 260 may include a laser source configured to emit light into tissue of a subject to generate detectable light scatter. Light source 260 may include any number of light sources having any suitable characteristics. In examples where a sensor array is used instead of sensing device 250, each sensing device may be configured to emit the same wavelength.

[0061] Detector 262 can be selected to be specifically sensitive to a selected target energy spectrum of light source 260. In some examples, detector 262 can be configured to detect the intensity of light that has been scattered by tissue, such as Figure 1 In some examples, a detector array may be used, and each detector in the array may be configured to detect the intensity of a single wavelength. In operation, light may enter detector 262 after passing through tissue of a subject (including skin, bone, and other tissue). Detector 262 may convert the intensity of the received light into an electrical signal. The light intensity may be directly related to light scattering from light scattering particles within the tissue, thereby producing speckle contrast, as described with respect to Figure 1 Discussed.

[0062] After converting the received light into electrical signals, detector 262 can transmit the detection signals to LSI device 200, which can process the detection signals and determine physiological parameters such as blood flow measurements. In some examples, one or more of the detection signals are pre-processed by sensing device 250 before being transmitted to LSI device 200.

[0063] The control circuit 245 can be coupled to the light driver circuit 240, the front-end processing circuit 216, and the back-end processing circuit 214, and can be configured to control the operation of these components. In some examples, the control circuit 245 is configured to provide a timing control signal to coordinate its operation. For example, the light driver circuit 240 can generate one or more light drive signals based on the timing control signal provided by the control circuit 245, and the light drive signal can be used to turn on and off the light source 260. The front-end processing circuit 216 can use the timing control signal to operate synchronously with the light driver circuit 240. For example, the front-end processing circuit 216 can synchronize the operation of the analog-to-digital converter and the demultiplexer with the light drive signal based on the timing control signal. In addition, the back-end processing circuit 214 can use the timing control signal to coordinate its operation with the front-end processing circuit 216.

[0064] As described above, the light driver circuit 240 can be configured to generate a light drive signal that is provided to the light source 260 of the sensing device 250. The light drive signal can, for example, control the intensity of the light source 260 and control the timing of when the light source 260 is turned on and off. In some examples, the light driver circuit 240 provides one or more light drive signals to the light source 260. In examples where the light source 260 is configured to emit two or more wavelengths of light, the light drive signal can be configured to control the operation of each wavelength of light. The light drive signal can include a single signal, or can include multiple signals (e.g., one signal for each wavelength of light).

[0065] Front-end processing circuitry 216 can perform any suitable analog conditioning on the detector signal. This conditioning can include any type of filtering (e.g., low-pass, high-pass, band-pass, notch, or any other suitable filtering), amplification, performing operations on the received signal (e.g., taking derivatives, averaging), performing any other suitable signal conditioning (e.g., converting a current signal into a voltage signal), or any combination thereof. The conditioned analog signal can be processed by an analog-to-digital converter of circuitry 216, which can convert the conditioned analog signal into a digital signal. Front-end processing circuitry 216 can operate on the analog or digital form of the detector signal to separate different signal components. Front-end processing circuitry 216 can also perform any suitable digital conditioning on the detector signal, such as low-pass, high-pass, band-pass, notch, averaging, or any other suitable filtering, amplification, performing operations on the signal, performing any other suitable digital conditioning, or any combination thereof. Front-end processing circuitry 216 can reduce the number of samples in the digital detector signal. In some examples, front-end processing circuitry 216 can also remove dark or ambient effects from the received signal.

[0066] Backend processing circuitry 214 may include processing circuitry 210 and memory 220. Processing circuitry 210 may include an assembly of analog or digital electronic components and may be configured to execute software, which may include an operating system and one or more application programs, as described herein with respect to, for example, Figure 1 The processing circuitry 210 may receive and further process the physiological signals received from the front-end processing circuitry 216. For example, the processing circuitry 210 may determine one or more physiological parameter values based on the received physiological signals. For example, the processing circuitry 210 may calculate one or more blood flow metrics and / or diagnostic metrics based on the LSI signals.

