A middle ear canal scanning system for CT contrast imaging

Abstract

The present invention provides a middle ear canal scanning system for CT contrast imaging, including a scanning device and an ear canal scanning system. The scanning device includes a 3D ear canal scanner and a CT scanning device. The ear canal scanning system includes a scanning assistance module, an imaging module, an image management module, a contrast module, and an abnormality recording module. Through the CT ear canal scanning system and the scanning assistance module, the patient is assisted in the positioning work before scanning, and then the scanning range is adjusted to limit the scanning range, solving the problem that the contrast imaging range is large, resulting in a high dose and lacking the function of accurately limiting the scanning range. The present invention can also combine the model image obtained by the 3D ear canal scanner and the imaging of the CT device contrast through the imaging management module to obtain multi-dimensional medical images and mark the images through the abnormality recording module to obtain medical images with clear marks, making the viewing of the images more intuitive, and at the same time saving the time for doctors to judge medical images.

Description

Technical Field

[0001] The present invention belongs to the technical field of medical imaging, and particularly relates to a middle ear canal scanning system for CT contrast. Background Art

[0002] Nowadays, the demand for ear canal scanning is increasing. Ear canal CT can generally be performed by plain scan. Patients need to first undergo a clinical examination of the ear and accurately describe their own clinical characteristics to the doctor. After the doctor's preliminary examination, it is determined whether ear canal CT examination is needed. During the examination, the patient cooperates with the doctor to adopt a suitable examination posture and angle. During the process of taking pictures and imaging, the head and body cannot be shaken randomly. In the prior art, during the CT contrast process of the ear canal, the contrast range is large, resulting in a high dose, lacking the function of accurately limiting the scanning range, and the scanning of the ear canal, especially the middle ear canal, requires a more precise range to output more accurate images. Therefore, there is an urgent need for a middle ear canal scanning system for CT contrast. Summary of the Invention

[0003] Therefore, the present invention provides a middle ear canal scanning system for CT contrast, which realizes slice scanning imaging within the ear canal range and obtains more accurate medical image data.

[0004] In a first aspect of the present invention, a middle ear canal scanning system for CT contrast is provided, including a scanning device and an ear canal scanning system;

[0005] The scanning device is configured to include a 3D ear canal scanner and a CT scanning device;

[0006] The ear canal scanning system includes a scanning assistance module, an imaging module, an image management module, a comparison module, and an abnormality recording module;

[0007] The scanning assistance module is configured to control the scanning angle, scanning depth, and contrast time of the CT scanning device, store the set scanning angle, scanning depth, and contrast time in each scan, and generate and store a configuration data set;

[0008] The imaging module is configured to obtain and visualize the spatial model data of the 3D ear canal scanner, and at the same time obtain multiple groups of cross-sectional medical images of the CT scanning device, and construct a medical image set including spatial model data and cross-sectional medical images;

[0009] The image management module is configured to store the medical image set, classify and sort it, and store it in an image storage library;

[0010] The comparison module is configured to obtain historical images and the newly scanned medical image set from a medical image library, and obtain image change data after comparison;

[0011] Anomaly recording module, configured to analyze a medical image set and mark the anomaly positions on the medical images.

[0012] As a preferred manner, the scanning assistance module includes a scanning range adjustment unit and a scanning positioning unit;

[0013] The scanning range adjustment unit is configured to control the scanning angle, scanning depth, and contrast time of the CT scanning device;

[0014] The scanning positioning unit is configured to configure and store the scanning points of the CT scanning device, as well as their scanning angles and scanning depths, according to the acquired spatial model data.

[0015] As a further preferred manner, the scanning assistance module is configured to perform the following steps:

[0016] Obtain spatial model data from a 3D ear canal scanner;

[0017] Obtain the distance data between the first position and the second position specified in the ear canal in the spatial model data, where the first position and the second position are the two extreme positions in the same section plane within the first region;

[0018] Configure the CT scanning device to use the first position as the scanning depth, set multiple scanning points in the first direction of the first region, and perform scans on each of the multiple section planes formed from the first position to the second position at each scanning point;

[0019] Configure the CT scanning device to gradually scan from the first position as the reference position to the second position in the direction of the second position;

[0020] Configure the CT scanning device to gradually scan to a preset depth range after scanning to the second position.

