Multimodal image processing method and system for in vivo magnetically controlled targeting enrichment

Abstract

The present application relates to the technical field of molecular image analysis, and particularly relates to a multimodal image processing method and system for in-vivo magnetic control targeted enrichment. The method comprises the following steps: obtaining preparation parameter data of a multimodal nano reagent, including a core-shell structure composition, surface modification and target ligand connection density parameters; selecting an animal model based on a target lesion type and position, and obtaining physiological structure and pathological characteristics of the animal model to obtain lesion characteristic data; determining the best injection dose of the multimodal nano reagent according to the lesion characteristic data to obtain best injection dose prediction data; and determining an injection mode to obtain injection mode parameter data. The present application realizes efficient enrichment of the nano reagent in the lesion area through external magnetic control, and improves the imaging signal and diagnostic accuracy.

Description

Technical Field

[0001] This invention relates to the field of molecular imaging analysis technology, and in particular to a multimodal image processing method and system for in vivo magnetically controlled targeted enrichment. Background Technology

[0002] In vivo magnetotargeting is a technology that uses an external magnetic field to control the movement and positioning of magnetic microparticles or microrobots within the body. It aims to precisely deliver drugs or other therapeutic agents to target sites, improving treatment efficacy and reducing side effects. Magnetic nanoparticles or micron-sized magnetic microparticles are typically used; these magnetic carriers can load drugs, genes, or other therapeutic substances. For example, superparamagnetic nanoparticles, with their excellent biocompatibility and magnetic responsiveness, are frequently used in in vivo magnetotargeting. Multimodal imaging processing methods for in vivo magnetotargeting enrichment utilize advanced nanotechnology to prepare multimodal nanoreagents with core-shell structures, surface modifications, and targeted ligand linkages. These nanoreagents simultaneously possess multiple imaging capabilities, including surface-enhanced Raman scattering, photoacoustics, and near-infrared II fluorescence, providing complementary information across different imaging platforms and significantly improving image resolution and signal-to-noise ratio. However, the challenges of "difficult enrichment and localization" inherent in traditional in vivo drug delivery and imaging have also been overcome. By combining an external adjustable magnetic field system, this method achieves precise directional migration and enrichment of nanoreagents in vivo, greatly improving the local concentration of reagents in the lesion area and overcoming the problem of weak imaging signals caused by uneven reagent diffusion and low enrichment efficiency in the past. Summary of the Invention

[0003] Therefore, it is necessary for the present invention to provide a multimodal image processing method and system for in vivo magnetically controlled targeted enrichment, in order to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, a multimodal image processing-based method for in vivo magnetically controlled targeted enrichment includes the following steps:

[0005] Step S1: Obtain the preparation parameter data of the multimodal nanoreagent, including core-shell structure composition, surface modification and target ligand connection density parameters;

[0006] Step S2: Select animal models based on the target lesion type and location, and obtain the physiological structure and pathological characteristics of the animal models to obtain lesion feature data;

[0007] Step S3: Based on the lesion characteristic data, determine the optimal injection dose of the multimodal nanoreagent from the preparation parameter data to obtain optimal injection dose prediction data; determine the injection method to obtain injection method parameter data; based on the optimal injection dose prediction data and injection method parameter data, introduce the multimodal nanoreagent into the animal model via intravenous injection, and perform image acquisition to obtain lesion three-dimensional image localization data; based on the lesion three-dimensional image localization data, set the parameters of the external adjustable magnetic field system to obtain magnetic field control parameter data;

[0008] Step S4: Based on the magnetic field control parameter data, the multimodal nanoreagent is directionally migrated to the lesion area for enrichment, and the maintenance enrichment data is obtained; the original multimodal imaging data of surface-enhanced Raman scattering, photoacoustic and near-infrared II fluorescence in the lesion area are acquired, and the enrichment efficiency of the reagent in the lesion area is analyzed based on the maintenance enrichment data to obtain the multimodal imaging enrichment performance data.

[0009] This invention achieves precise diagnosis and treatment of lesion areas through meticulously designed steps. First, by acquiring preparation parameter data for multimodal nanoreagents, including core-shell structure composition, surface modification, and targeting ligand linkage density parameters, nanoreagents with excellent performance can be precisely designed. This precise design optimizes the nanoreagents' magnetic responsiveness, biocompatibility, and targeting ability, providing a solid foundation for subsequent targeted enrichment and imaging. Second, animal models are selected based on the type and location of the target lesion, and the physiological structure and pathological characteristics of the animal models are acquired to obtain lesion characteristic data. This process ensures the specificity and reliability of the experiment. A comprehensive understanding of the lesion characteristics provides a scientific basis for the injection dosage and method of the nanoreagent, thereby improving the enrichment efficiency of the nanoreagent in the lesion area while reducing the impact on normal tissues. After determining the optimal injection dosage and method, the multimodal nanoreagent is introduced into the animal model via intravenous injection, and imaging is performed to obtain three-dimensional image localization data of the lesion. This process not only achieves precise delivery of the nanoreagent but also monitors the distribution of the nanoreagent in vivo in real time through image acquisition. This real-time monitoring capability enables more precise parameter settings for the externally adjustable magnetic field system. It allows for optimization of the magnetic field gradient, direction, and pulse modulation scheme based on the specific location, size, and depth of the lesion, thereby achieving efficient enrichment of nanoreagents in the lesion region. Finally, by controlling the magnetic field parameters, multimodal nanoreagents are directionally migrated to the lesion region for enrichment, and raw multimodal imaging data of surface-enhanced Raman scattering, photoacoustic, and near-infrared II fluorescence in the lesion region are acquired. This combination of multimodal imaging technologies not only provides rich lesion information but also further verifies the aggregation of nanoreagents in the lesion region through enrichment efficiency analysis. This combination of multimodal imaging and enrichment efficiency analysis not only improves the accuracy of lesion diagnosis but also provides strong support for subsequent treatment efficacy evaluation. The entire method, through precise nanoreagent design, lesion feature analysis, magnetic field control, and multimodal imaging analysis, achieves efficient targeted enrichment and accurate diagnosis of lesions, providing a new technical means for the early detection and treatment of diseases.

[0010] The present invention also provides a multimodal image processing system for in vivo magnetically controlled targeted enrichment, used to execute the above-described multimodal image processing method for in vivo magnetically controlled targeted enrichment, wherein the multimodal image processing system for in vivo magnetically controlled targeted enrichment includes:

[0011] The preparation parameter module is used to obtain preparation parameter data for multimodal nanoreagents, including core-shell structure composition, surface modification, and targeting ligand connection density parameters.

[0012] The lesion feature module is used to select animal models based on the target lesion type and location, and to obtain the physiological structure and pathological features of the animal models to obtain lesion feature data.

[0013] The magnetic field parameter module is used to determine the optimal injection dose of the multimodal nanoreagent based on lesion characteristic data, obtaining optimal injection dose prediction data; and to determine the injection method, obtaining injection method parameter data; based on the optimal injection dose prediction data and injection method parameter data, the multimodal nanoreagent is introduced into the animal model via intravenous injection, and image acquisition is performed to obtain three-dimensional image localization data of the lesion; based on the three-dimensional image localization data of the lesion, the parameters of the external adjustable magnetic field system are set to obtain magnetic field control parameter data;

[0014] The magneto-controlled enrichment and imaging module is used to directionally migrate multimodal nanoreagents to the lesion area for enrichment based on magnetic field control parameters, thereby obtaining maintenance enrichment data; it acquires raw multimodal imaging data of surface-enhanced Raman scattering, photoacoustic, and near-infrared fluorescence in the lesion area, and analyzes the enrichment efficiency of the reagents in the lesion area based on the maintenance enrichment data, thereby obtaining multimodal imaging enrichment performance data.

[0015] This invention provides a foundation for the precise design and optimization of multimodal nanoreagents by acquiring their core-shell structure composition, surface modification, and targeting ligand connectivity density parameters. This detailed understanding of nanoreagent preparation parameters ensures a high degree of consistency in the physicochemical properties and functions of the nanoreagents, laying the groundwork for their excellent performance in subsequent targeted enrichment and imaging processes. The lesion feature module selects appropriate animal models based on the type and location of the target lesion and acquires their physiological structure and pathological characteristics, providing precise lesion information for the application of nanoreagents. This process not only clarifies the anatomical location and physiological characteristics of the lesions but also provides important reference for subsequent dose optimization and magnetic field modulation. Comprehensive analysis of lesion features allows for a better match between the performance of the nanoreagents and the needs of the lesions, thereby improving the targeting of treatment and diagnosis. The magnetic field parameter module further determines the optimal injection dose of the nanoreagents based on the lesion feature data and optimizes the injection method. This process combines the preparation parameters of the nanoreagents with the physiological and pathological characteristics of the lesions, ensuring efficient enrichment of the nanoreagents in the lesion area by simulating and evaluating the distribution of nanoreagents at different doses. Simultaneously, the acquisition of three-dimensional image localization data of the lesion provides precise lesion location information for setting parameters of the external adjustable magnetic field system. This optimization of magnetic field parameters not only improves the targeting of the nanoreagent but also reduces the impact on non-target tissues, further enhancing the accuracy of the entire system. The magneto-controlled enrichment and imaging module is responsible for the directional migration of the nanoreagent to the lesion area under the action of the magnetic field and achieving enrichment. By maintaining the acquisition of enrichment data, the dynamic changes of the nanoreagent in the lesion area can be monitored in real time, ensuring the stability and efficiency of the enrichment process. This module is also responsible for acquiring raw multimodal imaging data of the lesion area, including surface-enhanced Raman scattering, photoacoustic, and near-infrared II fluorescence, and performing enrichment efficiency analysis on these data. This multimodal imaging method not only provides rich lesion information but also further verifies the targeting ability of the nanoreagent in the lesion area through the evaluation of enrichment efficiency data. The entire system realizes the precise design of nanoreagents, detailed analysis of lesion characteristics, optimization of magnetic field control, and efficient execution of multimodal imaging. This systematic approach not only improves the enrichment efficiency and imaging quality of nanoreagents in lesion areas but also provides strong technical support for early diagnosis and precision treatment of diseases. This highly integrated and intelligent design ensures the efficiency, precision, and reliability of the entire process, paving new avenues for the application of nanotechnology in the biomedical field. Attached Figure Description

[0016] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0017] Figure 1This is a schematic diagram of the steps of the multimodal image processing method for in vivo magnetically controlled targeted enrichment according to the present invention.

[0018] Figure 2 for Figure 1 A detailed flowchart of step S1;

[0019] Figure 3 for Figure 1 A detailed flowchart of step S2. Detailed Implementation

[0020] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0021] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0022] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0023] To achieve the above objectives, please refer to Figures 1 to 3 This invention provides a multimodal image processing method for in vivo magnetically controlled targeted enrichment, the method comprising the following steps:

[0024] Step S1: Obtain the preparation parameter data of the multimodal nanoreagent, including core-shell structure composition, surface modification and target ligand connection density parameters;

[0025] In this invention, a core-shell structure material is selected, wherein the core material can be gold nanoparticles or iron oxide nanoparticles, and the shell material can be silicon or a polymer coating layer. The core material is prepared using a solvothermal method or a co-precipitation method, and the shell layer is prepared using layer-by-layer self-assembly or silanization modification techniques. Subsequently, surface modification is performed using thiol-gold bonding or carboxyl-amine coupling techniques to immobilize targeting ligands (such as folic acid, antibodies, or peptides) on the nanoparticle surface using crosslinking agents to control their linkage density. The core-shell structure and surface modification of the nanoreagent are characterized using transmission electron microscopy (TEM) and X-ray photoelectron spectroscopy (XPS), and its particle size distribution, zeta potential, and targeting ligand linkage density are measured using ultraviolet-visible spectrophotometry and dynamic light scattering (DLS). Finally, the prepared nanoreagent is stored in PBS buffer or physiological saline for subsequent experimental use.

[0026] Step S2: Select animal models based on the target lesion type and location, and obtain the physiological structure and pathological characteristics of the animal models to obtain lesion feature data;

[0027] In this invention, appropriate animal models are selected based on the type and location of the target lesion (e.g., liver cancer, breast cancer, or glioma). For example, for liver cancer lesions, a DEN-induced orthotopic liver cancer model or a Hepa1-6 cell subcutaneous xenograft model can be used; for breast cancer, a 4T1 cell orthotopic xenograft model can be used; and for glioma, a GL261 cell intracranial xenograft model can be used. After the animal model is established, the location and morphology of the lesion in the animal are obtained through ultrasound, magnetic resonance imaging (MRI), or CT scans. Pathological features are analyzed through tissue sections and HE staining, and tumor cell density, angiogenesis, and microenvironment characteristics are recorded. Optical imaging technology is used to further measure the fluorescence signal distribution at the tumor site to obtain lesion characteristic data, providing basic parameters for subsequent experiments.

