阅读说明：本技术 一种灌注分析方法及系统 (Perfusion analysis method and system ) 是由 赵小芬 李阳 郭慧文 于 2021-09-14 设计创作，主要内容包括：本说明书实施例提供一种灌注分析方法及系统。灌注分析方法包括：获取多个时间点的灌注扫描数据；基于灌注扫描数据,确定各个时间点的运动幅度是否大于预设幅度；当存在第一时间点的运动幅度大于预设幅度时,根据第一时间点所处的灌注阶段确定是否剔除第一时间点的数据。(The embodiment of the specification provides a perfusion analysis method and system. The perfusion analysis method comprises the following steps: acquiring perfusion scan data of a plurality of time points; determining whether the motion amplitude of each time point is greater than a preset amplitude based on the perfusion scan data; and when the motion amplitude of the first time point is larger than the preset amplitude, determining whether the data of the first time point are rejected according to the perfusion stage where the first time point is located.)

1. A perfusion analysis method, comprising:

acquiring perfusion scan data of a plurality of time points;

determining whether the motion amplitude of each time point is greater than a preset amplitude based on the perfusion scan data;

and when the motion amplitude of the first time point is larger than the preset amplitude, determining whether the data of the first time point are rejected according to the perfusion stage where the first time point is located.

2. A perfusion analysis method according to claim 1, wherein the perfusion scan data comprises a perfusion scan image, and the determining whether the amplitude of motion at each time point is greater than a preset amplitude based on the perfusion scan data comprises:

acquiring the variable quantity between the perfusion scanning image of each time point and the perfusion scanning image of the adjacent time point; and

determining whether the motion amplitude of each time point is greater than a preset amplitude based on the variation of each time point and its neighboring time points; alternatively, the first and second electrodes may be,

and determining whether the motion amplitude of each time point is larger than a preset amplitude or not by using a trained motion amplitude abnormity determination model based on the perfusion scanning images of each time point, wherein the motion amplitude abnormity determination model is a machine learning model.

3. A perfusion analysis method according to claim 1, wherein the perfusion phases include an arterial phase and an inflow-outflow phase; the determining whether to reject the data at the first time point according to the perfusion stage at which the first time point is located includes:

when the first time point is in the artery phase, not rejecting data of the first time point;

and when the first time point is in the inflow and outflow period, rejecting the data of the first time point.

4. A perfusion analysis method according to claim 3, wherein the method further comprises:

according to the sampling interval time of the first time point and the adjacent time point, determining the perfusion stage where the first time point is located, wherein:

when the sampling interval time of the first time point and the adjacent time points before and after the first time point is greater than the preset interval time, determining that the first time point is in an inflow and outflow period;

and when at least one of the sampling interval time of the first time and the adjacent time points before and after the first time is less than or equal to the preset interval time, determining that the first time point is in the arterial phase.

5. A perfusion analysis method according to claim 3, wherein the method further comprises:

determining a perfusion time-density curve based on perfusion scan data for the plurality of time points;

determining the perfusion stage at the first time point based on the time-density curve, specifically comprising:

determining the perfusion stage of the first time point based on the curve slope corresponding to the first time point on the time-density curve; alternatively, the first and second electrodes may be,

determining a peak time based on the time-density curve; and

determining a perfusion phase at the first point in time based on the interval of the first point in time and the peak time.

6. A perfusion analysis method according to claim 3, wherein when the first point in time is in the arterial phase, further comprising:

and generating prompt information to prompt the user of the related information of the first time point.

7. The perfusion analysis method of claim 1, further comprising:

and performing perfusion analysis based on the perfusion scan data of the plurality of time points or the perfusion scan data of the time points left after the first time point is removed to obtain a perfusion time-density curve and/or perfusion parameters.

8. A perfusion analysis system is characterized by comprising an acquisition module, a motion amplitude abnormity determination module and a data elimination module;

the acquisition module is used for acquiring perfusion scanning data of a plurality of time points;

the motion amplitude determination module is used for determining whether the motion amplitude of each time point is larger than a preset amplitude or not based on the perfusion scanning data;

and the data removing module is used for determining whether to remove the data of the first time point according to the perfusion stage where the first time point is located when the motion amplitude of the first time point is larger than the preset amplitude.

9. A perfusion analysis device comprising a processor, wherein the processor is configured to perform the perfusion analysis method of any one of claims 1-7.

10. A computer readable storage medium storing computer instructions which, when read by a computer, cause the computer to perform the perfusion analysis method of any one of claims 1-7.

Technical Field

The present disclosure relates to the field of scanning diagnosis technologies, and in particular, to a perfusion analysis method and system.

