Medical image information classification management system

Abstract

The utility model discloses a medical image information classification management system, which comprises image acquisition equipment, a workstation, an FPGA (Field Programmable Gate Array) processing unit, an image acquisition gateway, a local storage server, a network switch, a high-speed network switch, a GPU (Graphics Processing Unit) server and a cloud data storage center, the image acquisition gateway is respectively connected with the image acquisition equipment and the local storage server; the FPGA processing unit is connected with the image acquisition gateway; the network switch is respectively connected with the work station and the GPU server; the high-speed network switch is respectively connected with the local storage server and the GPU server; and the cloud data storage center is connected with the high-speed network switch. According to the utility model, the medical image data acquired by the image acquisition equipment can be preprocessed and classified at low cost, and powerful support is provided for efficient management and application of medical image information of hospitals.

Description

Technical Field

[0001] This utility model relates to the field of medical data management technology, and in particular to a medical image information classification and management system. Background Technology

[0002] Medical imaging technology plays a crucial role in disease diagnosis, treatment planning, and disease monitoring. Hospitals generate a vast amount of medical imaging data daily, which is invaluable for medical decision-making. However, there are currently many challenges in managing medical imaging information.

[0003] Hospital-generated image data needs to be processed and classified before being transferred to storage devices for efficient retrieval, analysis, and utilization. However, existing processing methods have significant shortcomings. Currently, most hospitals rely primarily on manual processing and classification of image data. This method not only consumes a large amount of manpower and time but also suffers from inaccurate classification and low processing efficiency due to the limitations of manual operation, failing to meet the ever-increasing image data processing needs of hospitals. Additionally, some hospitals use dedicated servers to process data. While this method improves processing efficiency to some extent, it requires significant investment in hardware costs and maintenance expenses. Utility Model Content

[0004] The purpose of this invention is to provide a medical image information classification and management system, which enables low-cost preprocessing and classification of medical image data acquired by image acquisition equipment, providing strong support for the efficient management and application of hospital medical image information. The specific technical solution is as follows:

[0005] A medical image information classification and management system includes an image acquisition device, a workstation, an FPGA processing unit, an image acquisition gateway, a local storage server, a network switch, a high-speed network switch, a GPU server, and a cloud data storage center. The image acquisition gateway is connected to both the image acquisition device and the local storage server. The FPGA processing unit is connected to the image acquisition gateway. The network switch is connected to both the workstation and the GPU server. The high-speed network switch is connected to both the local storage server and the GPU server. The cloud data storage center is connected to the high-speed network switch.

[0006] Preferably, the FPGA processing unit includes an SFP module and an FPGA chip; the FPGA chip is installed inside the SFP module; and the SFP module is connected to the image acquisition gateway.

[0007] Preferably, it also includes a load balancer; the load balancer is connected to the local storage server, the high-speed network switch and the GPU server respectively.

[0008] Preferably, it also includes a router and a firewall; the router and firewall are located between the high-speed network switch and the cloud storage data center; one end of the router is connected to the high-speed network switch, and the other end is connected to one end of the firewall; the other end of the firewall is connected to the cloud storage data center.

[0009] Preferably, the cloud data storage center includes a hot data storage server and a cold data storage server; the hot data storage server and the cold data storage server are respectively connected to a high-speed network switch.

[0010] Preferably, the image acquisition equipment includes a CT device, an MRI device, an X-ray imaging device, and an ultrasound imaging device; the CT device, MRI device, X-ray imaging device, and ultrasound imaging device are respectively connected to the image acquisition gateway.

[0011] Preferably, the workstation includes a doctor workstation, a nurse workstation, and an administrator workstation; the doctor workstation, nurse workstation, and administrator workstation are each connected to a network switch.

[0012] Preferably, the GPU server is model XE9640.

