Target detection method and system

Abstract

The invention relates to a target detection method and system for imaging equipment. At least a portion of the target is located in the subject. The target detection method comprises the following steps: acquiring volume image data generated by scanning a region of interest of a detected object by an imaging device; converting the volume image data into a feature projection image, wherein the feature projection image comprises three orthogonal plane feature projection images; detecting coordinates of the target in the three orthogonal plane feature projection images to obtain corresponding projection coordinates of the target in respective coordinate systems of the three orthogonal plane feature projection images; and obtaining a global coordinate of the target in a global coordinate system of the imaging device based on the projection coordinate of the target.

Description

Technical Field

[0001] The present invention relates to the field of computer imaging, and in particular to a target detection method and system for an imaging device. The present invention also relates to a computer readable medium storing instructions for executing the target detection method and an imaging device including the target detection system. Background Art

[0002] In the field of imaging, the technology of detecting targets in slice images generated by imaging devices has a wide range of applications. For example, a puncture needle in a patient's body is identified from a medical image. However, a challenge currently encountered by such technology is that target detection takes a relatively long time, because in traditional target detection technology, the target needs to be detected in sequence in all generated slice images. Moreover, as the number of images increases, the time overhead of target detection will basically increase proportionally. The number of images can be proportional to, for example, the width and / or accuracy of the scan. The wider the scan width, the larger the image coverage and the more images there are. For example, under the default settings, a scan width of 10 mm (millimeter) can usually generate 16 images, a scan width of 20 mm can generate 32 images, and a scan width of 40 mm can generate 64 images. The higher the scan accuracy, the more images will be generated under the same scan width. For example, taking a scan width of 40 mm as an example, 32 images can be generated with a scan accuracy of 1.25 mm, while 64 images will be generated if the scan accuracy is increased to 0.625 mm. In certain cases, for example, a target (such as a puncture needle) may be bent, tilted, and / or need to enter the human body at a larger angle relative to the scanning plane for the purpose of avoiding obstacles (such as bones, blood vessels, etc.). Such phenomena will also lead to an increase in the number of images. For example, a scan width of 10 mm may require the generation of 24 images, and / or lead to the need for a larger scan width, resulting in a larger number of images in a single scan. In addition, the resolution of the image itself will also affect the time cost of target detection. The higher the image resolution, the more pixels contained in the image, and the longer the detection time. For example, the time required for target detection for a 512x512 dot matrix image is 4 times the time required for target detection for a 256x256 dot matrix image.

[0003] When the time cost of target detection increases, the tap cycle time of the imaging device increases accordingly. This will not only reduce the working efficiency of the imaging device, but also increase the radiation exposure of the inspected object and / or the imaging system operator, and also increase the discomfort of the living inspected object and affect their recovery.

[0004] Therefore, there is a need in the art for improved target detection methods and systems to reduce the time cost of target detection. In addition, when the volume contained in the image increases or the image data of the same volume slice increases, the time cost required for detecting the target remains basically unchanged. Summary of the invention

[0005] According to one aspect of the present invention, a target detection method is provided. At least a portion of the target may be located in the object under inspection. The target detection method may include: obtaining volume image data generated by scanning the region of interest of the object under inspection by an imaging device; converting the volume image data into a feature projection image, the feature projection image including three orthogonal plane feature projection images; detecting the coordinates of the target in the three orthogonal plane feature projection images respectively to obtain the corresponding projection coordinates of the target in the respective coordinate systems of the three orthogonal plane feature projection images; and obtaining the global coordinates of the target in the global coordinate system of the imaging device based on the projection coordinates of the target.

[0006] According to another aspect of the present invention, a target detection system is provided. At least a portion of the target may be located in the object under inspection. The target detection system may include a memory configured to store volume image data generated by scanning the region of interest of the object under inspection by an imaging device. The target detection system may further include a processor configured to: obtain volume image data from the memory; convert the volume image data into a feature projection image, the feature projection image including three orthogonal plane feature projection images; detect the coordinates of the target in the three orthogonal plane feature projection images respectively to obtain the corresponding projection coordinates of the target in the respective coordinate systems of the three orthogonal plane feature projection images; and obtain the global coordinates of the target in the global coordinate system of the imaging device based on the projection coordinates of the target. The target detection system may also include a display configured to display the aforementioned feature projection images.

[0007] According to another aspect of the present invention, a computer-readable medium is provided, on which instructions are provided. When the instructions are executed by a processor, the processor is caused to perform the steps of the target detection method described above.

[0008] According to yet another aspect of the present invention, there is provided an imaging device comprising the target detection system as described above.

[0009] These and other features and aspects of the present invention will become more apparent from the following detailed description taken with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to be able to understand the present invention in detail, a more specific description of the present invention briefly summarized above can be obtained by reference to the embodiments, some of which are shown in the accompanying drawings. In order to facilitate understanding, the same reference numerals have been used as much as possible to indicate the same elements common to the various figures. However, it should be noted that the drawings only show typical embodiments of the present invention and therefore should not be considered to limit the scope of the present invention, as the present invention may allow other equivalent embodiments. In the accompanying drawings:

[0011] Figure 1 is a perspective schematic diagram of an exemplary imaging system according to an embodiment of the present invention.

[0012] Figure 2 is a schematic block diagram of an exemplary imaging system according to an embodiment of the present invention.

[0013] Figure 3A A series of slice images according to an embodiment of the present invention are shown.

[0014] Figure 3B Another series of slice images according to an embodiment of the present invention is shown.

[0015] Figure 3C Shown according to Figure 3B A three-dimensional (3D) image obtained by stereoscopic rendering of a series of slice images.

[0016] Figure 4A A slice image under a large display field of view (FOV) according to an embodiment of the present invention is shown.

[0017] Figure 4B Shown for Figure 4A An image of a slice image showing a small FOV of a portion of the image.

[0018] Figure 5 is an exemplary flow chart of a target detection method according to an embodiment of the present invention.

[0019] Figure 6 Three orthogonal plane feature projection images according to an embodiment of the present invention are shown.

[0020] Fig. 7A and Figure 7B An axial tomographic image and a maximum intensity projection image of a needle target according to an embodiment of the present invention are respectively shown.

[0021] Figure 8A-8C Each shows a user interface including a conventional CT image (left) and a corresponding multi-planar reconstruction (MPR) image (right).

[0022] Fig. 9 is an example block diagram of a target detection system according to an embodiment of the present invention.

[0023] It is contemplated that elements of one embodiment of the invention may be beneficially applied to other embodiments without further recitation. DETAILED DESCRIPTION

[0024] The specific embodiments of the present invention will be described below. It should be noted that in the specific description of these embodiments, in order to be concise and concise, this specification cannot provide a detailed description of all the features of the actual embodiments. It should be understood that in the actual implementation of any embodiment, just as in the process of any engineering project or design project, in order to achieve the specific goals of the developer, in order to meet system-related or business-related restrictions, various specific decisions are often made, and this will also change from one embodiment to another. In addition, it can also be understood that although the efforts made in this development process may be complex and lengthy, for those skilled in the art related to the content disclosed in the present invention, some changes such as design, manufacturing or production based on the technical content disclosed in the present invention are only conventional technical means, and should not be understood as the content of the present invention is insufficient. On the other hand, many specific details are set forth in the following description to facilitate a full understanding of the present invention, but those skilled in the art should understand that the present invention can be implemented without some or all of these specific details, so the present invention is not limited to the specific embodiments disclosed below.