Background

Compared with the common CT flat scan and enhanced scan, the CT perfusion imaging only acquires data information of one Time point, obtains a Time-density Curve (TAC) of each voxel of the tissue or the organ by continuously scanning a plurality of Time phases, reflects the inflow and outflow processes (blood perfusion) of a contrast agent in the tissue, and calculates various perfusion parameters (such as cerebral blood volume CBF, local cerebral blood volume CBV, average passing Time MTT, peak Time TTP and the like) by using different mathematical models, thereby forming a perfusion parameter map, evaluating the tissue ischemia condition and guiding the formulation of a treatment scheme.

Because the perfusion data needs to be acquired for calculation at a plurality of continuous time points, the data at each time point needs to be consistent in structure, a patient cannot have a large motion amplitude, and even if the motion of the patient is corrected by the large amplitude, the data cannot be matched, so that calculation errors are caused. Conventional perfusion acquisitions take at least 1 minute in duration, and take long periods of time, and there may be uncontrolled motion of the patient's body, possibly resulting in large motion amplitudes at individual time points therein, thereby affecting the accuracy of the calculation. When a large movement amplitude occurs in the existing perfusion scanning, a user generally identifies and evaluates the movement condition autonomously, and artificially judges whether the data of unqualified time points can be removed and recalculated, but the user needs to be trained in advance, so that the training cost is high, the requirement on the user is high, the artificial dependency is high, and the accuracy of the data is not ideal enough.

Therefore, there is a need for a perfusion analysis method and system that can automatically evaluate the motion amplitude and reject the unqualified data.

Disclosure of Invention

One embodiment of the present disclosure provides a perfusion analysis method, including: acquiring perfusion scan data of a plurality of time points; determining whether the motion amplitude of each time point is greater than a preset amplitude based on the perfusion scan data; and when the motion amplitude of the first time point is larger than the preset amplitude, determining whether the data of the first time point are rejected according to the perfusion stage where the first time point is located.

In some embodiments, the perfusion scan data comprises a perfusion scan image, and the determining whether the motion amplitude at each time point is greater than a preset amplitude based on the perfusion scan data comprises: acquiring the variable quantity between the perfusion scanning image of each time point and the perfusion scanning image of the adjacent time point; determining whether the motion amplitude of each time point is greater than a preset amplitude based on the variation of each time point and its neighboring time points.

In some embodiments, when the variation amounts of the first time point and its adjacent time points are greater than a preset threshold, it is determined that the motion amplitude of the first time point is greater than the preset amplitude.

In some embodiments, the perfusion scan data comprises a perfusion scan image, and the determining whether the motion amplitude at each time point is greater than a preset amplitude based on the perfusion scan data comprises: and determining whether the motion amplitude of each time point is larger than a preset amplitude or not by using a trained motion amplitude abnormity determination model based on the perfusion scanning images of each time point, wherein the motion amplitude abnormity determination model is a machine learning model.

In some embodiments, the perfusion phase comprises an arterial phase and an inflow-outflow phase; the determining whether to reject the data at the first time point according to the perfusion stage at which the first time point is located includes: when the first time point is in the artery phase, not rejecting data of the first time point; and when the first time point is in the inflow and outflow period, rejecting the data of the first time point.

In some embodiments, the method further comprises: according to the sampling interval time of the first time point and the adjacent time point, determining the perfusion stage where the first time point is located, wherein: when the sampling interval time of the first time point and the adjacent time points before and after the first time point is greater than the preset interval time, determining that the first time point is in an inflow and outflow period; and when at least one of the sampling interval time of the first time and the adjacent time points before and after the first time is less than or equal to the preset interval time, determining that the first time point is in the arterial phase.

In some embodiments, the method further comprises: determining a perfusion time-density curve based on perfusion scan data for the plurality of time points; determining a perfusion phase at the first time point based on the time-density curve.

In some embodiments, said determining the perfusion phase at the first point in time based on the time-density curve comprises: and determining the perfusion stage at the first time point based on the curve slope corresponding to the first time point on the time-density curve.

In some embodiments, said determining the perfusion phase at the first point in time based on the time-density curve comprises: determining a peak time based on the time-density curve; determining a perfusion phase at the first point in time based on the interval of the first point in time and the peak time.

In some embodiments, when the first point in time is in the arterial phase, further comprising: and generating prompt information to prompt the user of the related information of the first time point.

In some embodiments, further comprising: and performing perfusion analysis based on the perfusion scan data of the plurality of time points or the perfusion scan data of the time points left after the first time point is removed to obtain a perfusion time-density curve and/or perfusion parameters.