[0013] Compared with existing technologies, this utility model has the following beneficial effects:

[0014] This invention employs an FPGA processing unit to preprocess and classify medical image data acquired by image acquisition equipment. This efficiently removes noise and extracts key features, enabling preliminary data screening and organization. It effectively removes invalid and redundant information, reducing the data processing burden on the server and preventing server overload from impacting overall system efficiency. Furthermore, the entire system covers the entire process from image acquisition, processing, transmission to storage. The collaborative work of each part ensures rapid and accurate processing and storage of image data, improving the efficiency and quality of hospital medical image information management. It reduces reliance on manual or dedicated server data processing, thereby lowering costs and providing strong support for the efficient management and application of hospital medical image information. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0016] Figure 1 This is a schematic diagram of the structure of this utility model. Detailed Implementation

[0017] The technical solutions of the present utility model will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present utility model, and not all embodiments. Based on the embodiments of the present utility model, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the protection scope of the present utility model.

[0018] In the description of this utility model, it should be noted that the terms "center", "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "top surface", "bottom surface", "inner", "outer", "inner side", "outer side", etc., indicating the orientation or positional relationship are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this utility model and simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this utility model.

[0019] In the description of this utility model, "several" means one or more, "multiple" means two or more, "greater than," "less than," and "exceeding" are understood to exclude the stated number, while "above," "below," and "within" are understood to include the stated number. If the terms "first," "second," and "third" are used in the description, they are for descriptive purposes and to distinguish technical features, and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the sequential relationship of the indicated technical features.

[0020] In the description of this utility model, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "setting" 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 utility model based on the specific circumstances. The embodiments of this utility model will now be described based on its overall structure.

[0021] Example 1

[0022] As shown in the figure, a medical image information classification and management system includes an image acquisition device, a workstation, an FPGA processing unit, an image acquisition gateway, a local storage server, a network switch, a high-speed network switch, a GPU server, and a cloud data storage center. The image acquisition gateway is connected to both the image acquisition device and the local storage server. The FPGA processing unit is connected to the image acquisition gateway. The network switch is connected to both the workstation and the GPU server. The high-speed network switch is connected to both the local storage server and the GPU server. The cloud data storage center is connected to the high-speed network switch.

[0023] Next, the working principle of this embodiment will be described in detail so that those skilled in the art can better understand this utility model:

[0024] After acquiring medical image information, the image acquisition device transmits it to the image acquisition gateway, which simultaneously sends the data to a local storage server for storage. The FPGA processing unit, connected to the image acquisition gateway, preprocesses and classifies the acquired data, performing operations such as noise removal and key feature extraction. It categorizes image data of different types or priorities, allowing for initial screening and organization of large amounts of raw image data, removing invalid or redundant information, and extracting key components, thus reducing the data processing burden on subsequent servers. The data processed by the FPGA processing unit is then transmitted to workstations and GPU servers via network switches. The network switches handle data transmission between workstations and GPU servers, while high-speed network switches connect the local storage server and GPU server, ensuring high-speed data exchange. The GPU server performs further analysis and processing of the medical image data, such as image reconstruction and feature extraction, to meet the diagnostic needs of doctors. Finally, the cloud data storage center is connected to the entire system via high-speed network switches, enabling remote data storage and backup, facilitating data access and sharing for medical personnel in different locations.

[0025] Example 2

[0026] The difference between this embodiment and embodiment 1 is that the FPGA processing unit includes an SFP module and an FPGA chip; the FPGA chip is installed inside the SFP module; and the SFP module is connected to the image acquisition gateway.

[0027] The image acquisition gateway transmits data acquired from the image acquisition device to the SFP module. The FPGA chip performs preliminary data processing within the SFP module (the SFP module is a commercially available SFP interface device equipped with an FPGA chip, and the image acquisition gateway is a gateway device with an SFP interface). The processed data is then transmitted through a network switch to subsequent devices such as workstations and GPU servers for further processing and analysis. At the same time, the local storage server also stores the data, while the cloud data storage center is responsible for remote storage and sharing of the data.

[0028] The working principle of this embodiment is the same as that of Embodiment 1.

[0029] Example 3

[0030] The difference between this embodiment and embodiment 2 is that it also includes a load balancer; the load balancer is connected to the local storage server, the high-speed network switch and the GPU server respectively.

