Quantitative laser speckle blood flow imaging method based on neuromorphic visual sensor

Abstract

The present invention discloses a quantitative laser speckle blood flow imaging method based on a neuromorphic visual sensor. This method relates to the field of biomedical engineering technology and addresses the problem in traditional laser speckle contrast imaging methods where static scattered light caused by physiological behavior and electronic noise from the imaging device adversely affect the quality of blood flow imaging. The method comprises the following steps: Step 1: Using a helium-neon laser as a light source, an expanded laser beam is irradiated onto a scattering medium to form a speckle pattern; Step 2: Using a microfluidic syringe, the syringe's inner diameter and flow rate are set to control the flow rate range; Step 3: Using an event camera to asynchronously sense changes in the speckle pattern's light intensity, and capturing the speckle pattern using a "frame" camera; Step 4: The event camera acquires event stream data, which is then processed using an algorithm. This method achieves real-time, high-resolution blood flow imaging and flow rate calculation, overcoming the inherent limitations of traditional methods.

Description

Technical Field

[0001] The present invention relates to the field of biomedical engineering technology, and in particular to a quantitative laser speckle blood flow imaging method based on a neuromorphic visual sensor. Background Art

[0002] Existing Laser Speckle Contrast Imaging (LSCI) methods can achieve full-field blood flow imaging, but they face challenges in balancing spatiotemporal resolution, real-time performance, and quantitative capabilities. Traditional LSCI methods analyze blood flow by calculating speckle contrast, a method that requires significant time and computational resources and cannot meet the clinical need for accurate, real-time blood flow measurement. Furthermore, due to its frame-based acquisition approach, the temporal resolution of traditional LSCI methods is limited by the camera's frame rate, making it impossible to accurately capture rapid blood flow changes. This poses a challenge for highly dynamic blood flow imaging applications.

[0003] Among existing LSCI technologies, common methods include spatial laser speckle contrast imaging (sLSCI), temporal laser speckle contrast imaging (tLSCI), and spatial-temporal laser speckle contrast imaging (stLSCI). These methods have improved the quality of blood flow imaging to a certain extent, but they still cannot achieve both high spatial and high temporal resolution. For example, while spatial laser speckle contrast imaging has good spatial resolution, its temporal resolution is low, making it unable to accurately depict rapidly changing blood flow. Temporal laser speckle contrast imaging, on the other hand, has good temporal resolution but poor spatial resolution, making it difficult to obtain detailed blood flow images.

[0004] Furthermore, existing LSCI methods have limitations in quantitatively measuring blood flow. To achieve quantitative blood flow velocity, multiple exposures are typically required. By analyzing the changes in speckle contrast at different exposure times, a nonlinear model is established to determine the relationship between speckle autocorrelation time and blood flow velocity. This method requires significant computing resources and carries high errors, making it difficult to meet high-precision requirements in real-time applications. This is particularly true during clinical surgery, where doctors need to monitor blood flow changes in the lesion area in real time to adjust surgical strategies. Traditional methods struggle to provide sufficient real-time support.

[0005] Noise is also a major difficulty in traditional LSCI methods. During the imaging process, static scattered light caused by physiological behaviors (such as heartbeat and breathing) and the electronic noise of the imaging device itself will have an adverse effect on the final blood flow imaging quality. This noise not only reduces the signal-to-noise ratio of the image but also affects the accuracy of quantitative measurements. Although some studies have proposed noise reduction methods based on image post-processing, these methods generally increase computational complexity and cannot be applied in real time during the imaging process; therefore, they do not meet existing needs. To this end, we propose a quantitative laser speckle blood flow imaging method based on a neuromorphic vision sensor. Summary of the Invention

[0006] The purpose of the present invention is to provide a quantitative laser speckle blood flow imaging method based on a neuromorphic visual sensor to solve the problem in the traditional LSCI method proposed in the above background art that static scattered light caused by physiological behavior and electronic noise of the imaging device itself will have an adverse effect on the final blood flow imaging quality.