[0067] Processing circuitry 210 may perform any suitable signal processing of the signal, such as any suitable bandpass filtering, adaptive filtering, closed-loop filtering, any other suitable filtering, and / or any combination thereof. Processing circuitry 210 may also receive input signals from additional sources not shown. For example, processing circuitry 210 may receive input signals from user interface 230 containing information about a treatment being provided to the subject. Processing circuitry 210 may use the additional input signals in any of the determinations or operations it performs based on backend processing circuitry 214 or the regional oximetry device.

[0068] Processing circuit 210 is an example of processing circuit 110 and is configured to perform the techniques of this disclosure. For example, processing circuit 210 is configured to receive a signal indicative of speckle contrast from patient tissue and determine a blood flow metric or other value indicative of perfusion and vascular function.

[0069] Memory 220 may comprise any suitable computer-readable medium capable of storing information interpretable by processing circuitry 210. In some examples, memory 220 may store light source and detection functionality, signal processing instructions, LSI signal processing instructions, blood flow metric calculation instructions, generated patient data, etc. Backend processing circuitry 214 may be communicatively coupled to user interface 230 and communication interface 290.

[0070] In some examples, the user interface 230 may include an input device 234, a display 232, and a speaker 236. The user interface 230 is Figure 1 The example of the user interface 130 is shown, and the display 232 is Figure 1 An example of a display 132 is shown. The user interface 230 can include, for example, any suitable device, such as one or more medical devices (e.g., a medical monitor displaying various physiological parameters, a medical alarm, or any other suitable medical device that displays physiological parameters or uses the output of the backend processing 214), one or more display devices (e.g., a monitor, a personal digital assistant (PDA), a cell phone, a tablet computer, a clinician workstation, any other suitable display device, or any combination thereof), one or more audio devices, one or more storage devices, one or more printing devices, any other suitable output device, or any combination thereof.

[0071] Input device 234 can include one or more of any type of user input device, such as a keyboard, a mouse, a touch screen, buttons, switches, a microphone, a joystick, a touchpad, or any other suitable input device or combination of input devices. In other examples, input device 234 can be a pressure-sensitive or presence-sensitive display included as part of display 232. Input device 234 can also receive input to select a model of sensing device 250. In some examples, processing circuitry 210 can determine a presentation type for display 232 based on user input received by input device 234. For example, processing circuitry 210 can be configured to present a graphical user interface via display 232.

[0072] Communication interface 290 may enable LSI device 200 to exchange information with other external devices or implanted devices. Communication interface 290 may include any suitable hardware, software, or both that allows LSI device 200 to communicate with an electronic circuit, device, network, server or other workstation, display, or any combination thereof.

[0073] The components of the LSI device 200 shown and described as separate components are shown and described for illustrative purposes only. In some examples, the functionality of some of the components can be combined into a single component. For example, the functionality of the front-end processing circuit 216 and the back-end processing circuit 214 can be combined in a single processor system. In addition, in some examples, the functionality of some of the components of the blood flow detection device shown and described herein can be divided into multiple components. For example, some or all of the functionality of the control circuit 245 can be performed in the front-end processing circuit 216, the back-end processing circuit 214, or both. In other examples, the functionality of one or more components can be performed in a different order or may not need to be performed. In some examples, all components of the blood flow detection device can be implemented in a processor circuit. In one example, the LSI device 200 includes a control circuit 245, which includes all of the functionality described herein with respect to the front-end processing circuit 216 and the back-end processing circuit 214.

[0074] Although transmission-based laser speckle imaging is generally described herein and can provide adequate blood flow readings within a tissue volume in most cases, reflectivity-based laser speckle imaging within an area can also provide valuable blood flow information. When the volume of tissue is too thick to allow light to adequately pass through, reflectivity-based laser speckle imaging, which still provides both blood flow characteristics and waveform features, can be employed by systems such as the blood flow detection device 100 or the LSI device 200. In this way, in some examples, the blood flow detection device 100, the LSI device 200, or another device can obtain an LSI signal from a reflectivity-based sensor configuration and generate a blood flow metric as described herein.

