CN114366163A

CN114366163A - Cerebral blood flow data acquisition method and system based on rapid scanning and intelligent terminal

Info

Publication number: CN114366163A
Application number: CN202210026043.1A
Authority: CN
Inventors: 熊飞; 丁旻昊; 陈苹; 肖振华
Original assignee: Shenzhen Delica Medical Equipment Co ltd
Current assignee: Shenzhen Delikai Medical Electronics Co ltd
Priority date: 2022-01-11
Filing date: 2022-01-11
Publication date: 2022-04-19
Anticipated expiration: 2042-01-11
Also published as: CN114366163B

Abstract

The application relates to a cerebral blood flow data acquisition method and system based on rapid scanning and an intelligent terminal. The cerebral blood flow data acquisition method comprises the following steps: determining a scanning sequence in a current region to be scanned, and sending path planning information to a scanning mechanism based on the scanning sequence; acquiring a scanning result sent by a scanning mechanism, and determining cerebral blood flow data information and an effective point set based on the scanning result; judging whether all acquisition points in the current area to be scanned are over-scanned or not; if not, executing a path updating step; and determining an area updating strategy based on the effective point set, updating the current area to be scanned according to the area updating strategy, and returning to the area selection step. According to the method and the device, the mode of dynamically adjusting the area to be scanned according to the actual scanning result is utilized, the time waste of scanning the collection points which can not collect blood flow signals in the scanning process is reduced, and the characteristic of improving the integral scanning speed of three-dimensional brain blood flow data collection is achieved.

Description

Cerebral blood flow data acquisition method and system based on rapid scanning and intelligent terminal

Technical Field

The application relates to the field of cerebral blood flow data acquisition technology, in particular to a cerebral blood flow data acquisition method and system based on rapid scanning and an intelligent terminal.

Background

At present, in order to observe the cerebral blood flow of a human body, an intracranial three-dimensional cerebral blood flow model needs to be constructed, and when the intracranial three-dimensional cerebral blood flow model is constructed, an observation object needs to be scanned by using an ultrasonic device so as to acquire three-dimensional cerebral blood flow data.

In the prior art, for example, an intracranial three-dimensional cerebral blood flow reconstruction method, a storage medium and an ultrasound apparatus disclosed in chinese patent application with application publication No. CN112043308A, which discloses an intracranial three-dimensional cerebral blood flow reconstruction method, the method includes: acquiring three-dimensional cerebral blood flow data of the intracranial of a detection object, and dividing the acquired three-dimensional cerebral blood flow data into a plurality of data layers; determining interpolation data corresponding to each data layer according to the motion track of an ultrasonic probe for detecting three-dimensional cerebral blood flow data; inserting the interpolation data corresponding to each data layer into each data layer to obtain target three-dimensional cerebral blood flow data; and determining a three-dimensional cerebral blood flow model corresponding to the detection object based on the target three-dimensional cerebral blood flow data.

In view of the above technical solutions, the inventor believes that three-dimensional cerebral blood flow data is obtained by scanning a skull of a detection object through a probe of an ultrasound device according to a preset motion trajectory, where the motion trajectory includes a plurality of acquisition points, and each acquisition point is a position point for acquiring data in a probe motion process. The probe automatically traverses all the acquisition points on the motion track, but for different detection objects, the blood vessel distribution of the cranium is different, so that whether all the acquisition points on the motion track can acquire blood flow signals or not is caused, a large amount of scanning on the acquisition points can reduce the overall speed of three-dimensional brain blood flow data acquisition, the time period of data acquisition and subsequent data processing is influenced, the burden of the probe control driving module is increased, and the service life of the probe control driving module is shortened.

Disclosure of Invention

The method for acquiring the cerebral blood flow data based on the rapid scanning has the advantage of improving the overall efficiency of data acquisition and scanning.

The above object of the present invention is achieved by the following technical solutions:

a cerebral blood flow data acquisition method based on rapid scanning is characterized by comprising the following steps:

area selection: determining a region to be scanned in the total planning region;

sequence determination: determining a scanning sequence in a current region to be scanned, and sending path planning information to a scanning mechanism based on the scanning sequence so that the scanning mechanism scans all acquisition points in the current scanning sequence; the area to be scanned comprises at least one scanning sequence, the scanning sequence comprises at least one acquisition point which is not subjected to overscan, and all the scanning sequences sequentially participate in scanning according to a scanning sequence;

scanning and analyzing: acquiring a scanning result sent by a scanning mechanism, and determining cerebral blood flow data information and an effective point set based on the scanning result; wherein the cerebral blood flow data information comprises all blood flow signals acquired in the current scanning sequence, and the effective point set comprises acquisition points capable of acquiring the blood flow signals;

and (4) executing judgment: judging whether all acquisition points in the current region to be scanned are over-scanned or not, and if so, outputting a data acquisition result based on cerebral blood flow data information; if not, executing a path updating step;

path updating: based on the effective point set, determining a region updating strategy, updating the current region to be scanned according to the region updating strategy, and returning to the region selection step;

wherein the region update policy comprises a contraction policy and an expansion policy; the contraction strategy is used for contracting the range of the current region to be scanned; the expansion strategy is used for expanding the range of the area to be scanned.