[0007] To achieve the above-mentioned object, the present invention provides the following technical solution: a quantitative laser speckle blood flow imaging method based on a neuromorphic visual sensor, comprising the following steps:

[0008] Step 1: Using a He-Ne laser as the light source, the expanded laser beam is irradiated on the scattering medium to form a speckle pattern.

[0009] Step 2: Use a microfluidic syringe to set the syringe inner diameter and flow rate parameters to control the flow rate range;

[0010] Step 3: Use an event camera to asynchronously sense changes in the speckle pattern's light intensity. Whenever the speckle pattern changes, the camera continuously outputs a pulse signal, forming a pulse data stream to detect rapidly changing blood flow information. Use a "frame" camera to capture the speckle pattern, calculate the speckle pixel size n in the image frame, calibrate the pixel physical size k, and calculate the average speckle size m using the formula m = n × k.

[0011] Step 4: Start the microfluidic syringe to move the fluid in the pipeline. The event camera collects event stream data. Combining the position and polarity information of the event with the pre-set event trigger threshold, the grayscale value of a specific pixel at a specific time is determined. Then, interpolation is performed at 20μs time intervals to obtain a curve of the grayscale value of the point over time, and the blood flow velocity is calculated.

[0012] Preferably, the wavelength of the helium-neon laser is 632 nm.

[0013] Preferably, the scattering medium includes hydrogel, capillary glass flakes, diamond slurry, or blood.

[0014] Preferably, the event camera is a camera based on a neuromorphic vision sensor.

[0015] Preferably, the flow rate range of the microfluidic syringe is 0.001 μL / min to 43.349 ml / min, and the flow rate range in step 2 is 0.1 to 2.0 mm / s.

[0016] Preferably, the blood flow velocity is the speckle movement velocity, and the calculation formula of the blood flow velocity v is:

[0017]

[0018] Where m is the average size of speckles, τ C is the speckle autocorrelation time.

[0019] Preferably, after the autocorrelation operation is performed after interpolation in step 4 to obtain an autocorrelation image, the middle peak of the image is taken, and the speckle autocorrelation time is the full width at half maximum of the peak.

[0020] Preferably, the calculation formula for the average speckle size m is:

[0021] m = n × k;

[0022] Where n is the speckle pixel size and k is the physical pixel size.

[0023] Preferably, the speckle pixel size is calculated by shooting image frames with a “frame” camera, and the pixel physical size is obtained by calibrating the “frame” camera.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] 1. The present invention achieves accurate capture of dynamic changes in blood flow by combining an event camera with laser speckle, breaking through the technical bottleneck of traditional frame cameras that are difficult to describe rapid dynamic changes under frame rate limitations. The present invention can asynchronously collect speckle change data with a time resolution of microseconds, directly respond to brightness changes caused by blood cell movement, and at the same time shield static areas that are not involved in changes, thereby effectively reducing data redundancy. In the blood flow imaging process, the present invention utilizes the characteristics of the event camera outputting event data streams to generate event responses only for pixels with brightness changes, significantly reducing the computational requirements for data storage and processing, and improving the response speed and processing efficiency of the system while maintaining unchanged system performance. In specific applications, this technical solution provides accurate blood flow dynamic information for clinical surgery by generating high-temporal and spatial resolution blood flow imaging data in real time, which can support doctors to quickly adjust surgical strategies and improve the safety and accuracy of surgery. In addition, the event camera has the advantage of high dynamic range and can maintain good imaging effects under low light conditions, ensuring its applicability under a variety of clinical and experimental conditions;

[0026] 2. By leveraging the asynchronous sensing, low latency, and low data redundancy characteristics of the event camera, this invention can achieve real-time, full-field imaging of blood flow within limited transmission bandwidth. This is extremely helpful for understanding blood flow behavior in complex vascular networks. For example, when assessing tissue blood supply and studying the occurrence and development of vascular lesions (such as cerebral infarction, varicose veins, and hemangiomas), more comprehensive information can be obtained through visualization of blood flow distribution. In addition, quantitative measurements can also provide reliable data support for disease diagnosis and treatment efficacy evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 Schematic diagram of the imaging principle of the event camera;

