Portable DR medical image data acquisition method and system

Abstract

The application relates to the technical field of medical image data collection, and discloses a portable DR medical image data collection method and system. The method can accurately identify the unreliable working state of a wired data transmission interface by continuously monitoring the wired data transmission interface in the whole process of wireless data transmission task operation and combining image quality quantitative evaluation.

Description

Technical Field

[0001] This invention relates to the field of medical image data acquisition technology, and more specifically, to a portable DR medical image data acquisition method and system. Background Technology

[0002] In primary healthcare institutions or mobile healthcare scenarios, portable DR (Digital Radiography) devices bear the crucial responsibility of rapidly acquiring patients' X-ray image data and seamlessly integrating this data with hospital information systems. However, in practical applications, these devices often face challenges such as low data acquisition efficiency, inconsistent storage formats, and difficulties in cross-system sharing. These problems can lead to delays in diagnostic results and even create information silos, hindering the effective flow of medical information. Especially in high-frequency and variable usage environments, the device's data transmission interfaces, particularly cable interfaces, are subjected to mechanical stress from repeated plugging and unplugging, leading to a series of transmission reliability issues. Summary of the Invention

[0003] This invention provides a portable DR medical image data acquisition method and system, aiming to solve the problem of transmission reliability caused by repeated plugging and unplugging of data transmission interfaces in the complex and ever-changing usage environment of portable DR devices, as well as the resulting technical problems such as interruption of medical image data transmission, damage to data integrity, and lag in diagnostic information.

[0004] The technical solution of this application is as follows: In a first aspect, this application discloses a portable DR medical image data acquisition method, the method comprising: The wireless data transmission task of the medical image file corresponding to the portable DR device is initiated. Based on the image content features of the bone region boundary and soft tissue region boundary in the medical image file, the medical image file is segmented into multiple independent image data blocks. Throughout the entire wireless data transmission task, the electrical status of the wired data transmission interface is continuously monitored using a preset low-frequency sampling method. The electrical status includes the interface voltage status, physical contact status, and transmission signal quality status. When physical connection signs are detected at the wired data transmission interface during background monitoring, a preset fixed-duration status observation period is triggered. During the status observation period, the image quality of each image data block that has been transmitted is quantitatively evaluated. The similarity of the image structure between each transmitted image data block and the original image data block is calculated, and the electrical status fluctuation of the wired data transmission interface is monitored in real time. After the observation period ends, if the similarity of the image structure of all transmitted image data blocks is higher than the preset quality threshold, and the electrical state fluctuation of the wired data transmission interface exceeds the preset stability threshold, then the wired data transmission interface is determined to be in an unreliable working state. The current data transmission mode is locked as wireless data transmission mode, and the wireless data transmission task is continuously executed until the current wireless data transmission task is completed, or until the electrical signal of the wired data transmission interface remains stable within a preset continuous time period, so as to ensure the quality of medical image data transmission.

[0005] This technical solution effectively addresses the problem of data transmission interruption caused by system misjudgment and repeated switching of transmission modes when the wired interface of portable DR devices is unstable. By combining image quality feedback with electrical status monitoring, the unreliable state of the wired interface is accurately identified, and the wireless transmission mode is locked in time, thereby ensuring the transmission quality and integrity of medical image data and avoiding diagnostic delays caused by transmission interruption.

[0006] Furthermore, the medical image file is subjected to region segmentation processing, splitting it into multiple independent image data blocks, including: The acquired raw medical image files are preprocessed with grayscale normalization to unify the range of grayscale values ​​and eliminate interference from differences in imaging brightness. Edge enhancement operations are performed on the medical images after grayscale normalization to highlight the edge contour features of different tissue regions in the image; Based on the location of gray-level abrupt changes in the edge enhancement image, the boundary contour between the skeletal region and the soft tissue region is accurately located and fitted. Based on the boundary contours, the skeletal region, soft tissue region, and image background region are encoded independently to generate multiple independent image data blocks.

[0007] Through this technical solution, this application can achieve fine segmentation of medical images, independently encoding bone, soft tissue and background regions, providing more refined data units for subsequent image quality evaluation, thereby improving the accuracy and relevance of image quality evaluation, and ensuring more effective quality monitoring of key areas during transmission.

[0008] Based on the above, the image structure similarity between each transmitted image data block and the original image data block is calculated, including: The image data blocks returned by the receiving end are compared with the original image data blocks stored by the sending end in terms of features one by one. The similarity between the two sets of image data blocks in three dimensions—overall brightness distribution matching degree, local contrast matching degree, and image texture structure matching degree—is quantified and calculated separately. The matching data from the three dimensions are weighted and fused to generate a comprehensive similarity index that can characterize the integrity and consistency of the transmitted images. The calculated comprehensive similarity index is used to determine the degree of image structural similarity between the two sets of image data blocks.

[0009] This application provides a multi-dimensional and comprehensive image structure similarity evaluation method through this technical solution. By quantifying and weighting the comparison and fusion of key features such as brightness, contrast and texture, it can more comprehensively and accurately evaluate the integrity and consistency of the transmitted image, thereby providing a more reliable image quality feedback basis for the reliability judgment of wired interfaces.

[0010] In some preferred embodiments, the method further includes a precise determination step for unstable wired interface states, which includes: When background monitoring detects signs of physical connection at the wired data transmission interface, the sampling frequency of the electrical status is switched from the preset low frequency to the high frequency sampling mode. In the high-frequency sampling mode, the real-time electrical signals of the wired data transmission interface are continuously and densely sampled to obtain complete electrical signal timing data; Waveform morphology analysis is performed on the electrical signal time-series data obtained by dense sampling to extract the fluctuation amplitude sequence and fluctuation frequency characteristics corresponding to the signal time-series changes. The extracted fluctuation amplitude sequence and fluctuation frequency features are matched with the pre-stored interface unstable signal feature template. When the matching degree reaches the preset matching threshold, it is determined that the wired data transmission interface has a real unstable working state, providing a basis for judgment for image transmission mode switching.

[0011] Through this technical solution, this application can more accurately identify the true unstable state of the wired interface by high-frequency sampling and waveform morphology analysis, avoiding misjudgments that may be caused by low-frequency sampling, thereby providing a more reliable basis for intelligent switching of transmission modes and further improving the robustness of the system.

[0012] To enhance functionality, the method further includes an electromagnetic interference avoidance step, which includes: Before each low-frequency sampling action is executed, the electrical signal of the wired data transmission interface is briefly presampled at a sampling frequency higher than the preset low frequency. Based on the pre-sampled electrical signal data, identify and determine whether there are external electromagnetic interference signals around the interface; If an external electromagnetic interference signal is detected, the execution timing of the current low-frequency sampling action is delayed until the intensity of the external electromagnetic interference signal attenuates to within the preset allowable intensity range; If no external electromagnetic interference signal is detected, the low-frequency sampling action will be performed normally according to the preset timing sequence to ensure the accuracy of interface status monitoring data and avoid interference affecting the image transmission quality judgment.