By adopting the technical scheme, the scanning mechanism refers to the path planning information which has already finished the path planning before executing the scanning task and scans based on the motion path in the path planning information. After scanning the scanning sequence once, the system records the positions of acquisition points capable of acquiring blood flow signals in the scanning sequence, determines an effective point set, and collects the blood flow signals acquired at all the positions to determine cerebral blood flow data information. When all the acquisition points in the area to be scanned have been scanned, the scanning task is finished, and based on the acquired cerebral blood flow data information, cerebral blood flow data of the cranium can be obtained.

The total planning area is a set area of each collection point expected to need scanning, the area to be scanned is an area needing scanning, and before the first scanning task is executed, the area to be scanned is the total planning area. After the scanning task starts, the range of the area to be scanned changes, and the change is influenced by each scanned acquisition point on one hand, namely the area to be scanned does not contain the scanned acquisition points; on the other hand, the area to be scanned is affected by the area update policy.

When a scanning task is executed, the system firstly determines a region to be scanned, then determines a scanning sequence based on the current region to be scanned, and then scans acquisition points on the scanning sequence, and after scanning the scanning sequence once, determines a corresponding region updating strategy to update the region to be scanned according to an effective point set, and then scans the next scanning sequence after the region to be scanned is updated.

In the process of determining the region updating strategy, if the number of the acquisition points in the effective point set is small, it indicates that there may be more acquisition points which cannot acquire blood flow signals in the range of the current region to be scanned, so that the range of the current region to be scanned needs to be shrunk by a shrinkage strategy, and the number of the acquisition points which need to be scanned subsequently is reduced; if there are more collection points in the effective point set, it can be inferred that there may be more collection points in the peripheral range of the collection points, which can collect blood flow signals but do not belong to the range of the current region to be scanned, so the range of the current region to be scanned needs to be increased by a contraction strategy to reduce the occurrence of data missing.

The mode of dynamically adjusting the region to be scanned according to the actual scanning result is utilized, the time waste of scanning acquisition points which can not acquire blood flow signals in the scanning process is reduced, the integral scanning speed of three-dimensional cerebral blood flow data acquisition is improved, the time period of data acquisition and subsequent data processing is further shortened, and the effects of reducing the burden of the probe control driving module and prolonging the service life of the probe control driving module can be achieved because the scanning of each acquisition point is completed by the movement of the probe.

Optionally, in the specific method of the scanning analysis step, the method includes:

determining a low matching area and a high matching area based on the current area to be scanned; wherein the probability that the acquisition point in the low matching region is expected to be able to acquire blood flow signals is less than the probability that the acquisition point in the high matching region is expected to be able to acquire blood flow signals, and both the low matching region and the high matching region change along with the update of the region to be scanned;

acquiring a scanning result sent by a scanning mechanism;

determining cerebral blood flow data information based on blood flow signals acquired by all acquisition points in the scanning result;

and determining an effective point set based on acquisition points of blood flow signals acquired by the low-matching area in the scanning result.

By adopting the technical scheme, in the current region to be scanned, the probability that the blood flow signals can be acquired by the acquisition points in the low matching region is lower than the probability that the acquisition points in the matching region correspond to the acquisition points, and the acquisition points capable of acquiring the blood flow signals basically do not appear under the estimation condition.

Optionally, in the specific method of the path updating step, the method includes:

judging whether the effective point set has acquisition points, if so, determining the area updating strategy as an expansion strategy, and executing an area expansion step; if not, determining the area updating strategy as a contraction strategy, and executing an area contraction step; wherein the content of the first and second substances,

and (3) expanding the area: determining an expansion area according to the position of each acquisition point in the effective point set and the current low matching area, performing range expansion on the current area to be scanned based on the expansion area, and returning to the area selection step;

area shrinkage: and according to the distribution of the current low matching area, reducing the range of the current area to be scanned, and returning to the area selection step.

By adopting the technical scheme, if an acquisition point capable of acquiring a blood flow signal appears in the low matching area, determining an expanded area based on the peripheral range of the acquisition point and the range which can be extended by the low matching area, and combining the expanded area with the current area to be scanned to update the current area to be scanned; if the acquisition point capable of acquiring the blood flow signal does not appear in the low matching area, the probability that the blood flow signal can be acquired by the acquisition point in the current low matching area is proved to be low, so that the current low matching area is removed from the current scanning area to narrow the range of the area to be scanned.

Optionally, in a specific method of the region expanding step, the method includes:

determining a dynamic index corresponding to a current region to be scanned and determining an expansion coefficient; the dynamic index is used for reflecting the current trend of expansion or contraction of the area to be scanned, and the expansion coefficient can influence the current frequency of expansion or contraction of the area to be scanned;

determining an expansion update index based on the expansion coefficient and the dynamic index;

performing numerical comparison based on the expansion update index and the expansion threshold, and performing a partial expansion step or a full expansion step based on the comparison result;

local expansion: determining a point expansion area based on the position of each acquisition point in the effective point set, taking the point expansion area as an expansion area, performing range expansion on the current area to be scanned based on the expansion area, replacing the dynamic index corresponding to the current area to be scanned with an expansion updating index, and returning to the area selection step;

and (3) overall expansion: determining a point expansion area based on the position of each acquisition point in the effective point set, determining an expansion area based on the point expansion area and the current low matching area, performing range expansion on the current area to be scanned based on the expansion area, resetting a dynamic index corresponding to the current area to be scanned, and returning to the area selection step;

in a particular method of the zone shrinking step, comprising:

determining a dynamic index corresponding to a current region to be scanned and determining a shrinkage coefficient; wherein the contraction coefficient can influence the current frequency of expansion or contraction of the area to be scanned;

determining a contraction update index based on the contraction coefficient and the dynamic index;

performing a numerical comparison based on the contraction update index and a contraction threshold, and performing an index contraction step or a range contraction step based on the comparison result;

and (3) exponential shrinkage: replacing the dynamic index corresponding to the current region to be scanned with the contraction updating index, and returning to the region selection step;

shrinkage of range: and according to the distribution of the current low matching area, reducing the range of the current area to be scanned, resetting the dynamic index corresponding to the current area to be scanned, and returning to the area selection step.