[0021] As a further preferred manner, the imaging module includes a model imaging unit and a contrast imaging unit;

[0022] The model imaging unit is configured to obtain the visualization conversion of the spatial model data from a 3D ear canal scanner through ContextCapture;

[0023] The contrast imaging unit is configured to obtain a medical image set by merging and reconstructing the sectional medical images of the CT scanning device;

[0024] As a further preferred manner, the imaging module is configured to perform the following steps, S1: Obtain the sectional image when any of the sampling points scans to the first position;

[0025] S2. Obtain a plurality of consecutive cross-sectional images obtained by scanning the sampling point from the first position in the first direction to the second direction, and label them as the first cross-sectional image to the Nth cross-sectional image;

[0026] S3. Obtain a plurality of consecutive cross-sectional images at each progressive position when the sampling point progressively scans from the first position to the second position, and perform the steps of S2 at each said progressive position;

[0027] S4. Obtain the cross-sectional images obtained when adjacent sampling points of one said sampling point;

[0028] S5. Traverse each sampling point and repeat the execution of S1 - S4 to obtain a medical image set;

[0029] S6. Obtain the ear canal image and the ear canal blood vessel image obtained by fitting and closing on the gray continuous threshold of each cross-sectional image;

[0030] S7. Write the medical image set into the spatial model data.

[0031] As a preferred mode, the image management module includes an image storage library responsible for storing the medical image set; an image management unit responsible for classifying and sorting the medical images in the medical image set.

[0032] As a preferred mode, the comparison module includes an image acquisition unit responsible for acquiring historical medical images and newly generated medical images from the graphic storage library; a historical image comparison unit for comparing the historical medical images and the new medical images to obtain image change data.

[0033] As a preferred mode, the abnormal record module includes an abnormal marking unit responsible for analyzing the medical images and marking the abnormal positions on the medical images; a marking management unit responsible for managing the markings on the medical images.

[0034] As a preferred mode, the image management unit has the functions of image classification and image sorting.

[0035] As a preferred mode, the abnormal marking unit has the functions of manual marking and machine intelligent marking.

[0036] The above technical solutions of the present invention have the following advantages compared with the prior art:

[0037] Through the CT ear canal scanning system of the present invention, with the aid of a scanning assistance module, the patient is assisted in the positioning work before scanning, and then the scanning range is adjusted to limit the scanning range, solving the problems of a large contrast range, resulting in a high dose, and lacking the function of accurately limiting the scanning range. The present invention can also combine the model image obtained by the 3D ear canal scanner and the imaging of the CT device contrast through the imaging management module to obtain multi-dimensional medical images and mark the images through the abnormal recording module to obtain medical images with clear markings, making the viewing of the images more intuitive, and at the same time saving the time for doctors to judge medical images. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a schematic structural diagram of the system provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0040] An embodiment of the present disclosure provides a middle ear canal scanning system for CT contrast, as Figure 1 shown, including a scanning device and an ear canal scanning system.

[0041] The scanning device is configured to include a 3D ear canal scanner and a CT scanning device;

[0042] The ear canal scanning system includes a scanning assistance module, an imaging module, an image management module, a comparison module, and an abnormal recording module;

[0043] The scanning assistance module is configured to control the scanning angle, scanning depth, and contrast time of the CT scanning device, store the scanning angle, scanning depth, and contrast time set in each scan, and generate and store a configuration data set;

[0044] The imaging module is configured to obtain and visualize the spatial model data of the 3D ear canal scanner, and at the same time obtain multiple groups of sectional medical images of the CT scanning device to construct a medical image set including spatial model data and sectional medical images;

[0045] The image management module is configured to store the medical image set, classify and sort it, and store it in an image storage library;

[0046] A comparison module, configured to obtain historical images and a newly scanned set of medical images from a medical image library, and obtain image change data after comparison;

[0047] An anomaly recording module, configured to analyze the set of medical images and mark the anomaly locations on the medical images.

