Precise targeted treatment and image navigation device for breast cancer

Abstract

The invention relates to the technical field of breast cancer targeted therapy, in particular to a breast cancer precise targeted therapy and image navigation device which comprises a multi-mode image navigation module, a dynamic target spot tracking module, a drug delivery module and a real-time feedback regulation and control module. A data transmission link is established between the output end of the multi-mode image navigation module and the input end of the dynamic target tracking module, the dynamic target tracking module is in bidirectional connection with the drug delivery module through a signal bus, and the state output end of the drug delivery module is connected into the real-time feedback regulation and control module. A regulation and control signal output end of the real-time feedback regulation and control module is respectively connected with control input ends of the multi-mode image navigation module, the dynamic target tracking module and the drug delivery module. According to the invention, through image fusion positioning, dynamic target tracking, precise drug delivery of the mechanical arm and real-time feedback regulation and control, precise targeted treatment of breast cancer is realized, and the treatment risk is reduced.

Description

Technical Field

[0001] This invention relates to the field of targeted therapy technology for breast cancer, and in particular to a precision targeted therapy and image navigation device for breast cancer. Background Technology

[0002] In the field of breast cancer treatment, although traditional radiotherapy and chemotherapy can inhibit the proliferation of cancer cells, they often damage normal tissues and cause many side effects. With the development of molecular biology technology, research has found that the growth and proliferation of breast cancer cells are closely related to specific molecular targets. These targets are abnormally expressed on the surface or inside of cancer cells, becoming key markers to distinguish cancer cells from normal cells. Based on this, targeted therapy technology has emerged. Its core idea is to design therapeutic drugs targeting these specific targets, aiming to achieve precise attack on cancer cells and reduce the impact on healthy cells, providing a new direction for breast cancer treatment.

[0003] In terms of localization, traditional targeted therapy is affected by detection technology and tumor heterogeneity, making it difficult to clearly define the expression characteristics of some targets. This results in the inability of therapeutic drugs to accurately anchor to the lesion, affecting the treatment effect. During drug delivery, existing carrier systems are easily affected by the internal environment, making it difficult to accurately control the release dose of drugs at the lesion site. This may lead to insufficient local drug concentration or drug accumulation in normal tissues. In addition, some targeted therapies require puncture for drug delivery. The low precision of the puncture process may cause complications such as bleeding and infection, limiting the flexibility and safety of the treatment. Summary of the Invention

[0004] In order to overcome the problems of poor treatment effect and easy damage to normal tissue caused by existing traditional breast cancer targeted therapy, such as vague target positioning, difficulty in controlling drug dosage and low puncture accuracy.

[0005] The technical solution of the present invention is as follows: a precision targeted therapy and image navigation device for breast cancer, comprising a multimodal image navigation module, a dynamic target tracking module, a drug delivery module, and a real-time feedback control module; the output end of the multimodal image navigation module establishes a data transmission link with the input end of the dynamic target tracking module, the dynamic target tracking module and the drug delivery module are bidirectionally connected through a signal bus, the status output end of the drug delivery module is connected to the real-time feedback control module, and the control signal output end of the real-time feedback control module is connected to the control input ends of the multimodal image navigation module, the dynamic target tracking module, and the drug delivery module, respectively.

[0006] Preferably, the multimodal image navigation module includes an ultrasound acquisition unit, an MRI acquisition unit, and an image fusion processing unit. The ultrasound acquisition unit is used to acquire two-dimensional and three-dimensional ultrasound images of the lesion area, and the MRI acquisition unit is used to acquire metabolic and structural images of the lesion and surrounding tissues. Both image data are synchronously transmitted to the image fusion processing unit. The image fusion processing unit uses a multimodal image fusion algorithm to perform pixel-level fusion of the real-time features of the ultrasound images and the detailed features of the MRI images to generate lesion localization images and transmit them to the dynamic target tracking module.