By adopting the technical scheme, the updating of the area to be scanned comprises the updating of the self range and the updating of the dynamic index, the dynamic index of the area to be scanned can be updated every time the area expanding step or the area shrinking step is triggered, but the self range of the area to be scanned can be expanded or shrunk in a larger range only when the dynamic index exceeds the range corresponding to the expanding threshold or the shrinking threshold. The numerical value of the dynamic index is influenced by the expansion coefficient and the contraction coefficient at the same time, and only when the influence of the expansion coefficient on the dynamic coefficient is accumulated to a certain amount, or when the influence of the contraction coefficient on the dynamic coefficient is accumulated to a certain amount, the range of the area to be scanned is changed, so that the range adjustment caused by a small amount of individual deviation is prevented, the frequent adjustment caused by a small amount of deviation is reduced, and the overall efficiency of data acquisition is improved.

Meanwhile, when the current dynamic index does not exceed the range corresponding to the expansion threshold, the current region to be scanned is not adjusted in a large range, but the point expansion region is still used as the expansion region, so that the region to be scanned extends around the effective point set, and the risk of data missing collection is reduced.

Optionally, in a specific method for determining a point spread region based on the position of each acquisition point in the effective point set, the method includes:

determining an expansion circle center based on the position of the effective point concentrated acquisition point, and determining an expansion distance;

determining a point expansion area based on the expansion circle center and the expansion distance; the point expansion areas comprise all areas which take the expansion circle center as the circle center and take the expansion areas as the radius.

By adopting the technical scheme, in view of the distribution rule of cranial blood vessels, the blood vessels may pass through a plurality of continuous acquisition points, so that the peripheral range of each acquisition point in an effective point set can be deduced, namely, acquisition points which can acquire blood flow signals but do not belong to the range of the current region to be scanned may exist in a point expansion region, and therefore, acquisition points which are not subjected to over-scanning in the point expansion region are required to be added into the region to be scanned, so that the range of the scanning region is enlarged, and the occurrence of data missing acquisition is reduced.

Optionally, in a specific method of the overall expanding step, the method comprises:

determining a point expansion area based on the position of each acquisition point in the effective point set;

determining a candidate region based on the current low-matching region; wherein the probability that the acquisition point in the candidate region is expected to acquire a blood flow signal is less than or equal to the probability that the acquisition point in the current low-matching region is expected to acquire a blood flow signal;

removing the area which is related to the scanned scanning sequence from the candidate area, and determining an expandable area;

determining an expansion area according to the point expansion area and the candidate area;

and increasing the range of the current region to be scanned based on the expanded region, resetting the dynamic index corresponding to the current region to be scanned, and returning to the region selection step.

By adopting the technical scheme, when the range of the area to be scanned is shrunk, a part of acquisition points are rejected from the area to be scanned due to the fact that the probability that the blood flow signals can be acquired is low, but the part of acquisition points still have the acquisition points which can possibly acquire the blood flow signals, and the candidate area aims to recall the part of acquisition points and add the part of acquisition points into the area to be scanned again so as to reduce the risk of data missing. The limitation of the expandable region is to reduce the influence of the scanned region on the expansion region, reduce repeated adjustment and improve the efficiency of region convergence.

Optionally, in a specific method for determining a candidate region based on a current low matching region, the method includes:

determining a statistical comparison table corresponding to the total planning area; a plurality of grade areas divided in the total planning area are recorded in the statistical comparison table, and the grade areas are sorted according to the probability that blood flow signals can be expected to be acquired by the acquisition points in the areas;

based on the current low-match region, a candidate region is determined from the statistical look-up table.

By adopting the technical scheme, the statistical comparison table records the division of a plurality of grade areas, and based on the current low matching area, the grade area with lower collected blood flow signals is searched downwards in the statistical comparison table, so that the candidate area can be determined.

Optionally, in a specific method of the range narrowing step, the method includes:

determining a contraction area based on a low matching area corresponding to a current area to be scanned;

and removing the contraction area from the current area to be scanned, and taking the removed area as the area to be scanned.

By adopting the technical scheme, the contraction area is used for reflecting the part which can be removed in the low matching area, and as the current low matching area does not have the collection point which can collect the blood flow signal, the probability that the blood flow signal can be collected by the collection point in the current low matching area is proved to be lower, so that the corresponding contraction area is removed from the current scanning area, the rest area is obtained to be a new area to be scanned, and the range of the area to be scanned is reduced to a more accurate range.

Optionally, in a specific method for determining a shrinkage area based on a low matching area corresponding to a current area to be scanned, the method includes:

determining a minimum scanning area in the total planning area;

judging whether the low matching area corresponding to the current area to be scanned has an overlapping area with the minimum scanning area,

if so, removing the overlapping area from the low matching area, and taking the removed area as a contraction area;

and if not, determining the high matching area as the contraction area.