One of the embodiments of the present specification provides a perfusion analysis system, which includes an acquisition module, a motion amplitude abnormality determination module, and a data elimination module; the acquisition module is used for acquiring perfusion scanning data of a plurality of time points; the motion amplitude determination module is used for determining whether the motion amplitude of each time point is larger than a preset amplitude or not based on the perfusion scanning data; and the data removing module is used for determining whether to remove the data of the first time point according to the perfusion stage where the first time point is located when the motion amplitude of the first time point is larger than the preset amplitude.

In some embodiments, a perfusion analysis module is further included; the perfusion analysis module is to: and performing perfusion analysis based on the perfusion scan data of the plurality of time points or the perfusion scan data of the time points left after the first time point is removed to obtain a perfusion time-density curve and/or perfusion parameters.

One of the embodiments of the present description provides a perfusion analysis device comprising a processor for performing a perfusion analysis method.

One of the embodiments of the present description provides a computer-readable storage medium storing computer instructions, and when the computer instructions in the storage medium are read by a computer, the computer executes a perfusion analysis method.

Drawings

The present description will be further explained by way of exemplary embodiments, which will be described in detail by way of the accompanying drawings. These embodiments are not intended to be limiting, and in these embodiments like numerals are used to indicate like structures, wherein:

FIG. 1 is an exemplary flow diagram of a perfusion analysis method according to some embodiments herein;

FIG. 2 is an exemplary flow diagram of a data culling determination method according to some embodiments herein;

FIG. 3 is an exemplary block diagram of a perfusion analysis system according to some embodiments herein;

FIG. 4 is an exemplary block diagram of a perfusion analysis device according to some embodiments of the present description;

fig. 5 is a schematic illustration of a perfusion time-density curve shown in accordance with some embodiments herein.

Detailed Description

In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings used in the description of the embodiments will be briefly described below. It is obvious that the drawings in the following description are only examples or embodiments of the present description, and that for a person skilled in the art, the present description can also be applied to other similar scenarios on the basis of these drawings without inventive effort. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.

It should be understood that "system," "unit," and/or "module" as used herein is a method for distinguishing different components, elements, components, parts, or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.

The use of "first," "second," and similar terms in the description and in the claims does not indicate any order, quantity, or importance, but rather is used to distinguish one element from another. Also, the use of the terms "a" or "an" and the like do not denote a limitation of quantity, but rather denote the presence of at least one. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements.

Flow charts are used in this description to illustrate operations performed by a system according to embodiments of the present description. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.

Fig. 1 is an exemplary flow diagram of a perfusion analysis method 100 shown in accordance with some embodiments herein.

The execution body performing the perfusion analysis method 100 may include a perfusion scanning device and/or a controller. In some embodiments, the perfusion scanning device may be a medical Imaging device including at least one of a Computed Tomography (CT) device, a Magnetic Resonance Imaging (MRI) device, an X-ray device, a Positron Emission Tomography (PET) device, and an ultrasound detection device. In some embodiments, the controller may be a part of a system integrated in the electronic device, or may be a separate electronic device, and the controller may also be set in a cloud Server (Online Server). For example, the controller may be various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices, or may be a part of a system (e.g., a central control device) integrated in a medical imaging device. In some embodiments, the perfusion analysis method 100 may be performed by the perfusion analysis system 300 or the perfusion analysis device 400.

In some embodiments, the perfusion analysis method 100 may include:

at step 110, perfusion scan data is acquired at a plurality of time points. In some embodiments, step 110 may be performed by the acquisition module 310.

Perfusion scan data may refer to data obtained after performing a perfusion scan. For example, the perfusion scan data may include raw data obtained by a perfusion scan, a reconstructed perfusion scan image obtained, and so forth. In some embodiments, perfusion scan data for multiple time points may be acquired by performing a perfusion scan, which may include scanning modalities that continuously scan multiple time points, such as non-enhanced scans and helical scans. In some embodiments, the acquired perfusion scan data may be perfusion scan data for a plurality of time points in succession, or may be perfusion scan data for all time points within a scan time.

In some embodiments, some scan preparation may be performed before performing the perfusion scan. For example, the scan preparation may include registering a patient for a perfusion scan, entering patient information, which may include the patient's name, sex, age, height, weight, etc. In some embodiments, the scan preparation may further include: a CT reference image examination is performed. In some embodiments, a user (e.g., an operator of a perfusion scan project) may plan a positioning slice and a large range slice of a perfusion scan (e.g., setting an area to be scanned) based on the reference image. In some embodiments, the scan preparation may further include: the scan interval, scan time and dose parameters of each phase of the perfusion scan are set. For example, the scanning interval of the arterial phase may be set to 1.5s-2s, the scanning interval of the inflow-outflow phase may be set to 3s-4s, the scanning time of the arterial phase may be set to 15s-30s after the start of scanning, and the total scanning time may be set to not less than 60 s. The scanning interval, scanning time and dosage parameters of the artery period and the inflow-outflow period can be preset in the execution subject, can also be set by the user, and can also be set in a mode that the user modifies the reference data range given by the execution subject.