[0025] Furthermore, it is to be appreciated that the various embodiments shown in the figures are illustrative and that the figures are not necessarily drawn to scale.

[0026] In the present disclosure, unless otherwise defined, the technical terms or scientific terms used in the claims and the specification shall have the usual meanings understood by ordinary technicians in the technical field to which the present invention belongs. The words "first", "second" and similar words used in the patent application specification and the claims of the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "One" or "one" and other similar words do not indicate a quantitative limitation, but indicate the existence of at least one. "Include" or "comprise" and other similar words mean that the elements or objects appearing before "include" or "comprise" cover the elements or objects listed after "include" or "comprise" and their equivalent elements, and do not exclude other elements or objects. "Connect" or "connected" and other similar words are not limited to physical or mechanical connections, nor are they limited to direct or indirect connections.

[0027] In the present disclosure, the term "scan width" may refer to the total width of the area covered by the inspection imaging scheme, that is, the total thickness of the image slices obtained by the detector scan. The term "scan accuracy" may refer to the thickness of a single image slice. The term "tap cycle time" may refer to the duration from the start of exposure of the imaging device to the generation of all the desired images. Taking a CT imaging system as an example, if the gantry rotates, the exposure time requires 0.5 seconds, and the imaging time requires 1.5 seconds, then its tap cycle is 2.0 seconds. The term "inspected object" may include living objects (such as human bodies, animals, etc.) and inanimate objects (such as luggage, implants, manufactured parts, etc.). For example, the inspected object may generally include, but is not limited to, human patients, animals, or other objects that can be detected by various imaging devices.

[0028] The XYZ three-dimensional coordinate system is used herein to represent three orthogonal planes, namely, the XY plane, the XZ plane, and the YZ plane. It should be understood that the present invention may also use any other suitable three-dimensional coordinate system. In an embodiment where the imaging device is a medical imaging device, the three orthogonal planes may generally be the axial plane, the sagittal plane, and the coronal plane of the subject.

[0029] An imaging system that can be used to practice the technology of the present invention is described in detail below in conjunction with the accompanying drawings.

[0030] Figure 1 and Figure 2 An exemplary imaging system, such as a CT imaging system, is provided. The imaging system can be used to detect objects in an image according to the techniques of the present invention.

[0031] Although the present invention is described in conjunction with a CT imaging system, it should be understood that the present invention may also be applicable to any other suitable type of imaging system, including but not limited to luggage x-ray machines, medical imaging systems, etc. Medical imaging systems may include other medical imaging modalities in addition to CT, such as magnetic resonance imaging (MRI) systems, C-arm imaging systems, positron emission tomography (PET) systems, single photon emission computed tomography (SPECT) systems, interventional imaging systems (such as angiography, biopsy), ultrasound imaging systems, x-ray radiographic imaging systems, x-ray fluoroscopic imaging systems, and the like. Different types of imaging systems may be suitable for detecting corresponding targets. The target may be any type of suitable target. As an example, a luggage x-ray machine may be suitable for detecting specific items in luggage. For medical imaging systems, detectable targets include interventional objects (such as needles, endoscopes, implants, catheters, guidewires, dilators, ablators, contrast agents, etc.), lesions (such as tumors, etc.), bones, organ tissue structures, vascular structures, and the like. On the other hand, for example, CT imaging systems may be used in the manufacturing industry, such as parts inspection, in addition to being used in the medical field.

[0032] Figure 1 An exemplary imaging system 100 is shown. The imaging system 100 is configured to image a subject 112. In some embodiments of the present disclosure, the imaging system 100 may be a CT imaging system, and the term "subject" may be used interchangeably with "patient," but it should be understood that, at least in some examples, a patient is a type of subject that can be imaged by a CT system, and the subject may include a patient. In some embodiments, the imaging system 100 may include a gantry 102. The gantry 102 may in turn include at least one x-ray radiation source 104, which may include an x-ray target made of graphite and metal. The at least one x-ray radiation source 104 may be configured to emit an x-ray radiation beam 106 for imaging the subject. Specifically, the radiation source 104 is configured to emit x-rays 106 toward a detector array 108 positioned on an opposite side of the gantry 102. Although Figure 1 Only a single radiation source 104 is shown in the figure, but in other embodiments, multiple radiation sources can be used to emit multiple x-rays 106 to obtain projection data corresponding to the object under examination at different energy levels.

[0033] In some embodiments, the imaging system 100 may include an imaging sensor 114 positioned on or outside the rack 102. As shown, the imaging sensor 114 is positioned on the outside of the rack 102 and is oriented to image the inspected object 112 when it is at least partially outside the rack 102. The imaging sensor 114 may include a visible light sensor and / or an IR sensor with an infrared (IR) light source. The IR sensor may be a three-dimensional depth sensor, such as a time-of-flight (TOF) sensor, a stereo sensor, or a structured light depth sensor, which is operable to generate a three-dimensional depth image, while in other embodiments, the IR sensor may be a two-dimensional IR sensor, which is operable to generate a two-dimensional IR image. In some embodiments, the two-dimensional IR sensor may be used to infer depth based on knowledge of IR reflection phenomena to estimate three-dimensional depth. Regardless of whether the IR sensor is a three-dimensional depth sensor or a two-dimensional IR sensor, the IR sensor may be configured to output a signal encoding an IR image to a suitable IR interface, which may be configured to receive a signal encoding an IR image from the IR sensor. In other examples, the imaging sensor may also include other components, such as a microphone, to enable reception and analysis of directional and / or non-directional sounds from the viewed subject and / or other sources.

[0034] In some embodiments, the imaging system 100 may include a processor such as an image processor 110. The processor 110 may be configured to implement the target detection techniques of the present invention, as will be described in further detail below. The processor 110 may also be configured to reconstruct an image of a target volume of the subject 112 using a suitable reconstruction method, such as an iterative or analytical image reconstruction method. For example, the image processor 110 may reconstruct an image of a target volume of the subject 112 using an analytical image reconstruction method such as filtered back projection (FBP). As another example, the image processor 110 may reconstruct an image of a target volume of the subject 112 using an iterative image reconstruction method, such as adaptive statistical iterative reconstruction (ASIR), conjugate gradient (CG), maximum likelihood expectation maximization (MLEM), model-based iterative reconstruction (MBIR), etc.

[0035] In some embodiments, the image processor 110 can be configured to perform multi-oblique plane reconstruction (MPR) based on the target detection technology of the present invention. MPR is a method of displaying a 3D data set that can generate cross-sectional images, such as original two-dimensional (2D) coronal, sagittal, and axial images. Curved MPR reconstructs cross-sectional images perpendicular to a specific curve created by the user. Target detection can serve as the basis for automatic MPR and tracking. This automatic MPR based on target detection can display the entire image of the target and the region of interest of the subject 112 in one image, allowing the physician to accurately assess the distance of the target (e.g., a puncture needle) from a specific part (e.g., a tumor) in the region of interest, thereby providing better guidance to the physician, such as Figure 8A-8C In addition, multiple consecutive reconstructed images can also show the trajectory of the target.