[0075] Figure 3 An example LSI signal 302 is shown representing blood flow detected using LSI technology. Figure 3 As shown in the example of , graph 300 includes an LSI signal 302 generated from detected scattered light passing through sample tissue. As described in U.S. Patent Application Publication No. 2013 / 0204112 to White et al., entitled "Perfusion Assessment Using Transmission Laser Speckle Imaging," which is incorporated herein by reference in its entirety, a transmission LSI can be used to measure blood flow. This transmission LSI can have practical uses in a medical setting. As described above, the LSI measures the motion of a light-scattering subject, and the resulting data can be interpreted to quantify blood flow. One capability conferred by the transmission LSI is the ability to longitudinally record changes in blood flow over time, for example in the LSI signal. In this way, the blood flow detection device 100 or the processing circuitry 110 of another device ( Figure 1 ) can accurately detect and record minute changes in blood flow that accompany the subject's heartbeat or other factors such as the cardiac cycle. When this data is analyzed over time, Figure 3 As shown, each change in blood flow due to a pressure wave from the contraction of the heart is generally referred to as a pulse waveform 304.

[0076] For example, changes in blood volume determined from PPG data differ from analyses of LSI data. Similarly, analysis of LSI data (e.g., related to pulse signals derived from pulsatile blood flow) has different implications than similar analysis performed on PPG data. Not all LSI signals are pulsed or oscillatory, such as during surgery requiring the use of a heart-lung machine, as such devices remove the pulsatile component of blood flow. However, these types of situations can also reduce or eliminate the need for appropriate blood flow monitoring using PPG technology.

[0077] An LSI signal acquired using a transmission LSI can be analyzed in a variety of ways. One non-limiting example provided herein relates to the analysis of one or more pulse waveforms derived using a transmission LSI, such as shown in LSI signal 302. These examples are not intended to be all-inclusive and are included for purposes of illustration only, not limitation.

[0078] LSI signal 302 may include features or characteristics that indicate how blood flows through the vasculature. For example, each of pulse waveforms 304 includes one or more distinct peaks, such as peak 306, peak 308, and peak 310. Such waveforms with three peaks may be referred to as triphasic waveforms. A waveform with two peaks may be referred to as a biphasic waveform, and a waveform with one peak may be referred to as a monophasic waveform. The presence, amplitude, slope, or other characteristics of peaks within a waveform may be used to determine waveform metrics that classify the waveform. Such classifications may indicate the magnitude of the functional quality and / or degradation of the waveform for blood flow through the vasculature. Additionally, the flow rate of a waveform may correspond to the flow rate measured over a period of time covering a portion or all of waveform 304, such as corresponding to peak 306 (e.g., the highest peak or first peak in time), distinct peaks, multiple peaks, the instantaneous flow rate corresponding to any one or more peaks or portions of waveform 304, or the area under the curve of waveform 304 for any portion of waveform 304. In this manner, the system can determine the flow for each waveform (e.g., waveform 304) based on the diastolic flow portion, the systolic flow portion, the average flow, or any other flow determination. The system can determine the flow value for waveform 304 as the average flow or median flow determined over a portion or all of one or more waveforms (e.g., waveform 304).

[0079] The pulse waveform analysis performed by processing circuitry 110 (or other processing circuitry) on transmitted LSI data, such as LSI signal 302, can include characteristics such as measurements of time delays between repetitive features, such as pulse waveform peak to pulse waveform peak, pulse waveform trough to pulse waveform trough, pulse waveform systolic peak to pulse waveform diastolic pressure foot, pulse waveform recurrent notch to pulse waveform recurrent notch, pulse waveform systolic peak to pulse waveform recurrent notch, pulse waveform diastolic pressure foot to pulse waveform recurrent notch, pulse waveform post-systolic oscillation to pulse waveform post-systolic oscillation, and / or half of the maximum pulse width. Processing circuitry 110 can use any one or a combination of these characteristics to generate a blood flow metric based on LSI signal 302.

[0080] In some examples, within a single pulse waveform (e.g., waveform 304) derived from the LSI signal 302, the processing circuit 110 may determine one or more of the following: pulse waveform amplitude (peak to valley), pulse waveform peak to pulse waveform recurrent notch amplitude, pulse waveform peak to pulse waveform secondary oscillation amplitude, pulse waveform width measured at any height (including the recurrent notch and half the peak to valley amplitude), area under the pulse waveform, pulse waveform systolic area, pulse waveform diastolic area, first derivative of the pulse waveform systolic slope, second derivative of the pulse waveform systolic slope, third derivative of the pulse waveform systolic slope, first derivative of the pulse waveform diastolic slope, second derivative of the pulse waveform diastolic slope, third derivative of the pulse waveform diastolic slope, pulse waveform linear regression slope, and / or changes in the prevalence of pulse waveform features (e.g., recurrent notches or post-systolic oscillations) due to physiological causes. Processing circuitry 110 may use any of these characteristics alone or in combination with other characteristics of LSI signal 302 to determine (eg, analyze) a waveform metric, and ultimately a blood flow metric, based on signal 302 .

