Detection system for handheld optical coherence tomography probe

Abstract

The invention relates to the field of medical devices, in particular to a detection system for a handheld optical coherence tomography probe, which is characterized in that imaging parameters of each local area in original imaging data are acquired, an image quality score is calculated, and a qualified imaging area and a to-be-optimized imaging area are divided according to a quality threshold value; the method comprises the following steps of: obtaining an image quality score of a to-be-optimized imaging region, subdividing and classifying the to-be-optimized imaging region on the basis of the image quality score, generating a reference subdivision image and a non-distinguishable subdivision image, performing interpolation processing on the non-distinguishable subdivision image according to the adjacent reference subdivision image, and finally outputting an optimized image. According to the invention, the defect identification and targeted adaptive optimization processing of the imaging image of the handheld probe are realized, the discernibility of the output image is improved, and the operation efficiency of scanning equipment is enhanced.

Description

Technical Field

[0001] This invention relates to medical devices, and more particularly to a detection system for a handheld optical coherence tomography probe. Background Technology

[0002] Handheld optical coherence tomography (OCT) probes have demonstrated advantages in high resolution and non-invasiveness in superficial tissue imaging in ophthalmology, dermatology, and other fields. However, the imaging quality of handheld OCT is extremely sensitive to the relative position of the probe and tissue, motion stability, and local tissue optical properties. In clinical practice, doctors often need to hold the probe with one hand for extended periods. Even minor vibrations, fluctuations in the distance between the probe tip and the tissue surface, or involuntary movements by the patient can lead to motion artifacts, decreased signal-to-noise ratio, and uneven structural contrast. Furthermore, differences in tissue optical properties between individuals and different lesion regions can introduce defects such as signal inhomogeneity and increased local artifact area. Existing handheld OCT systems largely rely on operator experience for repeated scans or manual screening afterward, lacking real-time detection and adaptive optimization methods for handheld probe imaging quality. This results in time-consuming clinical examinations and poor imaging consistency, limiting the widespread application of handheld OCT in rapid screening and accurate diagnosis.

[0003] Chinese Patent Publication No. CN110944573A discloses an optical probe for an optical coherence tomography (OCT) system according to an embodiment. The optical probe of the aforementioned OCT system includes: an optical fiber that receives light generated from a light source and transmits it to a plurality of lenses, receives light reflected from tissue from the plurality of lenses, and transmits it to an optical interferometer; a plurality of lenses, including a first lens located at the end of the optical fiber and a second lens located at any position along the length of the optical fiber; and a sheath capable of internally housing the optical fiber.

[0004] However, the following problems still exist in the existing technology: 1. In the existing technology, the local image quality is not evaluated in real time during the imaging process, and it is impossible to identify defective areas such as insufficient signal-to-noise ratio, motion artifacts or lack of contrast in time. 2. In the existing technology, the causes of the identified defect areas are not considered for tracing and differentiation, which makes it difficult for the probe to adapt and optimize even if it is scanned repeatedly, and still requires a lot of post-screening and manual identification. Summary of the Invention

[0005] Therefore, the present invention provides a detection system for a handheld optical coherence tomography probe to overcome the problems in the prior art that do not consider real-time quantitative evaluation of local image quality during the imaging process, and do not consider tracing the causes and differentiating the identified defect areas.

[0006] To achieve the above objectives, the present invention provides a detection system for a handheld optical coherence tomography probe, comprising: The image acquisition module is used to receive raw imaging data from the probe and acquire imaging parameters of each local region in the raw imaging data, including local signal-to-noise ratio, structural contrast, signal uniformity, and artifact area. An image analysis module, connected to the image acquisition module, is used to calculate the image quality score of each local region based on the imaging parameters; An imaging region evaluation module, which is connected to the image analysis module, is used to divide each local region into a qualified imaging region or an imaging region to be optimized based on the image quality score of each local region and a preset quality threshold. A processing output module, which is connected to the imaging region, is used to execute differentiated image optimization strategies, including: For the qualified imaging area, output its corresponding raw imaging data; For the imaging region to be optimized, the region is subdivided based on the image quality score, and a preset image enhancement algorithm is called to process the subdivided region, and the processed subdivided region image is output.