[0013] This technical solution effectively avoids the impact of external electromagnetic interference on interface status monitoring data. Through pre-sampling and delayed sampling mechanisms, it ensures low-frequency sampling in an environment with no interference or controllable interference, thereby improving the accuracy of interface status monitoring, avoiding misjudgments caused by interference, and further ensuring the accuracy of image transmission quality judgment.

[0014] As a technical improvement, the method also includes an interface local instability auxiliary determination step, which includes: Before determining that the wired data transmission interface is in an unreliable state, the real-time fluctuation feature values ​​of the interface are extracted in three dimensions: voltage state, contact state, and signal quality. Compare the fluctuation feature values ​​of each dimension with the preset normal range of the corresponding dimension one by one to determine whether there is a situation where the fluctuation feature value of a single dimension exceeds the corresponding abnormal threshold, while the fluctuation feature values ​​of the other dimensions are all within the normal range; If this situation exists, the wired data transmission interface will be marked as locally unstable, and the locally unstable state will be marked as an unreliable state of the interface.

[0015] Through this technical solution, this application can assist in the identification of local unstable states of wired interfaces. By independently analyzing three dimensions—voltage, contact, and signal quality—it can more meticulously identify potential problems of the interface. Even if an anomaly occurs in a single dimension, it can provide timely warnings, thereby improving the sensitivity and accuracy of determining unreliable states of the interface.

[0016] As a further improvement, the method also includes an early warning step for physical wear of the wired interface, which includes: In each low-frequency sampling electrical condition monitoring process, the central tendency feature value and dispersion feature value of the electrical signal are extracted; The central tendency feature value and dispersion feature value extracted in real time are compared and verified with the pre-stored normal value range; If the comparison results of multiple consecutive low-frequency samples all show that the feature values ​​deviate from the normal range, an early warning signal is generated indicating that there is physical wear and tear on the wired data transmission interface, thus avoiding the risk of abnormal image transmission in advance.

[0017] Through this technical solution, this application can achieve early warning of physical wear of wired interfaces. By continuously monitoring the concentration trend and dispersion characteristics of electrical signals and performing continuous comparison and verification, potential physical wear signs of the interface can be detected in time, thereby avoiding the risk of abnormal image transmission in advance and extending the service life of the equipment.

[0018] To optimize the structure, a transmission interference immunity optimization step is also included, which includes: The operating parameters of the current wireless data transmission task are adaptively adjusted to an anti-external interference transmission mode. The anti-external interference transmission mode includes at least one parameter optimization method: reducing the data transmission rate per unit time, increasing the redundancy of error correction coding in the transmitted data, and switching to a preset backup communication frequency band to stabilize the medical image data transmission process.

[0019] Through this technical solution, this application can further optimize the anti-interference capability of wireless transmission after locking the wireless transmission mode. By adaptively adjusting transmission parameters, such as reducing the rate, increasing error correction codes, or switching frequency bands, it can effectively cope with complex electromagnetic environments, thereby ensuring the stability and reliability of medical image data during wireless transmission.

[0020] To improve the scheme, the method also includes a sampling frequency adaptive adjustment step, which includes: Before initiating the wireless data transmission task, collect the real-time remaining battery power information of the portable DR device; Compare the real-time remaining battery power with the preset lower battery power threshold. When the real-time remaining battery power is lower than the preset lower limit threshold, the low-frequency sampling frequency of the wired interface background monitoring is reduced to achieve adaptive optimization of device power consumption and ensure stable operation of medical image transmission monitoring over a long period of time.

[0021] Through this technical solution, this application can achieve adaptive adjustment of the sampling frequency, dynamically adjust the low-frequency sampling frequency according to the battery power of the device, thereby optimizing the power consumption of the device, extending the working time of the device in battery-powered mode, and ensuring the stable operation of long-term medical image transmission and monitoring.

[0022] Secondly, this application also discloses a portable DR medical image data acquisition system for performing the above-mentioned portable DR medical image data acquisition method. The system includes: an image preprocessing module, a wireless transmission control module, an interface monitoring module, an image quality evaluation module, and a status determination module. The image preprocessing module is used to acquire medical image files collected by portable DR devices. Based on the image features of the boundaries of the skeletal region and the soft tissue region within the image, the medical image file is segmented into regions to generate multiple independent image data blocks. The wireless transmission control module is used to initiate the wireless data transmission task of medical image files. When the status determination module determines that the wired data transmission interface is unreliable, it locks the wireless data transmission mode and continues to execute the transmission task. At the same time, it adaptively adjusts the wireless transmission anti-interference parameters to ensure the quality of image data transmission. The interface monitoring module is used to perform background monitoring of the electrical status of the wired data transmission interface, including voltage status, contact status, and signal quality, at a preset low frequency during the operation of the wireless transmission task. It identifies physical connection signs of the interface and triggers a preset observation period, while simultaneously collecting electrical status fluctuation data. The image quality evaluation module is used to compare the multi-dimensional features of the transmitted image data block with the original image data block within the state observation period, quantify the image structure similarity, and complete the transmission image quality evaluation. The status determination module is connected to the interface monitoring module and the image quality evaluation module respectively. It is used to combine the image structure similarity threshold comparison results and the electrical state fluctuation amplitude threshold comparison results to comprehensively determine the working reliability status of the wired data transmission interface and realize intelligent switching of transmission mode based on image quality feedback.

[0023] This application provides a system that integrates image preprocessing, wireless transmission control, interface monitoring, image quality evaluation, and status determination. Through the collaborative work between modules, it achieves comprehensive management and intelligent control of the portable DR medical image data acquisition process. In particular, when the wired interface is unreliable, it can switch to and optimize wireless transmission in a timely manner, thereby effectively ensuring the transmission quality of medical image data and the stability of system operation. Beneficial effects

[0024] The portable DR medical image data acquisition method and system disclosed in this application can accurately identify unreliable working states of the wired data transmission interface by continuously monitoring the background of the wired data transmission interface throughout the entire operation of the wireless data transmission task and combining it with image quality quantitative evaluation.

[0025] Through the above technical solution, this application effectively solves the problem in the prior art where, when the wired interface of a portable DR device is physically worn or unstable, the main control program may misjudge the wired connection signal, leading to repeated interruptions of the wireless transmission task, and consequently causing medical image data transmission failure and diagnostic information delay. This application introduces an image quality feedback mechanism, using the actual quality of the transmitted data as the key criterion for judging the reliability of the interface, avoiding misjudgments that may arise from relying solely on electrical signal fluctuations. This mechanism, based on both image quality and electrical status judgment, enables the system to switch transmission modes more intelligently and accurately, thereby ensuring the integrity and continuity of medical image data transmission, avoiding data retransmission and diagnostic delays caused by transmission interruptions, and significantly improving the reliability and efficiency of data transmission in complex and variable environments for portable DR devices. Attached Figure Description

