Medical image processing apparatus, medical image processing method, and storage medium

Abstract

A medical image processing apparatus includes a generation unit that generates registration information indicating a registration relationship between a coordinate system of an ultrasound system and a coordinate system of another modality other than the ultrasound system; an acquisition unit that acquires two-dimensional ultrasound image data of an object at a predetermined time interval during movement of a probe and extracts a feature group from each of the two-dimensional ultrasound image data; a calculation unit that calculates a distance between the feature group at a current time and the feature group at a previous time; and a correction unit that corrects the registration information based on the calculated distance.

Description

Technical Field

[0001] This invention relates to a medical image processing device, a medical image processing method, and a storage medium capable of automatically correcting the registration relationship between the ultrasound system coordinate system and the three-dimensional CT / MR coordinate system established before surgery without interrupting the operation. Background Technology

[0002] In the field of medical imaging, during examinations or treatments, it is necessary to register the three-dimensional volumetric data of the examination site with the three-dimensional volumetric data obtained before the examination or treatment.

[0003] In existing technologies, before diagnosing or performing surgery on a patient's examination site, a CT (computed tomography) or MR (magnetic resonance) scan is typically performed to obtain three-dimensional CT or MR volumetric data with a good anatomical context. Then, before further diagnosis or surgery, a three-dimensional US (ultrasound) scan is performed on the same area to obtain three-dimensional US volumetric data in the current patient position. The three-dimensional CT or MR volumetric data is then registered with the three-dimensional US volumetric data. This allows for the rapid identification of the corresponding high-resolution anatomical plane in the CT or MR volumetric data, based on the real-time two-dimensional US volumetric data of the examination site. This facilitates accurate analysis and judgment by the physician, enabling correct diagnosis or appropriate surgical intervention.

[0004] The registration between the different modalities (CT, MR, US) mentioned above is called multimodal registration, which is a key technology in image-guided interventional surgery. Through registration between multiple modalities, a transformation matrix between the ultrasound system coordinate system and the MR / CT coordinate system can be obtained. This transformation matrix is ​​then used to register data from one modality to data from another. The resulting multimodal images combine the high resolution of CT / MR images with the real-time performance of US images.

[0005] However, existing multimodal registration techniques have the following problems: when the patient moves during surgery, the coordinate system of the ultrasound system and the patient's coordinate system no longer correspond, and the correspondence between the patient's ultrasound (US) images and CT / MR images is no longer correct. The surgeon must then interrupt the surgery, reacquire the 3D US data, and re-establish the correspondence between the 3D US and 3D CT / MR images. Considering that acquiring 3D US image data requires interrupting the surgery, the acquisition process is complex, and reconstruction takes a considerable amount of time, a technology is desired that can automatically determine whether correction is needed and automatically correct for any movement that occurs when the patient moves, avoiding the need to reacquire 3D US images. Summary of the Invention

[0006] Therefore, in view of the above, the present invention provides an image processing apparatus, an image processing method, and a storage medium for automatically correcting the registration relationship between the pre-established ultrasound system coordinate system and the three-dimensional CT / MR coordinate system based on two-dimensional ultrasound images acquired at specified time intervals.

[0007] Technical solution one relates to a medical image processing device, comprising: a generation unit that generates registration information representing the registration relationship between the coordinate system of an ultrasound system and the coordinate systems of other modalities besides the ultrasound system; an acquisition unit that acquires two-dimensional ultrasound image data of a subject at predetermined time intervals during probe movement and extracts feature groups from each of the two-dimensional ultrasound image data; a calculation unit that calculates the distance between the feature group at the current time and the feature group at the previous time; and a correction unit that corrects the registration information based on the calculated distance.

[0008] Technical solution two relates to a medical image processing method, comprising the following steps: a generation step, generating registration information representing the registration relationship between the coordinate system of the ultrasound system and the coordinate systems of other modalities outside the ultrasound system; an acquisition step, acquiring two-dimensional ultrasound image data of the subject at predetermined time intervals during probe movement, and extracting feature groups from each of the two-dimensional ultrasound image data; a calculation step, calculating the distance between the feature group at the current time and the feature group at the previous time; and a correction step, correcting the registration information based on the calculated distance.