And step 120, determining whether the motion amplitude of each time point is larger than a preset amplitude or not based on the perfusion scan data. In some embodiments, step 120 may be performed by the motion magnitude anomaly determination module 320.

The motion amplitude of a certain time point may refer to the motion amplitude of a scanning part (such as a head, a brain, etc.) at the time point, and the motion amplitude of the scanning part at the certain time point may be reflected by perfusion scan data at the time point. When the motion amplitude of the scanning part at a certain time point is too large, the perfusion scan data at the time point can have an abnormality (such as an image has an artifact), or the object in the perfusion scan image at the time point can have a deviation relative to the images at the adjacent time points. In some embodiments, the preset amplitude may refer to a maximum motion amplitude that the scanning portion can be allowed to move.

In some embodiments, the motion amplitude abnormality determination module 320 may obtain a variation between the perfusion scan image at each time point and the perfusion scan image at the adjacent time point, and determine whether the motion amplitude at each time point is greater than a preset amplitude based on the variation between each time point and the perfusion scan image at the adjacent time point. The variation may be a characteristic quantity that can characterize a difference between a perfusion scan image at a certain time point (e.g., a scan object in an image) and a perfusion scan image at a neighboring time point. For example, the variance may include a rotation variable and/or a translation variable, and the like. In some embodiments, the amount of change may be characterized as a rotational variable and a translational variable. Whether the motion amplitude at a certain time point is larger than the preset amplitude can be evaluated through the rotation variable and the translation variable of the perfusion scan image at the certain time point and the perfusion scan image at the adjacent time point. In some embodiments, the motion magnitude anomaly determination module 320 may select an image at one time point as a reference image and images at other time points (e.g., adjacent time points) as floating images for registration, thereby obtaining a rotation variable and a translation variable between the images.

In some embodiments, the motion amplitude abnormality determining module 320 may establish a preset threshold of the variation, where the preset threshold may refer to a maximum amount that the variation can reach within a preset amplitude. In some embodiments, when the variation of a time point and its adjacent time points is greater than a preset threshold, it is determined that the motion amplitude of the time point is greater than a preset amplitude. And when at least one of the variation of a certain time point and the variation of the time points before and after the certain time point is less than or equal to a preset threshold, determining that the motion amplitude of the certain time point is not greater than the preset amplitude. The former adjacent time point refers to a time point adjacent to the time point before the time point, and the latter adjacent time point refers to a time point adjacent to the time point after the time point. In some embodiments, the first and last time points of the plurality of time points may not be determined.

In some embodiments, the preset threshold may comprise a maximum magnitude that the rotational and translational variables, respectively, can reach within a preset amplitude. In some embodiments, when the rotation variable and the translation variable of the perfusion scan image at a certain time point and the perfusion scan images at the preceding and following adjacent time points are both greater than the corresponding preset threshold values, it is determined that the motion amplitude at the certain time point is greater than the preset amplitude. And when at least one of the rotation variable and the translation variable of the perfusion scan image at a certain time point and the time points adjacent to the former time point and the latter time point is less than or equal to the corresponding preset threshold value, determining that the motion amplitude of the time point is not greater than the preset amplitude. In some embodiments, when at least one of the rotational variable and the translational variable of the perfusion scan image at a time point and the perfusion scan images at the preceding and following adjacent time points is greater than the corresponding preset threshold, it may be determined that the motion amplitude at the time point is greater than the preset amplitude. When the rotation variable and the translation variable of the perfusion scan image at a certain time point and the time points adjacent to the former time point and the latter time point are both smaller than or equal to the corresponding preset threshold, it can be determined that the motion amplitude of the time point is not larger than the preset amplitude. In some embodiments, the preset threshold may be preset by a user (e.g., an expert in the art). In some embodiments, the preset threshold may be automatically determined from historical data.