[0026] Figure 1 This is a flowchart illustrating a portable DR medical image data acquisition method provided in an embodiment of the present invention; Figure 2 This is a flowchart of a method for splitting an image into multiple independent image data blocks according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of a portable DR medical image data acquisition system provided in an embodiment of the present invention. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] Reference Figure 1 , Figure 1 This is a flowchart illustrating a portable DR medical image data acquisition method provided in an embodiment of the present invention. The method includes: S11, start the wireless data transmission task of the medical image file corresponding to the portable DR device, and perform region segmentation processing on the medical image file according to the image content features of the bone region boundary and soft tissue region boundary in the medical image file to split it into multiple independent image data blocks; S12, throughout the entire operation of the wireless data transmission task, the electrical status of the wired data transmission interface is continuously monitored in the background using a preset low-frequency sampling method. The electrical status includes the interface voltage status, physical contact status, and transmission signal quality status. S13, when physical connection signs are detected in the wired data transmission interface during background monitoring, a preset fixed-duration status observation period is triggered. S14, during the state observation period, perform image quality quantitative evaluation on each image data block that has been transmitted, calculate the image structure similarity between each transmitted image data block and the original image data block, and monitor the electrical state fluctuation amplitude of the wired data transmission interface in real time. S15, after the state observation period ends, if the image structure similarity of all transmitted image data blocks is higher than the preset quality threshold, and the electrical state fluctuation of the wired data transmission interface exceeds the preset stability threshold, then the wired data transmission interface is determined to be in an unreliable working state. S16, lock the current data transmission mode to wireless data transmission mode, and continue to execute the wireless data transmission task until the current wireless data transmission task is completed, or until the electrical signal of the wired data transmission interface remains stable within a preset continuous time period, so as to ensure the quality of medical image data transmission.

[0029] The portable DR medical image data acquisition method proposed in this application aims to solve the problems of interrupted medical image data transmission and compromised data integrity caused by the instability of wired data transmission interfaces in complex and variable environments. This method ensures the reliability and efficiency of medical image data transmission through an intelligent transmission mode switching mechanism.

[0030] Specifically, a "portable DR device" refers to a mobile digital X-ray imaging device characterized by its small size and light weight, making it easy to use in primary healthcare institutions, mobile medical vehicles, or field settings, and enabling rapid acquisition of patients' X-ray image data. A "medical image file" typically refers to a digital image file conforming to the DICOM (Digital Imaging and Communications in Medicine) standard or other medical image formats, containing detailed image information of the patient's skeletal and soft tissue areas.

[0031] In the embodiments of this application, the portable DR medical image data acquisition method first initiates the wireless data transmission task of the medical image files corresponding to the portable DR device. This means that the device will prioritize sending out the acquired medical image files via a wireless network (such as Wi-Fi, Bluetooth, or cellular network). Throughout the wireless data transmission task, the system continuously monitors the electrical status of the wired data transmission interface using a preset low-frequency sampling method. This low-frequency sampling effectively reduces system power consumption without affecting the normal operation of wireless transmission. The monitoring of the electrical status covers the interface voltage status, physical contact status, and transmission signal quality status, all of which are key indicators for judging the health status of the wired interface.

[0032] For example, in one implementation, the wireless data transmission task can be initiated by the user selecting the "Wireless Transmission" option on the portable DR device's operating interface, or automatically by the device when it detects a wireless network is available. Medical image files can be stored in the device's internal memory, awaiting transmission.

[0033] In the processing of medical image files for region segmentation, the image file can be segmented into multiple independent image data blocks based on the image content features of the boundaries between skeletal and soft tissue regions. For example, image processing algorithms such as edge detection, thresholding, or deep learning-based semantic segmentation techniques can be used to identify and distinguish between skeletal and soft tissue regions in the image. Segmenting the original image into multiple independent image data blocks improves the flexibility and fault tolerance of data transmission; for instance, if the transmission of a data block fails, only that data block needs to be retransmitted, instead of retransmitting the entire file.

[0034] For continuous background monitoring of the electrical status of wired data transmission interfaces, a preset low-frequency sampling method can be used. For example, the electrical parameters such as voltage, current, and resistance of the wired interface can be sampled every few seconds or tens of seconds and their values ​​recorded. The physical contact status can be determined by detecting the continuity or impedance changes of the interface pins. The transmission signal quality status can be evaluated by monitoring indicators such as the signal-to-noise ratio and bit error rate.

[0035] When background monitoring detects signs of physical connection at a wired data transmission interface, the system triggers a pre-set, fixed-duration status observation period. Signs of physical connection may manifest as changes in interface voltage levels, a decrease in contact resistance, or the detection of a handshake signal from a wired transmission protocol. The status observation period is designed to provide a more detailed assessment of potential wired interface connections, avoiding misjudgments due to momentary fluctuations.

[0036] During the observation period, the system performs a quantitative evaluation of the image quality of each transmitted image data block, calculating the structural similarity between each transmitted image data block and the original image data block. For example, image quality can be quantified by calculating metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). Simultaneously, the system monitors the electrical status fluctuations of the wired data transmission interface in real time. For example, it can record the maximum fluctuation range or standard deviation of electrical parameters such as voltage and current within the observation period.

[0037] After the status observation period ends, the system will make a comprehensive judgment. If the image structure similarity of all transmitted image data blocks is higher than a preset quality threshold, it indicates that the data quality of wireless transmission is good and has not been significantly affected. Meanwhile, if the electrical state fluctuation of the wired data transmission interface exceeds a preset stability threshold, the wired data transmission interface is determined to be in an unreliable operating state. This means that although the wired interface may show signs of physical connection, its electrical performance is unstable and cannot provide reliable data transmission services.

[0038] Once the wired data transmission interface is determined to be in an unreliable operating state, the system will lock the current data transmission mode to wireless data transmission mode and continue executing the wireless data transmission task. This locking mechanism can effectively avoid wireless transmission interruptions caused by repeated misjudgments of the wired interface. The wireless data transmission task will continue until the current wireless data transmission task is completed, or until the electrical signal of the wired data transmission interface remains stable for a preset continuous time period. The latter provides a mechanism to restore wired transmission; that is, when the wired interface truly returns to stability, the system can reconsider switching the transmission mode.

[0039] This application discloses a portable DR medical image data acquisition method that intelligently determines the reliability of the wired interface by continuously monitoring its status during wireless transmission and combining this with the quality evaluation of transmitted image data blocks. When the wired interface is deemed unreliable, the system locks the wireless transmission mode, effectively avoiding transmission interruptions and data loss caused by wired interface instability. This method significantly improves the reliability and efficiency of medical image data transmission in complex environments, ensures the quality of medical image data transmission, and avoids the transmission failure cycle caused by repeated plugging and unplugging of the wired interface in traditional solutions, as well as the resulting diagnostic delays and information silos. Compared to existing technologies, the core innovation of this application lies in the introduction of an intelligent discrimination mechanism based on image quality feedback and electrical state fluctuation monitoring, which enables adaptive switching and locking of the transmission mode. This fundamentally solves the interference of wired interface instability on medical image data transmission, ensuring the integrity and timeliness of medical image data.

[0040] For details, please refer to Figure 2 , Figure 2 This is a flowchart of a method for splitting an image into multiple independent image data blocks according to an embodiment of the present invention. S11 may include the following steps: S111, perform grayscale normalization preprocessing on the acquired raw medical image files to unify the range of image grayscale values ​​and eliminate interference from differences in imaging brightness; S112, perform edge enhancement operation on the medical image after grayscale normalization to highlight the edge contour features of different tissue regions in the image; S113, based on the position of gray-level abrupt change in the edge enhancement image, accurately locate and fit the boundary contour between the bone region and the soft tissue region; S114, based on the boundary contour, the skeletal region, the soft tissue region and the image background region are independently encoded to generate multiple independent image data blocks.

