The Application of Puzhi in Medical Equipment Scenarios

In the context of rapid development in medical technology, medical equipment is quickly advancing towards high precision, real-time response, and deep intelligent integration. The Puzhi development board, with its powerful digital signal processing capabilities, flexible reconfigurable hardware architecture, and a rich variety of communication interface configurations, provides solid technical support for the innovative research and development and upgrading of medical equipment. This proposal explores how to leverage the Puzhi development board to build a highly reliable, computationally efficient, and data processing-accurate medical equipment solution to meet the diverse needs of modern medical fields for cutting-edge technology applications.

(1) High-Performance Computing Core

The Puzhi development board is equipped with a processor core that has strong parallel processing capabilities, integrating rich logic units and high-speed data pathways, with a computing speed of up to 736GFLOPS (FP32). This outstanding performance allows it to complete complex calculations of large-scale medical data in a very short time. For example, in processing high-resolution medical imaging data, when performing 3D reconstruction of a 512×512 resolution medical CT image, using the Puzhi development board can significantly reduce processing time by about 30% compared to traditional processors, greatly improving diagnostic efficiency and gaining valuable time for timely patient treatment.

(2) Rich Communication Interfaces

The development board integrates various communication interfaces such as USB 3.0, Gigabit Ethernet, SPI, and I2C to meet data transmission needs in different medical scenarios. Among them, the USB 3.0 interface has a data transmission rate of about 2.5Gbps, enabling rapid transmission of massive medical imaging data, ensuring real-time and stable transmission of high-resolution images; Gigabit Ethernet builds a high-speed and stable data exchange bridge between the development board and host computers, hospital information systems (HIS), and picture archiving and communication systems (PACS), strongly supporting remote medical diagnosis and data sharing. In remote consultation scenarios, high-definition medical images and patient physiological parameters can be transmitted in real-time through Gigabit Ethernet, with latency controlled within 50 milliseconds, ensuring the smoothness and efficiency of the consultation process.

(3) Programmable Logic Resources

Based on FPGA (Field Programmable Gate Array) or SoC (System on Chip) architecture, the Puzhi development board supports hardware description languages such as VHDL and Verilog, as well as high-level programming languages like C and C++ for software development. This allows developers to flexibly customize hardware logic and algorithms according to the specific functional requirements of different medical devices. For example, mapping convolutional neural network-based medical image classification algorithms to FPGA hardware resources can increase the execution speed of the algorithms by more than five times through clever hardware parallelization design, significantly improving the operational efficiency and response speed of the device.

(4) High-Reliability Hardware Design

To meet the stringent requirements for reliability and safety in medical devices, the Puzhi development board uses industrial-grade components, providing excellent anti-interference capability and stability. In hardware design, redundant power supply design, signal integrity optimization, and thermal management technology ensure that the device can operate stably for long periods in complex medical environments. In terms of power supply, a dual power supply redundancy design is adopted, allowing the backup power supply to seamlessly switch within 1 millisecond when the main power supply fails, ensuring continuous normal operation of the device; the thermal management system optimizes the design of intelligent fan speed control and heat sinks to precisely control the operating temperature of the device within a reasonable range, effectively extending the device's lifespan.

(1) Medical Image Processing

In medical imaging devices such as X-ray, CT, and MRI, the imaging principle is based on the differences in response of different tissues to radiation or magnetic fields, resulting in a massive amount of raw image data that inevitably contains noise, artifacts, and other interference information. Therefore, it is necessary to use filtering algorithms such as Gaussian filtering and bilateral filtering to remove noise, employ FBP algorithms (for CT image reconstruction), and back-projection algorithms (for X-ray image reconstruction) to restore the true structure of the image, and utilize image segmentation algorithms based on threshold segmentation, region growing, and deep learning U-Net networks to achieve precise identification and segmentation of lesion areas, providing strong support for doctors' diagnosis of conditions.

(2) Physiological Parameter Monitoring

Electrocardiogram monitors collect cardiac electrical activity signals through surface electrodes, blood pressure monitors measure blood pressure using oscillometric or Korotkoff methods, and blood glucose meters detect glucose concentration in the blood based on electrochemical principles. These devices need to collect patients' physiological parameters in real-time and use digital filtering algorithms such as Butterworth filters and Chebyshev filters to remove signal interference, obtaining key physiological features through R-wave detection algorithms for ECG signals and pulse wave feature extraction algorithms for blood pressure signals, and combining threshold judgment and trend analysis methods to achieve real-time monitoring and abnormal warning of patients' vital signs, promptly identifying potential health risks.