[0009] Technical solution three relates to a storage medium storing a program that causes a computer to perform the following steps: a generation step, generating registration information representing the registration relationship between the coordinate system of the ultrasound system and the coordinate systems of other modalities outside the ultrasound system; an acquisition step, acquiring two-dimensional ultrasound image data of the subject at predetermined time intervals during probe movement, and extracting feature groups from each of the two-dimensional ultrasound image data; a calculation step, calculating the distance between the feature group at the current time and the feature group at the previous time; and a correction step, correcting the registration information based on the calculated distance.

[0010] Invention Effects

[0011] According to the above-described configuration of the present invention, the registration relationship between the ultrasound system coordinate system and the three-dimensional CT / MR coordinate system established before surgery can be automatically corrected during the operation without interrupting the procedure, thereby improving accuracy and saving time. Furthermore, since the entire correction process can be performed automatically, manual intervention is eliminated, thus improving convenience. Attached Figure Description

[0012] Figure 1 This is a functional block diagram of the medical image processing apparatus according to the first embodiment.

[0013] Figure 2 This is a flowchart of the medical image processing method according to the first embodiment.

[0014] Figure 3 This is a flowchart of the medical image processing method according to the second embodiment. Detailed Implementation

[0015] Hereinafter, with reference to the accompanying drawings, a medical image processing apparatus, a medical image processing method, and a storage medium storing a computer program capable of implementing the medical image processing method will be described. The embodiments described below are merely examples and are not intended to limit the scope. Furthermore, the content described in one embodiment can, in principle, be applied to other embodiments as well.

[0016] (First Embodiment)

[0017] Before surgery, to obtain a comprehensive understanding of the examination site, a three-dimensional scan is typically performed to acquire 3D ultrasound (US), 3D CT, or 3D MR data of the site. The 3D CT or MR data is then registered with the 3D US data. During the surgical procedure, if the patient remains stationary, the ultrasound system coordinate system and the patient's coordinate system always correspond. However, sometimes patient movement can cause a mismatch between the actual patient coordinate system and the ultrasound system coordinate system.

[0018] Figure 1 This is a structural block diagram of the medical image processing apparatus according to the first embodiment.

[0019] The medical image processing device of this embodiment can automatically correct three-dimensional volume data, such as three-dimensional CT volume data and three-dimensional MR volume data, that have been established with the ultrasound system coordinate system before surgery without interrupting the operation.

[0020] like Figure 1 As shown, the medical image processing apparatus 1 includes a generation unit 10, an acquisition unit 11, a storage unit 12, a calculation unit 13, a judgment unit 14, and a correction unit 15. Furthermore, although... Figure 1 Not shown, but the medical image processing device 1 may also include a display control unit that displays three-dimensional volume data in real time on a display unit such as a monitor.

[0021] The generation unit 10 generates registration information that represents the registration relationship between the coordinate system of the ultrasound system and the coordinate systems of other modalities besides the ultrasound system. The coordinate system of the ultrasound system can be a coordinate system obtained from a three-dimensional US image scanned before surgery. The coordinate system of other modalities besides the ultrasound system is the coordinate system of three-dimensional volume data, which can be a coordinate system obtained from a three-dimensional CT image or a three-dimensional MR image scanned before surgery.

[0022] The acquisition unit 11 has the functions of acquiring ultrasound images, acquiring feature groups from ultrasound images, and acquiring a transformation matrix that represents the registration relationship between the coordinate system of the ultrasound system and the coordinate systems of other modes outside the ultrasound system. Depending on the object of acquisition, the acquisition unit 11 can be divided into an ultrasound image acquisition unit 11A, a feature group acquisition unit 11B, and a transformation matrix acquisition unit 11C.