[0028] Step S3: Based on the lesion characteristic data, determine the optimal injection dose of the multimodal nanoreagent from the preparation parameter data to obtain optimal injection dose prediction data; determine the injection method to obtain injection method parameter data; based on the optimal injection dose prediction data and injection method parameter data, introduce the multimodal nanoreagent into the animal model via intravenous injection, and perform image acquisition to obtain lesion three-dimensional image localization data; based on the lesion three-dimensional image localization data, set the parameters of the external adjustable magnetic field system to obtain magnetic field control parameter data;

[0029] Based on lesion characteristic data, this invention first utilizes pharmacokinetic analysis to evaluate the distribution, metabolism, and clearance rates of different doses of nano-reagents in animals. Preliminary intravenous injection experiments were conducted using 3-5 different concentrations of nano-reagents (e.g., 5 mg / kg, 10 mg / kg, 15 mg / kg, etc.). Blood and major organ samples were collected at 0h, 1h, 3h, 6h, 12h, and 24h. The concentration of nano-reagents at each time point was measured using a fluorescence spectrophotometer or ICP-MS, and the half-life and blood circulation time were calculated to determine the optimal injection dose. Subsequently, experiments were conducted using intravenous bolus injection, continuous intravenous infusion, or local arterial injection to analyze the impact of different injection methods on the enrichment efficiency of the nano-reagents, and the optimal injection method was selected. Finally, under the determined optimal injection dose and method, three-dimensional image localization data of the lesions were acquired using a small animal in vivo imaging system (e.g., multimodal optical imaging or MRI), and the image data was used to set the parameters of the magnetic field system to optimize the directional enrichment effect of the nano-reagents in vivo.

[0030] Step S4: Based on the magnetic field control parameter data, the multimodal nanoreagent is directionally migrated to the lesion area for enrichment, and the maintenance enrichment data is obtained; the original multimodal imaging data of surface-enhanced Raman scattering, photoacoustic and near-infrared II fluorescence in the lesion area are acquired, and the enrichment efficiency of the reagent in the lesion area is analyzed based on the maintenance enrichment data to obtain the multimodal imaging enrichment performance data.

[0031] This invention utilizes three-dimensional imaging localization data of lesions to adjust the direction, intensity, and gradient of an external controllable magnetic field to achieve enrichment of nanoreagents in the lesion region. The magnetic field strength is typically set within the range of 100mT to 500mT, and the gradient is controlled at 1050mT / mm, with real-time adjustment achieved through computer-controlled excitation current of the electromagnetic coil. After the nanoreagents are enriched in the lesion region, multimodal imaging raw data are acquired using a surface-enhanced Raman scattering (SERS) spectrometer, a photoacoustic imaging system (PAI), and a near-infrared II (NIR-II) imaging system. The SERS signal is used to analyze the distribution of nanoreagents at the cellular level, the PAI is used to assess their depth penetration within tissues, and the NIR-II imaging provides high-contrast lesion localization information. Subsequently, by comparing the imaging signal intensities at different time points and combining hemodynamic analysis, the cumulative concentration and residence time of the nanoreagents in the lesion region are calculated to evaluate the enrichment efficiency and ultimately obtain multimodal imaging enrichment performance data.

[0032] This invention achieves precise detection of lesion areas through meticulously designed steps. First, by acquiring the preparation parameters of the multimodal nanoreagent, including core-shell structure composition, surface modification, and targeting ligand linkage density parameters, nanoreagents with excellent performance can be precisely designed. This precise design optimizes the nanoreagent's magnetic responsiveness, biocompatibility, and targeting ability, providing a solid foundation for subsequent targeted enrichment and imaging. Second, animal models are selected based on the target lesion type and location, and the physiological structure and pathological characteristics of the animal models are acquired to obtain lesion characteristic data. This process ensures the specificity and reliability of the experiment. A comprehensive understanding of the lesion characteristics provides a scientific basis for the injection dosage and method of the nanoreagent, thereby improving the enrichment efficiency of the nanoreagent in the lesion area while reducing the impact on normal tissues. After determining the optimal injection dosage and method, the multimodal nanoreagent is introduced into the animal model via intravenous injection, and imaging is performed to obtain three-dimensional image localization data of the lesion. This process not only achieves precise delivery of the nanoreagent but also monitors the distribution of the nanoreagent in vivo in real time through image acquisition. This real-time monitoring capability enables more precise parameter settings for the externally adjustable magnetic field system. It allows for optimization of the magnetic field gradient, direction, and pulse modulation scheme based on the specific location, size, and depth of the lesion, thereby achieving efficient enrichment of nanoreagents in the lesion region. Finally, by controlling the magnetic field parameters, multimodal nanoreagents are directionally migrated to the lesion region for enrichment, and raw multimodal imaging data of surface-enhanced Raman scattering, photoacoustic, and near-infrared II fluorescence in the lesion region are acquired. This combination of multimodal imaging technologies not only provides rich lesion information but also further verifies the aggregation of nanoreagents in the lesion region through enrichment efficiency analysis. This combination of multimodal imaging and enrichment efficiency analysis not only improves the accuracy of lesion diagnosis but also provides strong support for subsequent treatment efficacy evaluation. The entire method, through precise nanoreagent design, lesion feature analysis, magnetic field control, and multimodal imaging analysis, achieves efficient targeted enrichment and accurate diagnosis of lesions, providing a new technical means for the early detection and treatment of diseases.

[0033] Preferably, step S1 includes the following steps:

[0034] Step S11: Obtain physicochemical property data of superparamagnetic iron oxide nanoparticles, including particle size distribution data and magnetization data;

[0035] This invention employs a co-precipitation method to synthesize superparamagnetic iron oxide nanoparticles. Ferric chloride (FeCl3·6H2O) and ferrous chloride (FeCl2·4H2O) are dissolved in deionized water at a molar ratio of 2:1 and stirred under an inert atmosphere. Ammonia (NH4OH) is then slowly added dropwise until the pH reaches 10, and stirring continues for 2 hours to form a black precipitate. After magnetic separation, the precipitate is washed repeatedly with deionized water and ethanol and dried to obtain superparamagnetic iron oxide nanoparticles. The particle size distribution is determined using dynamic light scattering (DLS), and the magnetization curve is measured at 300 K using a vibrating sample magnetometer (VSM) to obtain saturation magnetization, coercivity, and hysteresis loop data, thus providing particle size distribution and magnetization data.

[0036] Step S12: Magnetic response performance is evaluated based on magnetization intensity data and particle size distribution data to obtain magnetic response characteristic data of the core material;

[0037] This invention evaluates the magnetic response performance of iron oxide nanoparticles based on particle size distribution and magnetization data. First, the particle morphology is observed using transmission electron microscopy (TEM), and the uniformity of the particle size distribution is calculated. Then, under an applied alternating magnetic field (e.g., within the frequency range of 10–100 kHz), the magnetocaloric effect of the nanoparticles is recorded using a hysteresis loop measurement device, and the relationship between specific saturation magnetic susceptibility and magnetic field strength is calculated to evaluate its magnetic response sensitivity. Furthermore, through magnetocaloric conversion efficiency testing, the solution temperature rise rate is measured under an alternating magnetic field, and the specific absorptivity (SAR) is calculated to quantify the thermal response characteristics of the magnetic material, ultimately obtaining the magnetic response characteristic data of the core material.

[0038] Step S13: Optimize the synthesis parameters of the mesoporous silica shell based on the magnetic response characteristic data of the core material to obtain the shell synthesis parameter data;

[0039] This invention optimizes the synthesis parameters of a mesoporous silica (mSiO2) shell based on the magnetic response characteristics of the core material. A hydrothermal method is used to prepare the mesoporous silica coating layer. First, iron oxide nanoparticles are dispersed in a water-ethanol mixture. Then, hexamine (TEA) is added as a catalyst, and tetraethoxysilane (TEOS) is added dropwise under stirring. The shell thickness and pore structure are optimized by adjusting the TEOS concentration (e.g., 0.1M, 0.2M), reaction time (e.g., 2h, 4h), and pH value (e.g., 9.0~11.0). The specific surface area, pore size distribution, and pore volume of the shell are analyzed using nitrogen adsorption-desorption tests, ultimately obtaining the shell synthesis parameter data.

[0040] Step S14: Synthesize core-shell structured nanomaterials based on shell synthesis parameter data, and characterize the core-shell structured nanomaterials by transmission electron microscopy and dynamic light scattering to obtain core-shell structure composition data;

[0041] This invention synthesizes core-shell structured nanomaterials based on optimized shell synthesis parameters. First, iron oxide nanoparticles are reacted with an optimized concentration of TEOS under stirring conditions to synthesize core-shell structured mSiO2@Fe3O4 nanomaterials. After synthesis, the morphology and structure of the nanoparticles are observed using transmission electron microscopy (TEM), recording the core-shell thickness and particle uniformity. Dynamic light scattering (DLS) is used to determine the average hydrated particle size and Zeta potential in the aqueous dispersion system. Finally, the shell thickness distribution is calculated using image analysis software to ensure that the synthesized nanomaterials have a uniform core-shell structure, obtaining core-shell structure composition data.

[0042] Step S15: Based on the core-shell structure composition data, conduct imaging performance evaluation and modification strategy analysis based on nanoparticle loading and spectral characterization to obtain modification parameter data;

[0043] This invention evaluates the imaging performance of nanoparticles based on core-shell structure composition data. First, the absorption and emission characteristics of the nanomaterials in the near-infrared II (NIR-II) spectral range are measured using ultraviolet-visible (UV-Vis) and fluorescence spectroscopy to analyze their optical imaging capabilities. Second, the surface-enhanced Raman scattering (SERS) signal intensity is measured using Raman spectroscopy, and the photoacoustic response is measured in the 750–900 nm range using a photoacoustic imaging (PAI) system to evaluate its multimodal imaging capabilities. Furthermore, the relationship between signal intensity and particle loading is analyzed by comparing the imaging signals of nanoparticles at different concentrations (e.g., 0.1 mg / mL, 0.5 mg / mL, 1 mg / mL) to optimize the modification strategy. Finally, surface modification parameters, including the modification methods of nanoparticle surface functional groups (e.g., thiolization, amylation) and the grafting density of fluorescent probes, are optimized based on the experimental results to obtain modification parameter data.

[0044] Step S16: Based on the modification parameter data, the surface of the nanomaterial is PEGylated and targeted ligand linkage analysis is performed to obtain ligand linkage data, which includes ligand linkage density data and orientation control data.

[0045] This invention, based on modification parameter data, modifies the surface of nanomaterials with polyethylene glycol (PEG) to improve their biocompatibility and cycling stability. A silanization reaction is employed to introduce a silane coupling agent (e.g., mPEG-Silane) onto the nanoparticle surface. The PEGylation modification effect is optimized by adjusting the PEG chain length (e.g., 2kDa, 5kDa, 10kDa) and reaction concentration (e.g., 0.5mg / mL, 1mg / mL). Subsequently, 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide (EDC) and N-hydroxysuccinimide (NHS) are used to activate the carboxyl groups, and targeting ligands (e.g., folic acid, antibodies, RGD peptides) are linked via amide bonding. Finally, X-ray photoelectron spectroscopy (XPS) is used to determine the degree of PEG modification, and surface plasmon resonance (SPR) and ELISA are used to analyze the linkage density and orientation of the targeting ligands, obtaining ligand linkage data, including ligand linkage density data and orientation control data.

[0046] Step S17: Combine the core-shell structure composition data, modification parameter data, and ligand linkage data into preparation parameter data for multimodal nanoreagents.

[0047] This invention integrates core-shell structure composition data, modification parameter data, and ligand linkage data to form complete multimodal nanoreagent preparation parameter data. First, morphological characterization data (TEM, DLS), magnetic response data (VSM, SAR), and imaging data (UV-Vis, PAI, SERS) of nanoparticles are matched and analyzed to ensure that all performance parameters meet application requirements. Then, a data fusion algorithm is used to normalize the PEG modification degree, targeting ligand linkage density, and nanoparticle dispersion parameters, constructing the final preparation parameter database. Finally, statistical analysis methods are used to evaluate the impact of different preparation conditions on imaging performance, forming a standardized preparation parameter dataset for subsequent experiments.