[0007] Preferably, the dynamic target tracking module consists of a target feature extraction unit, a respiratory phase synchronization unit, and a target dynamic matching unit. The target feature extraction unit extracts the morphological and grayscale features of the lesion from the fused image. The respiratory phase synchronization unit collects the patient's respiratory signal and determines the stable phase in the respiratory cycle. The target dynamic matching unit tracks the lesion position change in real time based on the target features under the stable phase using a feature point matching algorithm, generates target coordinate data, and sends it to the drug delivery module.

[0008] Preferably, the drug delivery module includes a robotic arm drive unit, a microfluidic drug channel, and a puncture needle. After receiving the target coordinate data, the robotic arm drive unit drives the robotic arm to move the puncture needle to the lesion location. The microfluidic drug channel has a built-in flow sensor to calculate the drug delivery dose based on the lesion volume and injects the drug into the lesion through the puncture needle by pressure control.

[0009] Preferably, the real-time feedback control module includes a force sensing feedback unit, an image verification unit, and a control command generation unit. The force sensing feedback unit is integrated into the end of the puncture needle, collects force signals during the puncture process, and transmits them to the control command generation unit. The image verification unit collects post-puncture images in real time and compares them with pre-operative fused images. The control command generation unit generates robotic arm position adjustment commands or drug dosage correction commands based on the force signal and image comparison results.

[0010] Preferably, the execution steps of the multimodal image fusion algorithm are as follows: First, the ultrasound image and MRI image are subjected to grayscale normalization processing; second, the SIFT algorithm is used to extract feature points of the two types of images and establish spatial correspondence; third, fusion weights are assigned based on the salient features of the lesion area to generate a fused image that retains both the real-time dynamic features of ultrasound and the metabolic details of MRI, providing dual feature support for target localization.

[0011] Preferably, the respiratory phase synchronization unit collects respiratory signals through a chest and abdominal pressure sensor, divides the respiratory cycle into an inspiratory phase, an expiratory phase, and a breath-holding phase, and identifies a stable phase through the waveform characteristics of the respiratory signal, ensuring that the target tracking process is synchronized with the lesion movement pattern, and avoiding target deviation caused by respiratory movement.

[0012] Preferably, the robotic arm drive unit adopts a six-degree-of-freedom robotic arm, and its motion controller is integrated into the robotic arm drive unit. The motion controller receives target coordinate data and generates a smooth motion trajectory. The microfluidic drug channel has a built-in flow sensor. The drug delivery dose is calculated by the volume parameters in the lesion localization image and the preset drug ratio relationship, so as to achieve the matching of drug delivery with lesion size.

[0013] Preferably, the force sensing feedback unit is integrated into the end of the puncture needle to collect the interaction force signal between the puncture needle and the tissue in real time. When the signal changes abruptly, the control command generation unit immediately triggers the robotic arm braking program, and at the same time, the image verification unit starts real-time image scanning to confirm the position and status of the puncture needle by comparing lesion features.

[0014] Preferably, the device also includes a device calibration module, which establishes a data connection with the multimodal image navigation module and the dynamic target tracking module. Before each use, a calibration operation is performed using a standard phantom. During the calibration process, parameters are corrected for the resolution of the ultrasound acquisition unit and the signal intensity of the MRI acquisition unit. Calibration parameters are generated and synchronously updated to the multimodal image navigation module and the dynamic target tracking module to ensure the consistency of target positioning.