[0007] Furthermore, the image acquisition module is used to acquire imaging parameters of each local region in the original imaging data, including: The local signal-to-noise ratio is obtained by calculating the ratio of the average signal strength to the standard deviation within the local area. Structural contrast is obtained by calculating the maximum gradient magnitude of signal intensity between adjacent tissue structures within the local region. Signal uniformity is obtained by calculating the variance or information entropy value of the signal intensity within the local region; The motion artifact index is obtained by calculating the fluctuation variance of the signal intensity in the slow scan direction.

[0008] Furthermore, the image analysis module calculates the image quality score for each of the local regions based on the imaging parameters, including: Calculate the basic parameters of sharpness based on the local signal-to-noise ratio and structural contrast. The basic parameters of regularity are calculated based on the signal uniformity and motion artifact index. The weighted sum of the sharpness and regularity parameters is used as the image quality score of the local region.

[0009] Furthermore, the image analysis module calculates the basic parameters of sharpness based on the local signal-to-noise ratio and structural contrast, including: The signal-to-noise component is determined by the ratio of the local signal-to-noise ratio to the standard signal-to-noise ratio; The ratio of the structural contrast to the standard contrast is used to determine the contrast component; The weighted sum of the signal-to-noise component and the contrast component is used as the basic parameter for sharpness.

[0010] Furthermore, the image analysis module calculates the basic parameters of regularity based on the signal uniformity and motion artifact index, including: The uniformity component is determined by the ratio of the signal uniformity to a standard uniformity reference value. The ratio of the motion artifact index to the standard artifact index is used to determine the artifact influence coefficient. The result of subtracting the artifact influence component from 1 is determined as the artifact suppression component; The weighted sum of the uniformity component and the artifact suppression component is used as the basic parameter of regularity.

[0011] Furthermore, the imaging region evaluation module is used to divide each local region into a qualified imaging region or an imaging region to be optimized based on the image quality score of each local region and a preset quality threshold. If the image quality score corresponding to the local region is greater than or equal to the preset quality threshold, then the local region is classified as a qualified imaging region. If the image quality score corresponding to the local region is less than a preset quality threshold, then the local region is divided into an imaging region to be optimized.

[0012] Furthermore, the processing output module further subdivides the imaging region to be optimized based on the image quality score, including: The adjustment coefficient is determined based on the ratio of the preset quality threshold to the image quality score; The number of subdivisions is determined based on the mapping relationship between the adjustment coefficient and the number of subdivisions; Based on the aforementioned subdivision quantity, the imaging region to be optimized is segmented and classified for selection; The mapping relationship between the adjustment coefficient and the number of subdivisions is predetermined.

[0013] Furthermore, the processing output module performs segmentation and classification filtering of the imaging region to be optimized based on the number of subdivisions, including: The imaging region to be optimized is uniformly divided into a corresponding number of sub-regions according to the specified number of sub-regions; Calculate the image quality score for each sub-region and classify it into a baseline subdivision image and an indistinguishable subdivision image based on the image quality score of each sub-region.

[0014] Furthermore, the processing output module calls a preset image enhancement algorithm to process the subdivided regions, including: Based on the spatial location of the indistinguishable subdivision image, determine the nearest reference subdivision image; The indistinguishable subdivision image is filled in based on the nearby reference subdivision image to complete the processing of the subdivision region.

[0015] Furthermore, the processing output module fills in the indistinguishable subdivision image based on the neighboring reference subdivision image to complete the processing of the subdivision region, including: Extract the texture features and signal intensity distribution of the adjacent reference subdivision image; Based on the texture features and signal intensity distribution, fill data is generated at the corresponding spatial location of the indistinguishable subdivision image using a spatial interpolation algorithm; The filled data is fused with the residual valid signal of the indistinguishable subdivision image to generate the processed complete subdivision image data.