[0048] This invention, by acquiring particle size distribution and magnetization data of superparamagnetic iron oxide nanoparticles, enables precise understanding of the fundamental properties of the core material. This in-depth understanding of the core material provides crucial information for subsequent magnetic response performance evaluation, ensuring that the nanoparticles achieve optimal response in a magnetic field, thus laying the foundation for magneto-controlled targeted enrichment. Optimizing the synthesis parameters of the mesoporous silica shell based on magnetic response characteristic data further enhances the structural stability and functionality of the nanomaterial. Precise optimization of the shell synthesis parameters not only enhances the biocompatibility of the nanoparticles but also provides a good platform for subsequent functional modifications. After the synthesis of the core-shell structured nanomaterials, characterization using transmission electron microscopy and dynamic light scattering techniques allows for precise acquisition of the core-shell structure's compositional information, ensuring the nanomaterials' size uniformity and structural integrity. Based on this, imaging performance evaluation and modification strategy analysis of the core-shell structured nanomaterials further optimize their imaging capabilities. This process not only ensures the signal intensity and stability of the nanomaterials in multimodal imaging but also provides a scientific basis for surface modification. Through PEGylation treatment and targeted ligand linkage analysis, the biocompatibility and targeting ability of the nanomaterial surface are significantly improved. PEGylation improved the stability and cycle time of nanomaterials in vivo, while precise conjugation of targeting ligands ensured the specific enrichment of nanomaterials in lesion areas. Finally, the integration of core-shell structure data, modification parameter data, and ligand conjugation data into complete preparation parameter data provided comprehensive technical support for the efficient preparation and application of multimodal nanoreagents. This series of steps not only ensured the high performance and multifunctionality of the nanoreagents but also laid a solid foundation for their application in in vivo magnetically controlled targeted enrichment and multimodal imaging.

[0049] Preferably, step S15 includes the following steps:

[0050] Step S151: Based on the core-shell structure composition data, design the loading strategy for gold nanoclusters, near-infrared dye IR-780 and NaYF4:Yb,Er upconversion nanoparticles to obtain loading condition data.

[0051] Based on the core-shell structure composition data, this invention first selects suitable functional components, including gold nanoclusters (AuNCs), near-infrared dye IR-780, and NaYF4:Yb,Er upconversion nanoparticles (UCNPs), and determines the loading strategy. Specifically, the functional components are immobilized on the surface of the core-shell structured nanomaterials using electrostatic adsorption, self-assembly, or covalent bonding. Specifically, the gold nanoclusters can be synthesized via sodium citrate reduction and loaded onto the nanomaterial surface using thiol-gold interactions; the IR-780 dye can be embedded into the nanostructure channels through hydrogen bonding or hydrophobic interactions; and the NaYF4:Yb,Er upconversion nanoparticles can be covalently linked using silanization coupling agents. Adjust reaction conditions, such as solution pH (controlled at 6.5-7.5), temperature (controlled at 25-37℃), and concentrations of functional components (AuNCs concentration 0.1-1 mg / mL, IR-780 concentration 0.05-0.5 mg / mL, UCNPs concentration 0.2-2 mg / mL), to optimize loading effect and finally obtain loading condition data.

[0052] Step S152: Load the functional components based on the load condition data to obtain the functional component load efficiency data;

[0053] Based on the loading condition data obtained in step S151, this embodiment of the invention loads functional components in a suitable reaction environment. First, the core-shell structured nanomaterials are dispersed in deionized water or PBS buffer solution (pH 7.4) to ensure uniform dispersion. Then, functional components are added sequentially, and the mixture is stirred or sonicated for 30-120 min to ensure uniform adsorption or binding to the nanostructure surface. After self-assembly and adsorption, the successful loading of gold nanoclusters is confirmed using transmission electron microscopy (TEM) and ultraviolet-visible absorption spectroscopy (UV-Vis). The loading status is confirmed by analyzing the absorption peak (around 780 nm) of the IR-780 dye using fluorescence spectroscopy. The loading of UCNPs is detected by fluorescence emission spectroscopy (excitation at 980 nm) to determine whether the intensity of its characteristic emission peak increases. The loading efficiency of each component, i.e., the change in the concentration of free functional components in the solution before and after loading, is measured and calculated to obtain the functional component loading efficiency data.

[0054] Step S153: Based on the functional component loading efficiency data, perform multimodal spectral characterization on the loaded nanomaterials to obtain Raman scattering, photoacoustic signals and fluorescence signal information, thereby obtaining spectral intensity data;

[0055] Based on the functional component loading efficiency data obtained in step S152, this invention performs multimodal spectral characterization on the loaded nanomaterials to evaluate their optical properties. First, a Raman spectrometer (laser wavelength 532 nm or 785 nm) is used to detect the surface-enhanced Raman scattering (SERS) signal of the loaded gold nanoclusters to confirm the Raman scattering intensity. Then, a photoacoustic imaging system (excitation wavelength range 700-900 nm) is used to analyze the photoacoustic signal of the IR-780 dye, and its performance in near-infrared imaging is evaluated by measuring the photoacoustic signal intensity at different wavelengths. Finally, a fluorescence spectrometer (excitation wavelength 980 nm) is used to measure the upconversion fluorescence emission peaks (e.g., 520 nm, 540 nm, and 660 nm) of NaYF4:Yb,Er UCNPs to evaluate their fluorescence intensity. By combining the signal responses of each channel, Raman scattering, photoacoustic, and fluorescence signal information are obtained, ultimately yielding spectral intensity data.

[0056] Step S154: Evaluate imaging performance based on multimodal signal intensity data to obtain imaging index prediction data;

[0057] This invention evaluates the imaging performance of nanomaterials based on the multimodal spectral intensity data obtained in step S153. First, the loaded nanomaterials are dispersed at different concentrations (e.g., 10, 50, 100, 200 μg / mL) in physiological saline or cell culture medium, and Raman imaging, photoacoustic imaging, and fluorescence imaging experiments are performed respectively. Raman imaging uses a confocal microRaman system to record the signal intensity distribution at different locations to evaluate spatial resolution; photoacoustic imaging uses a photoacoustic microscopy system to measure the photoacoustic signal intensity and calculate the contrast enhancement effect; fluorescence imaging uses a confocal laser scanning microscope to analyze the uniformity and stability of the upconversion fluorescence signal. Based on the image data, key imaging indicators such as signal-to-noise ratio (SNR), contrast enhancement factor (CE), and spatial resolution are calculated, and a prediction model is established to finally obtain predicted data for the imaging indicators.

[0058] Step S155: Develop a surface modification strategy based on the imaging index prediction data and core-shell structure composition data to obtain modification parameter data for the PEG-silane coupling agent.

[0059] Based on the imaging index prediction data and core-shell structure composition data obtained in step S154, this invention formulates a surface modification strategy to improve the stability, biocompatibility, and targeting ability of nanomaterials. PEG-silane coupling agents (such as silanized polyethylene glycol, MW 2000-5000 Da) can be used for surface modification to reduce non-specific adsorption and increase blood circulation time. First, a suitable concentration of PEG-silane coupling agent (e.g., 1-5 mg / mL) is selected, and a silanization reaction is carried out in an ethanol-water mixed solution at pH 8-9, with stirring for 12-24 h to ensure uniform distribution of PEG chains on the nanomaterial surface. Subsequently, the success rate of PEG modification is confirmed by Fourier transform infrared spectroscopy (FTIR) and thermogravimetric analysis (TGA), and the hydration diameter and surface charge changes of the modified nanomaterials are measured. Based on the optimized modification parameters, the final modification parameter data of the PEG-silane coupling agent are obtained.

[0060] This invention, through analysis of core-shell structure composition data, designed a loading strategy for gold nanoclusters, the near-infrared dye IR-780, and NaYF4:Yb,Er upconversion nanoparticles. This process not only ensured the rational layout of multiple functional components but also provided precise guidance for subsequent loading, enabling the nanomaterials to simultaneously possess multiple imaging modalities, laying the foundation for multimodal imaging. During the functional component loading process, efficient functional component loading was achieved by precisely controlling the loading conditions, and loading efficiency data was obtained. This data provides an important basis for subsequent performance evaluation, ensuring the signal intensity and stability of the nanomaterials under different imaging modalities. Through multimodal spectral characterization, the intensity information of Raman scattering, photoacoustic signals, and fluorescence signals was obtained. These spectral intensity data reflect the performance of the nanomaterials under different imaging modalities, providing direct feedback for optimizing imaging performance. Imaging performance evaluation based on multimodal signal intensity data further predicts the performance of the nanomaterials in practical applications. This evaluation process not only verifies the feasibility of nanomaterials in multimodal imaging but also provides a scientific basis for subsequent surface modification strategies. Based on imaging index prediction data and core-shell structure composition data, a surface modification strategy for PEG-silane coupling agents was formulated, and modification parameter data were obtained. This surface modification process not only enhanced the biocompatibility and stability of the nanomaterials but also further optimized their imaging performance, resulting in superior performance in the in vivo environment. The functionalization and imaging performance of the nanomaterials were comprehensively optimized. These steps not only ensured efficient signal output of the nanomaterials in multimodal imaging but also guaranteed their stability and biocompatibility in in vivo applications. This systematic optimization process enables nanoreagents to better meet the needs of in vivo magnetically controlled targeted enrichment and multimodal imaging, providing strong technical support for the precise diagnosis and treatment of diseases.

[0061] Preferably, step S16 includes the following steps:

[0062] PEGylation of the nanomaterial surface was performed based on modification parameter data to obtain the coverage density and uniformity data of the polyethylene glycol layer. Stability tests were conducted under simulated blood flow shear stress conditions based on biocompatibility and blood circulation time, based on the coverage density and uniformity data. Click chemical linkage of the targeted ligand was performed based on imaging index prediction data to obtain ligand linkage data. The simulated blood flow shear stress condition was 20 dyne / cm². The ligand linkage data included ligand linkage density data and orientation control data. The targeted ligand linkage density was 1.2-1.8 ligand molecules per square nanometer.

[0063] Based on the PEG-silane coupling agent modification parameter data obtained in step S155, this invention performs PEGylation treatment on nanomaterials under suitable conditions to improve their water solubility, biocompatibility, and cycle stability. The specific operation is as follows: First, the surface-modified nanomaterials are dispersed in anhydrous ethanol solution, controlling the nanomaterial concentration within the range of 1-5 mg / mL. Then, silanized PEG (molecular weight 2000-5000 Da, concentration 1-5 mg / mL) is added, and the reaction is carried out in a buffer system with pH 8-9. The entire reaction process is carried out under inert gas (such as nitrogen or argon) protection with stirring for 12-24 h to ensure that the PEG chains are fully bound to the nanomaterial surface. After the reaction is completed, unbound PEG molecules are removed by ultracentrifugation (centrifugation speed 8000-12000 rpm, time 10-30 min), and the morphology of the PEG layer is observed using transmission electron microscopy (TEM) combined with negative staining technology. Simultaneously, the change in the hydration diameter of the nanomaterials after PEGylation treatment is measured by dynamic light scattering (DLS). The actual PEG loading was calculated using thermogravimetric analysis (TGA), and the chemical composition of the PEG layer was analyzed by X-ray photoelectron spectroscopy (XPS) to obtain data on the coverage density and uniformity of the polyethylene glycol layer. To verify the stability of the PEGylated nanomaterials in a simulated blood flow environment, a microfluidic system with controllable shear force was constructed to simulate in vivo blood flow conditions. First, the PEG-modified nanomaterials were suspended in simulated physiological fluids (PBS or DMEM medium containing 5% serum) and loaded into the microfluidic system at concentrations of 10-100 μg / mL. The shear force within the microfluidic device was set at 20 dyne / cm² to simulate arterial blood flow, and the system was continuously circulated for 4-12 h. Dynamic light scattering (DLS) was used to monitor the particle size changes of the nanomaterials to assess their aggregation, and UV-Vis spectroscopy was used to analyze changes in solution transmittance to further determine the stability of the nanomaterials. Furthermore, the zeta potential of the nanomaterials was measured to analyze changes in their surface charge, verifying the effect of PEGylation on charge shielding. Finally, the stability of PEG-modified nanomaterials in simulated blood flow environments was comprehensively evaluated based on experimental data, and the optimal PEGylation parameters were determined by combining imaging index prediction data to ensure the long-term stability and imaging performance of the nanomaterials in blood circulation. To improve the targeting ability of the nanomaterials, targeting ligands were introduced onto the PEGylated surface and specifically linked using click chemistry strategies (such as azido-acetylene cycloaddition reactions). The specific method is as follows: First, PEG with terminal azido groups (N3-PEG, MW 2000-5000 Da) was introduced during the PEG modification process, and the successful modification of the azido groups was confirmed by Fourier transform infrared spectroscopy (FTIR).Subsequently, suitable targeting ligands (such as folic acid, RGD peptides, or antibodies) were selected, and alkynyl functional groups were introduced onto the ligand molecules to facilitate subsequent click reactions. Then, the PEG-modified azide nanomaterials were dispersed in PBS buffer at pH 7.4, and the alkynylated targeting ligands were added. Click chemistry was carried out in the presence of a Cu(I) catalyst (such as the CuSO4-ascorbic acid system) for 6-12 hours to ensure efficient ligand binding. After the reaction, unbound ligands were removed by ultracentrifugation, and the binding efficiency was analyzed using UV-Vis absorption spectroscopy (UV-Vis) or high-performance liquid chromatography (HPLC). The distribution of ligands on the nanomaterial surface was observed using transmission electron microscopy (TEM) combined with immunogold labeling, and the ligand density per unit area was determined by surface plasmon resonance (SPR) or a quartz crystal microbalance (QCM). Finally, the ligand linkage density is calculated to ensure that the number of ligand molecules per square nanometer is controlled between 1.2 and 1.8. The orientation of the ligands is analyzed by circular dichroism spectroscopy (CD) or molecular simulation to obtain ligand linkage data, including ligand linkage density data and orientation control data.