[0023] The ultrasound image acquisition unit 11A acquires two-dimensional ultrasound images at predetermined time intervals as the probe moves after the start of surgery. The two-dimensional ultrasound images include: a reference two-dimensional ultrasound (two-dimensional US) image, and a series of two-dimensional ultrasound images acquired at predetermined time intervals after the reference two-dimensional ultrasound. The reference two-dimensional US image is a set of two-dimensional ultrasound images acquired at the stage immediately following the start of surgery, when the patient remains stationary, after the registration of the preoperative three-dimensional US volume data with the three-dimensional CT / MR volume data has been completed. The position of this reference two-dimensional ultrasound image in the ultrasound system coordinate system remains unchanged compared to the preoperative three-dimensional ultrasound image.

[0024] Regarding the selection of the baseline 2D US image, an ideal 2D US image, such as one taken under breath-holding conditions, can be chosen. Furthermore, regarding the examination site, users can select different sites based on the clinical scenario. For example, in cardiac scanning, users can select different chambers as examination sites to scan the baseline US image. The baseline 2D US image should include the feature groups described later.

[0025] The feature group acquisition unit 11B further extracts feature groups from the ultrasound images acquired by the ultrasound image acquisition unit 11A. The feature group is a set of characteristic regions within the examination site. In this embodiment, the feature group is a set of multiple anatomically significant feature points; when the liver is the examination site, these feature points are, for example, the intersections of hepatic vessels. In other words, in this embodiment, information related to the feature points (e.g., coordinate information in the ultrasound system coordinate system) is extracted from the two-dimensional ultrasound image data containing these feature points.

[0026] The transformation matrix acquisition unit 11C acquires the transformation matrix between the ultrasound system coordinate system and the CT / MR coordinate system obtained before surgery by registering the 3D US volume data with the 3D CT volume data / 3D MR volume data. This transformation matrix represents the correspondence between the ultrasound system coordinate system and the 3D CT / MR coordinate system. When the transformation matrix is ​​updated and changes, the registration relationship between the ultrasound system coordinate system and the 3D CT / MR coordinate system is updated and changes accordingly.

[0027] The storage unit 12 has a first storage unit 12A and a second storage unit 12B. The first storage unit 12A is used to store information related to the feature group at the previous time step, and in this embodiment, it stores information related to the feature points at the previous time step. The second storage unit 12B is used to store information related to the transformation matrix. The information in the first storage unit 12A and the second storage unit 12B is updated as the subject moves.

[0028] Specifically, after the ultrasound image acquisition unit 11A acquires a reference two-dimensional US image and the feature group acquisition unit 11B extracts information related to feature points (e.g., the coordinates of feature points in the ultrasound system coordinate system), the coordinates of the feature points in the reference two-dimensional US image are stored in the first storage unit 12A as the feature point information of the previous moment. This coordinate information of the feature points in the reference two-dimensional US image stored in the first storage unit 12A is replaced by the coordinates of the feature points at the moment of automatic correction when automatic correction occurs. Subsequently, whenever automatic correction occurs, the coordinates of the feature points at the moment of correction are replaced with the coordinates of the feature points at the previous moment and stored. The second storage unit 12B stores the original transformation matrix between the ultrasound system coordinate system and the CT / MR coordinate system obtained before surgery through registration of three-dimensional US volume data with three-dimensional CT volume data / three-dimensional MR volume data. When automatic correction occurs, after the transformation matrix is ​​updated, the updated transformation matrix is ​​stored. Subsequently, whenever automatic correction occurs, the updated transformation matrix is ​​replaced with the current transformation matrix and stored.

[0029] The calculation unit 13 calculates the distance between the feature point at the current time extracted by the feature group acquisition unit 11B and the feature point at the previous time stored in the first storage unit 12A. This distance can be the average Euclidean distance calculated using multiple corresponding feature point pairs, or it can be the maximum distance among multiple corresponding feature point pairs. For comparison with a range of distances, this distance is a parameter that represents the coordinate difference between multiple corresponding feature point pairs as a whole. Alternatively, other methods can be used to obtain the distance.