[0016] Compared with existing technologies, this invention collects imaging parameters of various local regions in the original imaging data and calculates image quality scores. Based on a quality threshold, it divides the imaging region into qualified and unoptimized regions. Then, based on the image quality scores, it further subdivides and classifies the unoptimized region, generating a baseline subdivision image and an indistinguishable subdivision image. Finally, it interpolates the indistinguishable subdivision image based on neighboring baseline subdivision images, ultimately outputting an optimized image. This invention achieves the identification and targeted adaptive optimization of defects in handheld probe imaging images, improving the discernibility of the output image and enhancing the operational efficiency of the scanning equipment.

[0017] In particular, this invention addresses local imaging defects caused by handheld optical coherence tomography (OCT) probes during clinical operations due to shaking or uneven tissue contact. In practice, raw imaging data often contains areas with insufficient signal-to-noise ratio or large areas of motion artifacts, affecting overall image interpretation. This invention achieves quantitative identification of imaging defect areas by acquiring imaging parameters such as local signal-to-noise ratio, structural contrast, signal uniformity, and motion artifact index, and calculating image quality scores accordingly.

[0018] In particular, this invention takes into account the potential for uneven quality within the imaging region to be optimized. In practice, uniformly processing the entire region to be optimized may result in excessive smoothing of details or loss of effective signal. This invention further subdivides the imaging region to be optimized based on image quality scores, dividing it into multiple sub-regions and distinguishing between a baseline subdivision image and an indistinguishable subdivision image, thus achieving a more refined and differentiated granularity of image optimization.

[0019] In particular, this invention takes into account the difficulty of independently reconstructing indistinguishable subdivision images due to severe signal loss. In practice, directly discarding or simply interpolating such regions can lead to image structural breaks or texture distortion. This invention determines neighboring reference subdivision images based on the spatial location of the indistinguishable subdivision image, and extracts their texture features and signal intensity distribution for spatial interpolation and signal fusion, thereby achieving high-fidelity filling and reconstruction of severely defective regions. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the structural connection of a detection system for a handheld optical coherence tomography probe according to an embodiment of the invention. Figure 2 This is a logic block diagram illustrating how each local region is divided into a qualified imaging region or an imaging region to be optimized, as an embodiment of the invention. Detailed Implementation

[0021] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0022] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0023] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0024] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0025] Please see Figure 1 As shown, Figure 1 This is a schematic diagram of the structural connection of a detection system for a handheld optical coherence tomography probe according to an embodiment of the invention. The detection system for a handheld optical coherence tomography probe of the present invention includes: The image acquisition module is used to receive raw imaging data from the probe and acquire imaging parameters of each local region in the raw imaging data, including local signal-to-noise ratio, structural contrast, signal uniformity, and artifact area. An image analysis module, connected to the image acquisition module, is used to calculate the image quality score of each local region based on the imaging parameters; An imaging region evaluation module, which is connected to the image analysis module, is used to divide each local region into a qualified imaging region or an imaging region to be optimized based on the image quality score of each local region and a preset quality threshold. A processing output module, connected to the imaging region, is used to execute differentiated image optimization strategies, including: For the qualified imaging area, output its corresponding raw imaging data; For the imaging region to be optimized, the region is subdivided based on the image quality score, and a preset image enhancement algorithm is called to process the subdivided region, and the processed subdivided region image is output.

[0026] Specifically, the image acquisition module is used to acquire imaging parameters of each local region in the original imaging data, including: The local signal-to-noise ratio is obtained by calculating the ratio of the average signal strength to the standard deviation within the local area. Structural contrast is obtained by calculating the maximum gradient magnitude of signal intensity between adjacent tissue structures within the local region. Signal uniformity is obtained by calculating the variance or information entropy value of the signal intensity within the local region; The motion artifact index is obtained by calculating the fluctuation variance of the signal intensity in the slow scan direction.