In some embodiments, the amount of change may be characterized as mutual information between the two images, the mutual information characterizing the similarity between the two images. The motion amplitude at a certain time point can be estimated by mutual information of the perfusion scan image at the time point and the perfusion scan images at the adjacent time points. In some embodiments, the preset threshold may refer to a lowest value (e.g., lowest similarity) that the mutual information can reach within a preset range. When the mutual information of the perfusion scan image at a certain time point and the forward adjacent time point and the backward adjacent time point is lower than a preset threshold value, the motion amplitude of the time point can be determined to be larger than a preset amplitude. When at least one of the mutual information of a certain time point and the preceding and following adjacent time points is less than or equal to a preset threshold, it may be determined that the motion amplitude of the time point is not greater than a preset amplitude.

In some embodiments, the motion amplitude anomaly determination module 320 may determine whether the motion amplitude at each time point is greater than a preset amplitude using a trained motion amplitude anomaly determination model based on the perfusion scan images at each time point. In some embodiments, the motion magnitude anomaly determination model may be a machine learning model. In some embodiments, a training set may be established to train the machine learning model to obtain a trained motion amplitude anomaly determination model. The training set may include scanned image data with a motion amplitude greater than a preset amplitude and scanned image data with a motion amplitude not greater than the preset amplitude, and the motion amplitude abnormality determination model trained by performing binary training on the scanned image data training set with a motion amplitude greater than the preset amplitude and a motion amplitude not greater than the preset amplitude may be used to determine whether the motion amplitude of the perfusion scanned image at each time point is greater than the preset amplitude.

Step 130, determining whether the motion amplitude of the first time point is larger than a preset amplitude. In some embodiments, step 130 may be performed by the motion magnitude anomaly determination module 320.

In some embodiments, the motion amplitude abnormality determination module 320 may determine a time point at which the motion amplitude is greater than a preset amplitude as the first time point. For example, when the variation of a certain time point and its adjacent time points before and after is greater than a preset threshold, the time point may be determined as a first time point at which the motion amplitude is greater than a preset amplitude. When there is a motion amplitude at the first time point greater than the preset amplitude, the perfusion analysis system 300 may perform step 140.

In some embodiments, the motion amplitude abnormality determination module 320 may determine that the first time point does not exist when the motion amplitude at any time point is not greater than the preset amplitude. When the first point in time is not present, the perfusion analysis system 300 may perform step 150.

And step 140, determining whether to eliminate the data at the first time point according to the perfusion stage at which the first time point is located. In some embodiments, step 140 may be performed by data culling module 330.

In some embodiments, the perfusion phase may be divided according to the flow of the injected contrast agent in the blood vessel. In some embodiments, the perfusion phase may include an inflow and outflow phase in which contrast agent flows to and from the scanned region (e.g., a portion of the body such as the skull or heart), and an arterial phase in which a bolus of contrast agent is concentrated in arterial vessels in the scanned region. In some embodiments, when the first time point is in an arterial phase, the data culling module 330 may not cull data of the first time point. In some embodiments, the data culling module 330 may cull data of the first time point when the first time point is in an inflow and outflow period. For more details regarding data culling, reference may be made to FIG. 2 and its associated description.

Step 150, performing perfusion analysis based on the perfusion scan data of the plurality of time points or the perfusion scan data of the remaining time points after the first time point is removed to obtain a perfusion time-density curve and/or perfusion parameters. In some embodiments, step 150 may be performed by perfusion analysis module 340.

In some embodiments, the perfusion parameters may include, but are not limited to, a combination of one or more of cerebral blood volume CBF, local cerebral blood volume CBV, mean transit time MTT, time to peak TTP, and the like. In some embodiments, when there is no first time point at which the motion amplitude is greater than the preset amplitude or there is a first time point but not rejected, the perfusion analysis module 340 may perform perfusion analysis based on the obtained perfusion scan data of the plurality of time points, thereby obtaining a perfusion time-density curve and/or perfusion parameters. In some embodiments, when there is a first time point at which the motion amplitude that can be rejected is greater than the preset amplitude, the perfusion analysis module 340 may perform perfusion analysis based on the perfusion scan data of the remaining time point after the first time point is rejected, thereby obtaining a perfusion time-density curve and/or perfusion parameters. In some embodiments, when determining the perfusion stage at the first time point based on the time-density curve, a preliminary time-density curve may be generated based on the perfusion scan data at the plurality of time points, and the perfusion stage at the first time point may be determined based on the preliminary time-density curve, and when the data at the first time point is not rejected, the perfusion analysis module 340 may determine the preliminary time-density curve as a final time-density curve; when the first time point is eliminated, the perfusion analysis module 340 may generate a final time-density curve again according to the perfusion scan data of the remaining time points after the first time point is eliminated.