[0036] The imaging system 100 may include a table 115 on which an object to be imaged may be positioned. The table 115 may be motorized so that the vertical position and / or lateral position of the table may be adjusted. Thus, the table 115 may include a motor 116 and a motor controller 118. The table motor controller 118 moves the table 115 by adjusting the motor 116 so that the object is properly positioned in the gantry 102 to acquire projection data corresponding to a target volume of the object 112. The table motor controller 118 may adjust the height of the table 115 (e.g., the vertical position relative to the ground on which the table is located) and the lateral position of the table 115 (e.g., the horizontal position of the table along an axis parallel to the axis of rotation of the gantry).

[0037] Figure 2 Shows something like Figure 11. In one embodiment, the system 200 includes a detector array 108 (see Figure 1 ). The detector array 108 also includes a plurality of detector elements 202 that together collect the x-ray beam 106 that passes through the subject 112 (see Figure 1 ) to acquire corresponding projection data. The system 200 stores the projection data in a storage device 218. The storage device 218 may include, for example, a hard disk drive, a floppy disk drive, a compact disk-read / write (CD-R / W) drive, a digital versatile disk (DVD) drive, a flash drive, and / or a solid-state storage device. Thus, in one embodiment, the detector array 108 is fabricated in a multi-slice configuration including multiple rows of cells or detector elements 202. In such a configuration, one or more additional rows of detector elements 202 are arranged in a parallel configuration for acquiring projection data.

[0038] In some embodiments, the system 200 is configured to traverse different angular positions around the subject 112 to obtain the desired projection data. Therefore, the gantry 102 and the components mounted thereon (such as the radiation source 104 and the detector 202) can be configured to rotate around the rotation center 206 to obtain, for example, projection data at different energy levels. Alternatively, in embodiments where the projection angle relative to the subject 112 changes over time, the mounted components can be configured to move along a general arc rather than along a section of a circle.

[0039] In one embodiment, the system 200 includes a control mechanism 208 to control the movement of components, such as the rotation of the gantry 102 and the operation of the x-ray radiation source 104. In some embodiments, the control mechanism 208 also includes an x-ray controller 210 configured to provide power and timing signals to the radiation source 104. Additionally, the control mechanism 208 includes a gantry motor controller 212 configured to control the rotational speed and / or position of the gantry 102 based on imaging requirements.

[0040] In some embodiments, the control mechanism 208 also includes a data acquisition system (DAS) 214 that is configured to sample analog data received from the detector element 202 and convert the analog data into digital signals for subsequent processing. The data sampled and digitized by the DAS 214 is transmitted to a computing device 216. In one example, the computing device 216 stores the data in a storage device 218. Although Figure 2 Only a single computing device 216 is shown in FIG. 2 , but in some examples, computing device 216 may be distributed across multiple physical devices.

[0041] In addition, the computing device 216 provides commands and parameters to one or more of the DAS 214, the x-ray controller 210, and the gantry motor controller 212 to control system operations, such as data acquisition and / or processing. In some embodiments, the computing device 216 controls system operations based on operator input. The computing device 216 receives operator input, such as commands and / or scan parameters, via an operator console 220 operatively coupled to the computing device 216. The operator console 220 may include a keyboard (not shown) or a touch screen to allow an operator to specify commands and / or scan parameters.

[0042] Although Figure 2 Only one operator console 220 is shown, but more than one operator console may be coupled to the system 200, e.g., for inputting or outputting system parameters, requesting exams, and / or viewing images. Additionally, in some embodiments, the system 200 may be coupled to multiple displays, printers, workstations, and / or the like located locally or remotely, e.g., within an institution or hospital or at an entirely different location, via one or more configurable wired and / or wireless networks, such as the Internet and / or a virtual private network.

[0043] For example, in one embodiment, the system 200 includes or is coupled to a picture archiving and communication system (PACS) 224. In an exemplary embodiment, the PACS 224 is further coupled to remote systems (such as a radiology department information system, a hospital information system) and / or to an internal or external network (not shown) to allow operators at different locations to supply commands and parameters and / or gain access to image data.

[0044] The computing device 216 uses operator-supplied and / or system-defined commands and parameters to operate the table motor controller 118, which in turn can control the powered table 115. For example, the computing device 216 can send commands to the motor controller 118, instructing the motor controller 118 to adjust the vertical position and / or lateral position of the table 115 via the motor 116.

[0045] As previously described, the DAS 214 samples and digitizes the projection data acquired by the detector elements 202. Subsequently, the image reconstructor 230 uses the sampled and digitized x-ray data to perform high-speed reconstruction. Figure 2The image reconstructor 230 is shown as a separate entity, but in some embodiments, the image reconstructor 230 may form part of the computing device 216. Alternatively, the image reconstructor 230 may not be present in the system 200, and instead the computing device 216 may perform one or more functions of the image reconstructor 230. In addition, the image reconstructor 230 may be located locally or remotely, and may be operably connected to the system 100 using a wired or wireless network. In particular, an exemplary embodiment may use computing resources in a "cloud" network cluster for the image reconstructor 230.

[0046] In one embodiment, the image reconstructor 230 stores the reconstructed image in the storage device 218. Alternatively, the image reconstructor 230 transmits the reconstructed image to the computing device 216 to generate available subject information for diagnosis and evaluation. In some embodiments, the computing device 216 transmits the reconstructed image and / or subject information to a display 232, which is communicatively coupled to the computing device 216 and / or the image reconstructor 230. In one embodiment, the display 232 allows the operator to evaluate the imaged anatomical structure. The display 232 can also allow the operator to select a volume of interest (VOI) and / or request subject information, for example, via a graphical user interface (GUI), for subsequent scanning or processing.

[0047] In some examples, the computing device 216 may include computer-readable instructions that are executable to send commands and / or control parameters to one or more of the DAS 214, the x-ray controller 210, the gantry motor controller 212, and the table motor controller 226 according to an examination imaging protocol that includes the clinical task / intent of the examination. For example, the clinical intent may inform the goals of the procedure based on clinical indications (e.g., general scanning or lesion detection, anatomical structures of interest, critical to quality (CTQ) parameters, or other goals), and may further define the required position and orientation of the subject during the scan (e.g., supine and feet first). The operator of the system 200 can then position the subject on the table according to the position and orientation of the subject specified by the imaging protocol. In addition, the computing device 216 can set and / or adjust various scan parameters (e.g., dose, gantry rotation angle, kV, mA, attenuation filter) according to the imaging protocol. For example, the imaging protocol may be selected by an operator from a plurality of imaging protocols stored in a memory on the computing device 216 and / or a remote computing device, or the imaging protocol may be automatically selected by the computing device 216 based on received subject information.

[0048] The following describes in detail techniques that can be used to reduce the time overhead of object detection with reference to the accompanying drawings.

[0049] As mentioned above, the time cost of target detection increases with the increase of the number of images, image resolution, etc. The number of images further increases with the increase of scanning width, scanning accuracy, etc. The image resolution is proportional to the number of pixels. Based on this, the present invention conceives various methods to reduce the time cost of target detection.