[0041] The grayscale normalization preprocessing of the acquired raw medical image files refers to mapping the pixel grayscale values ​​of the raw medical images to a uniform, preset numerical range, such as 0 to 255. The purpose is to eliminate inconsistencies in image brightness that may be caused by different imaging devices, different exposure parameters, or different acquisition conditions, thereby providing a standardized input for subsequent image processing and avoiding interference from brightness differences on the accuracy of region segmentation.

[0042] Furthermore, performing edge enhancement operations on the grayscale-normalized medical image can be understood as using specific image processing algorithms, such as the Sobel operator, Prewitt operator, or Laplacian operator, to highlight areas in the image where grayscale values ​​change drastically. These areas typically correspond to the boundaries between different tissues or structures in the image, such as the boundary between bone and soft tissue, or between soft tissue and the background. By enhancing these edge features, the subsequent boundary recognition process can be made more accurate and robust.

[0043] Specifically, accurately locating and fitting the boundary contour between skeletal and soft tissue regions based on abrupt gray-level changes in an edge-enhanced image involves using image analysis techniques to identify points or regions in the edge-enhanced image where gray-level values ​​change abruptly from one stable level to another. These gray-level abrupt change points are considered potential boundary points. By connecting and fitting these points, the boundary line between skeletal and soft tissue regions can be accurately delineated. For example, thresholding, region growing, or active contour models can be used to locate and fit the contour.

[0044] Based on this, and using the defined boundary contours, the skeletal region, the soft tissue region, and the image background region are independently encoded to generate multiple independent image data blocks. This means that after determining the skeletal region, the soft tissue region, and the remaining image background region, each region is considered an independent entity. These independent regions are then encoded separately, for example, using different compression algorithms or data formats, to generate their own independent image data blocks. This independent encoding process facilitates differentiated processing based on the importance or characteristics of different regions during subsequent data transmission, such as using higher-quality encoding for critical skeletal regions. It also facilitates the verification and recovery of individual data blocks during transmission, improving the flexibility and efficiency of transmission.

[0045] The solution in this application eliminates the interference of imaging brightness differences on subsequent processing by performing grayscale normalization preprocessing on the original medical image files, ensuring the consistency of image data. Through the above technical solution, this application can achieve high-precision region segmentation of medical image files, ensuring accurate identification and separation of bone and soft tissue regions. This refined segmentation process effectively avoids segmentation errors caused by uneven image brightness or blurred edges in traditional methods, thereby improving the accuracy of subsequent image analysis and diagnosis.

[0046] Meanwhile, splitting images into multiple independent, individually encoded data blocks makes data transmission more flexible and robust. Even under poor network conditions, individual data blocks can be retransmitted or optimized, effectively reducing the risk of data loss or corruption. This significantly improves the overall quality and reliability of medical image data transmission, providing doctors with clearer and more accurate diagnostic information.

[0047] In some of the embodiments described above in this application, during the state observation period, it is necessary to conduct a quantitative evaluation of the image quality of each image data block that has been transmitted, which involves calculating the degree of image structural similarity between each transmitted image data block and the original image data block.

[0048] Specifically, the methods for calculating image structural similarity include: The image data blocks returned by the receiving end are compared with the original image data blocks stored by the sending end in terms of features one by one. The similarity between the two sets of image data blocks in three dimensions—overall brightness distribution matching degree, local contrast matching degree, and image texture structure matching degree—is quantified and calculated separately. The matching data from the three dimensions are weighted and fused to generate a comprehensive similarity index that can characterize the integrity and consistency of the transmitted images. The calculated comprehensive similarity index is used to determine the degree of image structural similarity between the two sets of image data blocks.

[0049] Specifically, dimensional feature comparison refers to a detailed, multi-layered comparison of the image data block received at the receiving end with the original image data block stored at the sending end. This can include pixel-level difference analysis, comparison of statistical features of image regions, and even feature extraction and matching based on deep learning. Its purpose is to comprehensively capture any subtle changes or distortions that may occur in the image during transmission.

[0050] Overall brightness distribution matching can be understood as evaluating the consistency of two images in terms of overall brightness and grayscale distribution. For example, this can be achieved by comparing statistical measures such as the grayscale histogram, average brightness value, or brightness variance of the two images. Local contrast matching measures whether the brightness differences and detail sharpness in local areas of an image remain consistent. For example, it can be evaluated by calculating the local variance of the image, edge strength, or using local contrast enhancement algorithms. Image texture structure matching focuses on comparing the similarity of repeating patterns, details, and surface features in an image. For example, it can be quantified using methods such as the gray-level co-occurrence matrix (GLCM), wavelet transform coefficients, or Fourier descriptors.

[0051] In practical applications, weighted fusion calculations of the matching data across the three dimensions aim to comprehensively consider various aspects of image quality and avoid the one-sidedness of evaluations based on a single dimension. For example, different weights can be assigned to the importance of different dimensions in medical image diagnosis, and a comprehensive similarity index that fully reflects the integrity and consistency of the transmitted image can be generated through linear weighting or nonlinear fusion algorithms. The higher the value of this comprehensive similarity index, the greater the structural similarity between the transmitted image and the original image, and the better the image quality.

[0052] This application's solution comprehensively evaluates image transmission quality from multiple perspectives by performing a dimensional feature comparison between the transmitted image data blocks and the original image data blocks, and quantifying the overall brightness distribution matching degree, local contrast matching degree, and image texture structure matching degree. This multi-dimensional and refined evaluation method ensures that any image distortion caused by transmission instability, whether it be brightness deviation, detail blurring, or texture destruction, can be effectively captured and quantified. By weighted fusion of these matching degree data, a comprehensive similarity index is generated, thus providing a more robust and accurate image quality evaluation result.

[0053] In some embodiments of this application, by performing background monitoring of the electrical state of the wired data transmission interface using a preset low-frequency sampling method, and combining this with a quantitative evaluation of the image quality of the transmitted image data blocks, it is possible to preliminarily determine whether the wired data transmission interface is in an unreliable operating state. However, this determination method based on low-frequency sampling and macroscopic electrical state fluctuation amplitude may struggle to accurately identify the specific type or instantaneous fluctuation characteristics of the interface's unstable state, thereby affecting the accuracy and timeliness of decision-making regarding transmission mode switching, and posing a risk of misjudgment or delayed determination.

[0054] In response, this application further proposes a precise determination step for unstable wired interface states, which includes: When the background monitoring detects signs of physical connection at the wired data transmission interface, the sampling frequency of the electrical status is switched from a preset low frequency to a high frequency sampling mode. In the high-frequency sampling mode, the real-time electrical signals of the wired data transmission interface are continuously and densely sampled to obtain complete electrical signal timing data; Waveform morphology analysis is performed on the electrical signal time-series data obtained by dense sampling to extract the fluctuation amplitude sequence and fluctuation frequency characteristics corresponding to the signal time-series changes. The extracted fluctuation amplitude sequence and fluctuation frequency features are matched with the pre-stored interface unstable signal feature template. When the matching degree reaches the preset matching threshold, it is determined that the wired data transmission interface has a real unstable working state, providing a basis for judgment for image transmission mode switching.