[0028] Figure 2 This is a schematic diagram of the laser speckle blood flow imaging experimental platform system structure;

[0029] Figure 3 This is a flow chart of event stream data processing of the present invention;

[0030] Figure 4 A graph showing the grayscale value of the event stream data processing result of a specific pixel point over time;

[0031] Figure 5 This is a graph showing the autocorrelation calculation results of the event stream data processing results of a specific pixel point according to the present invention;

[0032] Figure 6 This is a schematic diagram of the laser speckle blood flow imaging verification experiment of the present invention;

[0033] Figure 7 This is a diagram showing the flow imaging results of diamond polishing liquid using the event camera of the present invention;

[0034] Figure 8 This is a diagram showing the blood flow imaging result of the event camera of the present invention;

[0035] Figure 9 Schematic diagram of the front of the hydrogel;

[0036] Figure 10 This is the result of imaging the flow of diamond polishing fluid covered by hydrogel using an event camera;

[0037] Figure 11 Speckle field map generated for frosted glass;

[0038] Figure 12 The original data and fitting curve of the time-varying speed of the frosted glass driven by the electric translation stage;

[0039] Figure 13 The original data and fitting curve of the correlation time of blood flow in the microfluidic chip changing with the speed;

[0040] Figure 14 This is a graph showing the correlation time of blood flow in the mouse ear as a function of laser irradiation time. DETAILED DESCRIPTION

[0041] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0042] See also Figures 1 to 14 The present invention provides an embodiment of a quantitative laser speckle blood flow imaging method based on a neuromorphic visual sensor, comprising the following steps:

[0043] Step 1: Build a laser speckle blood flow imaging experimental platform system, such as Figure 2 As shown, a helium-neon laser (Thorlabs, HNL210L, 21 mW) is used as the light source. The wavelength of the helium-neon laser is 632 nm. The expanded laser beam is used to irradiate the scattering medium to form a speckle pattern.

[0044] Step 2: Use a microfluidic syringe (model LSP01-1A, manufactured by Baoding DiChuang Electronic Technology Co., Ltd.) and set the syringe inner diameter and flow rate parameters. The flow rate range of the microfluidic syringe is 0.001 μL / min to 43.349 ml / min, and the flow rate range is controlled to be 0.1 to 2.0 mm / s.

[0045] Step 3: Use an event camera to asynchronously sense changes in the light intensity of the speckle pattern. The event camera model is the CeleX5-MP from Shanghai OmniVision Core, with a resolution of 1280×800, a pixel size of 9.8μm×9.8μm, and a photosensitive area of ​​12.5mm×7.8mm. Each pixel of the camera works independently and can asynchronously sense changes in light intensity. Whenever the speckle pattern changes, the camera continuously outputs a pulse signal to form a pulse data stream to detect high-speed changes in blood flow information. Use a "frame" camera to capture the speckle pattern. The camera model is the MV-CA020-10UM from Hikvision Robotics, with a resolution of 1624×1240 and a pixel size of 4.5μm×4.5μm. Calculate the speckle pixel size n in the image frame, calibrate the pixel physical size k, and calculate the average speckle size m using the formula m=n×k.

[0046] Step 4: Start the microfluidic syringe to make the fluid in the pipe move, and the event camera collects the event stream data. Next, process the data. The processing flow chart is as follows: Figure 3As shown in Figure 2, the position and polarity information of the event are combined with the pre-set event trigger threshold to determine the grayscale value of a specific pixel at a specific time. Then, interpolation is performed at 20μs time intervals to obtain a graph showing the grayscale value of the point changing with time, as shown in Figure 2. Figure 4 As shown, Figure 4 The horizontal axis is time, in microseconds, and the vertical axis is the grayscale value of the pixel. After interpolation, the autocorrelation operation is performed, and the result is as follows: Figure 5 As shown, take the middle peak of the image, the full width at half maximum of the peak is also the speckle autocorrelation time τ C , calculate the blood flow velocity, which is the speckle moving speed. The calculation formula of blood flow velocity v is:

[0047]

[0048] Where m is the average speckle size, and the calculation formula for the average speckle size m is:

[0049] m = n × k;

[0050] Where n is the speckle pixel size, k is the pixel physical size, the speckle pixel size is calculated by shooting image frames with a “frame” camera, and the pixel physical size is obtained by calibrating the “frame” camera.