[0064] This invention utilizes PEGylation based on modification parameter data to precisely control the coverage density and uniformity of the polyethylene glycol (PEG) layer. This precise PEGylation not only provides excellent biocompatibility for nanomaterials but also significantly prolongs their time in blood circulation. Uniform PEG layer coverage effectively reduces non-specific adsorption of nanomaterials in vivo, lowering the risk of immune responses and thus enhancing their stability in the in vivo environment. Following PEGylation, the nanomaterials undergo stability testing. This test is conducted under simulated blood flow shear stress conditions, mimicking the real environment of in vivo blood circulation, with a shear stress setting of 20 dyne / cm². This test comprehensively assesses the stability of nanomaterials in complex physiological environments, ensuring they do not aggregate or degrade during blood circulation, thereby maintaining their functional integrity. This stability test provides crucial assurance for the in vivo application of nanomaterials, enabling them to maintain good performance in complex physiological environments. Furthermore, click chemistry linking of targeting ligands based on imaging index prediction data further enhances the targeting capability of the nanomaterials. The binding density of the targeting ligands was precisely controlled at 1.2–1.8 ligand molecules per square nanometer. This density range ensures that the nanomaterials can efficiently recognize and bind to the target lesion area, while avoiding non-specific binding caused by excessive ligand density. Through click chemolinking, the targeting ligands can firmly bind to the surface of the nanomaterials, and the binding process is highly controllable and stable. This binding of the targeting ligands not only improves the targeting efficiency of the nanomaterials but also enhances their enrichment ability in the lesion area, further improving the accuracy of multimodal imaging. Through PEGylation, stability testing, and binding of the targeting ligands, the biocompatibility, stability, and targeting ability of the nanomaterials are comprehensively improved. These steps work together to enable the nanomaterials to circulate efficiently in the in vivo environment, accurately target the lesion area, and provide clear and reliable signals in multimodal imaging, providing strong technical support for disease diagnosis and treatment.

[0065] Preferably, step S2 includes the following steps:

[0066] Step S21: Obtain target lesion type classification data, including pathophysiological characteristics data of atherosclerosis and gastrointestinal tumors;

[0067] The process of obtaining target lesion type classification data in this embodiment of the invention first requires establishing a pathophysiological feature database including atherosclerosis and gastrointestinal tumors. Specifically, clinical case data is collected, including patient pathological sections, genome sequencing results, metabolomics data, and imaging data (CT, MRI, PET-CT, etc.). For atherosclerotic lesions, key feature data such as endothelial cell dysfunction, lipid deposition, expression of inflammatory factors (e.g., TNF-α, IL-6), macrophage infiltration level, fibrous cap thickness, and calcification degree are extracted from the lesion area. For gastrointestinal tumors, tissue samples from gastric cancer, colorectal cancer, etc., are collected, and their molecular subtypes (e.g., MSI, CIN, EBV+), angiogenesis, tumor cell proliferation index (Ki-67), immune cell infiltration status (CD4+, CD8+ T cells), and extracellular matrix components (collagen fiber density) are analyzed. Using multi-omics data fusion analysis, feature models for different lesion types are established, thereby forming target lesion type classification data.

[0068] Step S22: Select animal species and model construction methods based on the target lesion type classification data to obtain model construction scheme data;

[0069] In this embodiment of the invention, appropriate animal species and model construction methods are selected based on the obtained target lesion type classification data. Specifically, the similarity between different animal models and human lesions is first analyzed. For example, atherosclerotic lesions can be treated using ApoE- / - or LDLr- / - mice, with vascular plaque formation induced by a high-fat, high-cholesterol diet (45%-60% fat, 1.25% cholesterol). Gastrointestinal tumors can be treated using a nude mouse subcutaneous tumor transplantation model or an orthotopic tumor model, with transplanted cell lines selected from human colorectal cancer (HCT116, SW620) or gastric cancer (MGC-803, SGC-7901). Then, a model construction plan is formulated according to the experimental objectives, including the induction period (e.g., feeding on a high-fat diet for 12-24 weeks) and the cell seeding amount (e.g., 1×10⁻⁶ cells / year). 6 -5×10 6 The model was constructed using a combination of 1 cell / 100 μL PBS and tumorigenesis assessment metrics (e.g., a subcutaneous tumor diameter ≥5 mm was considered a successful model). Finally, the optimal model construction protocol was determined based on these parameters.

[0070] Step S23: Based on the model construction protocol data, establish an animal model by inducing a high-fat diet or by inoculating tumor cells, and perform histopathological verification to obtain data confirming lesion formation;

[0071] In this embodiment of the invention, a tumor model is established by inducing atherosclerosis through a high-fat diet or by inoculating tumor cells, according to the model construction scheme. Specifically, ApoE- / - mice are fed a high-fat, high-cholesterol diet, and their body weight and blood lipid levels (TC, LDL, HDL, TG) are monitored weekly. After 12 weeks, aortic tissue is collected for Oil Red O staining to observe lipid deposition, and plaque structure is analyzed using HE staining. For the tumor model, HCT116 cells are prepared into 1×10⁻⁶ cells. 6 One cell per 100 μL suspension was subcutaneously injected into the right axilla of nude mice. Tumor volume (V = 0.5 × length × width²) was measured weekly to observe tumor formation. When the tumor volume reached 150-500 mm³, histopathological verification was performed, including HE staining to observe cell arrangement and nuclear atypia, and immunohistochemical detection of proliferation and angiogenesis markers (Ki-67, CD31). Finally, the histological analysis results were combined to obtain data confirming lesion formation.

[0072] Step S24: Perform clinical imaging evaluation on the lesion formation confirmation data, including CT, MRI and ultrasound scans, to obtain preliminary data on lesion characteristics, including lesion size, location and morphology;

[0073] This invention, based on lesion formation confirmation data, employs various imaging techniques, including CT, MRI, and ultrasound scans, to acquire data on the size, location, and morphology of animal lesions. Specifically, firstly, a micro-CT scanner is used to image the blood vessels of the atherosclerosis model, using contrast agents (such as barium iodide solution) to enhance contrast and measure plaque volume and the degree of luminal stenosis. Secondly, T2-weighted MRI (7T small animal MRI) is used to perform T2-weighted imaging of the tumor model to assess tumor boundary clarity, necrotic areas, and vascular distribution. Finally, high-frequency ultrasound (30-40MHz) is used for real-time imaging to determine tumor growth dynamics and blood flow signal intensity. After 3D reconstruction, the image data is compared with histopathological data to obtain preliminary data on lesion size, location, and morphology.

[0074] Step S25: Analyze the lesion microenvironment based on the preliminary data of lesion characteristics to obtain lesion microenvironment characteristic data. The lesion microenvironment analysis specifically includes vascular permeability, hemodynamics, and interstitial pressure measurement.

[0075] This invention analyzes the lesion microenvironment based on preliminary data of lesion characteristics, obtaining characteristic data such as vascular permeability, hemodynamics, and interstitial pressure. Specifically, vascular permeability is assessed using fluorescent tracers (such as FITC-glucan or Evans blue). Tissue samples are collected at different time points (5 min, 30 min, 1 h) after tail vein injection of the fluorescent tracer, and the fluorescence signal intensity is measured to calculate the vascular extravasation coefficient. Laser Doppler Flowmetry (LDF) is used to measure hemodynamic parameters of the lesion tissue, including microcirculatory blood flow and vascular resistance, to obtain the blood flow difference between the tumor area and normal tissue. Microprobe pressure measurement technology is used, inserting a 0.5 mm diameter micro-pressure sensor inside the tumor to monitor interstitial fluid pressure (IFP) and assess the distribution of interstitial pressure. Finally, the experimental data are combined to form lesion microenvironment characteristic data.

[0076] Step S26: Combine the lesion formation confirmation data, preliminary lesion characteristic data, and lesion microenvironment characteristic data into lesion characteristic data.

[0077] This invention integrates lesion formation confirmation data, preliminary lesion characteristic data, and lesion microenvironment characteristic data to establish a complete lesion characteristic dataset. Specifically, the process involves importing histopathological data (HE staining, immunohistochemistry), imaging data (CT, MRI, ultrasound), and microenvironment data (vascular permeability, hemodynamics, interstitial pressure) into a bioinformatics analysis platform (such as MATLAB, Python, or R), and performing data dimensionality reduction and feature extraction using principal component analysis (PCA) or hierarchical clustering. Then, a lesion classification model is constructed based on machine learning algorithms (such as random forest or support vector machine), and the accuracy and stability of the model's predictions are determined through ROC curve analysis. Finally, the integrated lesion characteristic data can be used for subsequent targeted drug screening, image analysis optimization, and pathophysiological mechanism research.

[0078] This invention clarifies the fundamental characteristics of research subjects by acquiring classification data of target lesion types, including the pathophysiological characteristics of atherosclerosis and gastrointestinal tumors. This precise classification of lesion types provides a scientific basis for the subsequent selection and construction of animal models, ensuring the relevance and effectiveness of the experimental design. Based on the lesion type classification data, appropriate animal species and model construction methods are further selected, resulting in a detailed model construction plan. This step, by accurately matching lesion types with animal models, lays a solid foundation for subsequent experiments. Subsequently, animal models are established through methods such as high-fat diet induction or tumor cell inoculation, and histopathological verification is performed to ensure that lesion formation conforms to experimental expectations. This process not only verifies the effectiveness of the model but also provides a reliable basis for subsequent lesion characteristic analysis. After lesion formation is confirmed, preliminary data such as lesion size, location, and morphology are obtained through clinical imaging assessments (such as CT, MRI, and ultrasound scans). This multimodal imaging assessment not only provides macroscopic features of the lesions but also provides important references for subsequent microenvironment analysis. Further analysis of the lesion microenvironment, including measurements of vascular permeability, hemodynamics, and interstitial pressure, revealed the physiological and pathological state of the lesions. These microenvironmental characteristic data reflected the complexity and dynamic changes of the lesions, providing crucial background information for the targeted enrichment and imaging of nanoreagents. Finally, the lesion formation confirmation data, preliminary lesion characteristic data, and lesion microenvironment characteristic data were integrated into complete lesion characteristic data. This integration process provided a comprehensive basis for determining the optimal injection dosage and injection method of the nanoreagents, ensuring the scientific rigor and reliability of subsequent experiments. Through this series of steps, not only was precise lesion localization and characteristic analysis achieved, but detailed lesion information was also provided for the targeted enrichment and imaging of multimodal nanoreagents, laying a solid foundation for the implementation of the entire research methodology.