[0030] The judgment unit 14 determines the relationship between the distance calculated by the calculation unit 13 and a predetermined distance range. This distance range can be set by the user based on the sampling time interval of the ultrasound image. The judgment unit 14 determines whether the distance calculated by the calculation unit 13 is less than the lower limit of the distance range, falls within the distance range, or exceeds the upper limit of the distance range. For example, when examining the liver region, the distance range can be set to 5mm-30mm. In this case, the judgment unit 14 determines whether the distance calculated by the calculation unit 13 is less than 5mm, falls within the 5mm-30mm range, or exceeds 30mm. When the distance is less than the lower limit of the distance range, it can be considered that the movement of the subject is very small and no correction is needed; when it falls within the distance range, it can be considered that the movement of the subject is suitable for automatic correction, and subsequent correction steps are performed; when it exceeds the upper limit of the distance range, it is necessary to further determine the cause of the above result.

[0031] In other words, if the determination unit 14 determines that the distance calculated by the calculation unit 13 is greater than the upper limit of the distance range, it further determines whether the feature point at the current moment has been obtained in the calculation by the calculation unit 13. If it is determined that the feature point at the current moment has been obtained, it can be considered that the movement of the subject is too large, resulting in exceeding the upper limit. In this case, automatic correction is not suitable, and automatic correction will be stopped and "A large movement has occurred, and automatic correction cannot be completed" will be output. If it is determined that the feature point at the current moment has not been obtained, it can be considered that the user has input a stop command. In this case, automatic correction will be stopped and "Automatic correction has been stopped based on the user's stop command" will be output.

[0032] The above example illustrates the case where the judgment unit 14 is independent of the correction unit 15, but the judgment unit 14 can also be omitted so that the correction unit 15 has the function of the judgment unit 14.

[0033] The correction unit 15 has the functions of generating a correction matrix as correction information, updating the feature set of the previous time step, and updating the transformation matrix of the current time step (when the correction occurs). Depending on the functions, the correction unit 15 can be divided into a correction matrix generation unit 15A, a feature set updating unit 15B, and a transformation matrix updating unit 15C.

[0034] When the distance calculated by the calculation unit 13 is within a predetermined distance range, the correction matrix generation unit 15A generates a three-dimensional correction matrix based on the coordinate positions of the corresponding feature points at the current time and the previous time.

[0035] After the correction matrix generation unit 15A generates a three-dimensional correction matrix, the feature group update unit 15B updates the coordinate position of the feature point at the previous moment. That is, it replaces the coordinate position of the feature point at the previous moment with the coordinate position of the feature point at the current moment. The replaced coordinate position of the feature point at the current moment is stored in the first storage unit 12A and used as the coordinate position of the feature point at the previous moment when the next ultrasound image is sampled.

[0036] After the correction matrix generation unit 15A generates a three-dimensional correction matrix, the conversion matrix update unit 15C uses the generated three-dimensional correction matrix and the current conversion matrix stored in the second storage unit 12B to update the conversion matrix. The updated conversion matrix replaces the original conversion matrix and is stored in the second storage unit 12B.

[0037] By updating the transformation matrix, the corresponding 3D CT or 3D MR images established before surgery can be automatically corrected and displayed on the monitor.

[0038] Next, use Figure 2 The automatic correction process for three-dimensional volume data is explained.

[0039] In step S100, at the initial moment of the surgery, a set of reference two-dimensional US images are acquired. The reference two-dimensional US images are two-dimensional US images acquired after the start of the surgery while the patient remains stationary, and are images in the same coordinate system as the three-dimensional US volume data obtained before the surgery.

[0040] In step S200, a set of two-dimensional US images at the current moment is obtained. During the probe movement, a set of two-dimensional US images is acquired at intervals Δt, for example, Δt = 250ms. In this way, multiple sets of two-dimensional US image data can be acquired at a specified sampling interval throughout the entire automatic correction process. An automatic correction judgment is performed every time a set of US images is acquired at interval Δt. The sampling interval Δt of the two-dimensional US images can be set by the user.