[0051] The above-mentioned scattering media include hydrogel, capillary glass, diamond polishing fluid, and blood.

[0052] Figure 1 This diagram illustrates the principle of imaging a moving object using an event camera. Compared to data recorded by frame-based cameras, event cameras record data with greater temporal continuity and lower data redundancy. Event cameras offer significant advantages over traditional cameras in real-time data processing and long-term monitoring. For example, a high-speed camera with a resolution of 1 megapixel and a frame rate of 1000 frames per second generates 1GB of data per second, making it unsuitable for long-term operation. However, at the same spatial resolution and higher temporal resolution, an event camera generates approximately 40MB of data per second, which can be processed and stored in real time on a computer via a USB port. Therefore, event cameras are well-suited for long-term monitoring in industrial and biological fields.

[0053] Use the event camera to image the fluid and verify its imaging performance: To observe the imaging effect of the event camera on the fluid, a Figure 6 The experimental setup is shown in Figure 1. The laser light generated by a helium-neon laser is expanded by a plano-convex lens. The laser beam is irradiated on the capillary tube to form a speckle pattern. A microfluidic syringe pushes diamond abrasive slurry with particles of approximately 10 μm in diameter through the capillary glass tube to simulate blood flow. An event camera captures the changes in the speckle field. The measurement results are shown in Figure 1. Figure 7 As shown;

[0054] Testing the imaging effect of real blood flow, such as Figure 8 As shown in the figure, it can be seen that the proposed method can well image fluids containing scatterers. Compared with traditional methods, the imaging method proposed in the present invention does not require the calculation of speckle contrast and can achieve real-time imaging with an imaging frame rate of up to 1000 frames per second.

[0055] The diamond slurry covered by the hydrogel was imaged using an event camera to verify its imaging performance in complex situations: In order to simulate the occlusion and scattering effect of the skin on blood vessels, we used agar powder mixed with Intralipid to prepare a hydrogel with scattering properties similar to those of the skin. Figure 9 As shown, the hydrogel is covered on the capillary glass, and the imaging results are shown Figure 10 As shown in Figure 2, it can be seen that the proposed method can still effectively complete the imaging of diamond slurry in the presence of hydrogel coverage.

[0056] An event camera was used to image the speckle field formed by a capillary glass sheet and calculate the velocity to verify the feasibility of the velocity measurement method proposed in this invention: an experimental platform was built, a piece of frosted glass was fixed on the electric translation stage, and the electric translation stage drove the frosted glass to move at a constant speed. The speed range of the electric translation stage was set to 0.1-2.0 mm / s, with an interval of 0.1 mm / s. A total of 20 sets of data were measured, and the results are shown as follows: Figure 12 As shown. The results are fitted to obtain the fitting curve, and it can be seen that the fitting effect is good and there is no point with large deviation. By shooting the image frame, such as Figure 11 As shown in the figure, the calculated speckle pixel size is 3.2 pixels, and the calibrated physical pixel size is 5.1667 μm / pixel. Therefore, the theoretical average speckle size is 16.533 μm. The actual average speckle size obtained from the fitting curve is 16.8 μm, with an error of 1.61%. This experiment verifies the feasibility of our proposed method.