[0079] Preferably, the determination of the optimal injection dose and injection method in step S3 specifically includes:

[0080] Fabrication of multimodal nanoreagents based on preparation parameter data;

[0081] The extravascular extravasation rate of multimodal nanoreagents under different blood flow conditions was calculated using lesion characteristic data;

[0082] Construct a diffusion model of multimodal nanoreagents in the interstitial tissue;

[0083] Based on extravascular exudation rate and diffusion model simulation, time-concentration curves of different doses of multimodal nanoreagent in various tissues and organs throughout the body were obtained.

[0084] The drug targeting index and selective targeting coefficient are calculated based on the time-concentration curve to obtain targeting selectivity assessment data.

[0085] Based on the time-concentration curve and target selectivity assessment data, the dose range that maximizes the signal intensity in the lesion area is determined, and the optimal injection dose prediction data is obtained.

[0086] Based on the lesion type and its microenvironment characteristics, this invention selects suitable nanomaterials, such as gold nanoparticles, iron oxide nanoparticles, or polymer nanoparticles, and determines their physicochemical properties, including particle size, surface charge, hydrophobicity, and biocompatibility. Subsequently, based on preparation parameter data (including reaction temperature, stirring rate, solvent type, etc.), nanoparticles are synthesized using physical or chemical methods, for example, iron oxide nanoparticles are prepared via co-precipitation or polymer nanoparticles are synthesized via microemulsion. Next, imaging probes (such as fluorescent dyes, radioactive isotopes, or MRI contrast agents) are combined with the nanoparticles via covalent coupling or surface adsorption techniques. The prepared nanoreagents are then characterized for particle size, dispersion, and surface charge using methods such as transmission electron microscopy (TEM), dynamic light scattering (DLS), and Zeta potential analysis, obtaining multimodal nanoreagents that meet the preparation parameter requirements. Based on the characteristics of vascular permeability, blood flow velocity, and interstitial pressure in the lesion area, an in vitro microfluidic model was established, simulating capillary blood flow conditions using different flow rates (e.g., 10 μL / min, 50 μL / min, and 100 μL / min). Then, fluorescently labeled nanoreagents were added to the fluid system, and their residence time and exudation rate in the simulated vascular channels were recorded in real time. Light sheet fluorescence microscopy (LSFM) and flow cytometry were used to analyze the changes in extravascular permeation of the nanoreagents over time. Finally, by calculating the amount of nanoreagents exuding from the vascular lumen per unit time, the extravascular permeation rate under different blood flow conditions was obtained, and this rate was fitted and analyzed with the vascular permeability data of the lesion area to predict the permeation situation in the real physiological environment. Based on Frick's diffusion law and combined with the characteristics of the lesion microenvironment (e.g., interstitial space size, extracellular matrix density, and interstitial fluid flow rate), a finite element analysis model was established. The diffusion coefficients of nanoreagents with different particle sizes (e.g., 50 nm, 100 nm, 200 nm) in a three-dimensional collagen scaffold were experimentally determined, and the parameters of the diffusion model were adjusted based on in vivo analysis of lesion tissue sections. Subsequently, the real-time diffusion behavior of the nanoreagents in tissues was observed in an animal model using in vivo two-photon microscopy, and the results were compared and verified with simulation results to optimize the predictive ability of the diffusion model. Finally, a mathematical model that can reflect the actual diffusion of nanoreagents in lesion tissues was obtained. Based on the aforementioned extravascular permeation rate and tissue diffusion model, a multi-compartment model incorporating parameters such as blood circulation, liver metabolism, and renal clearance was established using pharmacokinetic software (e.g., PK-Sim or Simcyp).Then, in a mouse model, different doses (e.g., 1 mg / kg, 5 mg / kg, 10 mg / kg) of the nanoreagent were injected via tail vein. Blood samples and major organs (liver, kidney, spleen, and lesion area) were collected at different time points (0.5 h, 1 h, 4 h, 8 h, 24 h). The concentration distribution of the nanoreagent was determined by ICP-MS (inductively coupled plasma mass spectrometry) or autoradiography. The experimental data were input into the model for parameter correction to obtain accurate time-concentration curves. The area under the radiation (AUCtumor / AUCplasma) ratio of the lesion area to plasma was calculated based on the time-concentration curves to obtain the drug targeting index (TI). Then, the selective targeting coefficient (STI) was obtained by calculating the AUC ratio of the lesion area to non-targeted tissues (e.g., liver, kidney). Furthermore, correlation analysis was performed on these data with the lesion microenvironment characteristics to evaluate the targeting performance of different nanoreagent design schemes. In the experiment, multiple nanoreagent formulations (e.g., PEG-modified, targeted ligand-modified, etc.) were selected, and their TI and STI were compared to screen out the optimal nanoreagent preparation scheme. Based on the aforementioned TI and STI data, several candidate doses (e.g., 2 mg / kg, 5 mg / kg, 10 mg / kg) were selected and validated using dynamic imaging. Specifically, different doses of the nanoreagent were injected into animal models, and signal intensity changes in the lesion area were measured at preset time points (1 h, 4 h, 8 h) using MRI, PET, or optical imaging. Subsequently, nonlinear regression analysis was used to analyze the relationship between signal intensity and dose, calculating the dose range corresponding to maximizing the signal. Combined with animal toxicity test data, the safe dose range was optimized, ultimately obtaining the optimal injection dose prediction data, providing a reference for subsequent preclinical studies.

[0087] This invention utilizes preparation parameter data to fabricate multimodal nanoreagents, ensuring a high degree of consistency in the physicochemical properties and functions of the nanoreagents. This precise preparation process provides a high-quality reagent foundation for subsequent experiments, enabling them to maintain stable performance in complex biological environments. The extravascular exudation rate of the nanoreagents under different blood flow conditions is calculated using lesion characteristic data. This process, through in-depth analysis of the lesion microenvironment, accurately predicts the permeability of the nanoreagents in the blood vessel wall, providing an important basis for optimizing the delivery efficiency of the nanoreagents. Simultaneously, a diffusion model of the nanoreagents in the interstitial tissue is constructed to further simulate the dynamic behavior of the nanoreagents within the tissue. The establishment of this model not only considers the physical properties of the nanoreagents but also incorporates the physiological characteristics of the lesion tissue, providing theoretical support for the targeted delivery of the nanoreagents. By simulating the time-concentration curves of the nanoreagents in various tissues and organs throughout the body at different doses, the in vivo distribution of the nanoreagents can be comprehensively evaluated. This simulation process, combining the extravascular exudation rate and diffusion model, provides a scientific basis for optimizing the dosage of the nanoreagents. Furthermore, the drug targeting index and selective targeting coefficient are calculated using the time-concentration curves, yielding targeted selectivity evaluation data. This evaluation process not only quantified the targeting capability of the nanoreagent but also provided clear guidance for subsequent dose adjustments. Ultimately, based on the target selectivity evaluation data and time-concentration curves, the dose range that maximizes signal intensity in the lesion area was determined, yielding optimal injection dose prediction data. Determining this dose range not only ensured efficient enrichment of the nanoreagent in the lesion area but also reduced the impact on non-target tissues, improving the signal-to-noise ratio of imaging and diagnostic accuracy. Through this series of steps, precise delivery and enrichment of the nanoreagent in the lesion area were achieved, providing high-quality signal output for multimodal imaging and further advancing the application of nanotechnology in disease diagnosis and treatment.

[0088] Preferably, the determination of the injection method in step S3 specifically includes:

[0089] Drug delivery models for different injection routes were constructed based on lesion feature data to obtain injection route efficiency data;

[0090] This invention collects lesion characteristic data, including vascular density, blood flow velocity, permeability, interstitial space size, and extracellular matrix composition at the lesion site. Then, drug delivery models are constructed for different injection routes (e.g., intravenous injection, arterial injection, and local injection). Computational fluid dynamics (CFD) is used to simulate the transport of nanoparticles under different blood flow environments, and pharmacokinetic (PK) modeling is combined to calculate the residence time and concentration changes of the drug in the lesion area under each injection route. Experimental verification uses mouse or rat models to deliver fluorescently labeled nanoparticles via tail vein, femoral artery, and local injection at the lesion site. Blood and tissue samples are collected at different time points (0.5h, 1h, 4h, and 8h). The concentration of the nanoparticles in the lesion area and other organs is measured using a fluorescence spectrophotometer or ICP-MS. The cumulative lesion dose and targeting ratio per unit dose are calculated, ultimately obtaining efficiency data for different injection routes.

[0091] Single-injection and fractionated-injection regimens are designed based on the optimal injection dose prediction data.

[0092] This invention, based on optimal injection dose prediction data, determines comparative regimens for single-dose and fractionated injections. For example, a single injection of 5 mg / kg could be followed by two fractionated injections of 2 mg / kg and 3 mg / kg, or three fractionated injections of 1 mg / kg, 2 mg / kg, and 2 mg / kg. Then, pharmacokinetic simulation tools such as PK-Sim are used to input different dose delivery methods, calculating drug concentration change curves in the lesion region under each regimen. The effects of different regimens on lesion targeting are evaluated by considering blood drug clearance rate, liver and kidney metabolic capacity, and lesion tissue retention characteristics. In the experimental validation phase, mouse models are selected for single-dose and fractionated injections. In vivo imaging techniques (such as PET, MRI, or fluorescence molecular imaging) are used to monitor drug distribution in the lesion region, comparing the effects of single-dose and fractionated injections on lesion signal intensity and retention time, ultimately yielding a dose delivery regimen suitable for the characteristics of the lesion.

[0093] The dosage distribution strategy was obtained by simulating the area under the blood drug concentration curve and the peak concentration under each scheme;

[0094] Based on the aforementioned single-dose and fractionated injection regimens, this invention uses pharmacokinetic software to input parameters such as injection dose, injection time interval, plasma clearance rate, and tissue permeability to calculate blood drug concentration curves (i.e., drug concentration changes over time) for different regimens. Then, key pharmacokinetic parameters are extracted, such as the area under the curve (AUC) representing total drug exposure and peak concentration (Cmax) representing the highest blood drug concentration. The ratio of drug accumulation in the lesion area to drug distribution in non-target tissues (such as the liver and kidneys) is calculated. In the experimental section, blood samples are collected at different time points through animal experiments. Plasma drug concentrations are measured using LC-MS / MS or ICP-MS techniques, and the results are compared with simulation results to optimize the dosage distribution strategy, maximizing drug accumulation in the lesion area while avoiding excessive exposure to non-target organs that could lead to toxicity.

[0095] Based on injection route efficiency data and dose allocation strategies, the injection time interval is determined by analyzing the vascular permeability time window information in the lesion characteristic data, thus obtaining an injection time plan.

[0096] This invention utilizes pharmacokinetic simulations to analyze drug concentration changes in the lesion area under different injection routes and dosage distribution schemes, and combines this with the lesion area vascular permeability time window (i.e., the time range within which blood vessels are most open and the drug most easily penetrates into the lesion) for time matching. Vascular permeability time window data can be measured using fluorescent molecular probes or dynamic contrast-enhanced MRI (DCE-MRI) to observe changes in contrast agent penetration in the lesion area and determine the optimal drug delivery time. Then, the interval between injections is adjusted based on the time window information. For example, if the peak of lesion permeability occurs 2 hours after injection, subsequent doses can be delivered 2 hours later to improve drug retention efficiency. In the experimental phase, animal experiments were conducted using different time interval injection schemes (e.g., 2h, 4h, 6h), and in vivo imaging technology was used to monitor drug concentration changes in the lesion area to ultimately determine the optimal injection time scheme.

[0097] The injection rates of 0.1–2 mL / min were experimentally evaluated, and the dispersion and aggregation of the multimodal nanoreagents in simulated blood vessels were measured to obtain the optimal injection rate data.

[0098] This invention constructs a simulated blood vessel model within a microfluidic system. Transparent PDMS material is used to fabricate vascular channels with an inner diameter of 50-200 μm, which are then filled with a blood-simulating solution (PBS + plasma proteins) similar to physiological conditions. Nanoparticles are then injected at different rates (0.1 mL / min, 0.5 mL / min, 1.0 mL / min, 2.0 mL / min), and the dispersion of the nanoparticles is observed using a high-power microscope and dynamic light scattering (DLS) to analyze whether aggregation or precipitation occurs. Simultaneously, in animal experiments, nanoparticles are injected at different rates, and blood samples are collected at 5 min, 30 min, and 1 h after injection to measure changes in the concentration of the nanoparticles in the blood, evaluating the effect of different injection rates on drug distribution. Finally, the optimal injection rate that maintains stable dispersion of the nanoparticles while avoiding excessively rapid clearance is determined.