[0041] In step S300, a feature set is extracted from the two-dimensional ultrasound image obtained at the current moment. The feature set represents a collection of characteristic regions within the examined area. In this embodiment, the feature set represents a collection of multiple anatomically significant feature points. In other words, in this embodiment, multiple anatomically significant key points (such as the intersections of hepatic vessels) are extracted from the two-dimensional ultrasound image in step S300. Then, the coordinates of these feature points in the ultrasound system coordinate system are recorded. These coordinates are used for distance calculation in step S400 and for generating the correction matrix in step S500.

[0042] In step S400, the distance between the feature point at the current time and each feature point at the previous time is calculated. The distance can be calculated using the average Euclidean distance between the corresponding feature points or the maximum distance between the feature points, but is not limited to these methods.

[0043] In step S500, the calculated distance is compared with a predetermined distance range. If the distance is within the predetermined range (e.g., 5mm-30mm for the liver), proceed to step S600 to generate a correction matrix. If the distance is less than the lower limit of the predetermined range (e.g., 5mm for the liver), no correction is performed. If no feature point is detected, or the calculated distance exceeds the upper limit of the predetermined range (e.g., greater than 30mm), the entire automatic correction process stops, and information related to the reason for stopping is displayed, such as "Automatic correction has stopped based on the user's stop command" or "Significant movement has occurred, automatic correction cannot be completed." The distance range given above is merely an example; its specific value can be set by the user based on the sampling interval of the 2D US image.

[0044] In step S600, a correction matrix is ​​generated based on the feature points at the current time and the feature points at the previous time. Specifically, the position coordinates of multiple corresponding feature points are extracted from the two-dimensional ultrasound images at the current time and the previous time, and the correction matrix is ​​generated using these position coordinates. The correction matrix represents the registration relationship between multiple corresponding feature point pairs, and can be generated by minimizing the distance between the corresponding feature points, but the generation method of the correction matrix is ​​not limited to this.

[0045] In step S700, the transformation matrix is ​​updated based on the current transformation matrix and the generated correction matrix. Specifically, in the first update of the transformation matrix, after obtaining the three-dimensional correction matrix from step S600, the three-dimensional correction matrix is ​​combined with the original transformation matrix (between the ultrasound system coordinate system and the three-dimensional CT / MR coordinate system) to obtain the updated transformation matrix, which is stored in the second storage unit. In subsequent automatic corrections, this updated transformation matrix is ​​used as the current transformation matrix for the next automatic correction. Furthermore, whenever the transformation matrix is ​​updated, the registration relationship between the ultrasound system coordinate system and the three-dimensional MR / CT coordinate system is also updated, thereby completing the automatic correction.

[0046] In step S800, the coordinates of each feature point at the previous moment are updated. Specifically, the position coordinates of the corresponding feature points at the previous moment stored in the first storage unit are replaced with the position coordinates of each feature point at the current moment. In subsequent automatic corrections, the replaced position coordinates of each feature point are used as the position coordinates of each feature point at the previous moment in the next automatic correction.

[0047] In step 900, upon receiving a stop command from the user, the automatic correction is stopped based on the stop command. Specifically, this embodiment has the function of stopping automatic correction based on a stop command from the user. Normally, after automatic correction begins, it enters a looping process. Whenever new 2D US image data is acquired, it is compared with the 2D US image data from the previous moment to determine whether a correction matrix needs to be generated and the registration relationship between the ultrasound system coordinate system and the 3D MR / CT coordinate system needs to be updated. This automatic correction process can only stop when the distance exceeds the upper limit of a predetermined distance range. However, it is desirable that the user can actively stop the automatic correction process as needed.

[0048] In this embodiment, when a user's stop command is received, the two-dimensional ultrasound feature group (i.e., feature points) extracted at the current moment is discarded. Since there are no feature points at the current moment, the distance calculated in step S400 exceeds the upper limit of a predetermined distance range. Therefore, in step S500, the entire automatic correction process is stopped, and a message indicating that automatic correction is complete is displayed. Here, as an example of stopping automatic correction, the case of discarding the acquired feature points upon receiving a user's stop command is illustrated. However, this is not the only method; the acquisition of two-dimensional ultrasound images can also be stopped upon receiving a user's stop command. Furthermore, if the user needs to scan other locations, they can also stop the automatic correction of the current examination location by inputting a stop command, thereby obtaining reference two-dimensional ultrasound images of other locations.