[0027] Specifically, the image analysis module is used to calculate the image quality score of each local region based on the imaging parameters, including: Calculate the basic parameters of sharpness based on the local signal-to-noise ratio and structural contrast. The basic parameters of regularity are calculated based on the signal uniformity and motion artifact index. The weighted sum of the sharpness and regularity parameters is used as the image quality score of the local region.

[0028] Specifically, the weighting weight of the basic sharpness parameter is set to 0.4, and the weighting weight of the basic regularity parameter is set to 0.6. This weighting allocation is based on considerations of the characteristics of handheld optical coherence tomography (OCT) imaging. In clinical practice, motion artifacts and signal inhomogeneity introduced by slight shaking of the probe held by the doctor, patient micro-movements, or uneven tissue contact pressure are the primary factors leading to decreased image quality and affecting structural coherence and diagnostic discernibility. The basic regularity parameter is directly related to the motion artifact index and signal uniformity, so it is given a higher weight to emphasize sensitivity to image stability and consistency defects. In contrast, while the local signal-to-noise ratio and structural contrast reflected by the basic sharpness parameter are important for detail rendering, their degradation in handheld imaging scenarios is often secondary to the aforementioned instability factors. Therefore, by giving the basic regularity parameter a higher weight, the image quality score can more effectively identify imaging areas that require priority optimization due to operational instability.

[0029] Specifically, the image analysis module calculates the basic parameters of sharpness based on the local signal-to-noise ratio and structural contrast, including: The signal-to-noise component is determined by the ratio of the local signal-to-noise ratio to the standard signal-to-noise ratio; The ratio of the structural contrast to the standard contrast is used to determine the contrast component; The weighted sum of the signal-to-noise component and the contrast component is used as the basic parameter for sharpness.

[0030] Specifically, during the system calibration phase, a standard reflective sample is used, and multiple images are taken under ideal and stable alignment between the probe and the sample. The local signal-to-noise ratio (SNR) of several local regions in the large number of obtained test images is calculated and statistically analyzed, and the average value is determined as the standard SNR of the system under the current configuration. Specifically, during the system calibration phase, a standard reflective sample is used, and multiple images are taken under ideal and stable alignment between the probe and the sample. The structural contrast of several local regions in the large number of test images obtained is calculated and statistically analyzed, and the average value is determined as the contrast component of the system under the current configuration.

[0031] Specifically, the weighting weight of the signal-to-noise component is set to 0.5, and the weighting weight of the contrast component is set to 0.5. Specifically, the image analysis module calculates the basic parameters of regularity based on the signal uniformity and motion artifact index, including: The uniformity component is determined by the ratio of the signal uniformity to a standard uniformity reference value. The ratio of the motion artifact index to the standard artifact index is used to determine the artifact influence coefficient. The result of subtracting the artifact influence component from 1 is determined as the artifact suppression component; The weighted sum of the uniformity component and the artifact suppression component is used as the basic parameter of regularity.

[0032] Specifically, during the system calibration phase, a standard reflective sample is used, and multiple images are taken under ideal and stable alignment between the probe and the sample. The signal uniformity of several local regions in the large number of test images obtained is calculated and statistically analyzed, and the average value is determined as the standard uniformity reference value for the system under the current configuration.

[0033] Specifically, during the system calibration phase, a standard reflective sample is used, and multiple images are taken under ideal and stable alignment between the probe and the sample. The motion artifact index of several local regions in the large number of test images obtained is calculated and statistically analyzed, and its average value is determined as the standard artifact index of the system under the current configuration.

[0034] Specifically, the weighting weight of the uniformity component is set to 0.4, and the weighting weight of the homogeneity component is set to 0.6. Because the doctor's handheld operation inevitably introduces slight vibrations, and because the pressure distribution is uneven when the probe contacts the curved surface of biological tissue, motion artifacts typically have a much greater destructive effect on the image's structural coherence and the integrity of diagnostic information than the slight fluctuations in the signal's uniformity itself. Assigning a higher weight to the artifact suppression component makes the basic regularity parameter more sensitive to motion-related distortions, thereby ensuring that the image quality assessment system can accurately capture the image defects that most seriously affect image interpretation in clinical practice.