By adopting the technical scheme, the minimum scanning area comprises the acquisition points which are required to be scanned in the total planning area, and the minimum scanning belt area is used for limiting the contraction area, so that the risk of excessive contraction of the area to be scanned can be reduced, and the integrity of data acquisition is guaranteed.

The second purpose of the application is to provide a cerebral blood flow data acquisition system based on rapid scanning, which has the characteristic of improving the overall efficiency of data acquisition and scanning.

The second objective of the present invention is achieved by the following technical solutions:

the area selection module is used for determining an area to be scanned in the total planning area;

the sequence determining module is used for determining a scanning sequence in the current region to be scanned and sending path planning information to the scanning mechanism based on the scanning sequence so that the scanning mechanism scans all acquisition points in the current scanning sequence; wherein the area to be scanned comprises at least one scanning sequence comprising at least one acquisition point that is not overscan;

the scanning analysis module is used for acquiring a scanning result sent by the scanning mechanism and determining cerebral blood flow data information and an effective point set based on the scanning result; wherein the cerebral blood flow data information comprises all blood flow signals acquired in the current scanning sequence, and the effective point set comprises acquisition points capable of acquiring the blood flow signals;

the execution judgment module is used for judging whether all the acquisition points in the current region to be scanned are over-scanned or not, and if so, outputting a data acquisition result based on cerebral blood flow data information; if not, executing a path updating step;

a path updating module, which is used for determining an area updating strategy based on the effective point set, updating the current area to be scanned according to the area updating strategy and returning to the area selection step;

The third purpose of the application is to provide an intelligent terminal, which has the characteristic of improving the overall efficiency of data acquisition and scanning.

The third object of the invention is achieved by the following technical scheme:

an intelligent terminal comprises a memory and a processor, wherein the memory is stored with a computer program which can be loaded by the processor and executes the cerebral blood flow data acquisition method based on the quick scanning.

The fourth purpose of the present application is to provide a computer storage medium, which can store corresponding programs and has the characteristic of improving the overall efficiency of data acquisition and scanning.

The fourth object of the present invention is achieved by the following technical solutions:

a computer readable storage medium storing a computer program that can be loaded by a processor and executed to perform any of the above fast scan-based cerebral blood flow data acquisition methods.

Drawings

Fig. 1 is a schematic diagram of a motion trajectory of a probe in the related art.

Fig. 2 is a schematic view of the operation of the probe in scanning the skull.

Fig. 3 is a schematic flow chart of a cerebral blood flow data acquisition method based on fast scanning according to the present application.

Fig. 4 is a schematic diagram of a total planning area, an unexecuted area, an executed area, an area to be scanned, and an unscanned area.

Fig. 5 is a sub-flow diagram illustrating a scanning analysis step in the rapid scanning-based cerebral blood flow data acquisition method according to the present application.

Fig. 6 is a schematic distribution diagram of the areas a, B, C, and D in the plan view of the present application.

Fig. 7 is a sub-flowchart of the region contraction step in the fast scan-based cerebral blood flow data acquisition method according to the present application.

Fig. 8 is a schematic diagram of the start of a scan of the total planned area in an example of area shrinkage.

Fig. 9 is a schematic diagram of continuing to scan the region to be scanned shown in fig. 8 in an example of region shrinkage, and illustrates that a range shrinkage step is performed on the region to be scanned.

Fig. 10 is a sub-flowchart of the region expansion step in the method for acquiring cerebral blood flow data based on fast scan according to the present application.

Fig. 11 is a schematic diagram of scanning the region to be scanned shown in fig. 9 in the example of region expansion.

Fig. 12 is a schematic diagram of determining a spot expanded region of the region to be scanned shown in fig. 11 in an example of region expansion.

Fig. 13 is a schematic diagram after a local expansion step is performed on the region to be scanned shown in fig. 12 in the example of region expansion.

Fig. 14 is a schematic diagram of determining a spot expanded region of the region to be scanned shown in fig. 13 in the example of region expansion.

Fig. 15 is a schematic diagram after a full expansion step is performed on the region to be scanned shown in fig. 14 in the example of region expansion.

Fig. 16 is a block diagram of a brain blood flow data acquisition system based on fast scan according to the present application.

In the figure, 1, a region selection module; 2. a sequence determination module; 3. a scanning analysis module; 4. an execution judgment module; 5. a path update module; 6. and a result output module.

Detailed Description

Referring to fig. 1, in the related art, when an intracranial three-dimensional cerebral blood flow model is constructed for an observation object, an ultrasonic device is required to scan a skull of the observation object, and in a scanning process, the ultrasonic device drives a probe to automatically move in a planned area according to a preset motion track by controlling a driving module (usually, a motor or a multi-axis manipulator), wherein the planned area includes a plurality of acquisition points, the motion track passes through each acquisition point, and when the probe moves to the position of the acquisition point, data acquisition is performed at the position corresponding to the acquisition point. The preset motion track of the probe is usually in a snake shape or a 'return' shape.

The related art has technical defects that: the probe needs to traverse all the acquisition points on the motion trail, that is, the probe needs to move to the acquisition point for acquisition no matter whether the acquisition point can acquire cerebral blood flow data or not. The full traversal mode not only can increase the burden of the control drive module and shorten the service life of the probe control drive module, but also can increase the time period for automatically acquiring cerebral blood flow and generating three-dimensional cerebral blood flow data.