[0099] The injection time protocol and optimal injection rate data are combined into injection method parameter data.

[0100] Based on the aforementioned experimental data, this invention integrates the optimal injection time, interval, and rate under different lesion characteristics to construct a personalized injection protocol database. Then, using data modeling tools (such as Matlab or Python modeling), a multi-factor optimization model is established. By inputting lesion type, vascular permeability, and pharmacokinetic parameters, the recommended injection time and rate are automatically calculated, and an injection method parameter table suitable for preclinical experiments is generated. Finally, the optimized injection method is validated in animal models, measuring the cumulative drug amount, signal intensity, and drug exposure in non-target tissues within the lesion area. This ensures that the optimized injection method maximizes the lesion-targeting effect while reducing systemic toxicity, providing data support for subsequent clinical research.

[0101] This invention constructs drug delivery models for different injection routes based on lesion characteristic data, enabling precise evaluation of the efficiency of each route. This process considers not only the anatomical location and physiological characteristics of the lesion but also the physicochemical properties of the nanoreagent, providing a scientific basis for selecting the optimal injection route. The injection route efficiency data obtained through the delivery model clarifies the advantages and disadvantages of different routes, laying the foundation for subsequent dose allocation and timing. After determining the injection route, single-injection and fractionated-injection schemes are designed based on optimal dose prediction data. This flexible injection scheme design better adapts to the dynamic changes of the lesion and the in vivo behavior of the nanoreagent. By simulating the area under the plasma concentration curve and peak concentration under each scheme, the dose allocation strategy is further optimized. This strategy not only ensures the effective enrichment of the nanoreagent in the lesion area but also avoids insufficient treatment or side effects due to excessively high or low doses. Combining the injection route efficiency data and dose allocation strategy, the vascular permeability time window information in the lesion characteristic data is analyzed to determine the injection time interval. This process fully considers the dynamic changes in the lesion microenvironment, ensuring that the nanoreagents can enter the lesion area during the period of optimal vascular permeability, thereby improving targeting efficiency. The injection timing protocol obtained in this way provides crucial time assurance for the precise delivery of nanoreagents. Experimental evaluation of different injection rates (0.1-2 mL / min) and measurement of the dispersion and aggregation of nanoreagents in simulated blood vessels allows for the determination of the optimal injection rate. This rate optimization not only ensures the uniform distribution of nanoreagents in blood vessels but also avoids aggregation or dilution problems caused by injection that is too fast or too slow. Finally, the injection timing protocol and optimal injection rate data are integrated into injection method parameter data, providing comprehensive and precise injection guidance for the clinical application of nanoreagents. The nanoreagent injection process achieves comprehensive optimization from theoretical design to experimental validation. This optimization not only improves the enrichment efficiency of nanoreagents in the lesion area but also reduces the impact on non-target tissues, providing reliable technical support for multimodal imaging and targeted therapy.

[0102] Preferably, the parameter settings of the external adjustable magnetic field system in step S3 specifically include:

[0103] Based on the three-dimensional image localization data of the lesion, the depth, size and morphological characteristics of the lesion are quantitatively analyzed to obtain the spatial characteristic parameter data of the lesion;

[0104] This invention utilizes high-resolution three-dimensional medical imaging equipment (such as magnetic resonance imaging (MRI) or computed tomography (CT)) to acquire lesion image data of patients. Then, a machine learning-based image segmentation algorithm (such as U-Net or Mask R-CNN) is used to automatically identify the lesion region and calculate the lesion's three-dimensional spatial distribution information, including parameters such as lesion volume, longest diameter, sphericity, and boundary roughness. For depth calculation, the distance from the patient's surface to the lesion center is used as the lesion depth parameter, and combined with the density of surrounding tissues and vascular distribution, a spatial feature model is established to ultimately obtain the lesion's spatial feature parameter data. In specific application scenarios, such as magnetic-targeted therapy for liver tumors, the lesion's spatial feature data might include: lesion volume in the range of 5-30 cubic centimeters, longest diameter of 5-20 millimeters, lesion depth of 30-70 millimeters, and surrounding tissue vascular density of 0.6-1.2 mm² / mm³, etc.

[0105] The optimal magnetic field gradient distribution is calculated based on the spatial characteristic parameter data of the lesion, thereby obtaining the magnetic field gradient parameter data;

[0106] This invention utilizes finite element analysis software (such as COMSOL Multiphysics) to simulate and calculate the magnetic field distribution based on the spatial characteristic parameters of the lesion. Different magnetic field sources (such as permanent magnets and electromagnetic coils) are set and their arrangement optimized to ensure the maximization of the magnetic field gradient in the lesion area while maintaining magnetic field uniformity. In specific implementation, the rate of change of magnetic field intensity within the target area is used as the magnetic field gradient parameter. If the magnetization intensity of the magnetically responsive nanoparticles in the tumor area needs to reach 10-50 mT, the magnetic field gradient can be set to 50-200 T / m to ensure effective aggregation of nanoparticles in the lesion area. Finally, the magnetic field gradient parameter data is obtained through simulation calculation and verified in conjunction with experimental data.

[0107] The incident angle and direction of the magnetic field are determined based on the location of the lesion and the characteristics of the surrounding tissue structure in the spatial characteristic parameter data of the lesion, thereby obtaining the magnetic field direction positioning parameter data.

[0108] This invention combines lesion location and surrounding tissue structure features from spatial characteristic parameter data, and employs magnetic resonance imaging (MRI-compatible magnetic field detection) to analyze the optimal incident angle of the magnetic field. By rotating the magnetic field coil or adjusting the relative position of the external magnet, the main direction of the magnetic field lines is matched with the lesion location to maximize the magnetic targeting and aggregation effect of nanoparticles. For example, in magnetic targeting therapy for gliomas, if the lesion is located in the left frontal lobe, the magnetic field direction can be adjusted to a fronto-occipital direction, with the incident angle controlled between 30-60 degrees to optimize the magnetic effect. Finally, the magnetic field direction positioning parameters are determined by combining three-dimensional magnetic field simulation with in vitro experiments.

[0109] Based on the magnetic response characteristics of multimodal nanoreagents and blood circulation dynamics parameters, a magnetic field pulse modulation scheme was designed to obtain magnetic field pulse modulation parameter data, which includes the initial magnetic field strength, pulse frequency, intensity variation range, number of reverse magnetic field pulses and duration.

[0110] This invention designs a suitable magnetic field pulse modulation scheme based on the magnetic response characteristics (such as saturation magnetization and superparamagnetism) of multimodal nanoreagents and blood circulation dynamics (such as blood flow velocity and shear force). In specific operation, a dynamic magnetic field generator is used to generate a pulsed magnetic field, setting the initial magnetic field strength (e.g., 100 mT), pulse frequency (e.g., 1-10 Hz), intensity variation range (e.g., ±50 mT), number of reverse magnetic field pulses (e.g., 5-20 times), and duration (e.g., 5-30 minutes). For example, in a targeted delivery experiment to breast cancer tumors, a 5 Hz pulsed magnetic field is used for stimulation for 15 minutes to observe the deposition of nanoparticles at the lesion site. Parameters are adjusted to optimize the enrichment effect, ultimately obtaining the magnetic field pulse modulation parameter data.

[0111] Based on the preset target enrichment requirements, the real-time monitoring signal acquisition time interval and the threshold conditions for determining the steady state of enrichment are set to obtain enrichment monitoring parameter data.

[0112] This invention addresses the targeted enrichment process of magnetic nanoparticles by setting real-time monitoring signal acquisition intervals and threshold conditions for determining steady-state enrichment. Specifically, fluorescent probes are used to label magnetic nanoparticles, and fluorescence imaging or magnetic resonance imaging (MRI) is used to monitor the enrichment level of nanoparticles in the lesion area in real time. A monitoring interval is set (e.g., acquiring signals every 10 seconds), and steady-state enrichment is determined when the signal intensity change rate is less than 5%. For example, in the magnetic targeted delivery process for gliomas in the brain, if the monitoring signal stabilizes within 60 minutes and the fluorescence intensity in the lesion area reaches more than 80% of the initial value, the enrichment is considered to have reached its optimal state. Finally, enrichment monitoring parameters are obtained based on experimental data.

[0113] The magnetic field gradient parameter data, magnetic field direction positioning parameter data, magnetic field pulse modulation parameter data, and enrichment monitoring parameter data are integrated into complete magnetic field control parameter data.

[0114] In this embodiment of the invention, after all key parameters have been calculated, the magnetic field gradient parameter data, magnetic field direction positioning parameter data, magnetic field pulse modulation parameter data, and enrichment monitoring parameter data are integrated into complete magnetic field control parameter data. In specific implementation, computer-aided optimization algorithms (such as Particle Swarm Optimization (PSO) or Genetic Algorithm (GA)) are used to jointly optimize each parameter and form the final magnetic field control scheme. For example, for a liver tumor with a diameter of 10 mm and a depth of 40 mm, the final determined magnetic field strength is 150 mT, the gradient is 120 T / m, the pulse frequency is 5 Hz, the action time is 30 minutes, the monitoring signal acquisition interval is 10 seconds, and the enrichment steady-state threshold is set to 80%, ensuring that magnetic nanoparticles can efficiently target and accumulate in the lesion area.

[0115] This invention provides precise lesion location information for magnetic field modulation, ensuring that the magnetic field can accurately act on the target area and avoid affecting surrounding normal tissues. It optimizes the enrichment efficiency of nanoreagents in the lesion area, enabling them to migrate efficiently to the lesion site under the influence of the magnetic field. Simultaneously, the incident angle and direction of the magnetic field are determined based on the lesion location and the structural characteristics of the surrounding tissue, obtaining magnetic field direction positioning parameter data. This parameter determination further improves the accuracy of magnetic field modulation, ensuring that the nanoreagents reach the lesion area along the optimal path. This scheme not only considers the physical properties of the nanoreagents but also incorporates the dynamic changes in blood circulation within the body. By optimizing the initial magnetic field strength, pulse frequency, intensity variation range, and the number and duration of reverse magnetic field pulses, the enrichment capacity of the nanoreagents in the lesion area is further enhanced. The monitoring mechanism can provide real-time feedback on the enrichment status of the nanoreagents in the lesion area, ensuring the stability and reliability of the enrichment process. By integrating magnetic field gradient parameters, magnetic field direction positioning parameters, magnetic field pulse modulation parameters, and enrichment monitoring parameters into complete magnetic field modulation parameter data, comprehensive optimization of magnetic field modulation is achieved. The magnetic field modulation process realizes a systematic design from lesion localization to parameter optimization. This precise magnetic field modulation not only improves the enrichment efficiency of nanoreagents in lesion areas but also ensures the stability and real-time controllability of the enrichment process. Ultimately, it provides efficient and precise technical support for the targeted enrichment and imaging of multimodal nanoreagents, promoting the application of nanotechnology in disease diagnosis and treatment.

[0116] Preferably, step S4 includes the following steps:

[0117] Step S41: Activate the external adjustable magnetic field system according to the magnetic field control parameter data to generate an initial magnetic field and obtain the initial magnetic field state data; and execute the magnetic field pulse modulation program based on the initial magnetic field state data to realize the gradual enhancement of the gradient magnetic field from the outside to the inside, and obtain the gradient magnetic field distribution data.

[0118] In this embodiment of the invention, magnetic field control parameters are input into the magnetic field control system to activate the magnetic field generator, generating a uniform initial magnetic field. This initial magnetic field is then monitored in real time by a high-precision magnetic field sensor, obtaining data on the initial magnetic field state, including its intensity, direction, and distribution. Subsequently, a magnetic field pulse modulation program is executed, employing a time-series control strategy to gradually increase the magnetic field gradient from the peripheral region towards the lesion center. Specifically, this is achieved through dynamic adjustment of the coil current. A lower magnetic field intensity is initially applied at the outer edge of the lesion, and the magnetic field intensity in the core region is gradually increased at set time intervals. Simultaneously, the gradient change rate is adjusted to ensure effective aggregation of the magnetic nanoparticles in the lesion area. Throughout the process, real-time data on magnetic field changes are collected by the sensor and stored as gradient magnetic field distribution data, including magnetic field intensity change curves at different locations, for subsequent fine-tuning.