[0050] like Figure 3A As shown, it is shown that Figure 1 and Figure 2 The imaging systems 100, 200 shown scan the region of interest of the subject 112 to obtain 16 slice images (Slice 1 to Slice 16). The scanning accuracy is 0.625 mm and the scanning width is 10 mm. The resolution of each slice image is 512x512 pixels. The target to be detected by the scanning process is a needle, such as a puncture needle, a biopsy needle, etc. The needle is an interventional instrument with a large width-to-length ratio. For imaging technology, the needle is approximately a two-dimensional (2D) rotating body. The needle of the present invention can be made of any suitable material, such as metal. The needle can generally have a specification of, for example, 25G to 10G, and the needle of this specification has an outer diameter of about 0.515 to 3.404 mm. The needle may include a needle core and a needle shell that wraps the needle core, for example, for extracting human tissue. When the needle is inserted into the object to be examined (such as the human body), it is not always parallel to the imaging section. Therefore, image processing is required to identify the needle and then automatically generate a cross-sectional image where the needle is located to provide guidance to the physician, such as indicating the position or distance of the needle relative to a specific part of the area of ​​interest (for example, a suspected tumor or a confirmed tumor).

[0051] Conventionally, the target needle can be detected in the 16 slice images one by one, for example, by one or more methods selected from the group consisting of threshold segmentation, PCA enhancement, Gaussian convolution, Hessian matrix, D-Test and AI. If the detection time consumed by each slice image is constant, the time cost of target detection is O(n), where n is the number of slice images. If one or more slice images require an iterative algorithm to remove interference such as bones (because the CT values ​​of needles and bones are both high), the detection time of these slice images will increase accordingly, making the time cost of target detection much more complicated. In summary, the time cost T of target detection can be calculated by the following formula (1). Among them, t i represents the target detection time consumed by a single slice image, which can be associated with factors such as the resolution of the slice image; and n represents the number of slice images, which can be associated with factors such as the scanning width and scanning accuracy.

[0052] exist Figure 3AIn the example shown (CT intervention with 3D automatic MPR support), the detection of the target needle generally takes a total of about 4-5 seconds, for example 4.336 seconds, wherein the x-ray exposure time may be about 0.234 seconds.

[0053] It should be understood that not all slice images will have objects to be detected. Figure 3A In the example shown, the needle can be detected in slices 4 to 12. In addition, since the needle is approximately two-dimensional, the aspect ratio of the needle in the image (which depends on the angle between the needle and the scanning plane) is very important for needle detection. If the needle enters the human body at a large angle, the needle length in the slice image will be small (the actual length of the needle is relatively long); if the needle enters the human body at an angle perpendicular to the body surface, the needle length in the slice image will be close to the actual length of the needle.

[0054] In some embodiments of the present invention, the time cost T of target detection can be directly limited by setting an upper limit on the detection time consumed by slice images. For example, after a single exposure, only 3 seconds are allowed to detect the needle. If the needle is not detected within 3 seconds, the detection is abandoned and the physician is asked to use the plane layer image instead.

[0055] In other embodiments of the present invention, the time cost T of target detection can be reduced by adjusting various factors that affect the time cost of target detection (such as scan width, scan accuracy, image resolution, number of image pixels, etc.). Compared with the method of directly limiting the time cost T, such a method can avoid detection failure to a large extent.

[0056] For example, in one example, considering that the time spent on detecting a target in an image increases as the number of image pixels increases, the pixel array on the air area in the image (such as Figure 4A and Figure 4B As shown), the time cost T of target detection is reduced. Figure 4A A maximum display FOV of 50 cm (centimeter) is shown, as indicated by the outer circle in the figure. Figure 4B Shows Figure 4A The part represented by the small and medium circles is a smaller display FOV of 10 cm. In actual imaging operations, physicians generally directly select the area of ​​interest represented by the small circle for imaging (see Figure 4B ). Figure 4B For the image shown, excluding the air area has a relatively small effect on the computational complexity of target detection. Correspondingly, the effect of reducing the time overhead of target detection will also be relatively limited.

[0057] In another example, considering that the time cost of target detection increases with the number of images, the time cost T of target detection can be reduced by reducing the number of slice images n. Further, the scan width usually depends on the inspection imaging scheme, so for a specific target detection process, the number of slice images generated can usually be reduced by reducing the image scanning accuracy. For example, in the case of a 10mm scan width, a scanning accuracy of 0.625mm can generate 16 slice images, such as Figure 3A When the scanning accuracy is reduced to 1.25 mm, the number of slice images will be proportionally reduced to 8. Accordingly, the time cost T of target detection can be reduced to half of that at 0.625 mm scanning accuracy.

[0058] In another example, considering that the time spent on detecting a target in a single slice image increases with the increase of image resolution, the target detection time spent in each slice image can be reduced by reducing the image resolution of one or more slice images. i , thereby reducing the time cost T of target detection in general. For example, if a 256x256 dot matrix image is used instead of a 512x512 dot matrix image for target detection, the target detection time t consumed by the slice image is i It can be reduced to one-fourth of the level before replacement.

[0059] For example, a reduction in scanning accuracy and / or image resolution will result in a loss of detection accuracy. Specifically, a reduction in scanning accuracy will result in a loss of detection accuracy in the slice direction (e.g., Z direction). A reduction in image resolution will result in a loss of detection accuracy in the cross section (e.g., XY plane). In order to achieve a significant reduction in the time overhead of target detection without losing detection accuracy, the present invention further provides the following: Figure 5 The target detection method 500 shown in FIG. The method 500 performs target detection using feature projection images on three orthogonal planes. Figure 3A Under the same conditions as the example shown, Figure 5 The time cost of target detection of the method shown can be reduced to 1.68 seconds.

[0060] The "feature" used herein may refer to the characteristics of the target to be identified that are more significant than the object to be inspected. The "feature projection image" may be, for example, a maximum intensity projection (MIP) image, an average intensity projection (AIP) image, a minimum intensity projection (mIP) image, a standard deviation projection (SDP) image, or various combinations thereof. The specific selection of the feature projection image may depend on the characteristics of the target and its surrounding objects. For example, a significant feature of a needle target made of metal relative to human tissue may be that the image CT value of the needle is higher, so the maximum density projection image may be used to detect the needle target. For another example, for a low-dose high-noise image, an average density projection image may be used. For another example, a target with a lower density than the surrounding may be suitable for a minimum density projection image, such as a fiber-made instrument relative to a bone. Since the image CT value of the fiber is much lower than that of the bone, a minimum density projection image may be used to detect the fiber target. For targets and objects with obvious changes in density, standard deviation projection images may also be used to detect the target. In addition, a combination of multiple feature projection images, such as the difference between the maximum density projection image and the minimum density projection image, can be used to enhance the characteristics of the target to be detected. For images from multiple sources (CT / X-ray / MRI) or for images with multiple energy spectra, synthetic feature projection images from different sources and different energy spectra can be used.

