CN109191541B

CN109191541B - CT remote image reconstruction method

Info

Publication number: CN109191541B
Application number: CN201810883426.4A
Authority: CN
Inventors: 任毅
Original assignee: Shenyang Shengnuo Technology Co ltd
Current assignee: Shenyang Shengnuo Technology Co ltd
Priority date: 2018-08-06
Filing date: 2018-08-06
Publication date: 2023-08-25
Anticipated expiration: 2038-08-06
Also published as: CN109191541A

Abstract

The embodiment of the application relates to a CT remote image reconstruction method, which comprises the following steps: acquiring CT image diagnosis data; dividing the diagnostic data into two categories, namely raw data and compressed data; sending the original data to a cloud; transmitting the compressed data to a local area for image reconstruction, and judging the locally reconstructed image in real time; and sending a corresponding instruction to the cloud according to the judgment result. The application provides a method for reducing CT use cost, which is characterized in that data scanned by a CT machine are transmitted to a cloud server through a network, image reconstruction is carried out by means of a cloud, intelligent diagnosis is carried out on images by means of cloud big data, and results are fed back to a hospital. The method combines big data and cloud computing to image and intelligently diagnose CT data, reduces the computer cost of a workstation of the CT machine, reduces the dependence on doctor diagnosis and promotes the popularization of the CT machine.

Description

CT remote image reconstruction method

Technical Field

The application relates to the technical field of medical computed tomography, in particular to a CT remote image reconstruction method.

Background

The traditional CT machine data acquisition and image creation are completed by the cooperation of scanning hardware equipment and a workstation computer of a manufacturer. The process does not need to be linked with an external network, and the data acquisition and imaging process is completely closed and only depends on the performance of hardware resources. And at the same time, the doctor in the home is required to perform image diagnosis.

The traditional CT machine data acquisition and imaging process is as follows: the data received from the detector is sent to the fixed end of the rack through the slip ring and then sent to the image-forming machine through the Ethernet, the image-forming machine builds the image and sends the image to the console computer operated by the doctor, or the image-forming machine and the console computer are one machine, and then the image is directly displayed on the machine for diagnosis of the doctor.

On the one hand, the traditional method needs scarce doctor resources to carry out image diagnosis; on the other hand, in order to increase the imaging speed, a high-end imaging workstation computer is required, and the hardware cost is high. Both of the above aspects restrict the popularization of CT machines.

Therefore, there is a need to develop a new method for reconstructing a CT remote image to solve one of the above-mentioned problems.

Disclosure of Invention

The embodiment of the application provides a CT remote image reconstruction method, which aims to solve the technical problems of high cost and complex structure of the existing CT image reconstruction method.

In one aspect, the embodiment of the application provides a method for reconstructing a CT remote image, which comprises the following steps: acquiring CT image diagnosis data; dividing the diagnostic data into two types, namely raw data and compressed data, wherein the compression ratio of the compressed data is based on the fact that a scanning position can be determined; sending the original data to a cloud; transmitting the compressed data to a local area for image reconstruction, and judging the locally reconstructed image in real time; and sending a corresponding instruction to the cloud according to the judgment result.

Further, the client comprises a local area network terminal or an internet mobile terminal.

Further, the step of sending the image reconstruction instruction to the cloud end according to the determination result includes: judging whether the built image is a required image or not according to the local reconstructed image; if yes, sending an image reconstruction starting instruction to the cloud; and if not, sending a correction instruction to the cloud.

Further, the step of "if not, sending the correction instruction to the cloud" includes: transmitting a correction instruction generated according to the compensation algorithm to the cloud; and the cloud server reconstructs an image after correcting the received data according to the correction instruction.

Further, after the step of sending the corresponding instruction to the cloud according to the determination result, the method includes: the cloud server performs intelligent diagnosis by adopting big data and feeds the diagnosis result back to the user; and/or, intelligently adjusting the imaging protocol through big data, and reconstructing an image based on the adjusted optimal imaging protocol; and/or feeding back the optimized scanning and imaging parameters to the local client, and improving the quality of the diagnosis data.

In a second aspect, the present application provides a method for reconstructing a CT remote image, including the steps of: acquiring CT image diagnosis data; sending the diagnosis data to a cloud; receiving the image returned by the cloud in real time, and judging the quality of the received image; and sending a corresponding instruction to the cloud according to the judgment result.

Further, the step of sending the image reconstruction instruction to the cloud end according to the determination result includes: judging whether the built image is a required image or not according to the received cloud image; if yes, sending an instruction for continuing image reconstruction to the cloud; and if not, sending a correction instruction to the cloud.

In a third aspect, the present application provides a method for reconstructing a CT remote image, including the steps of: the cloud server acquires CT image diagnosis data; forming a reconstructed image in real time according to the diagnostic data; transmitting the reconstructed image to a local server, and carrying out image correction according to an instruction transmitted by the local server; the cloud server performs intelligent diagnosis by adopting big data and feeds the diagnosis result back to the user; and/or, intelligently adjusting the imaging protocol through big data, and reconstructing an image based on the adjusted optimal imaging protocol; and/or feeding back the optimized scanning and imaging parameters to the local client, and improving the quality of the diagnosis data.