[0015] The beneficial effects of this invention are: 1. The ultrasound acquisition unit and MRI acquisition unit of the multimodal image navigation module acquire different types of images respectively. The image fusion processing unit executes the multimodal image fusion algorithm to perform pixel-level fusion of the two types of images. The target feature extraction unit of the dynamic target tracking module extracts lesion features. The respiratory phase synchronization unit identifies stable respiratory phases. The target dynamic matching unit performs feature point matching and tracking based on stable phases to generate accurate target coordinate data. The entire process from image acquisition to target tracking is coordinated, which greatly improves the accuracy of breast cancer lesion target localization and dynamic tracking capability, and solves the problems of ambiguous target localization and susceptibility to respiratory motion in traditional targeted therapy. 2. After receiving the target coordinate data through the robotic arm drive unit of the drug delivery module, the six-degree-of-freedom robotic arm is driven to move the puncture needle to the lesion position. The microfluidic drug channel calculates the dosage according to the lesion volume and the preset drug ratio. The drug is injected into the lesion through the puncture needle through pressure control, realizing dual precise control of drug delivery position and dosage, avoiding drug waste and damage to surrounding normal tissues, and solving the problems of dosage mismatch and poor targeting in traditional drug delivery. 3. The force sensing feedback unit of the real-time feedback control module collects the interaction force signal between the puncture needle and the tissue. The image verification unit compares the post-puncture image with the pre-operative fused image in real time. The control command generation unit generates adjustment commands based on the two types of signals to control the image acquisition parameters of the multimodal image navigation module, the tracking strategy of the dynamic target tracking module, and the position of the robotic arm and the drug dosage of the drug delivery module, respectively. At the same time, the device calibration module calibrates the parameters of the multimodal image navigation module and the dynamic target tracking module before use, forming a closed-loop control throughout the entire process. This significantly improves the safety and effectiveness of the treatment process and solves the problems of inability to adjust in a timely manner and high risk in traditional puncture treatment. Attached Figure Description

[0016] Figure 1 The diagram shown is a schematic representation of the overall system workflow structure of the present invention. Figure 2 The diagram shown is a schematic diagram of the internal structure and flow of the multimodal image navigation module of the present invention; Figure 3 The diagram shown is a schematic representation of the internal structure and flow of the dynamic target tracking module of the present invention. Figure 4 The diagram shown is a schematic representation of the collaborative workflow structure for drug delivery and real-time feedback regulation according to the present invention.

[0017] In the attached figures: 101, Multimodal image navigation module; 102, Dynamic target tracking module; 103, Drug delivery module; 104, Real-time feedback control module; 105, Device calibration module; 201, Ultrasound acquisition unit; 202, MRI acquisition unit; 203, Image fusion processing unit; 301, Target feature extraction unit; 302, Respiratory phase synchronization unit; 303, Target dynamic matching unit; 401, Robotic arm drive unit; 402, Microfluidic drug channel; 403, Puncture needle; 501, Force sensing feedback unit; 502, Image verification unit; 503, Control command generation unit. Detailed Implementation

[0018] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0019] Example 1 refer to Figure 1The structure shown is a breast cancer precision targeted therapy and image navigation device, including a multimodal image navigation module 101, a dynamic target tracking module 102, a drug delivery module 103, and a real-time feedback control module 104. The output end of the multimodal image navigation module 101 establishes a data transmission link with the input end of the dynamic target tracking module 102. The dynamic target tracking module 102 and the drug delivery module 103 are bidirectionally connected through a signal bus. The status output end of the drug delivery module 103 is connected to the real-time feedback control module 104. The control signal output end of the real-time feedback control module 104 is connected to the control input ends of the multimodal image navigation module 101, the dynamic target tracking module 102, and the drug delivery module 103, respectively.

[0020] This device enables precise targeted therapy for breast cancer through a complete workflow chain, solving the core problems of traditional targeted therapy, such as ambiguous target location, inaccurate drug delivery dosage, high risk of puncture process and inability to dynamically adjust.