[0061] The three orthogonal planes usually refer to the XY plane, XZ plane and YZ plane in the XYZ three-dimensional coordinate system, but may also be other suitable three orthogonal planes. Figure 1 and Figure 2In the CT imaging systems 100, 200 shown, the three orthogonal planes generally refer to the axial plane, sagittal plane, and coronal plane of the subject 112. For images lacking sufficient position reference information, a plane parallel to the scanning plane can be selected as the axial plane, a plane parallel to the horizontal plane can be selected as the coronal plane, and a plane perpendicular to the above two planes can be selected as the sagittal plane. In an embodiment using a puncture needle, when there is puncture planning information (such as a needle entry point and a puncture target point), a plane perpendicular to the needle entry point and the puncture target point can be selected as the coronal plane, a plane including the needle entry point and the puncture target point and the three o'clock (horizontal) direction of the scanning plane can be selected as the axial plane, and a plane perpendicular to these two planes can be selected as the sagittal plane. The advantage of this selection is that when the trajectory of the puncture is highly consistent with the planned path, the needle in the coronal plane will degenerate into a gathering point, while the projections of the axial plane and the sagittal plane have projections consistent with the actual length of the needle. In addition, based on the relative position relationship between the imaging device operator and the patient, the plane consistent with the operator's observation direction can be selected as the sagittal plane, the plane in the direction perpendicular to the observation direction can be selected as the axial plane, and the plane perpendicular to these two planes can be selected as the coronal plane.

[0062] The targets disclosed herein may be located at least partially within a subject (e.g., Figure 1 and Figure 2 In some embodiments, the target may be a rotating body. The target may have two-dimensional features or three-dimensional features. As mentioned above, the target may include but is not limited to interventional objects, lesions, bones, organ tissue structures, vascular structures, etc. The target may also be defects such as cracks in parts. For example, interventional objects may include but are not limited to needles, endoscopes, implants, catheters, guide wires, dilators, ablators, contrast agents, etc. Lesions may include but are not limited to tumors, cysts, nodules, ulcers, plaques, etc. Bones may include but are not limited to bones, joints, etc. Interventional needles may include but are not limited to puncture needles, biopsy needles, ablation needles, nerve block needles, etc. Implants may include but are not limited to pacemakers, implantable defibrillators, stents, artificial joints, artificial valves, cochlear implants, bone nails, implant dialyzers, dental implants, embolic devices, etc.

[0063] The object detection method 500 described herein may include steps 510 - 540 .

[0064] In step 510, volumetric image data generated by scanning a region of interest of an object under examination by an imaging device may be obtained. The imaging device may be, for example, Figure 1 or Figure 2 The CT imaging device shown. Figure 1 and Figure 2In the illustrated embodiment, the subject is a human patient. The obtained volume image data is provided as projection data to a processor for processing, or can be stored in a storage device of the imaging device (such as Figure 2 The data is then stored in the storage device 218 as shown for subsequent processing.

[0065] In step 520, the volume image data may be converted into a feature projection image, which includes three orthogonal plane feature projection images. With respect to the above-described embodiment in which the target detection time overhead varies with the number of images, method 500 uses three feature projection images on three mutually orthogonal planes to perform target detection regardless of the number of slice images, and the target detection time overhead is the time consumed to detect only three feature projection images, so it is greatly reduced and can be substantially constant. In addition, the feature projection image of the plane where the cross section is located in the three orthogonal planes has an image lattice with the same number of pixels as a conventional slice image, such as 512x512, 256x256, or 1024x1024. Moreover, the number of pixels on one dimension (row or column) of each of the other two feature projection images is consistent with the number of pixels on the row or column of the single slice image lattice, and the number of pixels on the other dimension is equal to the number of slice images. Since the number of slice images (such as 16, 32, 64, etc.) is usually much smaller than the number of pixels in the rows or columns of the slice image pixel matrix (such as 256, 512, 1024, etc.), the pixel matrix of the other two feature projection images is much smaller than the pixel matrix of the feature projection image on the cross section, so that the time consumed to detect the three orthogonal plane feature projection images can be less than the time consumed to detect the conventional three slice images, thereby further reducing the time overhead of target detection.

[0066] As an example, refer to Figure 3B and Figure 3C An exemplary process for obtaining a maximum intensity projection (MIP) image from volumetric image data is described.

[0067] Figure 3B Shown with Figure 3A Six slice images from another series of 16 slice images generated by a similar body scan. The scanning accuracy is 1.25mm and the scanning width is 20mm. The resolution of each slice image is also 512x512 pixels. Figure 3B The 16 slice images of Figure 3C The three-dimensional image shown in FIG. 1 is a three-dimensional image of a subject, wherein the XY plane can be the axial plane of the subject, the XZ plane can be the coronal plane of the subject, and the YZ plane can be the sagittal plane of the subject. Figure 3B-3CThe example shown in the figure uses a 512x512 2D array to represent the value of each pixel in the slice image. Figure 3B There are 42 pixel values ​​in 6 columns and 7 rows on the first, second, third and sixteenth slice images of the 16 slice images in the shown example, and the remaining pixel values ​​are not shown but are similar.

[0068] Example of a 2D array of the first slice image (S-1): 31 119 60 153 209 … 95 236 23 95 193 142 … 246 117 109 28 11 129 … 44 244 88 64 43 195 … 153 160 194 57 149 7 … 85 203 172 218 234 242 … 214 … … … … … … … 176 7 106 125 89 … 12

[0069] Example of a 2D array of the second slice image (S-2): 134 17 51 62 108 … 17 208 94 208 64 62 … 243 94 221 65 42 178 … 0 32 152 107 51 5 … 53 168 39 18 148 19 … 22 218 54 224 5 11 … 161 … … … … … … … 2 150 206 202 210 … 166

[0070] Example of a 2D array of the third slice image (S-3): 154 10 192 111 110 … 127 3 81 88 19 51 … 250 146 158 74 80 221 … 162 79 155 12 236 112 … 170 94 39 26 124 238 … 231 144 40 52 156 99 … 63 … … … … … … … 42 203 97 102 39 … 51

[0071] Example of a 2D array of the sixteenth slice image (S-16):

[0072] Since the slice image is parallel to the vertical scanning center plane, the vertical scanning center plane can be selected as the axial plane of projection. The maximum intensity projection (MIP) image of the axial plane can be obtained by making the selected pixel take the maximum value of its corresponding pixel value in the two-dimensional array of each slice image. For example, for the upper left corner pixel, the corresponding pixel values ​​in the first, second, third and sixteenth slice images are 31, 134, 154 and 219 respectively; assuming that the values ​​of the upper left corner pixels of the fourth to fifteenth slice images are all less than 219, the value of the upper left corner pixel of the maximum intensity projection (MIP) image is 219. The same goes for other pixels. Figure 3B-3C A two-dimensional array representation of the axial maximum intensity projection (MIP) image of the example shown can be shown as follows: 219 119 192 153 209 … 174 236 183 208 217 222 … 250 146 221 74 89 221 … 201 244 155 107 236 195 … 170 168 194 79 149 238 … 231 218 172 235 234 242 … 214 … … … … … … … 176 203 206 202 210 … 166

[0073] The two-dimensional array representation of the maximum intensity projection (MIP) images of the sagittal and coronal planes can be obtained in a similar way. The difference is that the columns of the sagittal plane lattice and the rows of the coronal plane lattice are not 512 pixels, but only 16 pixels (i.e., the number of slice images).

[0074] Figure 6 The main part, left part and bottom part respectively show Figure 3B-3CThe axial MIP image (Axial MIP), sagittal MIP image (Sag MIP) and coronal MIP image (Cor MIP) of the example are shown. The axial MIP image may have a pixel matrix of 512x512, the sagittal MIP image may have a pixel matrix of 16x512, and the coronal MIP image may have a pixel matrix of 512x16. Since the puncture needle is made of high-density metal, the needle portion in the image is highlighted. Such embodiments use maximum density projection to retain the needle information to the greatest extent.