[0055] Specifically, when the background monitoring module initially identifies physical connection signs at the wired data transmission interface during continuous background monitoring—for example, detecting a change in interface voltage or a change in physical contact from disconnected to connected—the system immediately responds by switching the sampling frequency of the electrical state of the wired data transmission interface from the conventional preset low-frequency mode to a high-frequency sampling mode. The high-frequency sampling mode aims to capture instantaneous changes in electrical signals at a higher frequency and with more detailed time resolution, thereby revealing details that might be missed under low-frequency sampling.

[0056] Furthermore, after switching to high-frequency sampling mode, the system continuously and intensively samples the real-time electrical signals of the wired data transmission interface. This intensive sampling can obtain more complete and detailed electrical signal timing data, which includes all the minute fluctuations in voltage, current, impedance, etc., during the connection or operation of the interface, laying the foundation for subsequent in-depth analysis.

[0057] Based on this, a detailed waveform morphology analysis is performed on the densely sampled electrical signal timing data. By deeply analyzing the characteristics of the signal waveform, the fluctuation amplitude sequence and fluctuation frequency characteristics corresponding to the signal timing changes can be extracted. For example, the instantaneous peak value, valley value, rising edge, falling edge, periodic fluctuation, random noise, etc., of the signal can be analyzed, and these characteristics can be quantified to form a set of feature vectors describing the electrical behavior of the interface.

[0058] Subsequently, the extracted fluctuation amplitude sequence and fluctuation frequency features are matched with pre-stored interface instability signal feature templates. These feature templates, based on extensive experimental data and accumulated experience, represent typical electrical signal feature patterns of different types of wired interface instability states (e.g., poor contact, cable damage, electromagnetic interference, interface aging, etc.). By calculating the matching degree between real-time features and templates, such as using correlation coefficients, Euclidean distance, or machine learning classification algorithms, the similarity between the two can be quantified. When the matching degree reaches a preset matching threshold, it is considered that the real-time monitored electrical signal pattern highly matches a known unstable signal feature template, thus accurately determining that the wired data transmission interface has a real unstable operating state. This accurate determination result will serve as a reliable basis for initiating image transmission mode switching (e.g., switching from wired to wireless), avoiding transmission interruptions or data loss due to misjudgment.

[0059] This application's solution addresses the problem of inaccurate interface instability detection that may exist in the basic solution mentioned above. This is achieved by switching the electrical state sampling frequency of the wired data transmission interface from a preset low frequency to a high frequency, performing waveform morphology analysis on the densely sampled electrical signal time-series data, extracting fluctuation amplitude sequences and frequency characteristics, and then matching these with pre-stored interface instability signal feature templates. Specifically, while the preset low-frequency sampling method can detect physical connection signs of the interface during background monitoring, its sampling granularity is coarse, making it difficult to capture instantaneous or localized instability phenomena, such as brief contact defects or weak signal interference. By switching to the high-frequency sampling mode, the system can acquire complete time-series data of the electrical signals at a higher resolution, thereby revealing subtle fluctuations and transient characteristics that are unobservable under low-frequency sampling.

[0060] Furthermore, waveform morphology analysis of these densely sampled data allows for the extraction of more discriminative fluctuation amplitude sequences and frequency characteristics, which more accurately reflect the true operating state of the interface. Finally, matching these characteristics with pre-stored interface instability signal feature templates enables the system to identify specific types of instability states, such as distinguishing between those caused by poor physical contact, cable aging, or external electromagnetic interference. This avoids potential misjudgments based solely on macroscopic fluctuation amplitudes, significantly improving the accuracy of determining instability states in wired data transmission interfaces.

[0061] This application further proposes an electromagnetic interference avoidance step to improve the accuracy of interface status monitoring data and avoid interference affecting image transmission quality judgment.

[0062] Specifically, the above method also includes an electromagnetic interference avoidance step, which includes: Before each low-frequency sampling action is performed, the electrical signal of the wired data transmission interface is briefly pre-sampled at a sampling frequency higher than the preset low frequency. Based on the pre-sampled electrical signal data, identify and determine whether there are external electromagnetic interference signals around the interface; If the external electromagnetic interference signal is detected, the execution timing of the current low-frequency sampling action is delayed until the intensity of the external electromagnetic interference signal attenuates to a preset allowable intensity range; If no external electromagnetic interference signal is detected, the low-frequency sampling action is performed normally according to the preset timing sequence to ensure the accuracy of interface status monitoring data and avoid interference affecting the image transmission quality judgment.

[0063] "Brief pre-sampling" refers to a short, high-frequency acquisition of electrical signals before the formal low-frequency sampling. Its purpose is to quickly and sensitively capture any transient or periodic electromagnetic interference that may exist in the environment. A sampling frequency higher than the preset low frequency, for example, several times or even tens of times higher, allows for more precise capture of the waveform details of the electrical signal, making it easier to identify abnormal interference characteristics.

[0064] "Identifying and determining whether there are external electromagnetic interference signals around the interface" can be understood as performing spectral analysis and waveform feature extraction on the pre-sampled electrical signal data. For example, the frequency components of the signal can be analyzed by Fourier transform. If an abnormal increase in energy of a specific frequency (such as power frequency noise 50Hz / 60Hz and its harmonics, high-frequency radio frequency interference, etc.) is detected, or if unexpected spikes or glitches appear in the signal waveform, it can be determined that there are external electromagnetic interference signals.

[0065] "Delaying the execution of the current low-frequency sampling action" means that when electromagnetic interference is detected, the system will not immediately execute the originally planned low-frequency sampling, but will wait for a period of time. This waiting time can be dynamically adjusted according to the persistence and intensity attenuation of the interference signal. Low-frequency sampling will only resume when the intensity of the external electromagnetic interference signal attenuates to a preset allowable range—that is, when the energy or amplitude of the interference signal decreases to a level that will not significantly affect normal electrical condition monitoring. The preset allowable range is an empirical value or a threshold determined experimentally, used to define the acceptable level of interference.

[0066] The proposed solution proactively detects and identifies potential external electromagnetic interference by introducing high-frequency pre-sampling before each low-frequency sampling. When interference is detected, the system intelligently delays the formal low-frequency sampling until the interference intensity decreases to an acceptable level, thereby avoiding the acquisition of distorted electrical status data under interference conditions. This mechanism ensures that subsequent background monitoring data on the electrical status of the wired data transmission interface is pure and accurate, effectively eliminating the impact of external interference on interface reliability assessment.

[0067] This application further proposes an auxiliary step for determining local instability of an interface, the auxiliary step for determining local instability of an interface includes: Before determining that the wired data transmission interface is in an unreliable state, real-time fluctuation feature values ​​of the interface are extracted in three dimensions: voltage state, contact state, and signal quality. Compare the fluctuation feature values ​​of each dimension with the preset normal range of the corresponding dimension one by one to determine whether there is a situation where the fluctuation feature value of a single dimension exceeds the corresponding abnormal threshold, while the fluctuation feature values ​​of the other dimensions are all within the normal range; If the aforementioned situation exists, the wired data transmission interface is marked as a locally unstable state, and the locally unstable state is marked as an unreliable interface state.