[0048] As a further preferred embodiment, the scanning assistance module includes a scanning range adjustment unit and a scanning positioning unit;

[0049] The scanning range adjustment unit is configured to control the scanning angle, scanning depth, and contrast time of the CT scanning device;

[0050] The scanning positioning unit is configured to configure and store the scanning points of the CT scanning device, as well as their scanning angles and scanning depths, based on the obtained spatial model data.

[0051] As a preferred embodiment, the scanning assistance module includes a scanning range adjustment unit and a scanning positioning unit;

[0052] The scanning range adjustment unit is configured to control the scanning angle, scanning depth, and contrast time of the CT scanning device;

[0053] The scanning positioning unit is configured to configure and store the scanning points of the CT scanning device, as well as their scanning angles and scanning depths, based on the obtained spatial model data.

[0054] As a further preferred embodiment, the imaging module includes a model imaging unit and a contrast imaging unit;

[0055] The model imaging unit is configured to obtain spatial model data from a 3D ear canal scanner and perform visual conversion through ContextCapture;

[0056] The contrast imaging unit is configured to obtain sectional medical images of the CT scanning device and obtain a set of medical images through combined reconstruction

[0057] As a preferred embodiment, the image management module includes an image storage library responsible for storing the set of medical images; and an image management unit responsible for classifying and sorting the medical images in the set of medical images.

[0058] As a preferred embodiment, the comparison module includes an image acquisition unit responsible for obtaining historical medical images and newly generated medical images from the graphic storage library; and a historical image comparison unit for comparing the historical medical images and the new medical images to obtain image change data.

[0059] As a preferred mode, the abnormal record module includes an abnormal marking unit, which is responsible for analyzing medical images and marking the abnormal positions on the medical images; and a marking management unit, which is responsible for managing the markings on the medical images.

[0060] As a preferred mode, the image management unit has the functions of image classification and image sorting.

[0061] As a preferred mode, the abnormal marking unit has the functions of manual marking and machine intelligent marking.

[0062] In the embodiments of the present disclosure, the working principles of both are basically explained respectively.

[0063] The 3D ear canal scanner slowly enters the patient's ear canal for the first scan, outputs a 3D model of the ear canal, and then obtains the limit widths of the cross-sectional positions of the ear canal in the 3D model. Specifically, it includes the following steps:

[0064] From the spatial model data XYZ scanned by the 3D ear canal scanner, obtain the maximum value Z max and the minimum value Z min of Z among them, and the difference is the scanning distance of the CT device;

[0065] S2: Extract Z max from the spatial model data obtained in S1. Taking the Z max point as the center point, create a cross line, and then create a plane A. Then, taking the Z max point as the center point of the plane A, connect the Z min point to obtain a straight line Z max - Z min. Make the plane A perpendicular to the straight line Z max - Z min, and the angle between the plane A and the horizontal plane is used as the data basis for the CT scanning angle.

[0066] As a further preferred mode, the scanning assistance module is configured to perform the following steps:

[0067] Obtain the spatial model data from the 3D ear canal scanner;

[0068] Obtain the distance data of the first position and the second position specified in the ear canal in the spatial model data, where the first position and the second position are the two limit positions in the same cross-section in the first region;

[0069] Configure the CT scanning device to use the first position as the scanning depth, set multiple scanning points in the first direction of the first region, and perform scans on each of the multiple cross-sections formed from the first position to the second position at each scanning point;

[0070] Configure the CT scanning device to use the first position as the reference position and gradually scan in the direction of the second position until reaching the second position;

[0071] After configuring the CT scanning device to scan to the second position, gradually scan to a preset depth range.

[0072] In the embodiments of the present disclosure, the 3D point cloud spatial data obtained by the 3D ear canal scanner of the model imaging unit is visually transformed through Context Capture.

[0073] The contrast imaging unit combines the CT device scan images and the 3D visualization model.