[0049] By equipping the medical image processing device 1 with the aforementioned structure, the registration relationship between the ultrasound system coordinate system and the three-dimensional CT / MR coordinate system can be automatically corrected during surgery without interrupting the procedure, thus improving accuracy and saving time. Furthermore, since the entire correction process can be performed automatically, manual intervention is eliminated, thereby increasing convenience.

[0050] (Second Implementation)

[0051] The first embodiment illustrates a case where feature points are extracted using a typical two-dimensional B-mode ultrasound image and automatic correction is performed based on the feature points.

[0052] However, ultrasound images from other scanning modes besides the two-dimensional B-mode can also be used for automatic correction. That is, ultrasound images from multiple scanning modes can be used for feature group extraction and distance calculation. The aforementioned other scanning modes include, for example, ultrasound images in Doppler mode (also known as Doppler images) and ultrasound images in SMI (super micro-vascular imaging) mode (also known as SMI images), which represent hemodynamics (blood flow, blood flow velocity, etc.).

[0053] The Doppler image is generated by extracting components, such as those corresponding to blood flow or tissue, from the received signal and calculating information about blood flow or tissue at multiple points based on the extracted components. The Doppler image includes the energy, average velocity, and discrete values ​​of the components corresponding to blood flow or tissue.

[0054] Ultramicro blood flow imaging technology can achieve super-resolution imaging of microvessels by locating and time-accumulating individual microbubbles flowing in microvessels, thus overcoming the effects of acoustic diffraction.

[0055] In the second embodiment, ultrasound image data from other scanning modes can be superimposed on the acquired two-dimensional B-mode ultrasound image data. In other words, Doppler images or SMI images with blood flow information can be superimposed on the two-dimensional B-mode ultrasound image data, thereby displaying ultrasound images from multiple scanning modes in a superimposed manner.

[0056] The difference between the second and first embodiments is that the first embodiment uses a two-dimensional B-mode as the base scanning mode throughout the automatic correction process. Ultrasound images in the two-dimensional B-mode are used for feature group extraction, distance calculation, and automatic correction. The second embodiment uses ultrasound images from multiple scanning modes (the base scanning mode, i.e., B-mode, and other scanning modes, i.e., Doppler mode or SMI mode). Ultrasound images in B-mode can be used for feature group extraction and distance calculation, as can ultrasound images in Doppler mode or SMI mode. Similar to the two-dimensional B-mode ultrasound image data, the feature groups in Doppler mode or SMI mode can also be a set of anatomically significant feature points. When using Doppler mode, the feature groups extracted from the Doppler image are, for example, the bifurcation points of blood vessels.

[0057] Figure 3 This is a flowchart of the medical image processing method according to the second embodiment.

[0058] Compared with the first embodiment, in the second embodiment, the original step S200 is replaced by steps S201 and S202. That is, in the first embodiment, a two-dimensional B-mode ultrasound image at the current moment is obtained, while in the second embodiment, in addition to obtaining a two-dimensional B-mode ultrasound image, an SMI image or a Doppler image is also obtained.

[0059] Furthermore, while the second embodiment allows for feature group extraction and distance calculation of ultrasound images under other scanning modes (SMI mode or Doppler mode) at each sampling, performing feature point extraction and distance calculation under other scanning modes for each sampling would be time-consuming. Therefore, it is desirable to reduce this time. To address this, it is considered to use only two-dimensional B-mode ultrasound images for feature point extraction and distance calculation.

[0060] The blood flow or tissue information contained in Doppler and SMI images is beneficial for blood flow or tissue correction. When the region of interest is blood flow or tissue, using Doppler and SMI images for correction yields good results.