[0068] Specifically, before determining the unreliable state of the wired data transmission interface, the system is configured to first perform a more detailed analysis of the interface's electrical state. The real-time fluctuation characteristics of the interface across three dimensions—voltage state, contact state, and signal quality—can be understood as quantitative indicators reflecting the stability of these dimensions. For example, voltage state fluctuation characteristics could be the root mean square error of voltage, peak-to-peak value, or the frequency of instantaneous dips / overshoots; contact state fluctuation characteristics could be the rate of change in contact resistance or the number of intermittent connections; and signal quality fluctuation characteristics could be the bit error rate, signal-to-noise ratio, or signal jitter amplitude. These characteristics aim to capture the dynamic changes in each dimension during transmission.

[0069] Furthermore, the system compares these real-time extracted fluctuation feature values ​​of each dimension with preset normal value ranges one by one. The normal value ranges are determined based on historical data or industry standards under ideal operating conditions. Simultaneously, anomaly thresholds are set for each dimension, defining what level of fluctuation is considered abnormal. Through comparison, it can be determined whether a single dimension's fluctuation feature value exceeds its corresponding abnormal threshold, while the fluctuation feature values ​​of other dimensions remain within their respective normal value ranges.

[0070] Therefore, if a specific situation is identified where a single dimension is abnormal while other dimensions are normal, it indicates that the overall instability of the interface is not caused by a complete failure, but by a local problem in a specific dimension. In this case, the wired data transmission interface will be marked as locally unstable. This locally unstable state marker will then be included as a specific manifestation of the interface's unreliable state in the final unreliable state determination.

[0071] This application's solution overcomes the limitations of traditional comprehensive judgment methods in identifying localized interface anomalies by introducing an auxiliary judgment step for localized interface instability. Through this technical solution, the application achieves more accurate and detailed judgment of unreliable states of wired data transmission interfaces. Compared to judgment methods that rely solely on the overall electrical state fluctuation amplitude, this solution can identify and mark localized unstable states of the interface, thereby effectively improving the accuracy of interface fault diagnosis. This not only allows for earlier detection of potential interface problems but also provides maintenance personnel with specific fault location information, significantly improving the efficiency and focus of fault diagnosis. This refined state judgment mechanism helps avoid unnecessary comprehensive inspections, reduces maintenance costs, and ultimately ensures the continuous high quality of medical image data transmission and the stability of system operation.

[0072] This application further proposes a portable DR medical image data acquisition method, which also includes an early warning step for physical wear of the wired interface, the early warning step for physical wear of the wired interface including: In each low-frequency sampling electrical condition monitoring process, the central tendency feature value and dispersion feature value of the electrical signal are extracted; The central tendency feature value and dispersion feature value extracted in real time are compared and verified with the pre-stored normal value range; If the comparison results of multiple consecutive low-frequency samples all show that the feature value deviates from the normal range, an early warning signal is generated indicating that the wired data transmission interface has physical wear, thus avoiding the risk of abnormal image transmission in advance.

[0073] Specifically, during each low-frequency sampling and monitoring of the electrical status of the wired data transmission interface, the system is configured to synchronously extract the statistical characteristics of the electrical signal. The central tendency characteristic can be understood as a value characterizing the average level or central position of the electrical signal over a period of time, such as the average voltage, average current, or average signal strength. The dispersion characteristic can be understood as a value characterizing the fluctuation range or instability of the electrical signal, such as the standard deviation, variance, or peak-to-peak value. These characteristics reflect the stability and consistency of the interface's electrical performance.

[0074] Furthermore, the extracted real-time central tendency feature values ​​and dispersion feature values ​​are compared and verified against pre-stored normal value ranges. These normal value ranges are determined based on historical data or design specifications of the interface under healthy operating conditions and are used to define the normal fluctuation range of the interface's electrical signals. The purpose of the comparison and verification is to determine whether the current electrical performance of the interface deviates from its normal operating baseline.

[0075] Furthermore, to avoid misjudgments caused by momentary interference or occasional fluctuations, the solution in this application requires that "the comparison results of multiple consecutive low-frequency samples all show that the feature values ​​deviate from the normal range" before it is judged as having signs of physical wear. This means that the system will continuously track the changing trends of these feature values, and will only trigger an early warning when the phenomenon of deviation from the normal range continues to occur, indicating a persistent and non-accidental degradation of the interface performance. Once this condition is met, the system will generate an early warning signal indicating physical wear of the wired data transmission interface.

[0076] This application's solution effectively addresses the lack of proactive early warning for physical wear in the aforementioned basic solutions by continuously monitoring and comparing the central tendency and dispersion characteristics of electrical signals in wired data transmission interfaces. Physical wear typically leads to poor interface contact, impedance changes, or decreased signal integrity, which are reflected in the statistical characteristics of electrical signals. For example, as wear intensifies, the average voltage of the electrical signal may drift, or the noise level and jitter (i.e., dispersion) of the signal may increase significantly. By extracting these characteristic values ​​and comparing them with the normal range of values ​​under healthy interface conditions, these subtle and gradual changes can be captured. When these characteristic values ​​deviate from the normal range multiple times consecutively, it indicates that the degradation of interface performance is not accidental but caused by continuous physical wear. Therefore, this solution can achieve early identification and warning of physical wear of the interface based on the inherent changing trends of electrical signals, rather than just the instantaneous operating state.

[0077] This application further proposes a transmission anti-interference optimization step to enhance the robustness of wireless data transmission.

[0078] After locking the wireless data transmission mode, the method further includes a transmission anti-interference optimization step, which includes: The operating parameters of the current wireless data transmission task are adaptively adjusted to an anti-external interference transmission mode. The anti-external interference transmission mode includes at least one parameter optimization method: reducing the data transmission rate per unit time, increasing the redundancy of error correction coding in the transmitted data, and switching to a preset backup communication frequency band to stabilize the medical image data transmission process.

[0079] Specifically, the transmission anti-interference optimization step refers to the system, after locking the wireless data transmission mode, no longer simply maintaining the original wireless transmission parameters, but adaptively adjusting the operating parameters of the wireless data transmission task according to the current environment and transmission requirements to form an anti-external interference transmission mode. This mode aims to enhance the robustness of wireless transmission and ensure stable transmission of medical image data in complex electromagnetic environments. The anti-external interference transmission mode may include at least one parameter optimization method.

[0080] For example, the data transmission rate per unit time can be reduced, i.e., the amount of data transmitted per second can be decreased to improve the signal's anti-interference capability and transmission reliability. This is particularly effective in environments with poor signal quality or strong interference. Furthermore, the redundancy of error correction coding in transmitted data can be increased. This means adding more redundant information to the original data, enabling the receiver to recover complete and correct data even if some data is lost or erroneous, thereby significantly improving the fault tolerance of data transmission.