[0081] In cases where more than one pulse waveform is derived from LSI signal 302, processing circuitry may determine waveform-to-waveform variability, such as, but not limited to, one or more of: systolic peak variability, diastolic peak variability, mean flow variability, amplitude variability, width variability, slope variability, and all other variability metrics between parameters calculated using a single waveform as mentioned in the previous paragraph. Processing circuitry 110 may use any of these features alone or in combination with other features described herein to determine (e.g., analyze) LSI signal 302 to determine waveform metrics and, ultimately, blood flow metrics.

[0082] In some examples, analysis of LSI signal 302 for determining a blood flow metric (by processing circuitry 110) may include performing frequency decomposition to provide additional or independent hemodynamic information of clinical value. Frequency analysis of this data can provide structural details of the collected waveform or waveforms. This waveform structure is influenced by many physiological processes, such as blood flow velocity, vascular tone, blood pressure, cardiac output, and the extent of atherosclerosis. Similarly, frequency decomposition of the transmitted LSI waveform contains information that enables quantification of these conditions or processes, for example, for blood flow metric and / or diagnostic purposes. Processing circuitry 110 may analyze frequency domain information, including at least one transformation of time domain data into the frequency domain, for example, using a wavelet or Fourier transform, followed by quantification of amplitude, either alone or in relation to one or more other frequencies. In this manner, processing circuitry 110 can determine power spectral characteristics at one or more frequencies, determine waveform metrics, and ultimately, determine blood flow metrics. In some examples, processing circuitry 110 may be configured to determine a blood flow metric based on one or more elements of the waveform metric with or without blood flow values.

[0083] For example, the techniques implemented by processing circuitry 110 for determining flow and waveform metrics (and, therefore, blood flow metrics) can also be applied to multiple tissue locations, such as by measuring multiple digits simultaneously. The resulting measurements from each location can provide clinical information related to understanding physiological processes or conditions within a specific anatomical site. This information may be clinically valuable alone or when compared qualitatively or quantitatively with measurements from other locations. For example, different parts of the foot are primarily perfused by different arteries in the leg, which are generally referred to as vascular bodies. Analysis of transmitted LSI data from different sites (e.g., different digits) can provide clinical information specific to the artery or arteries responsible for perfusing a specific vascular body. This site-specific information may be helpful in diagnosing, prognosing, or monitoring vascular disease.

[0084] Transmission LSI can also benefit from its ability to adopt data processing algorithms previously used for reflection LSI. For example, this includes calculating the speckle flow index (SFI) based on the collected transmission LSI data, as shown below:

[0085] SFI=1 / (2TK 2 ), (3)

[0086] where T is the exposure time of the photodetector used and K is the speckle contrast calculated spatially or over time.

[0087] An additional data acquisition and analysis technique used in conventional reflectance LSIs but also applicable by processing circuitry 110 in a transmission LSI is multiple exposure LSI. Multiple exposure LSI expands the range of blood flow rates to which the transmission LSI is sensitive. Processing circuitry 110 can use any of these techniques to generate LSI signals 302 or other signals from which the system can determine one or more characteristics used to generate blood flow metrics for the tissue.

[0088] Figure 4 is a chart illustrating example waveforms and blood flow values corresponding to various blood flow states of a patient. This tape may represent an example data structure stored by the memory and referenced by the processing circuitry to determine a score as described herein. Figure 4 As shown in the example of , various waveform shapes and flow values correspond to corresponding scores. Lower scores indicate a greater degree of deterioration in blood flow, so higher scores may reflect normal blood flow in a generally healthy subject.