[0035] Please see Figure 2 As shown, Figure 2 This is a logic block diagram illustrating how to divide local regions into qualified imaging regions or imaging regions to be optimized, according to an embodiment of the invention. The imaging region evaluation module is used to divide each local region into qualified imaging regions or imaging regions to be optimized based on the image quality score of each local region and a preset quality threshold. If the image quality score corresponding to the local region is greater than or equal to the preset quality threshold, then the local region is classified as a qualified imaging region. If the image quality score corresponding to the local region is less than a preset quality threshold, then the local region is divided into an imaging region to be optimized.

[0036] Specifically, the processing output module subdivides the imaging region to be optimized based on the image quality score, including: The adjustment coefficient is determined based on the ratio of the preset quality threshold to the image quality score; The number of subdivisions is determined based on the mapping relationship between the adjustment coefficient and the number of subdivisions; Based on the aforementioned subdivision quantity, the imaging region to be optimized is segmented and classified for selection; The mapping relationship between the adjustment coefficient and the number of subdivisions is predetermined.

[0037] Specifically, the processing output module performs segmentation and classification filtering of the imaging region to be optimized based on the number of subdivisions, including: The imaging region to be optimized is uniformly divided into a corresponding number of sub-regions according to the specified number of sub-regions; Calculate the image quality score for each sub-region and classify it into a baseline subdivision image and an indistinguishable subdivision image based on the image quality score of each sub-region.

[0038] Specifically, the processing output module calls a preset image enhancement algorithm to process the subdivided regions, including: Based on the spatial location of the indistinguishable subdivision image, determine the nearest reference subdivision image; The indistinguishable subdivision image is filled in based on the nearby reference subdivision image to complete the processing of the subdivision region.

[0039] Specifically, the processing output module fills in the indistinguishable subdivision image based on the neighboring reference subdivision image to complete the processing of the subdivision region, including: Extract the texture features and signal intensity distribution of the adjacent reference subdivision image; Based on the texture features and signal intensity distribution, fill data is generated at the corresponding spatial location of the indistinguishable subdivision image using a spatial interpolation algorithm; The filled data is fused with the residual valid signal of the indistinguishable subdivision image to generate the processed complete subdivision image data.

[0040] Specifically, there are no restrictions on the specific form of the image acquisition module, image analysis module, imaging area evaluation module, and processing output module. They can be composed of logic components, including field-programmable processors, computers, or microprocessors in computers, which will not be elaborated further.

[0041] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A detection system for a handheld optical coherence tomography probe, characterized in that, include: The image acquisition module is used to receive raw imaging data from the probe and acquire imaging parameters of each local region in the raw imaging data, including local signal-to-noise ratio, structural contrast, signal uniformity, and artifact area. An image analysis module, connected to the image acquisition module, is used to calculate the image quality score of each local region based on the imaging parameters; An imaging region evaluation module, which is connected to the image analysis module, is used to divide each local region into a qualified imaging region or an imaging region to be optimized based on the image quality score of each local region and a preset quality threshold. A processing output module, connected to the imaging region, is used to execute differentiated image optimization strategies, including: For the qualified imaging area, output its corresponding raw imaging data; For the imaging region to be optimized, the region is subdivided based on the image quality score, and a preset image enhancement algorithm is called to process the subdivided region, and the processed subdivided region image is output.

2. The detection system for a handheld optical coherence tomography probe according to claim 1, characterized in that, The image acquisition module is used to acquire imaging parameters of each local region in the original imaging data, including... The local signal-to-noise ratio is obtained by calculating the ratio of the average signal strength to the standard deviation within the local area. Structural contrast is obtained by calculating the maximum gradient magnitude of signal intensity between adjacent tissue structures within the local region. Signal uniformity is obtained by calculating the variance or information entropy value of the signal strength within the local area; The motion artifact index is obtained by calculating the fluctuation variance of the signal intensity in the slow scan direction.