(3) Medical Equipment Control

Surgical robots require precise control of the motion trajectory of mechanical arms, with positioning accuracy reaching sub-millimeter levels. By calculating motor drive parameters through inverse kinematics algorithms and utilizing force feedback control technology to achieve real-time perception and adjustment of the contact force between surgical instruments and tissues, the precision and safety of surgical operations are ensured; automated biochemical analyzers need to accurately control the sample and reagent addition amounts, reaction times, and detection processes, using microfluidic technology to achieve precise liquid control, and employing sensor feedback control to automate the monitoring of the detection process, ensuring the accuracy and repeatability of detection results, providing reliable data for clinical diagnosis.

(1) Data Acquisition Module

Adapt corresponding sensors according to the characteristics and requirements of different medical application scenarios. For example, amorphous silicon flat panel detectors and CMOS image sensors for medical image acquisition; bioelectric sensors, pressure sensors, and biochemical sensors for physiological parameter monitoring. Use dedicated data acquisition chips, such as ADS1256 for high-precision bioelectric signal acquisition, to transmit data to the Puzhi development board via SPI or I2C interfaces, ensuring that data acquisition meets the requirements of high resolution, low noise, and real-time performance, providing a high-quality data foundation for subsequent data processing and analysis.

(2) Core of the Puzhi Development Board

The Puzhi development board serves as the core control unit of the entire medical equipment system, responsible for receiving, parsing, and processing data from the data acquisition module, and generating precise control instructions based on preset algorithms and logic to achieve accurate control of external devices. Its rich internal logic resources and high-speed processing capabilities can process multiple data channels in parallel, effectively enhancing the overall operational efficiency of the system and ensuring the efficient realization of various functions of medical equipment.

(3) Storage Module

To meet the storage needs of medical data, the development board is equipped with large-capacity high-speed storage devices, such as eMMC and solid-state drives (SSD). Among them, the eMMC storage capacity can reach 32GB, with fast data read and write speeds, suitable for storing collected raw medical data, processed intermediate results, and program codes and configuration parameters required for device operation, ensuring safe storage and quick retrieval of data, allowing medical personnel to review and analyze historical data at any time.

(4) Communication Module

The communication module is key to achieving data interaction between medical equipment and external systems. Through Ethernet interfaces, following the DICOM (Digital Imaging and Communications in Medicine) protocol, it establishes network connections with hospital information systems (HIS) and picture archiving and communication systems (PACS), completing efficient transmission and sharing of medical imaging data; using USB interfaces to connect external display devices, such as high-resolution medical monitors and printers, to visualize medical data and print reports, facilitating medical personnel's intuitive understanding of patient condition information.

(1) Driver Program Development

Based on Linux or RTOS (Real-Time Operating System) platforms, develop device driver programs for various sensors and communication interfaces. Following the device driver model of the operating system, such as character device drivers and block device driver models in Linux, to achieve stable communication and efficient control between hardware devices and the operating system kernel, ensuring that hardware devices can operate stably and provide reliable data transmission support for upper-layer applications.

(2) Algorithm Implementation

Using C and C++ languages combined with open-source libraries such as OpenCV and TensorFlow to implement various medical image processing and analysis algorithms. For example, convolutional neural network (CNN)-based medical image classification algorithms can automatically classify and identify medical images; support vector machine (SVM)-based physiological parameter anomaly detection algorithms can effectively detect abnormal changes in patients' physiological parameters. At the same time, using parallel computing technologies such as OpenMP and CUDA to optimize and accelerate algorithms significantly enhances the execution efficiency and real-time performance of algorithms, meeting the demand for rapid data processing in medical devices.

(3) User Interface Design

Using graphical interface development frameworks such as Qt and GTK to design intuitive and user-friendly user interfaces. This interface provides functions such as device parameter settings, real-time data display, and alarm prompts, optimizing the design of human-computer interaction interfaces to reduce the operational difficulty for medical staff and improve the efficiency of medical device usage, allowing medical personnel to focus more on patient diagnosis and treatment.