[0119] Step S42: Based on the gradient magnetic field distribution data, near-infrared fluorescence signals in the lesion area are collected every 30 seconds to obtain enrichment process monitoring data; the fluorescence intensity change rate of three consecutive monitoring is calculated based on the enrichment process monitoring data and compared with a preset threshold to determine whether the enrichment steady state has been reached, and enrichment state judgment data is obtained.

[0120] In this embodiment of the invention, during magnetic field modulation, a near-infrared II fluorescence imaging system is used to scan the lesion area every 30 seconds to acquire the fluorescence signal of the magnetic nanoreagent within the lesion and record its temporal changes, thus obtaining enrichment process monitoring data. Fluorescence acquisition employs a short exposure time (<100ms) and a high-sensitivity detector to ensure real-time performance and signal accuracy. Subsequently, the fluorescence intensity of three consecutive acquisitions is calculated, and its rate of change is analyzed using numerical differentiation. This rate of change is compared with a preset steady-state enrichment threshold (e.g., a rate of change within 5%) to determine whether enrichment has reached a stable state. This process is automated, and the enrichment state judgment data is updated after each acquisition to ensure the accuracy of magnetic field control.

[0121] Step S43: Based on the enrichment state judgment data, automatically adjust the magnetic field strength and direction. If a steady state is not reached, continue executing the magnetic field modulation program; if a steady state is reached, proceed to the next stage of magnetic field modulation to obtain magnetic field optimization adjustment data.

[0122] This invention, based on enrichment state judgment data, automatically adjusts magnetic field control parameters if enrichment has not reached a steady state. This includes appropriately increasing the magnetic field strength or optimizing the magnetic field direction to allow the magnetic nanoreagent to continue enriching. Specifically, the adjustment method involves calculating the optimal magnetic field direction based on the current magnetic field distribution data and the fluorescence intensity change rate, and dynamically adjusting the vector component of the coil current to make the magnetic field force converge towards the lesion center. When the fluorescence change rate reaches the steady-state threshold, the magnetic field system enters the next stage, performing magnetic field optimization adjustment to adapt to the distribution characteristics of the nanoreagent in different tissue environments, and storing the magnetic field optimization adjustment data for subsequent magnetic field reverse modulation.

[0123] Step S44: Based on the magnetic field optimization adjustment data, execute the reverse magnetic field pulse sequence to drive the intravascular multimodal nanoreagent through the blood vessel wall to the target lesion location, and obtain tissue penetration enhancement data; based on the tissue penetration enhancement data, perform long-term enrichment of the multimodal nanoreagent in the lesion area to obtain maintenance enrichment data;

[0124] This invention utilizes magnetic field optimization data to initiate a reverse magnetic field pulse program, rapidly changing the magnetic field direction within a short time. This causes some magnetic nanoparticles to undergo inertial motion, allowing them to penetrate the blood vessel wall and enter the lesion tissue. Specifically, a 1-5 ms reverse magnetic field pulse is applied, with a peak intensity 50%-100% stronger than the initial magnetic field, for 5-10 cycles to ensure sufficient magnetic force to drive the nanoparticle migration. During this process, ultrasound or photoacoustic imaging is used to assess the penetration of the nanoparticles, recording tissue penetration enhancement data, including the distribution ratio of the reagent across the blood vessel wall, penetration depth, and signal change trends. Subsequently, a stable magnetic field maintenance phase is initiated, allowing the nanoparticles to remain within the lesion tissue for an extended period. Maintenance enrichment data is recorded to ensure imaging and therapeutic efficacy.

[0125] Step S45: Based on the maintenance enrichment data, perform multi-time point surface-enhanced Raman scattering, photoacoustic and near-infrared two-region fluorescence multimodal imaging data acquisition to obtain the raw multimodal imaging data;

[0126] In this embodiment of the invention, during the maintenance of the magnetic field, surface-enhanced Raman scattering, photoacoustic imaging, and near-infrared II fluorescence multimodal imaging data are acquired at preset time intervals (e.g., 10 minutes, 30 minutes, 1 hour, etc.). Specifically, a high-sensitivity Raman spectrometer is used to excite the lesion area at a specific wavelength (e.g., 785 nm or 1064 nm) to analyze the Raman enhancement signal of the nanoreagent; simultaneously, a photoacoustic imaging device is used to detect the photothermal conversion characteristics of the nanoreagent under pulsed laser irradiation to acquire the photoacoustic signal; and near-infrared II fluorescence imaging is performed simultaneously to record the fluorescence distribution of the nanoreagent. These data are stored in real time and organized into raw multimodal imaging data for subsequent targeted enrichment effect analysis.

[0127] Step S46: Based on the original multimodal imaging data, calculate the signal contrast between the lesion area and surrounding tissues at different time points, determine the enrichment half-life, and evaluate the signal enhancement factor to obtain targeted enrichment effect data;

[0128] This invention, based on multimodal imaging raw data, calculates the contrast between lesion signals and surrounding tissue signals acquired at different time points. A standardized algorithm is used to calculate signal ratios; for example, the fluorescence intensity ratio can be determined by the ratio of the average fluorescence intensity of the lesion area to the fluorescence intensity of the background tissue. Simultaneously, the enrichment half-life is calculated, which is the time required for the fluorescence signal to decay to 50% of its initial value during the enrichment process of the nanoreagent. Furthermore, the aggregation efficiency of the nanoreagent is evaluated by the signal enhancement factor, which can be expressed as the ratio of the maximum fluorescence signal intensity to the initial signal intensity, thereby obtaining targeted enrichment effect data and providing a basis for subsequent imaging optimization.

[0129] Step S47: Based on the targeted enrichment effect data, register and fuse the original multimodal imaging data to construct a multidimensional characterization model of the lesion and obtain multimodal imaging enrichment performance data.

[0130] This invention utilizes targeted enrichment effect data to register and fuse raw multimodal imaging data. A feature point matching-based method is employed to align data from different modalities (Raman, photoacoustic, and fluorescence) and perform image fusion to construct a multidimensional characterization model of the lesion. Specifically, firstly, chemical composition information of the lesion is extracted based on Raman scattering data; then, tissue blood perfusion characteristics are obtained by combining photoacoustic data; and simultaneously, fluorescence signals are used to supplement the enrichment distribution of nanoreagents. Through multimodal joint analysis, an enrichment efficacy evaluation map is generated, and multimodal imaging enrichment efficacy data is output, providing guidance for accurate diagnosis and personalized treatment.

[0131] This invention ensures that nanoreagents can efficiently migrate to the lesion area along an optimized path, while avoiding nanoreagent aggregation or uneven distribution caused by sudden changes in magnetic field strength. During the enrichment process, the enrichment status of nanoreagents is monitored in real time by acquiring near-infrared II fluorescence signals from the lesion area every 30 seconds. This high-frequency signal acquisition can promptly reflect the dynamic changes of nanoreagents in the lesion area, and by calculating the fluorescence intensity change rate and comparing it with a preset threshold, it can accurately determine whether the enrichment has reached a steady state. This real-time monitoring mechanism provides a scientific basis for the dynamic adjustment of the magnetic field, ensuring the efficiency and stability of the enrichment process. When enrichment has not reached a steady state, the system automatically adjusts the magnetic field strength and direction and continues to execute the magnetic field modulation program; when enrichment reaches a steady state, it enters the next stage of magnetic field modulation. This intelligent magnetic field adjustment strategy not only improves the enrichment efficiency of nanoreagents but also ensures the precise controllability of the enrichment process. Furthermore, by executing a reverse magnetic field pulse sequence, the nanoreagents are guided from the blood vessel to the target lesion location, significantly enhancing the tissue penetration ability of the nanoreagents and achieving long-term enrichment, providing a stable signal source for subsequent multimodal imaging. During the imaging phase, multimodal imaging data, including surface-enhanced Raman scattering, photoacoustic imaging, and near-infrared fluorescence, are acquired at multiple time points to comprehensively and dynamically reflect the physiological and pathological information of the lesion area. This multimodal imaging method not only provides rich imaging information but also accurately reflects the targeted enrichment effect of nanoreagents in the lesion area through signal contrast calculation, enrichment half-life determination, and signal enhancement factor evaluation. Finally, through registration and fusion processing of the raw multimodal imaging data, a multidimensional characterization model of the lesion is constructed, providing comprehensive and accurate imaging evidence for disease diagnosis and treatment. This achieves end-to-end optimization of nanoreagents from precise enrichment to efficient imaging. This systematic operation not only improves the enrichment efficiency and imaging quality of nanoreagents in the lesion area but also provides strong technical support for the early diagnosis and precision treatment of diseases.

[0132] The present invention also provides a multimodal image processing system for in vivo magnetically controlled targeted enrichment, used to execute the above-described multimodal image processing method for in vivo magnetically controlled targeted enrichment, wherein the multimodal image processing system for in vivo magnetically controlled targeted enrichment includes:

[0133] The preparation parameter module is used to obtain preparation parameter data for multimodal nanoreagents, including core-shell structure composition, surface modification, and targeting ligand connection density parameters.

[0134] The lesion feature module is used to select animal models based on the target lesion type and location, and to obtain the physiological structure and pathological features of the animal models to obtain lesion feature data.

[0135] The magnetic field parameter module is used to determine the optimal injection dose of the multimodal nanoreagent based on lesion characteristic data, obtaining optimal injection dose prediction data; and to determine the injection method, obtaining injection method parameter data; based on the optimal injection dose prediction data and injection method parameter data, the multimodal nanoreagent is introduced into the animal model via intravenous injection, and image acquisition is performed to obtain three-dimensional image localization data of the lesion; based on the three-dimensional image localization data of the lesion, the parameters of the external adjustable magnetic field system are set to obtain magnetic field control parameter data;

[0136] The magneto-controlled enrichment and imaging module is used to directionally migrate multimodal nanoreagents to the lesion area for enrichment based on magnetic field control parameters, thereby obtaining maintenance enrichment data; it acquires raw multimodal imaging data of surface-enhanced Raman scattering, photoacoustic, and near-infrared fluorescence in the lesion area, and analyzes the enrichment efficiency of the reagents in the lesion area based on the maintenance enrichment data, thereby obtaining multimodal imaging enrichment performance data.

[0137] This invention provides a foundation for the precise design and optimization of multimodal nanoreagents by acquiring their core-shell structure composition, surface modification, and targeting ligand connectivity density parameters. This detailed understanding of nanoreagent preparation parameters ensures a high degree of consistency in the physicochemical properties and functions of the nanoreagents, laying the groundwork for their excellent performance in subsequent targeted enrichment and imaging processes. The lesion feature module selects appropriate animal models based on the type and location of the target lesion and acquires their physiological structure and pathological characteristics, providing precise lesion information for the application of nanoreagents. This process not only clarifies the anatomical location and physiological characteristics of the lesions but also provides important reference for subsequent dose optimization and magnetic field modulation. Comprehensive analysis of lesion features allows for a better match between the performance of the nanoreagents and the needs of the lesions, thereby improving the targeting of treatment and diagnosis. The magnetic field parameter module further determines the optimal injection dose of the nanoreagents based on the lesion feature data and optimizes the injection method. This process combines the preparation parameters of the nanoreagents with the physiological and pathological characteristics of the lesions, ensuring efficient enrichment of the nanoreagents in the lesion area by simulating and evaluating the distribution of nanoreagents at different doses. Simultaneously, the acquisition of three-dimensional image localization data of the lesion provides precise lesion location information for setting parameters of the external adjustable magnetic field system. This optimization of magnetic field parameters not only improves the targeting of the nanoreagent but also reduces the impact on non-target tissues, further enhancing the accuracy of the entire system. The magneto-controlled enrichment and imaging module is responsible for the directional migration of the nanoreagent to the lesion area under the action of the magnetic field and achieving enrichment. By maintaining the acquisition of enrichment data, the dynamic changes of the nanoreagent in the lesion area can be monitored in real time, ensuring the stability and efficiency of the enrichment process. This module is also responsible for acquiring raw multimodal imaging data of the lesion area, including surface-enhanced Raman scattering, photoacoustic, and near-infrared II fluorescence, and performing enrichment efficiency analysis on these data. This multimodal imaging method not only provides rich lesion information but also further verifies the targeting ability of the nanoreagent in the lesion area through the evaluation of enrichment efficiency data. The entire system realizes the precise design of nanoreagents, detailed analysis of lesion characteristics, optimization of magnetic field control, and efficient execution of multimodal imaging. This systematic approach not only improves the enrichment efficiency and imaging quality of nanoreagents in lesion areas but also provides strong technical support for early diagnosis and precision treatment of diseases. This highly integrated and intelligent design ensures the efficiency, precision, and reliability of the entire process, paving new avenues for the application of nanotechnology in the biomedical field.