Compared with the prior art, the application has the following technical effects:

according to the method for reducing the CT use cost, data scanned by the CT machine are transmitted to the cloud server through the network, on one hand, the original data are uploaded to the cloud end, the original data are analyzed through big data, and the scanning protocol is optimized, so that the CT of the local end is more and more intelligent, on the other hand, image reconstruction is carried out by means of the cloud end, intelligent diagnosis is carried out on images by means of the big data of the cloud end, optimal scanning parameters can be intelligently set, and unnecessary dose increase is avoided. Meanwhile, the imaging protocol can be intelligently adjusted through big data, more image data based on different protocols which can be used for diagnosis are obtained, and therefore the correct probability of intelligent diagnosis is increased. The method combines big data and cloud computing to carry out imaging and intelligent diagnosis on CT data, reduces the computer cost of a CT machine workstation, reduces the dependence on doctor diagnosis and promotes popularization of CT machines.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly described below, it will be apparent that the drawings in the following description are only some embodiments of the present application, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a schematic structural diagram of a CT remote image reconstruction method according to a first embodiment of the present application;

FIG. 2 is a flow chart of a method for reconstructing a CT remote image according to a first embodiment of the present application;

FIG. 3 is a schematic structural diagram of a CT remote image reconstruction method according to a second embodiment of the present application;

FIG. 4 is a flow chart of a CT remote image reconstruction method according to a second embodiment of the present application;

FIG. 5 is a flow chart of a CT remote image reconstruction method according to a third embodiment of the present application;

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described in further detail below with reference to the accompanying drawings, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

The terminology used in the embodiments of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise, the "plurality" generally includes at least two.

It should be understood that the term "and/or" as used herein is merely one relationship describing the association of the associated objects, meaning that there may be three relationships, e.g., a and/or B, may represent: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.

It should be understood that although the terms first, second, third, etc. may be used to describe … … in embodiments of the present application, these … … should not be limited to these terms. These terms are only used to distinguish … …. For example, the first … … may also be referred to as the second … …, and similarly the second … … may also be referred to as the first … …, without departing from the scope of embodiments of the present application.

The words "if", as used herein, may be interpreted as "at … …" or "at … …" or "in response to a determination" or "in response to a detection", depending on the context. Similarly, the phrase "if determined" or "if detected (stated condition or event)" may be interpreted as "when determined" or "in response to determination" or "when detected (stated condition or event)" or "in response to detection (stated condition or event), depending on the context.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a product or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such product or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a commodity or system comprising such elements.

Preferred embodiments of the present application will be described in detail below with reference to the accompanying drawings.

Example 1

As shown in fig. 1-2, the method for reconstructing a CT remote image provided by the embodiment of the present application includes the following steps:

s100: CT image diagnostic data is acquired.

Specifically, the CT machine scans the patient in real time and outputs scanning data in a wired or wireless mode.

S101: the diagnostic data is classified into two types, raw data and compressed data.

The data is directly transmitted through wireless route or wired broadband after being collected, and the other route is compressed and then transmitted to a local imaging computer.

Preferably, the compressed data compression ratio is based on the ability to determine the scan location. For example, the head may be scanned to see the outline of the head shape, and the leg may be scanned to see the outline of the entire leg. The preferred data compression ratio is 8:1.

S102: and sending the original data to a cloud.

For example, the data of a CT gantry scan can be split into two process transmissions. One path of original data is transmitted to the cloud end in a wired or wireless mode. Preferably, the transmission is performed by wire, and in this case, a transmission system having a large transmission data amount and a wide bandwidth is required.

S103: and sending the compressed data to the local area for image reconstruction, and judging the locally reconstructed image in real time.

The other way, the original raw data is compressed, for example, the following steps (8-20): 1 is transmitted to a local data server, and real-time imaging is carried out on the local data server, so that a doctor can check whether the currently scanned data contains the wanted human body organ.

The reconstructed preview image after compression can be transmitted to the hands of any remote professional staff with a mobile internet browsing terminal without being judged by a local operation technician, and the technician confirms whether the final imaging protocol is reasonable or not, so that the dependence on the professionality of the local radiological technician is reduced. Further promote the popularization and the use of CT machines.

S104: and sending a corresponding instruction to the cloud according to the judgment result.

After the doctor determines various conditions of the imaging protocol, the doctor starts the main imaging. The remote server starts image reconstruction.

Specifically, judging whether the built image is a required image or not according to the local reconstructed image; if yes, sending an image reconstruction starting instruction to the cloud; and if not, sending a correction instruction to the cloud.

Further, the step of "if not, sending the correction instruction to the cloud" includes: transmitting a correction instruction generated according to the compensation algorithm to the cloud; and the cloud server reconstructs an image after correcting the received data according to the correction instruction. The compensation algorithm can extract according to the characteristic value of the part which is displayed by scanning, and symmetrically compensates the part which is not displayed, for example, the scanning of the head is only half of the scanning, and then the image correction can be carried out through the data compensation algorithm. The corresponding algorithm is not excessively limited, and the basic requirement is that the corresponding all scanned images can be obtained.