(4) Data Management System

Build a medical data management platform based on database management systems such as SQLite and MySQL to achieve structured storage, rapid querying, statistical analysis, and data backup and recovery functions for patient medical data. Through effective management of medical data, provide comprehensive and accurate data support for medical decision-making, while facilitating the tracing and analysis of patients' historical data, providing reference for disease diagnosis and treatment.

(1) Data Acquisition

Various sensors collect patients' physiological parameters or medical imaging data in real-time according to preset sampling frequencies and acquisition accuracies, and convert analog signals into digital signals through the data acquisition module, efficiently transmitting them to the Puzhi development board, providing raw data for subsequent data processing and analysis.

(2) Data Processing and Analysis

The Puzhi development board preprocesses the collected data, including denoising, normalization, and format conversion, to improve data quality; then applies corresponding algorithms for feature extraction and data analysis, such as lesion identification in medical images and abnormal judgment of physiological parameters, generating scientifically reasonable diagnostic suggestions or precise control instructions based on the analysis results.

(3) Result Output and Display

The processed results are clearly displayed to medical staff through display devices, such as LCD screens and OLED screens, in graphical, chart, or text form, while the result data is stored in a local database or uploaded to the hospital information system for subsequent review and analysis; for abnormal situations, alarm devices provide timely alerts to medical staff, ensuring that patients' conditions receive timely attention and handling.

(4) Device Control

Based on the data analysis results, the Puzhi development board sends precise control instructions to the actuators of medical devices, such as motion control of surgical robots and sample processing flow control of automated biochemical analyzers, achieving precise operation and automated operation of medical devices, ensuring the efficient and accurate delivery of medical services.

(1) Enhanced Device Performance

The high-performance computing capabilities and hardware acceleration features of the Puzhi development board significantly shorten the time for medical data processing, greatly improving the detection accuracy and response speed of medical devices. In medical imaging diagnosis, the image reconstruction time can be reduced by [X]%, winning valuable time for early diagnosis and treatment of diseases, helping to improve patient cure rates and recovery outcomes.

(2) Reduced Development Costs

Compared to dedicated medical chips and customized development platforms, the Puzhi development board offers a higher cost-performance ratio. Its rich open-source resources and mature development toolchain can effectively reduce hardware procurement costs and software development cycles during the R&D process, lowering the overall R&D costs of medical devices, making it affordable for more medical institutions to develop and upgrade advanced medical equipment.

(3) Accelerated Product Launch

With high programmability and flexible hardware architecture, developers can quickly iterate product features based on market demand and timely adjust product designs. This greatly accelerates the product launch process for medical devices, allowing new products to be put into clinical applications faster, meeting the urgent market demand for advanced medical equipment and promoting technological progress in the medical industry.

(4) Ensured Data Security

By implementing comprehensive data encryption algorithms, such as AES encryption, strict access control, and reliable data backup mechanisms, the security and integrity of patient medical data during storage and transmission are ensured, effectively preventing data leakage and tampering, protecting patient privacy, and safeguarding patients' legal rights.

(1) Portable Electrocardiogram Monitor

The portable electrocardiogram monitor developed using the Puzhi development board employs single-lead or multi-lead electrocardiogram sensors to collect electrocardiogram signals, amplifying, filtering, and digitizing the signals through onboard signal conditioning circuits and data acquisition modules. Utilizing wavelet transform-based electrocardiogram signal analysis algorithms, it monitors patients' heart rates, rhythms, and other electrocardiogram parameters in real-time. When abnormal electrocardiogram signals are detected, the data is transmitted to mobile terminals, such as smartphones and tablets, via Bluetooth or Wi-Fi modules, sending alert information to patients and medical staff, enabling remote health monitoring and management of cardiovascular disease patients, providing convenience for patients' daily health management.

(2) Intelligent Medical Imaging Diagnosis System

The intelligent medical imaging diagnosis system built on the Puzhi development board connects to medical imaging devices such as CT and MRI, receiving raw imaging data and utilizing deep learning algorithms for automatic analysis of images. By employing convolutional neural network models to identify lung nodules, brain tumors, and other lesions, combined with image segmentation and feature extraction techniques, it accurately calculates the size, location, and morphological features of lesions, providing preliminary diagnostic suggestions to doctors, assisting them in improving diagnostic efficiency and accuracy, effectively reducing misdiagnosis and missed diagnosis rates, and providing strong support for accurate diagnosis and timely treatment of patients.