In order to solve the above technical problems, a scheme is needed to be provided, which can plan the motion trajectory of the probe, so that the probe only moves to a position where blood flow may occur as far as possible to perform data acquisition, thereby increasing the speed of scanning acquisition, and meanwhile, the motion trajectory can be adjusted and planned in real time by using the position where blood flow occurs as a reference, thereby reducing the possibility of data missing acquisition.

In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship, unless otherwise specified.

In addition, the reference numerals of the steps in this embodiment are only for convenience of description, and do not represent the limitation of the execution sequence of the steps, and in actual application, the execution sequence of the steps may be adjusted or performed simultaneously as needed, and these adjustments or substitutions all belong to the protection scope of the present invention.

Embodiments of the present application are described in further detail below with reference to figures 2-16 of the specification.

The embodiment of the application discloses a cerebral blood flow data acquisition method based on rapid scanning.

A brain blood flow data acquisition method based on rapid scanning is mainly described as follows.

Referring to fig. 2 and 3, S1, area selection: the area to be scanned in the total planning area is determined.

The total planning area refers to an area containing all the acquisition points, and is the most complete scanning area range, and the probability that each acquisition point in the total planning area can acquire a blood flow signal is different. In the actual detection, each acquisition point is actually distributed around the skull of the observation object, namely distributed in a three-dimensional space, the three-dimensional distribution is expanded into a two-dimensional plane, a two-dimensional planning view can be obtained, and the position of each acquisition point is recorded in the planning view in a two-dimensional coordinate mode.

Referring to fig. 4, in detail, the total planning area is composed of an executed area and an unexecuted area, wherein the executed area refers to an area where the point set of the acquired points is located, which has been scanned, and the unexecuted area refers to an area where the point set of the acquired points is located, which has not been scanned. In this embodiment, as each acquisition point is scanned sequentially, the executed area becomes gradually larger, and the unexecuted area becomes gradually smaller.

Further, the non-execution region is composed of a region to be scanned and a region not to be scanned, wherein the region to be scanned refers to a region where a point set of acquisition points which need to be scanned in the next step is located, the acquisition points are not scanned, and the probability that cerebral blood flow data can be acquired is high; the non-scanning region refers to a region where a point set of acquisition points that do not need to be scanned in the next step is located, and this portion of acquisition points is not scanned, but is not included in the scanning path because the probability that cerebral blood flow data can be acquired is low.

In this embodiment, as the executed region gradually becomes larger, the region to be scanned is updated according to the scanning result of the executed region, and the updating manner includes contraction and expansion, where the contraction means that the region to be scanned gradually approaches the region where the acquisition point with higher probability of being able to acquire cerebral blood flow data is located, and gradually reduces the range, thereby improving the overall rate of scanning acquisition; the expansion means that the region to be scanned is gradually expanded in a small range by taking an acquisition point with higher probability of acquiring cerebral blood flow data as a center so as to adjust the range, thereby reducing the possibility of data missing acquisition.

Referring to fig. 3, S2, sequence determination: and determining a scanning sequence in the current area to be scanned, and sending path planning information to the scanning mechanism based on the scanning sequence so that the scanning mechanism scans all acquisition points in the current scanning sequence.

The scanning sequence includes at least one acquisition point that is not scanned, and each scanning sequence may enter the next step in sequence according to a preset scanning order.

Specifically, the scanning sequence that has been subjected to the overscan is recorded in the executed area, and in the subsequent steps, the large-scale expansion of the area to be scanned is involved, and only the area range where the scanning sequence that has not been subjected to the overscan is located in the area to be scanned.

The path planning information contains the specific position of the scanning sequence and the scanning direction corresponding to the scanning sequence.

The scanning mechanism comprises a probe and a control driving module, wherein the control driving module is used for controlling the probe to move. And after the control driving module receives the path planning information, the control driving module controls the data acquisition of each acquisition point in the scanning sequence in sequence according to the scanning direction, so that the probe moves and scans in the planned motion path. After the probe finishes scanning of a scanning sequence, the probe sends a scanning result corresponding to the scanning, and the scanning result comprises blood flow signals collected by one or more collection points.

The scanning direction of the scanning sequence can be from left to right, can also be from right to left, and can also be the staggered direction; if the scanning sequence is set to be from left to right, the probe scans all the acquisition points in the scanning sequence from left to right in sequence; if the scanning sequence is set from right to left, the probe can sequentially scan all the acquisition points in the scanning sequence from right to left; if the scanning sequence is set to be in the staggered direction, the system records the scanning sequence of the last scanning sequence, and if the last scanning sequence is from left to right, the current scanning sequence is from right to left; if the previous direction is from right to left, the current direction is from left to right.

In this embodiment, the scanning sequence is set as the first row of the region to be scanned, and the direction is the interlaced direction. And scanning the rows in the area to be scanned from top to bottom in sequence. When only the last scanning sequence is left in the area to be scanned and the scanning sequence is finished, namely when the last row of acquisition points of the area to be scanned is finished, the task of scanning the cerebral blood flow data of the cranium is finished.

One of the functions of step S2 is to determine the moving path of the probe and define the update cycle of the region to be detected, and each time a scan sequence is completed, the scan result is statistically analyzed, the region to be scanned may be updated, and the scan sequence is updated with the region to be scanned.