According to the perfusion analysis method provided by the embodiment of the application, the first time point with the overlarge motion amplitude can be determined by evaluating the motion amplitude of each time point, and whether the data of the first time point are removed or not is judged according to the perfusion stage where the first time point is located. According to the method and the device, the accuracy of the perfusion scanning data can be automatically judged under the condition that the motion amplitude of the patient is too large, unqualified perfusion scanning data is analyzed and processed, and a relatively more accurate result is given to a user, so that the dependence on human judgment is reduced, and the training cost for the user can be reduced.

FIG. 2 is an exemplary flow diagram of a data culling determination method according to some embodiments herein. In some embodiments, when there is a motion amplitude of the first time point greater than the preset amplitude, the perfusion analysis system 300 may execute the data culling determination method 200, so as to determine whether to cull the data of the first time point according to the perfusion stage in which the first time point is located. In some embodiments, the steps in the data culling determination method 200 may be performed by the data culling module 330 and/or the perfusion analysis module 340.

Step 210, determining the perfusion stage at the first time point according to the sampling interval time of the first time point and the adjacent time point. In some embodiments, step 210 may be performed by data culling module 330.

In some embodiments, when the sampling interval time of the first time point and the time points adjacent to the first time point is greater than the preset interval time, the data culling module 330 may determine that the first time point is in the inflow and outflow period. In some embodiments, the data culling module 330 may determine that the first time point is in the arterial phase when at least one of the sampling interval times of the first time and its immediately adjacent time points is less than or equal to a preset interval time.

In some embodiments, the preset interval time may be set according to a scan interval time set before scanning. Since the artery period scanning interval time is less than the inflow and outflow period scanning interval time, the preset interval time may be set to be the artery period scanning interval time, may also be set to be slightly less than the inflow and outflow period scanning interval time, and may also be set to be a value between the artery period scanning interval time and the inflow and outflow period scanning interval time. For example, the scanning interval time in the arterial phase is 1.5s, the scanning interval time in the inflow/outflow phase is 3s, and the preset interval time may be set to 1.5s, 2.9s, 2.5s, or the like.

In some alternative embodiments, the preset interval time may be set as an inflow and outflow period scanning interval time, and when the sampling interval times of the first time point and its preceding and succeeding adjacent time points are equal to the preset interval time, it may be determined that the first time point is in the inflow and outflow period; when at least one of the sampling interval times of the first time and its immediately preceding and succeeding neighboring time points is less than the preset interval time, it may be determined that the first time point is in the arterial phase.

The filling stage where the first time point is located is determined according to the sampling interval time of the first time point and the adjacent time point, the stage where the first time point is located can be quickly determined, so that whether the data of the first time point are eliminated or not can be quickly judged, and the speed and accuracy of filling analysis are effectively improved.

In some embodiments, the data culling module 330 may perform step 210 to determine the perfusion phase at the first point in time. In some embodiments, the data culling module 330 may perform steps 220 and 230 to determine the perfusion phase at the first point in time.

Based on the perfusion scan data at multiple Time points, a perfusion Time-density Curve (TAC) is determined, step 220.

The perfusion time-density curve may reflect the density versus time of the contrast agent flowing through the perfusion scan site. Fig. 5 is a schematic illustration of a perfusion time-density curve shown in accordance with some embodiments herein. As shown in fig. 5, during a perfusion scan, the density of contrast agent at the site of the perfusion scan (e.g., the brain) increases and then decreases over time.

Based on the time-density curve, a perfusion phase at the first time point is determined, step 230.

In some embodiments, the data culling module 330 may determine the perfusion phase at the first time point based on a slope of a curve corresponding to the first time point on the time-density curve. The time-density curve may be a smoothed curve. In some embodiments, the data culling module 330 may determine the perfusion phase at the first time point according to a magnitude of an absolute value of a slope of a corresponding point of the first time point on the time-density curve. For example, the first time point is determined to be in the arterial phase when the absolute value of the slope of the corresponding point of the first time point is greater than a preset threshold (e.g., 1, 1.5, etc.), and the first time point is determined to be in the inflow and outflow phase when the absolute value of the slope of the corresponding point of the first time point is less than or equal to the preset threshold. In some embodiments, when the slope of the corresponding point at the first time point is equal to 0, it may be determined that the first time point is at the peak, i.e., the first time point is also in the arterial phase. As can be seen from the perfusion time-density curve shown in fig. 5, the time-density curve changes faster and the slope absolute value is larger during the arterial phase; while the time-density curve changes more slowly and the absolute value of the slope is smaller during the inflow period. The perfusion stage where the first time point is located can be determined quickly and accurately by the curve slope corresponding to the first time point on the basis of the time-density curve.