Further, after the step of sending the corresponding instruction to the cloud according to the determination result, the method includes: and the cloud server performs intelligent diagnosis by adopting big data and feeds the diagnosis result back to the user. And/or, intelligently adjusting the imaging protocol through big data, and reconstructing an image based on the adjusted optimal imaging protocol; and/or feeding back the optimized scanning and imaging parameters to the local client, and improving the quality of the diagnosis data.

The intelligent diagnosis comprises the steps of grabbing corresponding similar case diagnosis and treatment schemes according to image analysis results and intelligently analyzing according to the big data, and selecting an optimal scheme to feed back to a user, wherein the user can be a doctor of a hospital or the patient.

Example 2

As will be appreciated with reference to fig. 3-4, the present application provides a method for reconstructing a CT remote image, including the following steps:

s201: CT image diagnostic data is acquired.

S202: and sending the diagnosis data to a cloud.

At this time, the original data scanned by the CT gantry is directly transmitted to the cloud end in a wired or wireless manner. Preferably, the transmission is performed by wire, and in this case, a transmission system having a large transmission data amount and a wide bandwidth is required.

S203: receiving the image returned by the cloud in real time, and judging the quality of the received image

Specifically, after the cloud is initially built, the image is returned to a local check to determine whether the scanning data is accurate or not and whether the scanning data is the whole outline of the part to be scanned or not. In the judging process, the cloud can receive data, but can pause reconstructing the image, and the follow-up task is carried out after the local instruction is received.

The returned preview image can be transmitted to the hands of local radiological technicians, or to the hands of any remote professional technician holding a mobile internet browsing terminal (such as a mobile phone, a computer, a PAD, etc.), and the technician confirms whether the final imaging protocol is reasonable, thereby reducing the dependence on the professionals of the radiological technicians. Further promote the popularization and the use of CT machines.

S204: and sending a corresponding instruction to the cloud according to the judgment result.

Specifically, the step of sending the image reconstruction instruction to the cloud end according to the judgment result includes: judging whether the built image is a required image or not according to the received cloud image; if yes, sending an instruction for continuing image reconstruction to the cloud; and if not, sending a correction instruction to the cloud.

Example 3

With reference to fig. 5, the present application further provides a method for reconstructing a CT remote image, including the following steps:

s301: and the cloud server acquires CT image diagnosis data.

The cloud server acquires the uploaded data in a wired or wireless mode and stores the data. The cloud server may be centralized or distributed.

S302: and forming a reconstructed image in real time according to the diagnosis data.

The cloud end adopts the cloud reconstruction image server to reconstruct images at the cloud end, so that hardware equipment for locally storing data and reconstructing images is saved, and the mobility of data sharing is improved.

S303: and sending the reconstructed image to a local server, and carrying out image correction according to an instruction sent by the local server.

And generating a preview image in real time, sending the preview image to the local of the CT machine for a doctor to preview, and making whether final imaging is carried out or correction is carried out according to the preview structure.

S304: and the cloud server performs intelligent diagnosis by adopting big data and feeds the diagnosis result back to the user. And/or, intelligently adjusting the imaging protocol through big data, and reconstructing an image based on the adjusted optimal imaging protocol; and/or feeding back the optimized scanning and imaging parameters to the local client, and improving the quality of the diagnosis data.

The apparatus embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separate. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for reconstructing a CT remote image, comprising the steps of:

acquiring CT image diagnosis data;

dividing the diagnostic data into two types, namely raw data and compressed data, wherein the compression ratio of the compressed data is based on the fact that a scanning position can be determined;

sending the original data to a cloud;

the compressed data is sent to a client for local image reconstruction, and real-time judgment is carried out on the local reconstructed image;

sending a corresponding instruction to the cloud according to the judgment result;

the sending the corresponding instruction to the cloud according to the judging result comprises the following steps:

judging whether the built image is a required image or not according to the local reconstructed image;

if yes, sending an image reconstruction starting instruction to the cloud;

if not, a correction instruction generated according to a compensation algorithm is sent to the cloud end, so that the cloud end server can reconstruct an image after correcting the received data according to the correction instruction, wherein the compensation algorithm extracts according to the characteristic value of the part which is displayed by scanning, and symmetrically compensates the part which is not displayed.

2. The CT remote image reconstruction method as recited in claim 1, wherein: the client comprises a local area network terminal or an Internet mobile terminal.

3. The CT remote image reconstruction method as recited in any one of claims 1-2, wherein: the step of sending the corresponding instruction to the cloud according to the judgment result comprises the following steps:

the cloud server performs intelligent diagnosis by adopting big data and feeds the diagnosis result back to the user; and/or the number of the groups of groups,

the image-building protocol is intelligently adjusted through big data, and image reconstruction is carried out based on the adjusted optimal image-building protocol; and/or the number of the groups of groups,

and the optimized scanning and imaging parameters are fed back to the local client, so that the quality of the diagnosis data is improved.