[0021] The workflow is as follows: Before use, the device calibration module 105 establishes a data connection with the multimodal image navigation module 101 and the dynamic target tracking module 102. It corrects the resolution of the ultrasound acquisition unit 201 and the signal intensity of the MRI acquisition unit 202 using a standard phantom, generating calibration parameters and updating them synchronously to both modules to ensure consistency in subsequent target localization. Subsequently, the ultrasound acquisition unit 201 of the multimodal image navigation module 101 acquires two-dimensional and three-dimensional ultrasound images of the lesion area, and the MRI acquisition unit 202 acquires metabolic and structural images of the lesion and surrounding tissues. Both types of image data are synchronously transmitted to the image fusion processing unit 203, where a multimodal image fusion algorithm is used to generate lesion localization images. The target feature extraction unit 301 of the dynamic target tracking module 102 extracts the morphological and grayscale features of the lesion from the fused images. The respiratory phase synchronization unit 302 acquires respiratory signals and divides the respiratory cycle using a chest and abdominal pressure sensor, identifying a stable phase. The target dynamic matching unit 303, based on the target features under the stable phase, uses a feature point matching algorithm to perform real-time... The system tracks changes in lesion location, generates target coordinate data, and sends it to the drug delivery module 103. After receiving the target coordinate data, the robotic arm drive unit 401 of the drug delivery module 103, with its integrated motion controller, generates a smooth motion trajectory, driving the six-degree-of-freedom robotic arm to move the puncture needle 403 to the lesion location. The flow sensor built into the microfluidic drug channel 402, combined with the volume parameters in the lesion localization image and the preset drug ratio, calculates the drug delivery dose. The drug is then injected into the lesion through the puncture needle 403 via pressure control. Simultaneously, the system... The force sensing feedback unit 501 of the time feedback control module 104 is integrated at the end of the puncture needle 403. It collects the interaction force signal between the puncture needle 403 and the tissue in real time and transmits it to the control command generation unit 503. The image verification unit 502 collects the post-puncture image in real time and compares it with the preoperative fused image. The control command generation unit 503 sends control commands to the multimodal image navigation module 101, the dynamic target tracking module 102, and the drug delivery module 103 respectively according to the force signal and image comparison results, so as to realize the dynamic optimization of the entire treatment process.

[0022] Example 2 Based on the above embodiment 1, in order to address the problems in traditional targeted therapy such as insufficient single-image modality information leading to limited target localization accuracy, respiratory motion causing lesion position displacement and thus affecting tracking accuracy, insufficient smoothness of robotic arm motion trajectory leading to puncture position deviation, mismatch between drug delivery dose and lesion size, and inability to promptly sense tissue stress changes and positional deviations during puncture and make adjustments, refer to Figures 2-4 The structure shown is as follows: Furthermore, the multimodal image navigation module 101 includes an ultrasound acquisition unit 201, an MRI acquisition unit 202, and an image fusion processing unit 203. The ultrasound acquisition unit 201 is used to acquire two-dimensional and three-dimensional ultrasound images of the lesion area, and the MRI acquisition unit 202 is used to acquire metabolic and structural images of the lesion and surrounding tissues. Both image data are synchronously transmitted to the image fusion processing unit 203. The image fusion processing unit 203 uses a multimodal image fusion algorithm to perform pixel-level fusion of the real-time features of the ultrasound images and the detailed features of the MRI images to generate lesion localization images and transmit them to the dynamic target tracking module 102.

[0023] The ultrasound acquisition unit 201 acquires two-dimensional and three-dimensional ultrasound images of the lesion area, while the MRI acquisition unit 202 acquires metabolic and structural images of the lesion and surrounding tissues. The two types of image data are simultaneously transmitted to the image fusion processing unit 203. The image fusion processing unit 203 executes a multimodal image fusion algorithm. First, the ultrasound images and MRI images are normalized to grayscale. Then, the SIFT algorithm is used to extract feature points of the two types of images and establish spatial correspondence. Finally, based on the salient features of the lesion area, fusion weights are assigned to generate a fused image that retains both the real-time dynamic features of ultrasound and the metabolic details of MRI. This provides a precise lesion localization basis for the dynamic target tracking module 102, solving the problem that a single image modality cannot simultaneously take into account both real-time performance and detail, and improving the initial accuracy of target localization.

[0024] Furthermore, the dynamic target tracking module 102 consists of a target feature extraction unit 301, a respiratory phase synchronization unit 302, and a target dynamic matching unit 303. The target feature extraction unit 301 extracts the morphological and grayscale features of the lesion from the fused image. The respiratory phase synchronization unit 302 collects the patient's respiratory signal and determines the stable phase in the respiratory cycle. Based on the target features under the stable phase, the target dynamic matching unit 303 uses a feature point matching algorithm to track the lesion position change in real time, generates target coordinate data, and sends it to the drug delivery module 103.