In some embodiments, the data culling module 330 may determine a peak time from the time-density curve and determine the perfusion phase at the first time point based on an interval between the first time point and the peak time. For example, the data culling module 330 may determine a corresponding time of a peak point on the time-density curve as a peak time, and in some embodiments, the data culling module 330 may determine the perfusion phase at the first time according to a size of an interval between the first time point and the peak time. For example, when the interval between the first time point and the peak time is less than or equal to 10s, the first time point is determined to be in the arterial phase, and when the interval between the first time point and the peak time is greater than 10s, the first time point is determined to be in the inflow-outflow phase. In some embodiments, the data culling module 330 may determine the perfusion phase at the first time based on forward and backward interval sizes of the first time point and the peak time. For example, a time point within a range from 15s forward to 5s backward from the peak time is determined to be in the arterial phase, and a first time point not within the time range is determined to be in the inflow-outflow phase. This flexible way of dividing the arterial phase is advantageous to accommodate the difference in the time taken for the contrast agent to flow into the artery for each patient. By determining the perfusion phase at which the first point in time is based on the interval between the first point in time and the peak time, the determination process can be made faster and the determination result more accurate.

In step 240, it is determined whether the first time point is in an arterial phase. In some embodiments, step 210 may be performed by data culling module 330.

When it is determined that the first time point is not in the arterial phase (e.g., in the inflow-outflow phase), then the data culling module 330 may perform step 250: and eliminating the data at the first time point. By eliminating the data of the first time point which is not in the artery phase, the condition that the inaccuracy of the data of the first time point influences the accuracy of the overall calculation can be avoided. After culling the data at the first time point, the perfusion analysis system 300 may perform step 260.

When it is determined that the first time point is in the arterial phase, the data culling module 330 may perform step 270: the data at the first time point is not culled. By not removing the data of the first time point in the artery phase, the problem that the accuracy of the calculation result is influenced due to the fact that the scanning interval of the artery phase is too large after the first time point in the artery phase is removed can be avoided. Upon determining not to cull data at the first time point, the perfusion analysis system 300 may perform step 280 and/or step 290.

Step 260, performing perfusion analysis based on the perfusion scan data of the remaining time point after the first time point is removed to obtain a perfusion time-density curve and/or perfusion parameters. In some embodiments, step 260 may be performed by perfusion analysis module 340.

Based on the perfusion scan data at the plurality of time points, a perfusion analysis is performed to obtain a perfusion time-density curve and/or perfusion parameters, step 280. In some embodiments, step 280 may be performed by perfusion analysis module 340. In some embodiments, when there is a first point in time and the data at the first point in time is not culled, the perfusion analysis module 340 may perform perfusion analysis based on the perfusion scan data for the multiple points in time acquired in step 110.

In step 290, a prompt message is generated to prompt the user about the information at the first time point. In some embodiments, step 290 may be performed by perfusion analysis system 300 (e.g., perfusion analysis module 340).

In some embodiments, the reminder information may include one or more of the following: the information of the existence of the first time point, the time information of the first time point, the perfusion scanning image information of the first time point, the position information of the time-density curve of the first time point, the information that the motion amplitude of the first time point is larger than the preset amplitude, the information that the result is inaccurate due to the existence of the first time point, and the like. In some embodiments, the presentation form of the prompt message may include, but is not limited to, one or more of a voice prompt, an animated prompt, an image prompt, a text prompt, a pop-up prompt, and the like. The user is prompted with the relevant information of the first time point through the prompting information, so that the user can know that the motion amplitude of the first time point is large, and the user is assisted to make correct analysis and judgment. In some embodiments, perfusion analysis system 300 may present optional operational information to the user while presenting the reminder information. For example, the optional operational information may include deleting the first point in time, re-performing the perfusion scan, re-performing the perfusion analysis, and the like.

Fig. 3 is an exemplary block diagram of a perfusion analysis system 300, shown in accordance with some embodiments herein. In some embodiments, perfusion analysis system 300 may be implemented by perfusion analysis device 400 (e.g., processor 420). As shown in fig. 3, the perfusion analysis system 300 may include an acquisition module 310, a motion amplitude anomaly determination module 320, a data culling module 330, and a perfusion analysis module 340.

The acquisition module 310 may be used to acquire data and/or information during perfusion analysis. In some embodiments, the acquisition module 310 may be used to acquire perfusion scan data for multiple points in time.

The motion amplitude abnormality determination module 320 may be configured to determine whether the motion amplitude at a certain time point is abnormal. In some embodiments, the motion amplitude anomaly determination module 320 may determine whether the motion amplitude at each time point is greater than a preset amplitude based on the perfusion scan data. In some embodiments, the motion amplitude abnormality determination module 320 may obtain a variation between the perfusion scan image at each time point and the perfusion scan image at the adjacent time point, and determine whether the motion amplitude at each time point is greater than a preset amplitude based on the variation between each time point and the perfusion scan image at the adjacent time point.