[0074] As a further preferred method, the imaging module is configured to perform the following steps: S1, obtain a sectional image when any one of the sampling points scans to the first position;

[0075] S2, obtain a plurality of consecutive sectional images obtained by scanning the sampling point from the first direction to the second direction at the first position, and label them as the first sectional image to the Nth sectional image;

[0076] S3, obtain a plurality of consecutive sectional images of each progressive position when the sampling point gradually scans from the first position to the second position, and perform the steps of S2 at each progressive position;

[0077] S4, obtain a sectional image when the adjacent sampling point of one sampling point is obtained;

[0078] S5, traverse each sampling point and repeat S1 - S4 to obtain a medical image set;

[0079] S6, obtain an ear canal image and an ear canal blood vessel image obtained by fitting and closing on the gray - level continuous threshold of each sectional image;

[0080] S7, write the medical image set into the spatial model data.

[0081] The image management unit has image classification and image sorting functions, classifies according to the scanning part of the image, and sorts according to the generation time of the medical image. The classification method is as follows:

[0082] S1: Data processing: Process the medical images into tensors as needed, randomly form batches, normalize, standardize, and perform some data enhancement effects;

[0083] S2: Model definition: Define a Res Net model, design the number of network layers and the categories of layers according to the medical images in the image repository, including the settings of convolution, pooling, batch normalization, and activation functions;

[0084] S3: Parameter update: Generate a model object according to the medical images in the image repository, initialize these layers, accept batch data, and update the medical image model parameters in the object according to the defined optimization method.

[0085] S4: Model Selection: Each time after the parameters are updated, test whether the model meets the conditions. If it meets, stop the algorithm; otherwise, continue.

[0086] In the embodiment of the present disclosure, the abnormal marking unit has an artificial marking function and a machine intelligence marking function. Among them, the steps of machine intelligence marking are as follows:

[0087] S10: Machine Learning: Train the machine through deep learning to perform more standard medical image marking work;

[0088] S20: Anomaly Detection: Based on the density-based anomaly detection method, the data points in the high-density area of the medical image are regarded as anomaly points;

[0089] S30: Abnormal Region Marking: Divide the abnormal region according to the range where the anomaly points gather,

[0090] S40: Anomaly Point Remarks: Add text remarks to each mark.

[0091] In the embodiment of the present disclosure, the 3D ear canal scanner slowly enters the patient's ear canal to perform a preliminary scan on the patient's ear canal, and outputs a 3D model of the patient's ear canal. The 3D model data of the ear canal is input into the CT ear canal scanning system. The scanning auxiliary module analyzes the 3D model data to obtain the widest range of the model, and uses this as the data basis to obtain the width of the ear canal scan. Based on this data, the scanning distance is determined. The imaging module integrates the scanned medical image and the 3D ear canal model to obtain a visual model and modeling data, which are saved in the image storage library. The image management unit sorts and classifies the medical images, classifies the images based on the dense point distribution area in the medical images, and sorts them based on the difference in the comparison results of the medical images to obtain the medical images that need to be prioritized for attention, so as to assist the doctor in deciding the treatment plan. By retrieving and comparing the historical medical images in the image storage library through the comparison module, intuitive comparative medical images are obtained. Through the abnormal record module, the anomaly points on the image are marked based on machine intelligence marking. After the intelligent marking machine based on deep learning establishes a model, it is continuously updated to gradually enhance the accuracy of machine intelligence marking. The marked medical images can be edited and remarked through the marking management unit. The doctor can also perform manual marking operations through the marking management unit. The marked image that meets the actual application standard can be provided to the intelligent marking machine as a model for deep learning.

[0092] And in the embodiment of the present disclosure, the medical images obtained by the present invention are multi-dimensional images, that is, including three-dimensional images and two-dimensional images, and also including multi-dimensional images formed by the combination of three-dimensional images and two-dimensional images. The working process of generating multi-dimensional images is as follows:

[0093] The 3D model data is analyzed by a scanning assistance module to obtain the widest range of the model. Based on this data, the width of the ear canal scan is obtained, and based on this data, the scanning distance is determined. The medical images obtained by scanning and the 3D ear canal model are integrated by an imaging module. The combination method is as follows:

[0094] The combination of the stereoscopic image and the planar image is achieved by obtaining a channel map and a depth map on the planar image, smoothing the stereoscopic image model while establishing a more abundant model, implanting and matching the planar image, and using it as a detailed drawing of a specific part of the model to obtain a visualized model and modeling data, which are saved in an image storage library.