[0089] For example, waveform 400 indicates a relatively minimal pulse structure, which indicates low vascular function. Conversely, waveform 412 indicates a relatively healthy pulse structure, which indicates normal or healthy vascular function. Each of waveforms 400, 402, 404, 406, 408, 410, and 412 represents increasing levels of vascular function, with correspondingly higher scores. As can be seen from the waveforms, in some cases, lower vascular function corresponds to a reduced number of peaks and / or lower flow values in each pulse waveform. Flow values are displayed as normalized values without units, but any flow value can be classified into different tiers for different corresponding scores.

[0090] Despite Figure 4 In the example of FIG. 1 , both flow values and waveform shapes are categorized into seven different metric values, but in other examples, processing circuitry 110 may use a fewer or greater number of categories to generate an output via user interface 130 or user interface 230. Furthermore, in other examples, flow values and waveform shape metrics may have a different number of categories. As described herein, processing circuitry 110 may determine flow values from some or all waveforms as, for example, an instantaneous flow value corresponding to an appropriate portion of the waveform, or an area under a curve of the waveform, or a combination thereof. Processing circuitry 110 may determine waveform shape based on, for example, the number of peaks in each pulse waveform, the inter-peak spacing, a comparison to a waveform template, or any of the other techniques described herein.

[0091] Figure 5 is a diagram illustrating an example blood flow state determined based on example blood flow velocity and waveform metrics. Figure 5 As shown in the example of FIG, the processing circuit 110 can apply the scores from the blood flow rate or volume and waveform metrics to Figure 5 The graph of the blood flow and waveform metrics provided by processing circuitry 110 may be used to determine a diagnostic metric, which may be output to a clinician via user interface 130 or user interface 230. In some examples, the blood flow metric may be taken as the sum of the scores of the blood flow and waveform metrics provided by processing circuitry 110. This blood flow metric may provide insight into the overall vascular function of the tissue and / or subject.

[0092] In some examples, blood flow metrics or components of blood flow metrics generated by processing circuitry 110 are classified to determine a diagnostic metric. For example, the system may apply blood flow and waveform metrics to Figure 5 If the blood flow and waveform metric values fall within region A, processing circuitry 110 may generate a diagnostic metric indicative of healthy vascular function. Conversely, if the blood flow and waveform metric values fall within region B, processing circuitry 110 may generate a diagnostic metric indicative of impaired vascular function, such as that associated with peripheral vascular disease (PVD) or peripheral arterial disease (PAD). In this manner, threshold 500 may separate lower blood flow metrics from higher blood flow metrics in a stepwise manner. Threshold 500 may be predetermined or determined based on a specific patient, patient population, or any other data and stored in memory. In other examples, threshold 500 may be equal to a single blood flow metric. In some examples, processing circuitry 110 may employ multiple thresholds to indicate different risk levels for a particular condition, such as a low-risk level, a moderate-risk level, and a high-risk level for PVD.

[0093] Example areas of medicine where the types of analysis described above may be applicable include anesthesia depth monitoring, peripheral vascular disease diagnosis, prognosis, and monitoring, early hypovolemic shock detection, sleep apnea detection, diabetes progression monitoring, exercise monitoring, vascular surgery monitoring, dialysis fistula assessment, dialysis monitoring, endothelial function assessment, cyanide poisoning monitoring, non-invasive blood pressure measurement, heart attack, and the like. As previously mentioned, since the transmission LSI has the ability to measure blood flow, the actual list of potential applications may include other types of monitoring or condition determination not specifically mentioned herein. The foregoing topics have been described to highlight topics of potential interest, and this list is not intended to exclude use in applications not mentioned.

[0094] Detailed examples of methods and algorithms for diagnosing and / or monitoring a specific vascular disease state (peripheral vascular disease) are presented in the following examples.

[0095] Peripheral vascular disease (PVD / PAD) is a relatively common condition affecting approximately 12 million people in the United States. The ankle-brachial index (ABI) is an initial screening test that can be used to diagnose and grade PVD. It is also used to determine prognosis regarding limb salvage, wound healing, and future cardiovascular morbidity.

[0096] The ABI is calculated by measuring the average systolic blood pressure in the leg (at the ankle) and arm (at the forearm) and comparing these two values. The result is the ratio of the ankle systolic blood pressure to the brachial systolic blood pressure. This value is usually one or less, and PVD is diagnosed if the ABI is less than or equal to 0.9. If the ABI falls between 0.4 and 0.9, PVD is graded as mild to moderate, while an ABI of less than 0.4 corresponds to severe PVD. An ABI above 1.3 indicates incompressible blood vessels. Because the ABI is performed as a comparative ratio rather than an absolute value, it is a normalized measurement. This accounts for and eliminates the effects of natural variations in blood pressure due to everyday factors such as time of day, diet, stress, exercise, alcohol consumption, etc.