3. The detection system for a handheld optical coherence tomography probe according to claim 1, characterized in that, The image analysis module is used to calculate the image quality score of each local region based on the imaging parameters, including... Calculate the basic parameters of sharpness based on the local signal-to-noise ratio and structural contrast. The basic parameters of regularity are calculated based on the signal uniformity and motion artifact index. The weighted sum of the sharpness and regularity parameters is used as the image quality score of the local region.

4. The detection system for a handheld optical coherence tomography probe according to claim 3, characterized in that, The image analysis module calculates the basic parameters of sharpness based on the local signal-to-noise ratio and structural contrast, including... The signal-to-noise component is determined by the ratio of the local signal-to-noise ratio to the standard signal-to-noise ratio; The ratio of the structural contrast to the standard contrast is used to determine the contrast component; The weighted sum of the signal-to-noise component and the contrast component is used as the basic parameter for sharpness.

5. The detection system for a handheld optical coherence tomography probe according to claim 3, characterized in that, The image analysis module calculates the basic parameters of regularity based on the signal uniformity and motion artifact index, including: The uniformity component is determined by the ratio of the signal uniformity to a standard uniformity reference value. The ratio of the motion artifact index to the standard artifact index is used to determine the artifact influence coefficient. The result of subtracting the artifact influence component from 1 is determined as the artifact suppression component; The weighted sum of the uniformity component and the artifact suppression component is used as the basic parameter of regularity.

6. The detection system for a handheld optical coherence tomography probe according to claim 1, characterized in that, The imaging region evaluation module is used to divide each local region into a qualified imaging region or an imaging region to be optimized based on the image quality score of each local region and a preset quality threshold. If the image quality score corresponding to the local region is greater than or equal to the preset quality threshold, then the local region is classified as a qualified imaging region. If the image quality score corresponding to the local region is less than a preset quality threshold, then the local region is divided into an imaging region to be optimized.

7. The detection system for a handheld optical coherence tomography probe according to claim 1, characterized in that, The processing output module subdivides the imaging region to be optimized based on the image quality score, including: The adjustment coefficient is determined based on the ratio of the preset quality threshold to the image quality score; The number of subdivisions is determined based on the mapping relationship between the adjustment coefficient and the number of subdivisions; Based on the aforementioned subdivision quantity, the imaging region to be optimized is segmented and classified for selection; The mapping relationship between the adjustment coefficient and the number of subdivisions is predetermined.

8. The detection system for a handheld optical coherence tomography probe according to claim 7, characterized in that, The processing output module performs segmentation and classification filtering of the imaging region to be optimized based on the specified number of subdivisions, including... The imaging region to be optimized is uniformly divided into a corresponding number of sub-regions according to the specified number of sub-regions; Calculate the image quality score for each sub-region and classify it into a baseline subdivision image and an indistinguishable subdivision image based on the image quality score of each sub-region.

9. The detection system for a handheld optical coherence tomography probe according to claim 1, characterized in that, The processing output module calls a preset image enhancement algorithm to process the subdivided regions, including... Based on the spatial location of the indistinguishable subdivision image, determine the nearest reference subdivision image; The indistinguishable subdivision image is filled in based on the nearby reference subdivision image to complete the processing of the subdivision region.

10. The detection system for a handheld optical coherence tomography probe according to claim 9, characterized in that, The processing output module fills in the indistinguishable subdivision image based on the neighboring reference subdivision image to complete the processing of the subdivision region, including... Extract the texture features and signal intensity distribution of the adjacent reference subdivision image; Based on the texture features and signal intensity distribution, fill data is generated at the corresponding spatial location of the indistinguishable subdivision image using a spatial interpolation algorithm; The filled data is fused with the residual valid signal of the indistinguishable subdivision image to generate the processed complete subdivision image data.

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