[0095] The above description and the drawings fully illustrate the embodiments of the present disclosure so that those skilled in the art can practice them. Other embodiments may include structural, logical, electrical, process, and other changes. The embodiments only represent possible variations. Unless explicitly required, the individual components and functions are optional, and the order of operations can vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. Moreover, the terms used in this application are only for describing the embodiments and are not used to limit the claims. As used in the description of the embodiments and the claims, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are intended to also include the plural forms. Similarly, as used in this application, the term "and / or" means any and all possible combinations including one or more of the associated listed items. Additionally, when used in this application, the term "comprise" and its variants "comprises" and / or "comprising" etc. mean the presence of the stated features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or groups of these. Without further limitation, an element defined by the statement "comprising one..." does not exclude the presence of additional identical elements in the process, method, or device including the element. In this article, each embodiment may focus on the differences from other embodiments, and the same or similar parts between the embodiments may be referred to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method part disclosed in the embodiments, the relevant parts may refer to the description of the method part.

[0096] Those skilled in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner can depend on the specific application and design constraints of the technical solution. The skilled person can use different methods for each specific application to achieve the described functions, but such implementation should not be considered to exceed the scope of the embodiments of the present disclosure. The skilled person can clearly understand that for the convenience and conciseness of description, the specific working processes of the above-described devices, devices, and units can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0097] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of the devices, methods, and computer program products according to the embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions marked in the block may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. Each block in the block diagram and / or flowchart, as well as the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware device for performing the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

Claims

1. A middle ear canal scanning system for CT angiography, characterized in that: Includes a scanning device and an ear canal scanning system; The scanning device is configured to include a 3D ear canal scanner and a CT scanning device; The ear canal scanning system includes a scanning auxiliary module, an imaging module, an image management module, a comparison module and an abnormality recording module; The scanning auxiliary module is configured to control the scanning angle, scanning depth and contrast time of the CT scanning device, store the scanning angle, scanning depth and contrast time set in each scan, and generate and store a configuration data set; The scanning auxiliary module includes a scanning range adjustment unit and a scanning positioning unit; The scanning range adjustment unit is configured to control the scanning angle, scanning depth and imaging time of the CT scanning device; The scanning positioning unit is configured to configure and store the scanning points, scanning angles and scanning depths of the CT scanning device according to the acquired spatial model data; The scanning auxiliary module is configured to perform the following steps: Acquire spatial model data from a 3D ear canal scanner; Acquire distance data of a first position and a second position specified in the ear canal in the spatial model data, wherein the first position and the second position are two extreme positions in the same cutting plane in the first region; The CT scanning device is configured to use the first position as a scanning depth, to set a plurality of scanning points in a first direction of the first area, and to perform scanning respectively on a plurality of sections formed by each scanning point from the first position to the second position; The CT scanning device is configured to use the first position as a reference position and progressively scan toward the second position until reaching the second position; After configuring the CT scanning device to scan to the second position, it progressively scans to a preset depth interval; The imaging module is configured to obtain and visualize the spatial model data of the 3D ear canal scanner, and simultaneously obtain multiple sets of cross-sectional medical images of the CT scanning device, and construct a medical image set including the spatial model data and the cross-sectional medical images; The image management module is configured to store, classify and sort the medical image set and store it in a medical imaging library; A comparison module is configured to obtain historical images and a newly scanned medical image set from a medical image library, and obtain image change data after comparison; The abnormality recording module is configured to analyze the medical image set and mark abnormal locations on the medical images.

2. The middle auditory canal scanning system for CT angiography according to claim 1, characterized in that: The imaging module includes a model imaging unit and a contrast imaging unit; The model imaging unit is configured to obtain spatial model data from a 3D ear canal scanner and perform visualization conversion via ContextCapture; The contrast imaging unit is configured to obtain the cross-sectional medical images of the CT scanning device and obtain a medical image set by merging and reconstructing them.

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