[0025] The target feature extraction unit 301 extracts the morphological and grayscale features of the lesion from the fused image. The respiratory phase synchronization unit 302 collects respiratory signals through a chest and abdominal pressure sensor, divides the respiratory cycle into inspiratory, expiratory, and breath-holding phases, and identifies the stable phase through the waveform features of the respiratory signal. The target dynamic matching unit 303 uses a feature point matching algorithm to capture the changes in the lesion position in real time based on the morphological and grayscale features of the lesion under the stable phase, generates accurate target coordinate data, and sends it to the drug delivery module 103. This effectively counteracts the influence of respiratory motion on the lesion position, avoids deviation during target tracking, and improves the dynamic accuracy of target tracking.

[0026] Furthermore, the drug delivery module 103 includes a robotic arm drive unit 401, a microfluidic drug channel 402, and a puncture needle 403. After receiving the target coordinate data, the robotic arm drive unit 401 drives the robotic arm to move the puncture needle 403 to the lesion location. The microfluidic drug channel 402 has a built-in flow sensor to calculate the drug delivery dose based on the lesion volume and injects the drug into the lesion through the puncture needle 403 by pressure control.

[0027] In this system, after receiving the target coordinate data sent by the dynamic target tracking module 102, the robotic arm drive unit 401 analyzes the coordinate data and generates a smooth motion trajectory, driving the six-degree-of-freedom robotic arm to precisely move the puncture needle 403 to the lesion location according to the trajectory. The flow sensor built into the microfluidic drug channel 402 monitors the drug flow status in real time. At the same time, based on the volume parameters in the lesion positioning image generated by the multimodal image navigation module 101 and the preset drug ratio, the precise drug delivery dose is calculated. Through the pressure control mechanism, the drug is stably injected into the lesion through the puncture needle 403, realizing dual precise control of drug delivery position and dose, avoiding drug waste and damage to surrounding normal tissues.

[0028] Furthermore, the real-time feedback control module 104 is equipped with a force sensing feedback unit 501, an image verification unit 502, and a control command generation unit 503. The force sensing feedback unit 501 is integrated into the end of the puncture needle 403, which collects the force signal during the puncture process and transmits it to the control command generation unit 503. The image verification unit 502 collects the post-puncture image in real time and compares it with the preoperative fused image. The control command generation unit 503 generates a robotic arm position adjustment command or a drug dosage correction command based on the force signal and the image comparison result.

[0029] The force sensing feedback unit 501 is integrated at the end of the puncture needle 403. During the insertion of the puncture needle 403 into the tissue, it collects the interaction force signal between the puncture needle 403 and the tissue in real time and transmits the signal to the control command generation unit 503. The image verification unit 502 collects the image of the lesion area after puncture in real time and compares it pixel by pixel with the fused image generated by the preoperative multimodal image navigation module 101. The control command generation unit 503 performs a comprehensive analysis of the force signal and the image comparison result. When the force signal is abnormal or the image comparison shows a position deviation, it immediately generates a robotic arm position adjustment command and sends it to the robotic arm drive unit 401 of the drug delivery module 103, or generates a drug dosage correction command and sends it to the microfluidic drug channel 402, so as to realize the real-time dynamic adjustment of the puncture position and drug dosage, and improve the safety and effectiveness of treatment.

[0030] Furthermore, the execution steps of the multimodal image fusion algorithm are as follows: First, the ultrasound image and MRI image are subjected to grayscale normalization processing; second, the SIFT algorithm is used to extract feature points of the two types of images and establish spatial correspondence; third, fusion weights are assigned based on the salient features of the lesion area to generate a fused image that retains both the real-time dynamic features of ultrasound and the metabolic details of MRI, providing dual feature support for target localization.

[0031] First, the ultrasound and MRI images are subjected to grayscale normalization, mapping the grayscale values ​​of both types of images to the [0, 255] interval. The calculation formula is as follows: ,in The normalized grayscale value. The original image pixel grayscale values, The minimum grayscale value of the original image. The original image is used to extract feature points. A Gaussian difference pyramid is constructed to detect scale space extrema. These extrema are then fitted and corrected to obtain accurate keypoints. The gradient direction histogram of neighboring pixels is calculated and assigned a principal direction, generating a 128-dimensional keypoint descriptor. This establishes a spatial correspondence between the two types of image feature points. Finally, the fusion weight is calculated based on the saliency features of the lesion region. These saliency features are calculated using a local contrast algorithm, with the formula being... ,in For pixels The significance value, For pixel grayscale values, The neighborhood mean Assign weights to neighborhood standard deviations based on significance values. and Generate fused images ,in These are the pixel values ​​of the ultrasound image. By using MRI image pixel values, the fused image can simultaneously possess the real-time dynamic features of ultrasound and the metabolic details of MRI, providing dual feature support for target localization and significantly improving the accuracy of target localization.