[0075] Fig. 7A A target (a needle in this example) is shown in a conventional axial slice image; Figure 7B Figure 2 shows the target in an axial MIP image. Fig. 7A and Figure 7B The images shown have the same specifications, but the needle target has a larger aspect ratio in the MIP image, which makes it easier to detect the needle target.

[0076] In step 530, the coordinates of the target may be detected in the three orthogonal plane feature projection images respectively to obtain the corresponding projection coordinates of the target in the coordinate systems of the three orthogonal plane feature projection images. In an embodiment of the present invention, the detection of the projection coordinates may be based on one or more methods selected from the group consisting of a threshold segmentation method, a PCA enhancement method, a Gaussian convolution method, a Hessian matrix method, a D-Test method, and an AI method.

[0077] Taking the CT medical imaging system as an example, according to the commonly used coordinate system of CT scanning, the rows and columns of the axial MIP image correspond to the Y axis and X axis respectively, the rows and columns of the sagittal MIP image correspond to the Y axis and Z axis respectively, and the rows and columns of the coronal MIP image correspond to the Z axis and X axis respectively. Accordingly, the projection coordinates of the needle target detected in the axial MIP image can be expressed as (Ax i ,Ay j ), the projection coordinates of the needle target detected in the sagittal MIP image can be expressed as (Sy j ,Sz k ), the projection coordinates of the needle target detected in the coronal MIP image can be expressed as (Cx i ,Cz k ), where i represents the pixel column number of the projection coordinate on the corresponding feature projection image, j represents the pixel row number of the projection coordinate on the corresponding feature projection image, and k represents the pixel row or column number of the projection coordinate on the corresponding feature projection image. In one example, M projection coordinates of the needle target can be detected in the axial MIP image, N projection coordinates of the needle target can be detected in the sagittal MIP image, and O projection coordinates of the needle target can be detected in the coronal MIP image.

[0078] Next, in step 540 , the global coordinates of the target in the global coordinate system of the imaging device may be obtained based on the projection coordinates of the target obtained in step 530 .

[0079] In some embodiments, the projection coordinates can be converted to global coordinates by the following formula (2): Where, ^ represents an estimated value. Where, Represents the component of the global coordinate on the X-axis, Represents the component of the global coordinate on the Y axis, Represents the component of the global coordinate on the Z axis, Ax i Ay represents the component of the projection coordinate on the X-axis related to the XY orthogonal plane feature projection image in the three orthogonal plane feature projection images, j Represents the component of the projection coordinate on the Y axis related to the XY orthogonal plane feature projection image in the three orthogonal plane feature projection images, Sy j Sz represents the component of the projection coordinate on the Y axis related to the YZ orthogonal plane feature projection image in the three orthogonal plane feature projection images. k Cx represents the component of the projection coordinates on the Z axis related to the YZ orthogonal plane feature projection image in the three orthogonal plane feature projection images. i Cz represents the component of the projection coordinate on the X-axis related to the XZ orthogonal plane feature projection image in the three orthogonal plane feature projection images. k Represents the component of the projection coordinates on the Z axis related to the XZ orthogonal plane feature projection image among the three orthogonal plane feature projection images.

[0080] Taking the aforementioned CT medical imaging system as an example, the number of projection coordinates M, N, O detected on the three characteristic projection images is not always equal and is usually unequal, because the lengths of the projections are often unequal. Therefore, in order to facilitate the conversion of two-dimensional projection coordinates into three-dimensional global coordinates, one or more of the projection coordinates on the axial MIP image, the sagittal MIP image, and the coronal MIP image may be interpolated, for example, and all may be divided into 100 equal parts. Thus, the projection coordinates on the axial MIP image, the sagittal MIP image, and the coronal MIP image may be expressed as (Ax l ,Ay l )、(Sy l ,Sz l ) and (Cx l ,Cz l ), where l = 1, 2, 3, ... 100, and the above formula (2) can be changed to the following formula (3):

[0081] In extreme cases, one of the three feature projection images may degenerate to correspond to the periphery of the target. For example, when the trajectory of the needle target is perpendicular or substantially perpendicular to the coronal plane, the coronal MIP image can degenerate into a collection of points corresponding to the outer diameter of the needle target, rather than a linear target. In the case of coronal MIP image degradation, the width-to-length ratio of the area covered by the coronal image lattice is less than 1.414, and the absolute area is equivalent to the cross-sectional area of ​​the detection target. At this time, the component Ax on the X-axis of the projection coordinate corresponding to the axial MIP image is i The component Sz of the projection coordinates corresponding to the sagittal MIP image on the Z axis k In this case, the axial MIP image and the sagittal MIP image can be mainly used to reconstruct the image of the needle target, and the coronal MIP image can be used as an auxiliary.

[0082] For the degenerate X-axis component Ax of the target projection coordinates on the axial plane MIP image i , the degenerate Z-axis component Sz of the target projection coordinates on the sagittal MIP image k , and the components Cx of the target projection coordinates on the degenerate coronal MIP image on the two coordinate axes (X axis and Z axis) i and Cz k , the average value can be used Thus, the above formula (2) for calculating the global coordinates of the target in the global coordinate system of the imaging device can be changed into the following formula (4): Among them, ^ represents the estimated value, and ~ represents the average value. Among them, Represents the component of the global coordinate on the X-axis, Represents the component of the global coordinate on the Y axis, Represents the component of the global coordinate on the Z axis, Ay represents the average value component of the projection coordinates on the X-axis related to the XY orthogonal plane feature projection images in the three orthogonal plane feature projection images, j Represents the component of the projection coordinate on the Y axis related to the XY orthogonal plane feature projection image in the three orthogonal plane feature projection images, Sy j represents the component of the projection coordinates on the Y axis related to the YZ orthogonal plane feature projection image in the three orthogonal plane feature projection images, represents the average value component of the projection coordinates on the Z axis related to the YZ orthogonal plane feature projection images in the three orthogonal plane feature projection images, represents the average value component of the projection coordinates on the X-axis related to the XZ orthogonal plane feature projection images in the three orthogonal plane feature projection images, Represents the average value component on the Z axis of the projection coordinates related to the XZ orthogonal plane feature projection images among the three orthogonal plane feature projection images.

[0083] In addition, for the remaining non-degenerate target projection coordinate axis components Ay j and Sy j , can also be expressed by the interpolation with equal number of points as described above. In this way, the above formula (4) can be changed to the following formula (5):

[0084] Although the method of the present invention is described in the above order, the execution of the method of the present invention should not be limited to the above order. On the contrary, some steps of the method of the present invention can be performed in different orders or simultaneously, or some steps can not be performed in some embodiments. In addition, any step of the method of the present invention can be performed with a module, a unit, a circuit or any other suitable means for performing these steps.

[0085] After the global coordinates of the target are obtained, image reconstruction may be performed based on the global coordinates, for example, by the multi-planar image reconstruction (MPR) described above.