[0138] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0139] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A multimodal image processing-based method for in vivo magnetically controlled targeted enrichment, characterized in that, Includes the following steps: Step S1: Obtain the preparation parameter data of the multimodal nanoreagent, including core-shell structure composition, surface modification and target ligand connection density parameters; Step S2: Select animal models based on the target lesion type and location, and obtain the physiological structure and pathological characteristics of the animal models to obtain lesion feature data; Step S3: Based on the lesion characteristic data, determine the optimal injection dose of the multimodal nanoreagent from the preparation parameter data to obtain optimal injection dose prediction data; determine the injection method to obtain injection method parameter data; based on the optimal injection dose prediction data and injection method parameter data, introduce the multimodal nanoreagent into the animal model via intravenous injection, and perform image acquisition to obtain lesion three-dimensional image localization data; based on the lesion three-dimensional image localization data, set the parameters of the external adjustable magnetic field system to obtain magnetic field control parameter data; Step S4: Based on the magnetic field control parameter data, the multimodal nanoreagent is directionally migrated to the lesion area for enrichment, and the maintenance enrichment data is obtained; the original multimodal imaging data of surface-enhanced Raman scattering, photoacoustic and near-infrared II fluorescence in the lesion area are acquired, and the enrichment efficiency of the reagent in the lesion area is analyzed based on the maintenance enrichment data to obtain the multimodal imaging enrichment performance data.

2. The multimodal image processing method for in vivo magnetically controlled targeted enrichment according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Obtain physicochemical property data of superparamagnetic iron oxide nanoparticles, including particle size distribution data and magnetization data; Step S12: Magnetic response performance is evaluated based on magnetization intensity data and particle size distribution data to obtain magnetic response characteristic data of the core material; Step S13: Optimize the synthesis parameters of the mesoporous silica shell based on the magnetic response characteristic data of the core material to obtain the shell synthesis parameter data; Step S14: Synthesize core-shell structured nanomaterials based on shell synthesis parameter data, and characterize the core-shell structured nanomaterials by transmission electron microscopy and dynamic light scattering to obtain core-shell structure composition data; Step S15: Based on the core-shell structure composition data, conduct imaging performance evaluation and modification strategy analysis based on nanoparticle loading and spectral characterization to obtain modification parameter data; Step S16: Based on the modification parameter data, the surface of the nanomaterial is PEGylated and targeted ligand linkage analysis is performed to obtain ligand linkage data, which includes ligand linkage density data and orientation control data. Step S17: Combine the core-shell structure composition data, modification parameter data, and ligand linkage data into preparation parameter data for multimodal nanoreagents.

3. The multimodal image processing-based method for in vivo magnetically controlled targeted enrichment according to claim 2, characterized in that, Step S15 includes the following steps: Step S151: Based on the core-shell structure composition data, design the loading strategy for gold nanoclusters, near-infrared dye IR-780 and NaYF4:Yb,Er upconversion nanoparticles to obtain loading condition data. Step S152: Load the functional components based on the load condition data to obtain the functional component load efficiency data; Step S153: Based on the functional component loading efficiency data, perform multimodal spectral characterization on the loaded nanomaterials to obtain Raman scattering, photoacoustic signals and fluorescence signal information, thereby obtaining spectral intensity data; Step S154: Evaluate imaging performance based on multimodal signal intensity data to obtain imaging index prediction data; Step S155: Develop a surface modification strategy based on the imaging index prediction data and core-shell structure composition data to obtain modification parameter data for the PEG-silane coupling agent.

4. The multimodal image processing-based method for in vivo magnetically controlled targeted enrichment according to claim 3, characterized in that, Step S16 includes the following steps: PEGylation of the nanomaterial surface was performed based on modification parameter data to obtain the coverage density and uniformity data of the polyethylene glycol layer. Stability tests were conducted under simulated blood flow shear stress conditions based on biocompatibility and blood circulation time, based on the coverage density and uniformity data. Click chemical linkage of the targeted ligand was performed based on imaging index prediction data to obtain ligand linkage data. The simulated blood flow shear stress condition was 20 dyne / cm². The ligand linkage data included ligand linkage density data and orientation control data. The targeted ligand linkage density was 1.2-1.8 ligand molecules per square nanometer.

5. The multimodal image processing method for in vivo magnetically controlled targeted enrichment according to claim 4, characterized in that, Step S2 includes the following steps: Step S21: Obtain target lesion type classification data, including pathophysiological characteristics data of atherosclerosis and gastrointestinal tumors; Step S22: Select animal species and model construction methods based on the target lesion type classification data to obtain model construction scheme data; Step S23: Based on the model construction protocol data, establish an animal model by inducing a high-fat diet or by inoculating tumor cells, and perform histopathological verification to obtain data confirming lesion formation; Step S24: Perform clinical imaging evaluation on the lesion formation confirmation data, including CT, MRI and ultrasound scans, to obtain preliminary data on lesion characteristics, including lesion size, location and morphology; Step S25: Analyze the lesion microenvironment based on the preliminary data of lesion characteristics to obtain lesion microenvironment characteristic data. The lesion microenvironment analysis specifically includes vascular permeability, hemodynamics, and interstitial pressure measurement. Step S26: Combine the lesion formation confirmation data, preliminary lesion characteristic data, and lesion microenvironment characteristic data into lesion characteristic data.

6. The multimodal image processing-based method for in vivo magnetically controlled targeted enrichment according to claim 5, characterized in that, Determining the optimal injection dose and injection method in step S3 specifically includes: Fabrication of multimodal nanoreagents based on preparation parameter data; The extravascular extravasation rate of multimodal nanoreagents under different blood flow conditions was calculated using lesion characteristic data; Construct a diffusion model of multimodal nanoreagents in the interstitial tissue; Based on extravascular exudation rate and diffusion model simulation, time-concentration curves of different doses of multimodal nanoreagent in various tissues and organs throughout the body were obtained. The drug targeting index and selective targeting coefficient are calculated based on the time-concentration curve to obtain targeting selectivity assessment data. Based on the time-concentration curve and target selectivity assessment data, the dose range that maximizes the signal intensity in the lesion area is determined, and the optimal injection dose prediction data is obtained.

7. The multimodal image processing-based method for in vivo magnetically controlled targeted enrichment according to claim 6, characterized in that, The determination of the injection method in step S3 specifically includes: Drug delivery models for different injection routes were constructed based on lesion feature data to obtain injection route efficiency data; Single-injection and fractionated-injection regimens are designed based on the optimal injection dose prediction data. The dosage distribution strategy was obtained by simulating the area under the blood drug concentration curve and the peak concentration under each scheme; Based on injection route efficiency data and dose allocation strategies, the injection time interval is determined by analyzing the vascular permeability time window information in the lesion characteristic data, thus obtaining an injection time plan. The injection rates of 0.1–2 mL / min were experimentally evaluated, and the dispersion and aggregation of the multimodal nanoreagents in simulated blood vessels were measured to obtain the optimal injection rate data. The injection time protocol and optimal injection rate data are combined into injection method parameter data.

8. The multimodal image processing method for in vivo magnetically controlled targeted enrichment according to claim 7, characterized in that, The parameter settings for the external adjustable magnetic field system mentioned in step S3 specifically include: Based on the three-dimensional image localization data of the lesion, the depth, size and morphological characteristics of the lesion are quantitatively analyzed to obtain the spatial characteristic parameter data of the lesion; The optimal magnetic field gradient distribution is calculated based on the spatial characteristic parameter data of the lesion, thereby obtaining the magnetic field gradient parameter data; The incident angle and direction of the magnetic field are determined based on the location of the lesion and the characteristics of the surrounding tissue structure in the spatial characteristic parameter data of the lesion, thereby obtaining the magnetic field direction positioning parameter data. Based on the magnetic response characteristics of multimodal nanoreagents and blood circulation dynamics parameters, a magnetic field pulse modulation scheme was designed to obtain magnetic field pulse modulation parameter data, which includes the initial magnetic field strength, pulse frequency, intensity variation range, number of reverse magnetic field pulses and duration. Based on the preset target enrichment requirements, the real-time monitoring signal acquisition time interval and the threshold conditions for determining the steady state of enrichment are set to obtain enrichment monitoring parameter data. The magnetic field gradient parameter data, magnetic field direction positioning parameter data, magnetic field pulse modulation parameter data, and enrichment monitoring parameter data are integrated into complete magnetic field control parameter data.

9. The multimodal image processing method for in vivo magnetically controlled targeted enrichment according to claim 8, characterized in that, Step S4 includes the following steps: Step S41: Activate the external adjustable magnetic field system according to the magnetic field control parameter data to generate an initial magnetic field and obtain the initial magnetic field state data; and execute the magnetic field pulse modulation program based on the initial magnetic field state data to realize the gradual enhancement of the gradient magnetic field from the outside to the inside, and obtain the gradient magnetic field distribution data. Step S42: Based on the gradient magnetic field distribution data, near-infrared fluorescence signals in the lesion area are collected every 30 seconds to obtain enrichment process monitoring data; the fluorescence intensity change rate of three consecutive monitoring is calculated based on the enrichment process monitoring data and compared with a preset threshold to determine whether the enrichment steady state has been reached, and enrichment state judgment data is obtained. Step S43: Based on the enrichment state judgment data, automatically adjust the magnetic field strength and direction. If a steady state is not reached, continue executing the magnetic field modulation program; if a steady state is reached, proceed to the next stage of magnetic field modulation to obtain magnetic field optimization adjustment data. Step S44: Based on the magnetic field optimization adjustment data, execute the reverse magnetic field pulse sequence to drive the intravascular multimodal nanoreagent through the blood vessel wall to the target lesion location, and obtain tissue penetration enhancement data; based on the tissue penetration enhancement data, perform long-term enrichment of the multimodal nanoreagent in the lesion area to obtain maintenance enrichment data; Step S45: Based on the maintenance enrichment data, perform multi-time point surface-enhanced Raman scattering, photoacoustic and near-infrared two-region fluorescence multimodal imaging data acquisition to obtain the raw multimodal imaging data; Step S46: Based on the original multimodal imaging data, calculate the signal contrast between the lesion area and surrounding tissues at different time points, determine the enrichment half-life, and evaluate the signal enhancement factor to obtain targeted enrichment effect data; Step S47: Based on the targeted enrichment effect data, register and fuse the original multimodal imaging data to construct a multidimensional characterization model of the lesion and obtain multimodal imaging enrichment performance data.

10. A multimodal image processing-based system for in vivo magnetically controlled targeted enrichment, characterized in that, For performing the multimodal image processing system for in vivo magnetically controlled targeted enrichment as described in claim 1, the system comprises: The preparation parameter module is used to obtain preparation parameter data for multimodal nanoreagents, including core-shell structure composition, surface modification, and targeting ligand connection density parameters. The lesion feature module is used to select animal models based on the target lesion type and location, and to obtain the physiological structure and pathological features of the animal models to obtain lesion feature data. The magnetic field parameter module is used to determine the optimal injection dose of the multimodal nanoreagent based on lesion characteristic data, obtaining optimal injection dose prediction data; and to determine the injection method, obtaining injection method parameter data; based on the optimal injection dose prediction data and injection method parameter data, the multimodal nanoreagent is introduced into the animal model via intravenous injection, and image acquisition is performed to obtain three-dimensional image localization data of the lesion; based on the three-dimensional image localization data of the lesion, the parameters of the external adjustable magnetic field system are set to obtain magnetic field control parameter data; The magneto-controlled enrichment and imaging module is used to directionally migrate multimodal nanoreagents to the lesion area for enrichment based on magnetic field control parameters, thereby obtaining maintenance enrichment data; it acquires raw multimodal imaging data of surface-enhanced Raman scattering, photoacoustic, and near-infrared fluorescence in the lesion area, and analyzes the enrichment efficiency of the reagents in the lesion area based on the maintenance enrichment data, thereby obtaining multimodal imaging enrichment performance data.

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