[0032] Furthermore, the respiratory phase synchronization unit 302 collects respiratory signals through a chest and abdominal pressure sensor, divides the respiratory cycle into an inspiratory phase, an expiratory phase, and a breath-holding phase, and identifies a stable phase through the waveform characteristics of the respiratory signal to ensure that the target tracking process is synchronized with the lesion movement pattern and avoids target deviation caused by respiratory movement.

[0033] The chest and abdomen pressure sensor is placed close to the patient's chest and abdomen to collect pressure change signals in real time during respiration. The pressure signal is converted into an electrical signal and transmitted to the respiratory phase synchronization unit 302. The respiratory phase synchronization unit 302 filters and amplifies the electrical signal and divides the respiratory cycle into the inspiratory phase (pressure rises from the trough to the peak), the expiratory phase (pressure falls from the peak to the trough), and the breath-hold phase (pressure remains stable) based on the peak and trough values ​​of the signal waveform. By analyzing the waveform characteristics of multiple respiratory cycles, the breath-hold phase with pressure fluctuation amplitude of less than 5% is identified as the stable phase. The stable phase signal is sent to the target dynamic matching unit 303, so that the target dynamic matching unit 303 performs feature point matching and tracking based on lesion features only under the stable phase. This ensures that the target tracking process is consistent with the lesion movement pattern, fundamentally avoiding the target deviation problem caused by respiratory movement.

[0034] Furthermore, the robotic arm drive unit 401 adopts a six-degree-of-freedom robotic arm, and its motion controller is integrated into the robotic arm drive unit 401. The motion controller receives target coordinate data and generates a smooth motion trajectory. The microfluidic drug channel 402 has a built-in flow sensor. The drug delivery dose is calculated by the volume parameters in the lesion localization image and the preset drug ratio relationship, so as to achieve the matching of drug delivery with lesion size.

[0035] The motion controller of the six-degree-of-freedom robotic arm is integrated inside the robotic arm drive unit 401. After receiving the target coordinate data sent by the dynamic target tracking module 102, the motion controller calculates the target angle of each joint of the robotic arm through forward kinematics, using the formula: ,in The joint angle vector. The target coordinates, The inverse kinematics function is used to generate a smooth joint motion trajectory based on the target angle, driving the coordinated movement of each joint of the robotic arm, thereby precisely moving the puncture needle 403 to the lesion location; the flow sensor built into the microfluidic drug channel 402 collects the drug flow rate signal in real time, and combined with the lesion positioning image generated by the multimodal image navigation module 101, the lesion volume is calculated through a three-dimensional reconstruction algorithm. According to the preset drug ratio (in For drug delivery dosage, The precise drug delivery dose is calculated by using a constant drug dosage per unit volume of lesion. The pressure in the microfluidic drug channel 402 is adjusted by a pressure controller so that the drug is injected into the lesion at a constant rate through the puncture needle 403, achieving a precise match between drug delivery and lesion size, and avoiding insufficient or excessive drug dosage.

[0036] Furthermore, the force sensing feedback unit 501 is integrated at the end of the puncture needle 403 to collect the interaction force signal between the puncture needle 403 and the tissue in real time. When the signal changes abruptly, the control command generation unit 503 immediately triggers the robotic arm braking program, and at the same time, the image verification unit 502 starts real-time image scanning to confirm the position and status of the puncture needle 403 by comparing lesion features.