Data culling module 330 may be used to interpret and cull data. In some embodiments, the data culling module 330 may determine whether to cull data at the first time point based on the perfusion phase at the first time point. In some embodiments, when the first time point is in an arterial phase, the data culling module 330 may not cull data of the first time point. In some embodiments, the data culling module 330 may cull data of the first time point when the first time point is in an inflow and outflow period.

The perfusion analysis module 340 may be used to perform perfusion analysis operations. In some embodiments, the perfusion analysis module 340 may be configured to perform perfusion analysis based on perfusion scan data at a plurality of time points, or excluding perfusion scan data at time points remaining after a first time point, to obtain a perfusion time-density curve and/or perfusion parameters.

It should be noted that the above description of the perfusion analysis system and module is merely for convenience of description and should not limit the present application to the scope of the illustrated embodiments. It will be appreciated by those skilled in the art that, given the teachings of the present system, any combination of modules or sub-system configurations may be used to connect to other modules without departing from such teachings. In some embodiments, the acquisition module 310, the motion amplitude abnormality determination module 320, the data culling module 330, and the perfusion analysis module 340 disclosed in fig. 3 may be different modules in a system, or may be a module that implements the functions of two or more of the above modules. For example, each module may share one memory module, and each module may have its own memory module. Such variations are within the scope of the present application.

Fig. 4 is an exemplary block diagram of a perfusion analysis device 400 according to some embodiments of the present description. As shown in fig. 4, perfusion analysis device 400 may include a memory 410, a processor 420, and a communication bus. The memory 410 and the processor 420 may implement a communication process through a communication bus. The processor 420 may be configured to perform the perfusion analysis method provided by any of the embodiments described above in the present application.

In some embodiments, processor 420 may be implemented using a central processor, a server, a terminal device, or any other possible processing device. In some embodiments, the central processor, server, terminal device, or other processing device described above may be implemented on a cloud platform. In some embodiments, the central processor, server, or other processing device may be interconnected with various terminal devices, which may perform information processing tasks or portions thereof.

In some embodiments, memory 410 (or computer-readable storage medium) may store data and/or instructions (e.g., computer instructions). In some embodiments, the memory 410 may store a preset scan interval, a scan time, a dose parameter, a preset amplitude, a preset threshold, a preset interval time, a motion amplitude abnormality determination model training set, and the like. In some embodiments, the memory 410 may store computer instructions that the processor 420 (or computer) may read to perform the perfusion analysis methods provided by any of the embodiments herein. In some embodiments, the storage device may include mass storage, removable storage, volatile read-write memory, read-only memory (ROM), and the like, or any combination thereof. In some embodiments, the storage device may be implemented on a cloud platform.

The beneficial effects that may be brought by the embodiments of the present application include, but are not limited to: (1) providing an automatic perfusion analysis workflow which can automatically evaluate the perfusion motion condition of each time point and automatically judge whether the time point can be deleted according to the phase that the contrast agent at the time point rapidly flows into the outflow period; (2) the perfusion analysis method can give a relatively more accurate result to a user through automatic judgment, evaluation, processing and analysis under the conditions of patient reasons and unqualified data quality control; (3) the perfusion analysis method can promote the processing workflow, reduce the dependence on the user experience and reduce the training cost; (4) the perfusion analysis method can reduce the rescanning condition caused by unqualified data and limited experience of users to a certain extent, and reduce the dosage of patients. It is to be noted that different embodiments may produce different advantages, and in different embodiments, any one or combination of the above advantages may be produced, or any other advantages may be obtained.

Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be considered merely illustrative and not restrictive of the broad application. Various modifications, improvements and adaptations to the present application may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present application and thus fall within the spirit and scope of the exemplary embodiments of the present application.

Also, this application uses specific language to describe embodiments of the application. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the present application is included in at least one embodiment of the present application. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this application are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the present application may be combined as appropriate.

Additionally, the order in which elements and sequences of the processes described herein are processed, the use of alphanumeric characters, or the use of other designations, is not intended to limit the order of the processes and methods described herein, unless explicitly claimed. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.

Similarly, it should be noted that in the preceding description of embodiments of the application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to require more features than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.

Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present application. Other variations are also possible within the scope of the present application. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the present application can be viewed as being consistent with the teachings of the present application. Accordingly, the embodiments of the present application are not limited to only those embodiments explicitly described and depicted herein.