[0097] Despite its ubiquity, the ABI can have several limitations. For example, the ABI may be unreliable in patients with arterial calcification, renal failure, or heavy smoking. As another example, the ABI may be insensitive to mild peripheral arterial disease. As another example, the ABI can be heavily dependent on the individual performing the measurement and requires a skilled operator to obtain accurate results. The ABI can also take a significant amount of time to perform, for example, approximately 15 minutes.

[0098] An alternative to using a normalized ratio of measured blood pressure to diagnose PVD is to measure blood flow velocity in the peripheral digits (fingers and / or toes) directly using transmitted laser speckle analysis (e.g., using the LSI signal described herein), which can then perform a comparative analysis, such as a ratio of blood flow velocity in the fingers and toes, or perform an absolute analysis of flow velocity and / or waveform from the fingers or toes. Comparative calculations between the fingers and toes can normalize the laser speckle measurements, thereby maintaining the diagnostic potential of the ABI.

[0099] The laser speckle-based technology described in this article can provide several advantages over ABI. For example, laser speckle measurements do not require an arm cuff to be occluded, thereby avoiding discomfort to the patient. Laser speckle-based technology can also be used for patients with arterial calcification, incompressible arteries, and who cannot be diagnosed using ABI. However, the blood flow velocity of these patients can be quantified and used to make diagnostic measurements. As another example, the transmission laser speckle analysis system can be built as a simple finger-clip probe (or another relatively small form factor) and automated, which can help eliminate variability in the output between operators. The simple clip design can also eliminate the need for the skills and training required to perform ABI.

[0100] Figure 6 is a flow chart illustrating an example technique for determining a patient's blood flow status using waveforms determined using LSI technology. Figure 6 is relative to the blood flow detection device 100 ( Figure 1 ) of the processing circuit 110, in other examples, the processing circuit 210, the processing circuit 214 and / or the processing circuit 216 ( Figure 2 ) or other processing circuits alone or in combination with the processing circuit 110 may perform Figure 6 any part of the technology.

[0101] exist Figure 6 In an example, processing circuit 110 receives a laser speckle contrast signal obtained from tissue of a subject (700). In some examples, processing circuit 110 can control light source 142 to deliver light to the tissue of interest and control light sensor 152 to detect scattered light. Processing circuit 110 can receive an LSI signal from light detection circuit 150 or generate an LSI signal based on a raw signal received from light detection circuit 150.

[0102] The processing circuit 110 then determines a flow value based on at least a portion of the laser speckle contrast signal (702) and determines a waveform metric based on at least a portion of the laser speckle contrast signal (704). The processing circuit 110 determines a blood flow metric for the tissue based on the flow value and the waveform metric (706). The processing circuit 110 then outputs a representation of the blood flow metric (708) for display. In some examples, the processing circuit 110 may also or alternatively determine a diagnostic metric based on the blood flow metric to indicate a probability or likelihood that the patient has a particular condition, such as PVD or PAD.

[0103] although Figure 6 While the example described herein includes determining a flow value and a waveform metric based on the LSI signal to determine a blood flow metric, in other examples, the processing circuitry 110 may determine the blood flow metric based on other characteristics of the LSI signal. For example, the processing circuitry 110 may determine the blood flow metric based on a power spectrum of one or more frequencies and / or another characteristic described herein.

[0104] The present disclosure contemplates computer-readable storage media comprising instructions that cause a processor to perform any of the functions and techniques described herein. The computer-readable storage medium can take the form of any volatile, non-volatile, magnetic, optical, or electrical medium, such as RAM, ROM, NVRAM, EEPROM, or flash memory. The computer-readable storage medium can be referred to as non-transitory. Programmers such as patient programmers or clinician programmers, or other computing devices can also contain more portable removable memory types to enable easy data transfer or offline data analysis.