[0037] The force sensing feedback unit 501 uses a strain gauge force sensor integrated into the side wall of the end of the puncture needle 403. When the puncture needle 403 pierces the tissue, the sensor senses the reaction force of the tissue on the puncture needle 403 in real time, converts the force signal into a voltage signal and transmits it to the control command generation unit 503. When the rate of change of the voltage signal exceeds a preset threshold (i.e., the force signal changes abruptly), the control command generation unit 503 immediately sends a braking signal to the robotic arm drive unit 401. After receiving the signal, the robotic arm drive unit 401 controls the joints of the robotic arm to stop moving. At the same time, the control command generation unit 503 sends a start signal to the image verification unit 502. The image verification unit 502 starts real-time image scanning, acquires the image of the current position of the puncture needle 403, compares it with the preoperative fused image for lesion features, confirms whether the puncture needle 403 has touched blood vessels or other important tissues, promptly detects abnormalities in the puncture process and terminates the operation, avoids damage to the surrounding normal tissue by the puncture needle 403, and improves the safety of the puncture process.

[0038] Furthermore, it also includes a device calibration module 105, which establishes a data connection with the multimodal image navigation module 101 and the dynamic target tracking module 102. Before each use, a calibration operation is performed using a standard phantom. During the calibration process, parameters are corrected for the resolution of the ultrasound acquisition unit 201 and the signal intensity of the MRI acquisition unit 202. Calibration parameters are generated and synchronously updated to the multimodal image navigation module 101 and the dynamic target tracking module 102 to ensure the consistency of target positioning.

[0039] The device calibration module 105 establishes a bidirectional data connection with the multimodal image navigation module 101 and the dynamic target tracking module 102 via a data bus. Before each use, the standard phantom is placed in the device's detection area. The device calibration module 105 controls the ultrasound acquisition unit 201 to perform an ultrasound scan on the standard phantom, acquiring ultrasound images of the standard phantom and comparing them with preset ultrasound images of the standard phantom. The resolution deviation of the ultrasound acquisition unit 201 is calculated, and the resolution parameters are corrected by gain adjustment. Simultaneously, the device calibration module 105 controls the MRI acquisition unit 202 to perform an MRI scan on the standard phantom, acquiring standard phantom images. The MRI images of the model are compared with preset MRI images to calculate the signal intensity deviation. The signal intensity parameters are corrected by adjusting the magnetic field strength. The corrected resolution parameters and signal intensity parameters are integrated into calibration parameters and synchronously updated to the image fusion processing unit 203 of the multimodal image navigation module 101 and the target feature extraction unit 301 of the dynamic target tracking module 102 via the data bus. This ensures that the image data acquired by the multimodal image navigation module 101 and the feature data extracted by the dynamic target tracking module 102 are consistent, providing accurate basic data support for subsequent target localization and tracking.

[0040] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. A precision targeted therapy and image-guided device for breast cancer, characterized in that: It includes a multimodal image navigation module (101), a dynamic target tracking module (102), a drug delivery module (103), and a real-time feedback control module (104). The output end of the multimodal image navigation module (101) establishes a data transmission link with the input end of the dynamic target tracking module (102). The dynamic target tracking module (102) and the drug delivery module (103) are bidirectionally connected through a signal bus. The status output end of the drug delivery module (103) is connected to the real-time feedback control module (104). The control signal output end of the real-time feedback control module (104) is connected to the control input ends of the multimodal image navigation module (101), the dynamic target tracking module (102), and the drug delivery module (103), respectively.

2. The breast cancer precision targeted therapy and image navigation device according to claim 1, characterized in that: The multimodal image navigation module (101) includes an ultrasound acquisition unit (201), an MRI acquisition unit (202), and an image fusion processing unit (203). The ultrasound acquisition unit (201) is used to acquire two-dimensional and three-dimensional ultrasound images of the lesion area, and the MRI acquisition unit (202) is used to acquire metabolic and structural images of the lesion and surrounding tissues. The two image data are synchronously transmitted to the image fusion processing unit (203). The image fusion processing unit (203) uses a multimodal image fusion algorithm to perform pixel-level fusion of the real-time features of the ultrasound image and the detailed features of the MRI image to generate a lesion localization image and transmit it to the dynamic target tracking module (102).