[0081] Furthermore, as a preferred implementation, the system can switch to a preset backup communication frequency band. That is, when the currently used wireless communication frequency band is severely interfered with, the system can automatically or manually switch to a preset backup frequency band with less interference for data transmission, so as to avoid interference from the current frequency band and stabilize the medical image data transmission process.

[0082] This application's solution effectively addresses the stability issues that may arise in wireless transmission under complex external interference environments by introducing an anti-interference optimization step after locking the wireless data transmission mode. Through this technical solution, the application further enhances the anti-interference capability of wireless transmission by switching to wireless transmission mode when the wired interface is unreliable. This solution can intelligently adjust wireless transmission parameters according to actual external interference conditions, effectively avoiding electromagnetic interference and signal attenuation problems that may be encountered during wireless transmission, significantly improving the stability and reliability of medical image data transmission in complex environments. This not only ensures the integrity and accuracy of medical image data, avoiding the risk of diagnostic delays or misdiagnosis due to transmission quality issues, but also extends the effective working time of portable DR devices in various complex environments, improving the overall practicality of the device and the user experience.

[0083] This application further proposes a sampling frequency adaptive adjustment step, which aims to optimize device power consumption by intelligently adjusting the monitoring frequency, thereby extending device battery life and ensuring stable operation of long-term medical image transmission monitoring.

[0084] This application further proposes a sampling frequency adaptive adjustment step, which includes: Before initiating the wireless data transmission task, the real-time remaining battery power information of the portable DR device is collected; Compare the real-time remaining battery power with the preset lower battery power threshold. When the real-time remaining battery power is lower than the preset lower limit threshold, the low-frequency sampling frequency of the wired interface background monitoring is reduced to achieve adaptive optimization of device power consumption and ensure stable operation of long-term medical image transmission monitoring.

[0085] Specifically, before initiating the wireless data transmission task, the system actively acquires the real-time remaining battery power information of the portable DR device. This information can be understood as the device's current available power reserve, usually expressed as a percentage or specific unit of power. Subsequently, the acquired real-time remaining battery power is compared with a preset lower battery power threshold. This preset lower battery power threshold is the benchmark for the system to determine whether the device has entered a low battery state; for example, it can be set to 20% or 30% remaining power. When the real-time remaining battery power is lower than this preset lower battery power threshold, the system will automatically reduce the low-frequency sampling frequency used for wired interface background monitoring. For example, if the original preset low-frequency sampling frequency is once per second, it can be adjusted to once every five seconds or lower in a low battery state. The purpose of this is to reduce the power consumption of the interface monitoring module, thereby achieving adaptive optimization of device power consumption, and ensuring that the portable DR device can maintain stable operation of medical image transmission monitoring for extended periods even when the battery power is low, avoiding monitoring interruptions due to power depletion.

[0086] This application's solution effectively solves the problem of excessive power consumption that may result from continuous background monitoring by introducing an adaptive sampling frequency adjustment step. Through this technical solution, the application can intelligently adjust the sampling frequency of the wired interface background monitoring based on the actual power status of the portable DR device. This adaptive adjustment mechanism significantly reduces the power consumption of the device in low-power states, effectively extending the overall battery life of the portable DR device, enabling it to continuously acquire and transmit medical image data for longer periods, which is particularly suitable for scenarios where charging is not readily available, such as in the field or during emergency situations. Furthermore, this solution ensures stable operation of the device over extended periods while avoiding monitoring interruptions or data transmission risks due to power depletion, thus improving the flexibility and reliability of portable DR devices in practical applications.

[0087] refer to Figure 3 , Figure 3 This is a schematic diagram of a portable DR medical image data acquisition system provided in an embodiment of the present invention, used to execute the above-described portable DR medical image data acquisition method. The system includes: an image preprocessing module, a wireless transmission control module, an interface monitoring module, an image quality evaluation module, and a status determination module. The image preprocessing module is used to acquire medical image files collected by the portable DR device, and to perform region segmentation on the medical image files based on the image features of the bone region boundary and soft tissue region boundary within the image, generating multiple independent image data blocks; The wireless transmission control module is used to initiate the wireless data transmission task of medical image files, and when the status determination module determines that the wired data transmission interface is unreliable, it locks the wireless data transmission mode and continues to execute the transmission task, while adaptively adjusting the wireless transmission anti-interference parameters to ensure the quality of image data transmission. The interface monitoring module is used to perform background monitoring of the electrical status of the wired data transmission interface, including voltage status, contact status, and signal quality, at a preset low frequency during the operation of the wireless transmission task. It identifies physical connection signs of the interface and triggers an observation period of a preset duration, while simultaneously collecting electrical status fluctuation data. The image quality evaluation module is used to compare the multi-dimensional features of the transmitted image data block and the original image data block within the state observation period, quantify the image structure similarity, and complete the transmission image quality evaluation. The status determination module is communicatively connected to the interface monitoring module and the image quality evaluation module, respectively. It is used to combine the image structure similarity threshold comparison results and the electrical state fluctuation amplitude threshold comparison results to comprehensively determine the working reliability status of the wired data transmission interface and realize intelligent switching of transmission mode based on image quality feedback.

[0088] The portable DR medical image data acquisition system proposed in this application aims to provide a solution to effectively address the problems of interrupted medical image data transmission and compromised data integrity caused by unstable wired data transmission interfaces in complex and ever-changing environments. This system, through a modular design, organically combines image preprocessing, wireless transmission control, interface status monitoring, image quality evaluation, and intelligent transmission mode determination into a collaborative whole. The image preprocessing module segments medical images, improving the flexibility of data transmission; the interface monitoring module continuously monitors the wired interface to promptly detect potential connection problems; the image quality evaluation module assesses the quality of transmitted data, providing a basis for intelligent switching of transmission modes; and finally, the status determination module intelligently judges the reliability of the wired interface based on multi-source information, while the wireless transmission control module locks the wireless transmission mode and adaptively adjusts transmission parameters. This ensures the reliability and efficiency of medical image data transmission, avoiding the transmission failure cycle caused by repeated plugging and unplugging of the wired interface in traditional solutions, as well as the resulting diagnostic delays and information silos.

[0089] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A portable DR medical image data acquisition method, characterized in that, The method includes: The wireless data transmission task of the medical image file corresponding to the portable DR device is initiated. Based on the image content features of the bone region boundary and soft tissue region boundary in the medical image file, the medical image file is segmented into multiple independent image data blocks. Throughout the entire wireless data transmission task, the electrical status of the wired data transmission interface is continuously monitored using a preset low-frequency sampling method. The electrical status includes the interface voltage status, physical contact status, and transmission signal quality status. When physical connection signs are detected in the wired data transmission interface during background monitoring, a preset fixed-duration status observation period is triggered. During the state observation period, image quality quantitative evaluation is carried out on each image data block that has been transmitted. The image structure similarity between each transmitted image data block and the original image data block is calculated, and the electrical state fluctuation amplitude of the wired data transmission interface is monitored in real time. After the state observation period ends, if the image structure similarity of all transmitted image data blocks is higher than the preset quality threshold, and the electrical state fluctuation of the wired data transmission interface exceeds the preset stability threshold, then the wired data transmission interface is determined to be in an unreliable working state. The current data transmission mode is locked as wireless data transmission mode, and the wireless data transmission task is continuously executed until the current wireless data transmission task is completed, or until the electrical signal of the wired data transmission interface remains stable within a preset continuous time period, so as to ensure the quality of medical image data transmission.