[0086] According to an embodiment of the present invention, a target detection system 900 may also be provided. Fig. 9 The target detection system 900 may be configured to perform the various methods described above. The target detection system may be included in an imaging device, such as Figure 1 and Figure 2 The imaging device in the imaging system 100, 200 shown. The target detection system 900 may include a volatile or non-volatile memory or storage device 920 for storing volumetric image data generated by the imaging device scanning the region of interest of the subject. The volatile memory may include, for example, a random access memory (RAM), such as a static random access memory (SRAM) and a dynamic random access memory (DRAM). The non-volatile memory may include, for example, a hard disk, a floppy disk, an optical disk, a flash memory, etc. The target detection system 900 may further include, for example, Figure 1 and Figure 2 The processor 910 such as the image processor 110 and the image reconstructor 230 shown in the figure can execute various commands. In addition, the target detection system 900 can also include a processor such as Figure 2 A display 930 such as the display 232 is shown for displaying various images, such as the slice images and feature projection images and reconstructed images described herein.

[0087] According to an embodiment of the present invention, a computer readable medium may be further provided, having instructions thereon, which, when executed by a processor, cause the processor to perform the steps of the method of the present invention. The computer readable medium may include, but is not limited to, a non-transient tangible arrangement of an article manufactured or formed by a machine or device, including a storage medium, such as: a hard disk; any other type of disk, including a floppy disk, an optical disk, a CD-ROM, a CD-RW, and a magneto-optical disk; a semiconductor device, such as a read-only memory (ROM), a random access memory (RAM) such as a dynamic random access memory (DRAM) and a static random access memory (SRAM), an erasable programmable read-only memory (EPROM), a flash memory, an electrically erasable programmable read-only memory (EEPROM); a phase change memory (PCM); a magnetic card or an optical card; or any other type of medium suitable for storing electronic instructions. The computer readable medium may be installed in an imaging device, or in a separate control device or computer that remotely controls the imaging device.

[0088] According to an embodiment of the present invention, an imaging device may be further provided. The imaging device includes the aforementioned target detection system of the present invention.

[0089] The techniques described in the present invention may be implemented, at least in part, in hardware, software, firmware, or any combination thereof. For example, aspects of the techniques may be implemented in one or more microprocessors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or any other equivalent integrated or discrete logic circuits, and any combination of such components embodied in a programmer (such as a physician or patient programmer, stimulator, or other device). The terms "processor," "processing circuit," "controller," or "control module" may generally refer to any of the above logic circuits (alone or in combination with other logic circuits), or any other equivalent circuit (and alone or in combination with other digital or analog circuits).

[0090] The following provides examples of various embodiments of the present invention. Various details in the examples can be used in one or more embodiments of the present invention and can be combined with each other to form unique embodiments.

[0091] Example 1 is a target detection method. At least a portion of the target is located in the object under inspection. The target detection method includes: obtaining volume image data generated by scanning the region of interest of the object under inspection by an imaging device; converting the volume image data into a feature projection image, wherein the feature projection image includes three orthogonal plane feature projection images; detecting the coordinates of the target in the three orthogonal plane feature projection images respectively to obtain the corresponding projection coordinates of the target in the respective coordinate systems of the three orthogonal plane feature projection images; and obtaining the global coordinates of the target in the global coordinate system of the imaging device based on the projection coordinates of the target.

[0092] Example 2 includes a target detection method according to Example 1, wherein the target includes a rotating body having two-dimensional features.

[0093] Example 3 includes a target detection method according to Example 1, wherein the target includes an intervening object, a lesion, a bone, an organ tissue structure, and / or a vascular structure.

[0094] Example 4 includes a target detection method according to Example 1, wherein the target includes a needle, an endoscope, an implant, a catheter, a guidewire, a dilator, an ablator, and / or a contrast agent.

[0095] Example 5 includes a target detection method according to Example 1, wherein the feature projection image includes one or more of the following items: a maximum density projection image, an average density projection image, a minimum density projection image, a standard deviation projection image, and a combination of two or more thereof.

[0096] Example 6 includes the target detection method according to Example 1, wherein the three orthogonal planes are the axial plane, the sagittal plane and the coronal plane of the subject.

[0097] Example 7 includes the target detection method according to Example 1, wherein obtaining the global coordinates of the target includes: converting the projection coordinates into the global coordinates by the following formula: in, represents the component of the global coordinate on the X-axis, represents the component of the global coordinate on the Y axis, Represents the component of the global coordinate on the Z axis, Ax i Ay represents the component of the projection coordinates on the X-axis related to the XY orthogonal plane feature projection image in the three orthogonal plane feature projection images, j represents the component of the projection coordinates on the Y axis related to the XY orthogonal plane feature projection image in the three orthogonal plane feature projection images, Sy jSz represents the component of the projection coordinates on the Y axis related to the YZ orthogonal plane feature projection image in the three orthogonal plane feature projection images, k represents the component of the projection coordinates on the Z axis related to the YZ orthogonal plane feature projection image in the three orthogonal plane feature projection images, Cx i represents the component of the projection coordinates on the X-axis related to the XZ orthogonal plane feature projection image in the three orthogonal plane feature projection images, Cz k Represents the component of the projection coordinates on the Z axis related to the XZ orthogonal plane feature projection image among the three orthogonal plane feature projection images.

[0098] Example 8 includes a method according to any one of Examples 1-7, wherein obtaining the global coordinates of the target includes: performing an interpolation operation with equal number of points on one or more projection coordinates of the target in the coordinate system of the three orthogonal plane feature projection images.

[0099] Example 9 includes the target detection method according to Example 8, wherein obtaining the global coordinates of the target includes: performing an averaging operation on one or more of the interpolated projection coordinates.

[0100] Example 10 includes a target detection method according to Example 1, wherein the dot matrix of two of the three orthogonal plane feature projection images is smaller than the dot matrix of the other image.

[0101] Example 11 includes a target detection method according to Example 1, wherein a dot matrix of one of the three orthogonal plane feature projection images corresponds to the periphery of the target.

[0102] Example 12 includes the target detection method according to Example 1, further comprising: performing multi-planar reconstruction based on the global coordinates to display the overall appearance of the target and the region of interest in a reconstructed image.

[0103] Example 13 includes the target detection method according to Example 12, wherein the display includes a trajectory of the target.

[0104] Example 14 is a target detection system. At least a portion of the target is located in the object under inspection. The target detection system includes a memory, which is configured to store volume image data generated by scanning the region of interest of the object under inspection by an imaging device. The target detection system further includes a processor, which is configured to: obtain the volume image data from the memory; convert the volume image data into a feature projection image, the feature projection image including three orthogonal plane feature projection images; detect the coordinates of the target in the three orthogonal plane feature projection images respectively to obtain the corresponding projection coordinates of the target in the respective coordinate systems of the three orthogonal plane feature projection images; and obtain the global coordinates of the target in the global coordinate system of the imaging device based on the projection coordinates of the target. The target detection system also includes a display, which is configured to display a reconstructed image and a feature projection image of the reconstructed image.

[0105] Example 15 includes a target detection system according to Example 14, wherein the target includes a rotating body having two-dimensional features.

[0106] Example 16 includes a target detection system according to Example 14, wherein the target includes an interventional object, a lesion, a bone, an organ tissue structure, and / or a vascular structure.

[0107] Example 17 includes a target detection system according to Example 14, wherein the target includes a needle, an endoscope, an implant, a catheter, a guidewire, a dilator, an ablator, and / or a contrast agent.