[0061] Taking the above considerations into account, and to save automatic calibration time, feature points in Doppler or SMI images should only be extracted if movement of the subject is detected. Before that, two-dimensional B-mode ultrasound image data is used to determine whether movement has occurred. Taking liver detection as an example, feature points are only extracted from Doppler or SMI images and used to generate a calibration matrix if the distance between the current feature point and the previous feature point in B-mode is within the calibration range of 5mm-30mm.

[0062] The following uses Figure 3 The automatic correction cycle of the second embodiment will be explained.

[0063] In step S100, a reference ultrasound image is acquired, which includes a two-dimensional B-mode ultrasound image, an SMI-mode ultrasound image, or a Doppler-mode ultrasound image.

[0064] In step S201, a two-dimensional B-mode ultrasound image at the current moment is acquired.

[0065] In step S202, the SMI mode ultrasound image or Doppler mode ultrasound image at the current moment is acquired.

[0066] In step S300, feature points are extracted from the current two-dimensional B-mode ultrasound image or other scanning mode (SMI mode or Doppler mode) ultrasound image.

[0067] In step S400, the distance between the extracted feature point at the current time and the feature point at the previous time is calculated.

[0068] In step S500, the relationship between the calculated distance and the predetermined distance range is determined. If the calculated distance is less than the lower limit of the predetermined distance range, no correction is performed. If the calculated distance is greater than the upper limit of the predetermined distance range, automatic correction is stopped and the reason for stopping is displayed. If the calculated distance is within the predetermined distance range, the process proceeds to step S600 and correction begins.

[0069] In step S600, a correction matrix is ​​generated based on the coordinates of feature points extracted from ultrasound images of other scanning modes (SMI mode or Doppler mode).

[0070] In step S700, the current transformation matrix is ​​updated based on the generated correction matrix and the previously stored current transformation matrix, and stored in the corresponding storage unit, so that the CT / MR volume data is automatically corrected.

[0071] In step S800, the coordinates of feature points in other scanning modes at the previous moment are replaced with the coordinates of feature points in other scanning modes at the current moment, and stored in the corresponding storage unit.

[0072] In step 900, automatic calibration is stopped based on a stop command from the user.

[0073] The parts not mentioned above are the same as in the first embodiment, and are omitted here for repetition.

[0074] (Other implementation methods)

[0075] In addition to the embodiments described above, different components in each embodiment can be added to, subtracted from, or combined with each other to obtain new embodiments. These new embodiments are also included in the spirit of this invention.

[0076] Furthermore, the constituent elements of the illustrated device are functional concepts and do not necessarily need to be physically arranged as shown in the illustration. That is, the specific way in which the devices are distributed or unified is not limited to the illustration, and can be arranged in any unit, either functionally or physically, according to the actual situation.

[0077] Furthermore, the names and combinations of information shown in the above figures are merely examples and can be changed according to actual needs.

[0078] Furthermore, the medical image processing method described in the above embodiments and variations can be implemented using a pre-prepared program. This program can be obtained via a network such as the Internet. Additionally, the program implementing the medical image processing method of the present invention can also be recorded on a computer-readable storage medium such as a hard disk, flexible disc (FD), CD-ROM, MO, or DVD, and executed by a computer from the storage medium.

[0079] The foregoing has described several embodiments of the present invention, but these embodiments are merely illustrative and are not intended to limit the scope of the invention. These new embodiments can be implemented in various other ways, and various omissions, substitutions, and modifications can be made without departing from the scope of the invention's spirit. These embodiments and their variations are included in the scope and spirit of the invention, and are also included in the scope of the invention and its equivalents described in the technical solution.

Claims

1. A medical image processing device, comprising: The generation unit generates registration information that represents the registration relationship between the coordinate system of the ultrasound system and the coordinate systems of other modes outside the ultrasound system. The acquisition unit acquires two-dimensional ultrasound image data of the subject at specified time intervals after the start of surgery during probe movement, and extracts feature groups from each of the two-dimensional ultrasound image data. The computing unit calculates the distance between a first set of features extracted from two-dimensional ultrasound image data acquired at a first acquisition time after the start of the surgery and a second set of features extracted from two-dimensional ultrasound image data acquired at a second acquisition time earlier than the first acquisition time after the start of the surgery. as well as The calibration unit corrects and updates the registration information based on the calculated distance.