2. The portable DR medical image data acquisition method according to claim 1, characterized in that, The medical image file is segmented into multiple independent image data blocks, including: The acquired raw medical image files are preprocessed with grayscale normalization to unify the range of grayscale values ​​and eliminate interference from differences in imaging brightness. Edge enhancement operations are performed on the medical images after grayscale normalization to highlight the edge contour features of different tissue regions in the image; Based on the location of gray-level abrupt changes in the edge enhancement image, the boundary contour between the skeletal region and the soft tissue region is accurately located and fitted. Based on the defined boundary contour, the skeletal region, the soft tissue region, and the image background region are encoded independently to generate multiple independent image data blocks.

3. The portable DR medical image data acquisition method according to claim 1, characterized in that, The step of calculating the image structure similarity between each transmitted image data block and the original image data block includes: The image data blocks returned by the receiving end are compared with the original image data blocks stored by the sending end in terms of features one by one. The similarity between the two sets of image data blocks in three dimensions—overall brightness distribution matching degree, local contrast matching degree, and image texture structure matching degree—is quantified and calculated separately. The matching data from the three dimensions are weighted and fused to generate a comprehensive similarity index that can characterize the integrity and consistency of the transmitted images. The calculated comprehensive similarity index is used to determine the degree of image structural similarity between the two sets of image data blocks.

4. The portable DR medical image data acquisition method according to claim 1, characterized in that, The method further includes a precise determination step for unstable wired interface states, which includes: When the background monitoring detects signs of physical connection at the wired data transmission interface, the sampling frequency of the electrical status is switched from a preset low frequency to a high frequency sampling mode. In the high-frequency sampling mode, the real-time electrical signals of the wired data transmission interface are continuously and densely sampled to obtain complete electrical signal timing data; Waveform morphology analysis is performed on the electrical signal time-series data obtained by dense sampling to extract the fluctuation amplitude sequence and fluctuation frequency characteristics corresponding to the signal time-series changes. The extracted fluctuation amplitude sequence and fluctuation frequency features are matched with the pre-stored interface unstable signal feature template. When the matching degree reaches the preset matching threshold, it is determined that the wired data transmission interface has a real unstable working state, providing a basis for judgment for image transmission mode switching.

5. The portable DR medical image data acquisition method according to claim 1, characterized in that, The method further includes an electromagnetic interference avoidance step, which includes: Before each low-frequency sampling action is performed, the electrical signal of the wired data transmission interface is briefly pre-sampled at a sampling frequency higher than the preset low frequency. Based on the pre-sampled electrical signal data, identify and determine whether there are external electromagnetic interference signals around the interface; If the external electromagnetic interference signal is detected, the execution timing of the current low-frequency sampling action is delayed until the intensity of the external electromagnetic interference signal attenuates to a preset allowable intensity range; If no external electromagnetic interference signal is detected, the low-frequency sampling action is performed normally according to the preset timing sequence to ensure the accuracy of interface status monitoring data and avoid interference affecting the image transmission quality judgment.

6. The portable DR medical image data acquisition method according to claim 1, characterized in that, The method further includes an interface local instability auxiliary judgment step, which includes: Before determining that the wired data transmission interface is in an unreliable state, real-time fluctuation feature values ​​of the interface are extracted in three dimensions: voltage state, contact state, and signal quality. Compare the fluctuation feature values ​​of each dimension with the preset normal range of the corresponding dimension one by one to determine whether there is a situation where the fluctuation feature value of a single dimension exceeds the corresponding abnormal threshold, while the fluctuation feature values ​​of the other dimensions are all within the normal range; If it exists, the wired data transmission interface is marked as a locally unstable state, and the locally unstable state is marked as an unreliable state of the interface.

7. The portable DR medical image data acquisition method according to claim 1, characterized in that, The method further includes an early warning step for physical wear of the wired interface, which includes: In each low-frequency sampling electrical condition monitoring process, the central tendency feature value and dispersion feature value of the electrical signal are extracted; The central tendency feature value and dispersion feature value extracted in real time are compared and verified with the pre-stored normal value range; If the comparison results of multiple consecutive low-frequency samples all show that the feature value deviates from the normal range, an early warning signal is generated indicating that the wired data transmission interface has physical wear, thus avoiding the risk of abnormal image transmission in advance.

8. The portable DR medical image data acquisition method according to claim 1, characterized in that, The method further includes a transmission interference optimization step, which includes: The operating parameters of the current wireless data transmission task are adaptively adjusted to an anti-external interference transmission mode. The anti-external interference transmission mode includes at least one parameter optimization method: reducing the data transmission rate per unit time, increasing the redundancy of error correction coding in the transmitted data, and switching to a preset backup communication frequency band to stabilize the medical image data transmission process.

9. The portable DR medical image data acquisition method according to claim 1, characterized in that, The method further includes a sampling frequency adaptive adjustment step, which includes: Before initiating the wireless data transmission task, the real-time remaining battery power information of the portable DR device is collected; Compare the real-time remaining battery power with the preset lower battery power threshold. When the real-time remaining battery power is lower than the preset lower limit threshold, the low-frequency sampling frequency of the wired interface background monitoring is reduced to achieve adaptive optimization of device power consumption and ensure stable operation of long-term medical image transmission monitoring.

10. A portable DR medical image data acquisition system, characterized in that, The system for performing the portable DR medical image data acquisition method according to any one of claims 1 to 9 includes: an image preprocessing module, a wireless transmission control module, an interface monitoring module, an image quality evaluation module, and a status determination module; The image preprocessing module is used to initiate the wireless data transmission task of the medical image file corresponding to the portable DR device. Based on the image content features of the bone region boundary and soft tissue region boundary in the medical image file, the medical image file is segmented into multiple independent image data blocks. The wireless transmission control module is used to continuously monitor the electrical status of the wired data transmission interface in a preset low-frequency sampling mode throughout the entire operation of the wireless data transmission task. The electrical status includes the interface voltage status, physical contact status, and transmission signal quality status. The interface monitoring module is used to trigger a preset fixed-duration status observation period when physical connection signs are detected in the wired data transmission interface during background monitoring. The image quality evaluation module is used to conduct quantitative evaluation of the image quality of each image data block that has been transmitted within the state observation period, calculate the image structure similarity between each transmitted image data block and the original image data block, and monitor the electrical state fluctuation amplitude of the wired data transmission interface in real time. The state determination module is used to determine that the wired data transmission interface is in an unreliable working state if, after the state observation period ends, the image structure similarity of all transmitted image data blocks is higher than a preset quality threshold, and the electrical state fluctuation amplitude of the wired data transmission interface exceeds a preset stability threshold; then, the current data transmission mode is locked to the wireless data transmission mode, and the wireless data transmission task is continuously executed until the current wireless data transmission task is completed, or until the electrical signal of the wired data transmission interface remains stable within a preset continuous time period, thereby ensuring the quality of medical image data transmission.

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