[0031] When data is transmitted between local storage servers, high-speed network switches, and GPU servers, the load balancer allocates data traffic reasonably based on the load of each device, ensuring balanced data transmission across all devices and preventing overload of any one device from impacting the overall system performance. When multiple GPU servers are configured, if one GPU server is handling a heavy workload, the load balancer can offload some data to other relatively idle GPU servers for processing, or adjust the data transmission order and quantity according to the actual situation to optimize the overall system efficiency.

[0032] The working principle of this embodiment is the same as that of Embodiment 1.

[0033] Example 4

[0034] The difference between this embodiment and embodiment 3 is that it also includes a router and a firewall; the router and firewall are located between the high-speed network switch and the cloud storage data center; one end of the router is connected to the high-speed network switch, and the other end is connected to one end of the firewall; the other end of the firewall is connected to the cloud storage data center.

[0035] When data is transmitted from a high-speed network switch to the cloud storage data center, it first passes through a router for path selection and data forwarding, ensuring accurate and fast transmission. Simultaneously, a firewall performs security checks and filters on the transmitted data to prevent malicious attacks and unauthorized access from external networks from entering the cloud storage data center, protecting the security and privacy of medical imaging data. Data returning from the cloud storage data center also undergoes processing by the firewall and routers to ensure the security and reliability of data transmission. Adding routers and firewalls enhances the system's data security and confidentiality. The firewall effectively defends against external network threats, preventing unauthorized access and data leakage.

[0036] The working principle of this embodiment is the same as that of Embodiment 1.

[0037] Example 5

[0038] The difference between this embodiment and embodiment 4 is that the cloud data storage center includes a hot data storage server and a cold data storage server; the hot data storage server and the cold data storage server are respectively connected to a high-speed network switch.

[0039] Hot data storage servers are used to store frequently accessed or currently in-use medical image data. This type of data is often needed by doctors, nurses, and other medical personnel during diagnosis and treatment. Cold data storage servers, on the other hand, are used to store image data that has not been accessed for a long time and is in an archived state, such as patient image records from many years ago that have been fully treated and have not been retrieved for a long time. When data access is required, if hot data is requested, the high-speed network switch will quickly retrieve it from the hot data storage server and transmit it to the corresponding workstation; if cold data is requested, it will be read from the cold data storage server.

[0040] The working principle of this embodiment is the same as that of Embodiment 1.

[0041] Example 6

[0042] The difference between this embodiment and embodiment 5 is that the image acquisition device includes a CT device, an MRI device, an X-ray imaging device, and an ultrasound imaging device; the CT device, MRI device, X-ray imaging device, and ultrasound imaging device are respectively connected to the image acquisition gateway.

[0043] In the process of medical image acquisition, CT equipment mainly performs tomographic scanning imaging of organs and tissues inside the patient's body; MRI equipment uses magnetic fields and radio frequency pulses for imaging; X-ray imaging equipment is based on the imaging principle that X-rays penetrate different tissues in the human body to quickly acquire images of bones, chest, and other parts; and ultrasound imaging equipment uses ultrasound reflection imaging to monitor the activity status of internal organs in real time. The image data acquired by these different types of equipment are all transmitted to the image acquisition gateway, and then the gateway distributes the data to the subsequent FPGA processing unit for preprocessing and classification. This enables more comprehensive and accurate coverage of various medical imaging acquisition needs and meets the image type requirements of different departments and different disease diagnoses.

[0044] The working principle of this embodiment is the same as that of Embodiment 1.

[0045] Example 7

[0046] The difference between this embodiment and embodiment 6 is that the workstation includes a doctor workstation, a nurse workstation, and an administrator workstation; the doctor workstation, nurse workstation, and administrator workstation are each connected to a network switch.

[0047] The doctor's workstation, nurse's workstation, and administrator's workstation are each equipped with a computer and a corresponding PC client. The doctor's workstation is primarily used by doctors to view and analyze patient imaging data. Doctors can also record diagnostic results and review historical patient images for comparative analysis. The nurse's workstation focuses on nurses managing patient imaging examination processes, such as scheduling appointments, monitoring progress, and verifying that image acquisition meets requirements, to assist patients in completing each imaging examination efficiently and systematically. The administrator's workstation is primarily responsible for the operation and maintenance of the entire medical imaging information classification and management system, including user permission allocation, system parameter configuration, equipment status monitoring, troubleshooting and repair, ensuring stable system operation and normal data transmission and interaction between workstations, servers, and storage devices.