S3, scanning analysis: and acquiring a scanning result sent by a scanning mechanism, and determining cerebral blood flow data information and an effective point set based on the scanning result.

The scanning result comprises blood flow signals which correspond to all the acquisition points in the scanning sequence one by one, the scanning result is analyzed, all the blood flow signals obtained by the current scanning sequence are recorded in cerebral blood flow data information, and the specific positions of the acquisition points which can acquire the blood flow signals in the current scanning sequence are recorded in an effective point set.

In this embodiment, the valid point set may not include all the acquisition points capable of acquiring blood flow signals in the current scanning sequence, and the valid point set does not include the acquisition points on the row of the scanned scanning sequence, i.e., the scanned scanning sequence that has been processed does not participate in the statistics of the valid point set.

Referring to fig. 5 and 6, in step S3, the method includes:

and S31, determining a low matching area and a high matching area based on the current area to be scanned.

The low matching area and the high matching area are areas which are distinguished by the size of the probability that the blood flow signals can be expected to be acquired by the acquisition points in the areas, and the probability that the blood flow signals can be expected to be acquired by the acquisition points in the low matching area is smaller than the probability that the blood flow signals can be expected to be acquired by the acquisition points in the high matching area.

Specifically, the division between the low matching region and the high matching region is related to the actual blood flow position (or blood vessel position) distribution of the cranium, and the setting mode is as follows:

1) the history data is acquired using a scanning method in the related art. The historical data is used for reflecting whether each acquisition point in the motion trail can acquire the blood flow signals or not.

2) From the historical data, a statistical model of the blood flow signals acquired at the acquisition points is generated. Wherein the statistical model is partitioned based on the probability that the blood flow signal can be acquired by the acquisition points, such as the acquisition points located in the high probability region are the acquisition points where the blood flow signal with the intensity higher than the threshold value will appear with high probability (e.g., > 90%); acquisition points located in the medium probability region are those for which a medium probability (e.g., > 70%) would occur for which a blood flow signal having an intensity above a threshold value; the acquisition points in the low probability area are the acquisition points with low probability (> 5%) of blood flow signals with the intensity higher than the threshold value; the other regions are considered as small probability regions (≦ 5%).

3) And determining a low matching area and a high matching area according to the statistical model.

In this embodiment, the total planning area has 4 matching level areas divided based on a statistical model, which includes: the probability that blood flow signals can be acquired by the acquisition points in the 4 areas is gradually reduced, namely the highest D area in the area A is the smallest. The area B is distributed around the area A, the area C is distributed around the area B, and the area D is distributed around the area C; the area enclosed by the outer edge of the area A, the area enclosed by the outer edge of the area B, the area enclosed by the outer edge of the area C and the area enclosed by the outer edge of the area D are reduced in sequence.

The low matching area and the high matching area are relatively divided and correspond to the current area to be scanned, when the low matching area changes, the high matching area also changes, and when the range of the area to be scanned changes, both the low matching area and the high matching area may be updated. If in the initial state, the low matching area is the D area, the high matching area is the C area; if the acquisition point of the D area does not exist in the area to be scanned, the low matching area is the C area, and the high matching area is the B area; and if the acquisition point of the C area or the D area does not exist in the area to be scanned, the low matching area is the B area, and the high matching area is the A area.

Referring to fig. 5, S32, the scanning result sent by the scanning mechanism is acquired.

And acquiring a scanning result generated by the scanning of the current scanning sequence by the probe, so as to determine whether the current scanning sequence has an acquisition point capable of acquiring a blood flow signal in subsequent steps.

And S33, determining cerebral blood flow data information based on the blood flow signals acquired by all the acquisition points in the scanning result.

All the acquisition points and all the corresponding position coordinates of the blood flow signals which can be acquired in the current scanning sequence are counted and recorded in the cerebral blood flow data information.

And S34, determining an effective point set based on the acquisition points of the blood flow signals acquired by the low matching area in the scanning result.

Wherein, the effective point set comprises an acquisition point which can acquire blood flow signals in a low matching area. In a subsequent region expansion step, new acquisition points may be added to the region to be scanned. However, the valid point set does not count the acquisition points in the row of the scan sequence that have been scanned.

For example, the scan sequence has a top-down scan order, and in the case of no expansion, the scan sequences in the respective rows sequentially perform the scan from top to bottom in the plan view, such as the scan sequence of the first row, the scan sequence of the second row, and the scan sequence of the last row …. However, in the case of subsequent expansion, it may happen that the scanning sequence of the first line has already been scanned, but the scanning sequence of the first line still scans at this time because a new acquisition point appears in the scanning sequence, but because the line has already been counted, the scanning result of the scanning sequence of this time is not included in the statistics of the valid point set.

S4, executing judgment: judging whether all acquisition points in the current region to be scanned are over-scanned or not, and if so, outputting a data acquisition result based on cerebral blood flow data information; if not, step S5 is executed.

When all the acquisition points in the area to be scanned are scanned, scanning-available acquisition points do not exist in the subsequent steps, no unexecuted area exists, the task of scanning the cerebral blood flow data of the cranium is completed, a cerebral blood flow data report can be generated based on the cerebral blood flow data information, and the cerebral blood flow data report is used for constructing an intracranial three-dimensional cerebral blood flow model.