3. The breast cancer precision targeted therapy and image navigation device according to claim 1, characterized in that: The dynamic target tracking module (102) consists of a target feature extraction unit (301), a respiratory phase synchronization unit (302), and a target dynamic matching unit (303); the target feature extraction unit (301) extracts the morphological and grayscale features of the lesions from the fused images. The respiratory phase synchronization unit (302) collects the patient's respiratory signal and determines the stable phase in the respiratory cycle. The target dynamic matching unit (303) uses a feature point matching algorithm to track the lesion position change in real time based on the target features under the stable phase, generates target coordinate data and sends it to the drug delivery module (103).

4. The breast cancer precision targeted therapy and image navigation device according to claim 1, characterized in that: The drug delivery module (103) includes a robotic arm drive unit (401), a microfluidic drug channel (402), and a puncture needle (403). After receiving the target coordinate data, the robotic arm drive unit (401) drives the robotic arm to move the puncture needle (403) to the lesion location. The microfluidic drug channel (402) has a built-in flow sensor to calculate the drug delivery dose based on the lesion volume. The drug is injected into the lesion through the puncture needle (403) by pressure control.

5. The breast cancer precision targeted therapy and image navigation device according to claim 1, characterized in that: The real-time feedback control module (104) is equipped with a force sensing feedback unit (501), an image verification unit (502), and a control command generation unit (503). The force sensing feedback unit (501) is integrated at the end of the puncture needle (403), collects the force signal during the puncture process and transmits it to the control command generation unit (503). The image verification unit (502) collects the puncture image in real time and compares it with the preoperative fused image. The control command generation unit (503) generates a robotic arm position adjustment command or a drug dosage correction command based on the force signal and image comparison results.

6. The breast cancer precision targeted therapy and image navigation device according to claim 2, characterized in that: The execution steps of the multimodal image fusion algorithm are as follows: First, the ultrasound image and MRI image are subjected to grayscale normalization processing. Second, the SIFT algorithm is used to extract feature points of the two types of images and establish spatial correspondence. Third, fusion weights are assigned based on the salient features of the lesion area to generate a fused image that retains both the real-time dynamic features of ultrasound and the metabolic details of MRI, providing dual feature support for target localization.

7. The breast cancer precision targeted therapy and image navigation device according to claim 3, characterized in that: The respiratory phase synchronization unit (302) collects respiratory signals through a chest and abdominal pressure sensor, divides the respiratory cycle into an inspiratory phase, an expiratory phase, and a breath-holding phase, and identifies a stable phase through the waveform characteristics of the respiratory signal to ensure that the target tracking process is synchronized with the movement pattern of the lesion and avoids target deviation caused by respiratory movement.

8. The breast cancer precision targeted therapy and image navigation device according to claim 4, characterized in that: The robotic arm drive unit (401) adopts a six-degree-of-freedom robotic arm, and its motion controller is integrated into the robotic arm drive unit (401). The motion controller receives target coordinate data and generates a smooth motion trajectory. The microfluidic drug channel (402) has a built-in flow sensor. The drug delivery dose is calculated by the volume parameters in the lesion positioning image and the preset drug ratio relationship, so as to achieve the matching of drug delivery with the lesion size.

9. The breast cancer precision targeted therapy and image navigation device according to claim 5, characterized in that: The force sensing feedback unit (501) is integrated at the end of the puncture needle (403) to collect the interaction force signal between the puncture needle (403) and the tissue in real time. When the signal changes abruptly, the control command generation unit (503) immediately triggers the robotic arm braking program. At the same time, the image verification unit (502) starts real-time image scanning and confirms the position and status of the puncture needle (403) by comparing lesion features.

10. The breast cancer precision targeted therapy and image navigation device according to claim 1, characterized in that: It also includes a device calibration module (105), which establishes a data connection with the multimodal image navigation module (101) and the dynamic target tracking module (102). Before each use, a calibration operation is performed through a standard phantom. During the calibration process, the resolution of the ultrasound acquisition unit (201) and the signal intensity of the MRI acquisition unit (202) are corrected, calibration parameters are generated and synchronously updated to the multimodal image navigation module (101) and the dynamic target tracking module (102) to ensure the consistency of target positioning.

Images