[0048] The working principle of this embodiment is the same as that of Embodiment 1.

[0049] Example 8

[0050] The difference between this embodiment and embodiment 7 is that the GPU server is model XE9640.

[0051] The GPU server, model XE9640, has image data processing performance that meets the image analysis needs of the medical imaging field, providing doctors with higher quality and more efficient diagnostic support tools.

[0052] The working principle of this embodiment is the same as that of Embodiment 1.

[0053] In summary, this invention utilizes an FPGA processing unit to preprocess and classify medical image data acquired by image acquisition equipment. This efficiently removes noise, extracts key features, and performs preliminary data screening and organization, effectively eliminating invalid and redundant information. This reduces the data processing burden on the server and avoids server overload affecting the overall system efficiency. Furthermore, the entire system covers the entire process from image acquisition, processing, transmission to storage. The collaborative work of each part ensures the rapid and accurate processing and storage of image data, improving the efficiency and quality of hospital medical image information management. It reduces reliance on manual or dedicated server data processing, thereby lowering costs and providing strong support for the efficient management and application of hospital medical image information.

[0054] The foregoing description of specific exemplary embodiments of the present invention is for illustrative and explanatory purposes. These descriptions are not intended to limit the present invention to the precise forms disclosed, and it is obvious that many changes and variations can be made in accordance with the above teachings. Although embodiments of the present invention have been shown and described, these specific embodiments are merely explanations of the present invention and are not intended to limit the invention. The specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. The purpose of selecting and describing exemplary embodiments is to explain the specific principles of the present invention and its practical application, so that those skilled in the art, after reading this specification, can make modifications, substitutions, variations, and various choices and changes to the embodiments as needed without departing from the principles and spirit of the present invention, provided that such modifications, substitutions, variations, and choices and changes are within the scope of the claims of the present invention and are protected by patent law.

Claims

1. A medical image information classification management system characterized by comprising: The system comprises an image acquisition device, a workstation, an FPGA processing unit, an image acquisition gateway, a local storage server, a network switch, a high-speed network switch, a GPU server and a cloud data storage center; the image acquisition gateway is connected with the image acquisition device and the local storage server respectively; the FPGA processing unit is connected with the image acquisition gateway; the network switch is connected with the workstation and the GPU server respectively; the high-speed network switch is connected with the local storage server and the GPU server respectively; and the cloud data storage center is connected with the high-speed network switch.

2. The medical image information classification management system according to claim 1, wherein The FPGA processing unit comprises an SFP module and an FPGA chip; the FPGA chip is installed in the interior of the SFP module; and the SFP module is connected with the image acquisition gateway.

3. The medical image information classification management system of claim 1, wherein The system further comprises a load balancer; the load balancer is connected with the local storage server, the high-speed network switch and the GPU server respectively.

4. The medical image information classification management system of claim 1, wherein The system further comprises a router and a firewall; the router and the firewall are arranged between the high-speed network switch and the cloud storage data center; one end of the router is connected with the high-speed network switch, and the other end of the router is connected with one end of the firewall; the other end of the firewall is connected with the cloud storage data center.

5. The medical image information classification management system of claim 1, wherein The cloud data storage center comprises a hot data storage server and a cold data storage server; the hot data storage server and the cold data storage server are connected with the high-speed network switch respectively.

6. The medical image information classification management system of claim 1, wherein The image acquisition device comprises a CT device, an MRI device, an X-ray imaging device and an ultrasonic imaging device; the CT device, the MRI device, the X-ray imaging device and the ultrasonic imaging device are connected with the image acquisition gateway respectively.

7. The medical image information classification management system of claim 1, wherein The workstation comprises a doctor workstation, a nurse workstation and an administrator workstation; the doctor workstation, the nurse workstation and the administrator workstation are connected with the network switch respectively.

8. The medical image information classification management system of claim 1, wherein, The model of the GPU server is XE9640.

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