When there is an acquisition point which is not scanned in the region to be scanned, the task of scanning the cranial cerebral blood flow data is not completed, and planning and updating are required to be performed on the remaining unexecuted region.

S5, path updating: and based on the effective point set, determining an area updating strategy, updating the current area to be scanned according to the area updating strategy, and returning to the step S1.

Wherein the region updating strategy comprises a contraction strategy and an expansion strategy. The contraction strategy can contract the range of the current region to be scanned according to the high matching region corresponding to the current region to be scanned; the expansion strategy can expand the range of the current region to be scanned according to the position of each acquisition point in the effective point set and the high matching region corresponding to the current region to be scanned.

Referring to fig. 5, step S5 includes:

s51, judging whether the effective point set has collecting points, if not, determining the area updating strategy as a contraction strategy, and executing the step S52; if yes, the area update policy is determined to be the expansion policy, and step S53 is performed.

In actual detection, for different detection objects, the blood vessel distribution of the cranium is different, and the statistical model can only predict the probability that each acquisition point can acquire blood flow signals, so that the low-matching region can only evaluate the probability, the capability that the acquisition points can acquire the blood flow signals is not directly denied, and the purpose of the effective point set is to detect the deviation between the statistical model and an actual result, and plan and adjust the current region to be scanned in subsequent steps based on the deviation.

Specifically, because the probability that the blood flow signals can be acquired by the acquisition points in the low matching area is low, if more acquisition points capable of acquiring the blood flow signals exist in the effective point set, it is indicated that the current low matching area cannot accurately reflect the low probability area, and the current area to be scanned needs to be expanded, so that the purpose of reducing data missing acquisition can be achieved; if fewer acquisition points capable of acquiring blood flow signals exist in the effective point set, it is indicated that the current low-matching area can accurately reflect the low-probability area, and the current area to be scanned needs to be contracted, so that the purpose of improving the overall scanning efficiency can be achieved.

S52, shrinking the area: and judging whether the current dynamic index meets the range contraction condition or not based on the dynamic index corresponding to the current to-be-scanned area, performing range reduction on the current to-be-scanned area based on the current low matching area according to the judgment result, and returning to the step S1.

Wherein the valid set of points is capable of reflecting a deviation between the statistical model and the actual detection, which deviation affects the magnitude of the dynamic index. The dynamic index is used for reflecting the trend that the current region to be scanned needs to expand or contract, and only after the dynamic index meets the range contraction condition, the region to be scanned is subjected to range reduction, so that the range adjustment caused by a small amount of individual deviation is prevented, the frequent adjustment caused by a small amount of deviation is reduced, and the overall efficiency of data acquisition is improved.

Referring to fig. 6 and 7, in step S52, the method includes:

and S521, determining a dynamic index corresponding to the current region to be scanned, and determining a shrinkage coefficient.

After the area to be scanned is updated, the dynamic index changes, and the initial value of the dynamic index is a preset value, which is preset to 1 in this embodiment. The shrinkage factor can affect the frequency of expansion or shrinkage of the current region to be scanned and the reduction range of the dynamic index, and the value is set to be in the range of 0-1, in this embodiment, the shrinkage factor is preset to be 0.8.

And S522, determining a contraction updating index based on the contraction coefficient and the dynamic index.

Wherein, the product of the contraction coefficient and the dynamic index is calculated and determined as the contraction renewal index.

S523, judging whether the contraction updating index is greater than or equal to the contraction threshold value, if so, executing a step S524; if not, go to step S525.

The contraction threshold is a preset value, and when the initial value of the dynamic index is greater than or equal to 1, the contraction threshold should be less than or equal to the contraction coefficient, and in this embodiment, the contraction threshold is preset to be 0.75.

The shrinkage threshold is smaller than the initial value of the dynamic index, the value of the dynamic index is gradually reduced along with the multiplication of the dynamic index and the shrinkage coefficient, and when the dynamic index is smaller than the shrinkage threshold, the dynamic index meets the range shrinkage condition. The multiplication process of the dynamic index and the contraction coefficient is equivalent to the accumulation of the deviation value, the more times of the multiplication of the dynamic index and the contraction coefficient, the larger the trend of the range contraction of the area to be scanned is, before the trend does not meet the range contraction condition, the area to be scanned is equivalent to be in a state of receiving the influence of errors, and the range adjustment is temporarily not carried out; before the trend meets the range contraction condition, the range of the area to be scanned is adjusted.

In summary, in step S52, there are two ways to update the to-be-scanned area, which are corresponding dynamic index update and corresponding area range update, respectively, where after the scanning of each group of scanning sequences is completed, the dynamic index of the to-be-scanned area is updated; and the area range of the area to be scanned is updated only when the dynamic index reaches the threshold condition.

S524, exponential shrinkage: and replacing the dynamic index corresponding to the current region to be scanned with the contraction update index, and returning to the step S1.

And updating the contraction updating index into the dynamic index of the current region to be scanned so as to enable the dynamic index to be changed under the influence of the deviation brought by the effective point set.

S525, range shrinkage: according to the distribution of the current low matching area, the range of the current area to be scanned is narrowed, the dynamic index corresponding to the current area to be scanned is reset, and the process returns to step S1.

The current low matching area is reasonably removed from the current area to be scanned so as to update the range of the area to be scanned, meanwhile, the area distribution of the area to be scanned is readjusted, and the original high matching area is used as the updated low matching area of the area to be scanned.