[0105] The techniques described in this disclosure, including those attributable to the blood flow detection device 100 and LSI device 200 and any other processing circuits or circuits and various constituent components, may be implemented, at least in part, in hardware, software, firmware, or any combination thereof. For example, various aspects of the technology may be implemented within one or more processors, including one or more microprocessors, DSPs, ASICs, FPGAs, or any other equivalent integrated or discrete logic circuits in a programmer (e.g., a physician or patient programmer), a stimulator, a remote server, or other device, and any combination of these components. The term "processor" or "processing circuitry" may generally refer to any one of the aforementioned logic circuits, alone or in combination with other logic circuits, or any other equivalent circuitry.

[0106] As used herein, the term "circuitry" refers to an ASIC, electronic circuit, processor (shared, dedicated, or group) and memory, combinational logic, and / or other suitable components that execute one or more software or firmware programs to provide the described functionality. The term "processing circuitry" refers to one or more processors distributed across one or more devices. For example, a "processing circuitry" may include a single processor or multiple processors on a device. A "processing circuitry" may also include processors on multiple devices, where the operations described herein may be distributed across the processors and devices.

[0107] Such hardware, software, and firmware may be implemented in the same device or in separate devices to support the various operations and functions described in this disclosure. For example, any of the techniques or processes described herein may be performed in one device, or at least partially distributed among two or more devices. In addition, any of the described units, modules, or components may be implemented together or individually as discrete but interoperable logical devices. Depicting different features as modules or units is intended to highlight different functional aspects and does not necessarily imply that such modules or units must be implemented by separate hardware or software components. Instead, the functions associated with one or more modules or units may be performed by separate hardware or software components, or may be integrated within a common or separate hardware or software component.

[0108] The techniques described in this disclosure may also be embodied or encoded in an article of manufacture comprising a non-transitory computer-readable storage medium encoded with instructions. The instructions embedded or encoded in the article of manufacture comprising the encoded non-transitory computer-readable storage medium may cause one or more programmable processors or other processors to implement one or more of the techniques described herein, for example, when the instructions contained or encoded in the non-transitory computer-readable storage medium are executed by one or more processors. Exemplary non-transitory computer-readable storage media may include RAM, ROM, programmable ROM (PROM), erasable programmable ROM (EPROM), electronically erasable programmable ROM (EEPROM), flash memory, hard disk, compact disc ROM (CD-ROM), floppy disk, magnetic cassette, magnetic media, optical media, or any other computer-readable storage device or tangible computer-readable medium.

[0109] In some instances, computer-readable storage media include non-transitory media. The term "non-transitory" may indicate that the storage medium is not embodied in a carrier wave or propagated signal. In some examples, non-transitory storage media may store data that may change over time (e.g., in RAM or cache memory). The components of the devices and circuits described herein, including but not limited to the blood flow detection device 100 and the LSI device 200, may be programmed using various forms of software. One or more processors may be at least partially implemented as or include, for example, one or more executable applications, application modules, libraries, classes, methods, subjects, routines, subroutines, firmware, and / or embedded code.

[0110] Various examples of the present disclosure have been described. Any combination of the described systems, operations, or functions is contemplated. These and other examples are within the scope of the following claims.

Claims

1. A system for determining a blood flow metric using laser speckle imaging, comprising: a light driving circuit configured to cause the coherent light source to emit light of a single wavelength; a light detection circuit configured to generate a signal indicative of the detected light; processing circuitry configured to: controlling the light driving circuit to cause the coherent light source to emit light of the single wavelength; receiving a signal from the light detection circuit indicative of detected light, the detected light being scattered from the single wavelength of light, generating a laser speckle contrast signal based on a signal indicative of the detected light, the detected light being scattered by tissue of the subject and from the coherent light source emitting light of the single wavelength; determining a flow value according to the laser speckle contrast signal; determining a waveform metric based on the laser speckle contrast signal; determining a blood flow metric for the tissue based on both the flow value and the waveform metric; as well as A representation of the blood flow metric is output for display on a display device.

2. The system of claim 1 , wherein the processing circuit is configured to determine the waveform metric at least by determining the waveform metric based on the laser speckle contrast signal, the laser speckle contrast signal comprising at least one of a triphasic waveform, a biphasic waveform, or a monophasic waveform representing a single cardiac cycle.