[0108] Example 18 includes a target detection system according to Example 14, wherein the feature projection image includes one or more of the following items: a maximum density projection image, an average density projection image, a minimum density projection image, a standard deviation projection image, and a combination of two or more thereof.

[0109] Example 19 is a target detection system according to Example 14, wherein the three orthogonal planes are the axial plane, sagittal plane and coronal plane of the object under examination.

[0110] Example 20 includes the target detection system according to Example 14, wherein obtaining the global coordinates of the target includes: converting the projection coordinates into the global coordinates by the following formula: in, represents the component of the global coordinate on the X-axis, represents the component of the global coordinate on the Y axis, Represents the component of the global coordinate on the Z axis, Ax iAy represents the component of the projection coordinates on the X-axis related to the XY orthogonal plane feature projection image in the three orthogonal plane feature projection images, j represents the component of the projection coordinates on the Y axis related to the XY orthogonal plane feature projection image in the three orthogonal plane feature projection images, Sy j Sz represents the component of the projection coordinates on the Y axis related to the YZ orthogonal plane feature projection image in the three orthogonal plane feature projection images, k represents the component of the projection coordinates on the Z axis related to the YZ orthogonal plane feature projection image in the three orthogonal plane feature projection images, Cx i represents the component of the projection coordinates on the X-axis related to the XZ orthogonal plane feature projection image in the three orthogonal plane feature projection images, Cz k Represents the component of the projection coordinates on the Z axis related to the XZ orthogonal plane feature projection image among the three orthogonal plane feature projection images.

[0111] Example 21 includes a system according to any one of Examples 14-20, wherein obtaining the global coordinates of the target includes: performing an interpolation operation with equal number of points on one or more projection coordinates of the target in the coordinate system of the three orthogonal plane feature projection images.

[0112] Example 22 includes a target detection system according to Example 21, wherein obtaining the global coordinates of the target includes: performing an averaging operation on one or more of the interpolated projection coordinates.

[0113] Example 23 includes a target detection system according to Example 14, wherein the dot matrix of two of the three orthogonal plane feature projection images is smaller than the dot matrix of the other image.

[0114] Example 24 includes a target detection system according to Example 14, wherein the dot matrix of one of the three orthogonal plane feature projection images corresponds to the periphery of the target.

[0115] Example 25 includes the target detection system according to Example 14, further comprising: performing multi-planar reconstruction based on the global coordinates to display the overall appearance of the target and the region of interest in a reconstructed image.

[0116] Example 26 includes a target detection system according to Example 25, wherein the display includes a trajectory of the target.

[0117] Example 27 is a computer-readable medium having instructions stored thereon, which, when executed by a processor, causes the processor to perform the steps of the target detection method as described in any one of Examples 1-13.

[0118] Example 28 is an imaging device comprising a target detection system as described in any of Examples 14-26.

[0119] Some exemplary embodiments of the present invention have been described above. However, it should be understood that various modifications may be made to the exemplary embodiments described above without departing from the spirit and scope of the present invention. For example, if the described techniques are performed in a different order and / or if the components in the described systems, architectures, devices, or circuits are combined in different ways and / or replaced or supplemented by other components or their equivalents, suitable results may be achieved, and accordingly, these modified other embodiments also fall within the scope of protection of the claims.

Claims

1. A target detection method, wherein at least a portion of the target is located in an object to be detected, the target detection method comprising: Obtaining volumetric image data generated by scanning a region of interest of the subject by an imaging device; Converting the volume image data into a feature projection image, wherein the feature projection image includes three orthogonal plane feature projection images; Detecting the coordinates of the target in the three orthogonal plane feature projection images respectively to obtain corresponding projection coordinates of the target in the respective coordinate systems of the three orthogonal plane feature projection images; as well as The global coordinates of the object in the global coordinate system of the imaging device are obtained based on the projection coordinates of the object.

2. The target detection method according to claim 1, characterized in that: The target includes a rotating body having two-dimensional features.

3. The target detection method according to claim 1, characterized in that: The targets include needles, endoscopes, implants, catheters, guidewires, dilators, ablators, and / or contrast agents.

4. The target detection method according to claim 1, characterized in that: The characteristic projection image includes one or more of the following items: a maximum density projection image, an average density projection image, a minimum density projection image, a standard deviation projection image, and a combination of two or more thereof.

5. The target detection method according to claim 1, characterized in that: The three orthogonal planes are the axial plane, the sagittal plane and the coronal plane of the subject.

6. The method according to any one of claims 1 to 5, characterized in that The obtaining of the global coordinates of the target includes: performing an interpolation operation with equal number of points on one or more projection coordinates of the target in the coordinate system of the three orthogonal plane feature projection images.

7. The target detection method according to claim 6, characterized in that: The obtaining of the global coordinates of the target includes: performing an average operation on one or more projection coordinates among the interpolated projection coordinates.

8. The target detection method according to claim 1, further comprising: Multi-planar reconstruction is performed based on the global coordinates to display the entirety of the object and the region of interest in one reconstructed image.

9. The target detection method according to claim 8, characterized in that: The display includes a trajectory of the target.

10. A target detection system, wherein at least a portion of the target is located in an object to be detected, the target detection system comprising: A memory configured to store volume image data generated by scanning a region of interest of the subject by an imaging device; A processor configured to: obtaining the volumetric image data from the memory; Converting the volume image data into a feature projection image, wherein the feature projection image includes three orthogonal plane feature projection images; Detecting the coordinates of the target in the three orthogonal plane feature projection images respectively to obtain corresponding projection coordinates of the target in the respective coordinate systems of the three orthogonal plane feature projection images; as well as Obtaining the global coordinates of the target in the global coordinate system of the imaging device based on the projection coordinates of the target; as well as A display is configured to display the reconstructed image and feature projection images of the reconstructed image.

11. The target detection system according to claim 10, characterized in that: The target includes a rotating body having two-dimensional features.

12. The target detection system according to claim 10, characterized in that: The targets include needles, endoscopes, implants, catheters, guidewires, dilators, ablators, and / or contrast agents.

13. The target detection system according to claim 10, characterized in that: The characteristic projection image includes one or more of the following items: a maximum density projection image, an average density projection image, a minimum density projection image, a standard deviation projection image, and a combination of two or more thereof.

14. The target detection system according to claim 10, characterized in that: The three orthogonal planes are the axial plane, the sagittal plane and the coronal plane of the subject.

15. The system according to any one of claims 10 to 14, characterized in that: The obtaining of the global coordinates of the target includes: performing an interpolation operation with equal number of points on one or more projection coordinates of the target in the coordinate system of the three orthogonal plane feature projection images.

16. The target detection system according to claim 15, characterized in that: The obtaining of the global coordinates of the target includes: performing an average operation on one or more projection coordinates among the interpolated projection coordinates.

17. The target detection system according to claim 10, further comprising: Multi-planar reconstruction is performed based on the global coordinates to display the entirety of the object and the region of interest in one reconstructed image.

18. The target detection system according to claim 17, characterized in that: The display includes a trajectory of the target.

19. A computer-readable medium having instructions stored thereon, wherein when the instructions are executed by a processor, the processor is caused to perform the steps of the target detection method according to any one of claims 1 to 9.

20. An imaging device comprising the target detection system according to any one of claims 10-18.

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