2. The medical image processing device as described in claim 1, The feature group is a collection of multiple anatomical feature points.

3. The medical image processing device as described in claim 1, Ultrasound images generated under multiple scanning modes were used for the extraction of the feature set and the calculation of the distance.

4. The medical image processing device as described in claim 3, The feature group is a collection of multiple feature points in a Doppler image or an ultra-micro blood flow imaging image.

5. The medical image processing apparatus as described in any one of claims 1 to 4, If the distance calculated by the calculation unit is within a specified distance range, the correction unit generates correction information and corrects the registration information based on the generated correction information.

6. The medical image processing apparatus as described in any one of claims 1 to 4, If the distance calculated by the calculation unit is less than the lower limit of the specified distance range, the correction unit does not perform correction.

7. The medical image processing apparatus as described in any one of claims 1 to 4, If the distance calculated by the calculation unit is greater than the upper limit of the specified distance range, the medical image processing device stops the correction of the registration information and displays information related to the reason for the stop.

8. The medical image processing device as described in claim 5, The correction unit generates the correction information in a manner that minimizes the distance between corresponding feature points.

9. The medical image processing device as described in claim 1, The medical image processing device stops correcting the registration information based on a stop command from the user.

10. The medical image processing apparatus as described in claim 5, After generating the correction information, the correction unit replaces the second feature group of the second timing acquisition with the first feature group of the first timing acquisition.

11. The medical image processing apparatus as described in claim 1, It also has a display control unit. The display control unit enables the display unit to display three-dimensional volume data in other modalities besides the ultrasound system in real time.

12. The medical image processing apparatus as described in claim 10, It also has: A first storage unit that stores information related to the second feature group acquired at the second acquisition time; and A second storage unit that stores information related to the registration information. After the correction information is generated, the information related to the second feature group of the second acquisition timing stored in the first storage unit is replaced by information related to the first feature group of the first acquisition timing. After the registration information is corrected, the registration information stored in the second storage unit is replaced by the corrected registration information.

13. The medical image processing apparatus as described in claim 1, The acquisition unit acquires two-dimensional ultrasound image data including the feature group as reference image data.

14. The medical image processing apparatus as described in claim 1, Other modalities besides ultrasound systems include computed tomography (CT) and magnetic resonance imaging (MR).

15. The medical image processing apparatus as described in claim 5, The above correction information is the correction matrix. The registration information mentioned above is a transformation matrix.

16. A medical image processing method, comprising: The generation step generates registration information that represents the registration relationship between the coordinate system of the ultrasound system and the coordinate systems of other modes outside the ultrasound system. The acquisition steps involve acquiring two-dimensional ultrasound image data of the subject at specified time intervals after the start of surgery during probe movement, and extracting feature groups from each of the two-dimensional ultrasound image data. The calculation step involves calculating the distance between a first set of features extracted from two-dimensional ultrasound image data acquired at a first acquisition time after the start of the surgery and a second set of features extracted from two-dimensional ultrasound image data acquired at a second acquisition time earlier than the first acquisition time after the start of the surgery. as well as The correction step involves correcting and updating the registration information based on the calculated distance.

17. A storage medium storing a program that causes a computer to perform the following steps: The generation step generates registration information that represents the registration relationship between the coordinate system of the ultrasound system and the coordinate systems of other modes outside the ultrasound system. The acquisition steps involve acquiring two-dimensional ultrasound image data of the subject at specified time intervals after the start of surgery during probe movement, and extracting feature groups from each of the two-dimensional ultrasound image data. The calculation step involves calculating the distance between a first set of features extracted from two-dimensional ultrasound image data acquired at a first acquisition time after the start of the surgery and a second set of features extracted from two-dimensional ultrasound image data acquired at a second acquisition time earlier than the first acquisition time after the start of the surgery. as well as The correction step involves correcting and updating the registration information based on the calculated distance.

Images