3. The system of claim 1 , wherein the processing circuit is configured to determine the waveform metric by at least: comparing the waveform shape of the laser speckle contrast signal with a plurality of different waveform templates; and The waveform metric corresponding to one of the plurality of different waveform templates that best fits the waveform shape is selected. 4 . The system of claim 1 , wherein the processing circuit is configured to determine the blood flow metric based on the flow value and a total score of the waveform metric.

5. The system of claim 1, wherein the processing circuit is configured to determine the blood flow metric based on a lookup table defining a relationship between the flow value and the waveform metric.

6. The system of claim 1 , wherein the processing circuit is configured to: determining the flow value by at least determining an average flow of a plurality of waveforms of the laser speckle contrast signal; and The waveform metric is determined by at least determining an average slope of the plurality of waveforms of the laser speckle contrast signal.

7. The system of claim 1, wherein the processing circuit is configured to determine a peripheral vascular disease metric based on the blood flow metric of the tissue.

8. The system of claim 1, wherein the tissue comprises a toe of the subject.

9. The system of claim 1, further comprising a display device configured to present the representation of the blood flow metric.

10. The system of claim 1, further comprising: the coherent light source; and A light detector is configured to detect light scattered by the tissue from the coherent light source and to generate a signal indicative of the detected light.

11. A method for determining a blood flow metric using laser speckle imaging, comprising: controlling, by the processing circuit, a light driving circuit configured to cause the coherent light source to emit light of a single wavelength; generating, by light detection circuitry, a signal indicative of detected light scattered from the single wavelength of light; receiving, by the processing circuitry, a signal from the light detection circuitry indicative of the detected light; generating, by the processing circuitry, a laser speckle contrast signal based on a signal indicative of the detected light, the detected light being scattered by tissue of the subject and from the coherent light source emitting light of the single wavelength; determining a flow rate value based on the laser speckle contrast signal by the processing circuit; determining, by the processing circuit, a waveform metric based on the laser speckle contrast signal; determining, by the processing circuitry and based on both the flow value and the waveform metric, a blood flow metric for the tissue; as well as A representation of the blood flow metric is output by the processing circuitry and for display on a display device.

12. The method of claim 11, wherein determining the waveform metric comprises determining the waveform metric based on the laser speckle contrast signal comprising one of a triphasic waveform, a biphasic waveform, or a monophasic waveform representing a single cardiac cycle.

13. The method of claim 11 , wherein determining the waveform metric comprises: comparing the waveform shape of the laser speckle contrast signal with a plurality of different waveform templates; as well as The waveform metric corresponding to one of the plurality of different waveform templates that best fits the waveform shape is selected.

14. The method of claim 11, wherein determining the blood flow metric comprises determining the blood flow metric based on the flow value and a total score of the waveform metric.

15. The method of claim 11, wherein determining the blood flow metric comprises determining the blood flow metric from a lookup table defining a relationship between the flow value and the waveform metric.

16. The method of claim 11, wherein: Determining the flow value includes calculating an average flow of a plurality of waveforms of the laser speckle contrast signal; and Determining the waveform metric includes calculating an average slope of the plurality of waveforms of the laser speckle contrast signal.

17. The method of claim 11, further comprising determining a peripheral vascular disease metric based on the blood flow metric of the tissue.

18. The method of claim 11, wherein the tissue comprises a toe of a subject, and wherein the method further comprises: delivering coherent light to the tissue of the toe via the coherent light source; detecting, via a light detector, the light scattered by the tissue of the toe from a coherent light source; as well as A signal indicative of the detected light is generated via the light detector and by the light detection circuit.

19. The method of claim 11, further comprising presenting the representation of the blood flow metric via the display device.

20. A non-transitory computer-readable medium comprising instructions that, when executed, cause a processing circuit to: controlling the optical driving circuit so that the coherent light source emits light of a single wavelength; receiving a signal indicative of detected light from a light detection circuit, wherein the light detection circuit is configured to generate a signal indicative of the detected light; generating a laser speckle contrast signal based on a signal indicative of the detected light, the detected light being scattered by tissue of the subject and from the coherent light source emitting light of the single wavelength; determining a flow value according to the laser speckle contrast signal; determining a waveform metric based on the laser speckle contrast signal; determining a blood flow metric for the tissue based on both the flow value and the waveform metric; as well as A representation of the blood flow metric is output for display on a display device.

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