[0057] The feasibility of the velocity measurement method proposed in this invention was verified by using an event camera to image the fluid in the microfluidic chip and calculate the flow rate. To further simulate the flow of blood in capillaries, we used a microfluidic chip to simulate capillaries and observe the flow of blood in tiny channels. The channel width of the microfluidic chip is 0.2mm and the channel depth is 30μm. The results are as follows: Figure 13 As shown, it can be seen that the fitting effect is good. We use R 2 Measure the fitting effect and calculate R 2 =0.9984. This indicates that the fitting curve is very consistent with the calculation formula of blood flow velocity v and satisfies the inverse function relationship.

[0058] An event camera was used to image the ears of mice, and thrombus formation was induced in the ears by the optical thrombosis method. The changes in blood flow before and after were observed to verify the application potential of the method proposed in the present invention in biomedical engineering: The chemical reagent used in this embodiment is Bengal rose red, also known as tiger red. 1.5% tiger red (2ml / kg) was injected through the tail vein of the mouse, and the mouse ears were illuminated after waiting for 3 minutes. The laser light source used was a small semiconductor laser from Daheng Optoelectronics, model GCI-07-520-20, with an output power ≥20mW and a central wavelength of 520±10nm. The correlation time reflects the relative change in blood flow, such as Figure 14 As shown, 5 minutes after laser irradiation, blood flow dropped to 37.80% of the original level.

[0059] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

Claims

1. A quantitative laser speckle blood flow imaging method based on a neuromorphic visual sensor, characterized in that: The steps include: Step 1: Using a He-Ne laser as the light source, the expanded laser beam is irradiated on the scattering medium to form a speckle pattern. Step 2: Use a microfluidic syringe to set the syringe inner diameter and flow rate parameters to control the flow rate range; Step 3: Use an event camera to asynchronously sense changes in the light intensity of the speckle pattern. Whenever the speckle pattern changes, the camera continuously outputs a pulse signal to form a pulse data stream to detect high-speed changes in blood flow information; Use a "frame" camera to capture the speckle pattern, calculate the speckle pixel size n in the image frame, calibrate the pixel physical size k, and calculate the average speckle size m using the formula: ; Step 4: Start the microfluidic syringe to make the fluid in the pipe move. The event camera collects the event stream data. Combining the position and polarity information of the event with the pre-set event trigger threshold, the grayscale value of the specific pixel at a specific time is determined. The time interval is interpolated to obtain the gray value of the point over time curve and calculate the blood flow velocity.

2. The quantitative laser speckle blood flow imaging method based on a neuromorphic visual sensor according to claim 1, characterized in that: The wavelength of the helium-neon laser is 632 nm.

3. The quantitative laser speckle blood flow imaging method based on a neuromorphic visual sensor according to claim 1, characterized in that: The scattering medium includes hydrogel, capillary glass flakes, diamond polishing fluid, and blood.

4. The quantitative laser speckle blood flow imaging method based on a neuromorphic visual sensor according to claim 1, characterized in that: The event camera is a camera based on a neuromorphic vision sensor.

5. The quantitative laser speckle blood flow imaging method based on a neuromorphic visual sensor according to claim 1, characterized in that: Flow range of the microfluidic syringe , the flow rate range in step 2 is .

6. The quantitative laser speckle blood flow imaging method based on a neuromorphic visual sensor according to claim 1, characterized in that: The blood flow velocity is the speckle moving speed, and the calculation formula of the blood flow velocity v is: ； Where m is the average speckle size, is the speckle autocorrelation time.

7. The quantitative laser speckle blood flow imaging method based on a neuromorphic visual sensor according to claim 5, characterized in that: After the autocorrelation operation is performed after interpolation in step 4 to obtain the autocorrelation image, the middle peak of the image is taken, and the speckle autocorrelation time is the full width at half maximum of the peak.

8. The quantitative laser speckle blood flow imaging method based on a neuromorphic visual sensor according to claim 1, characterized in that: The calculation formula of the average speckle size m is: Where n is the speckle pixel size and k is the physical pixel size.

9. The quantitative laser speckle blood flow imaging method based on a neuromorphic visual sensor according to claim 1, characterized in that: The speckle pixel size is calculated by shooting image frames with a "frame" camera, and the pixel physical size is obtained by calibrating the "frame" camera.

Images