And resetting the dynamic index of the area to be scanned to an initial value while updating the area range of the area to be scanned.

In step S525, the method includes:

s5251, determining a contraction area based on the low matching area corresponding to the current area to be scanned.

The contraction area is located in the current area to be scanned, and the contraction area refers to an area which needs to be removed in order to contract the current area to be scanned.

Referring to fig. 7 and 8, in step S5251, the method includes:

s52511, a minimum scan area in the total planning area is determined.

The minimum scanning area comprises acquisition points which need to be scanned in the total planning area, the minimum scanning area is the minimum shrinkage range of the area to be scanned, and the area to be scanned cannot be shrunk to a range smaller than the minimum scanning area, so that the shrinkage area is limited, the risk of over shrinkage of the area to be scanned can be reduced, and the integrity of data acquisition is guaranteed.

Specifically, the minimum scanning area should be selected as an area with a higher probability of the statistical model, and in this embodiment, the minimum scanning area is the B area.

S52512, judging whether the low matching area corresponding to the current area to be scanned has an overlapping area with the minimum scanning area, if so, executing the step S52513; if not, go to step S52514.

Wherein the overlap region comprises an intersection between the low matching region and the minimum scan region. In order to shrink the current region to be scanned, the range corresponding to the low matching region needs to be reasonably removed, and the requirement of reasonable removal is that the acquisition point related to the minimum scanning region cannot be removed, that is, the acquisition point in the overlapping region cannot be removed from the region to be scanned.

S52513, the overlap area is removed from the low-matching area, and the removed area is used as a contraction area.

And the acquisition points containing the minimum scanning area in the overlapping area are removed from the low matching area, so that the shrinkage area does not interfere with the minimum scanning area, and the shrinkage area can be obtained.

S52514, determining the high matching area as a contraction area.

And if no overlapping area exists between the low matching area and the minimum scanning area, directly taking the high matching area as a contraction area.

S5252, the contraction region is removed from the current region to be scanned, and the removed region is used as the region to be scanned, and the process returns to step S1.

The acquisition points in the contraction area are removed from the current area to be scanned, so that the number of the acquisition points needing to be scanned in the area to be scanned can be reduced, and the range of the area to be scanned is reasonably reduced.

The following is a specific example description of step S52:

referring to fig. 8, it is assumed that the scanning task starts, and the low matching area is the D area and the high matching area is the C area.

The scanning sequence is the first row of the region to be scanned, namely the first row of the planning view, the acquisition points in the first row are all located in the low matching region, after all the acquisition points in the scanning sequence are scanned, no acquisition point capable of acquiring blood flow signals exists in the low matching region, and then the effective point set does not have an acquisition point, and the region contraction step is executed.

At this time, the dynamic index I =1 Is an initial value, the contraction coefficient s =0.8, the contraction renewal index Is =0.8, since the contraction threshold Ts =0.75, the contraction renewal index Is greater than the contraction threshold Ts, and the dynamic index does not satisfy the range contraction condition, the index contraction step Is executed to replace the value of the dynamic index I with the value of the contraction renewal index Is.

Referring to fig. 9, it is assumed that the scanning task continues to start, where the low matching area is the D area and the high matching area is the C area.

The scanning sequence is a first row of a region to be scanned, namely a second row of the planning view, the acquisition points of the second row are partially located in the low matching region and partially located in the high matching region, after all the acquisition points in the scanning sequence are scanned, the acquisition points which can acquire blood flow signals in the high matching region are found, but the acquisition points which can acquire the blood flow signals still do not exist in the low matching region, and then the region contraction step is executed if no acquisition points exist in the effective point set.

When the dynamic index I =0.8 and the contraction coefficient s =0.8, the contraction renewal index Is =0.64, and since the contraction threshold Ts =0.75 and the contraction renewal index Is smaller than the contraction threshold Ts, and the dynamic index satisfies the range contraction condition, the range contraction step Is performed:

and removing the acquisition points which are not in the minimum scanning area (area B) in the low matching area from the area to be scanned to shrink the range of the area to be scanned, wherein after the area to be scanned is updated, the low matching area is changed into the area C, and the high matching area is the area B.

The dynamic index I is reset to 1.

In summary, for the logical description and example description of the region contraction step, the logical description and example description of the region expansion step are as follows.

Referring to fig. 10, S53, zone expansion: and judging whether the current dynamic index meets the comprehensive expansion condition or not based on the dynamic index corresponding to the current region to be scanned, determining an expansion region based on the positions of all the collection points in the effective point set and the current low matching region according to the judgment result, performing range expansion on the current region to be scanned based on the expansion region, and returning to the step S1.

The expansion area is located outside the current area to be scanned, and the expansion area refers to an area of the current area to be scanned, which needs to be reasonably extended for expansion.

Similarly, in step S52, the dynamic index is used to reflect the trend that the current region to be scanned needs to expand or contract, and only when the dynamic index satisfies the overall expansion condition, the region to be scanned will be subjected to range expansion, so as to prevent the range adjustment caused by a small amount of individual deviations, reduce the frequent adjustment caused by a small amount of deviations, and improve the overall efficiency of data acquisition.

In step S53, the method includes:

and S531, determining a dynamic index corresponding to the current region to be scanned, and determining an expansion coefficient.

Similarly, in step S531, after the area to be scanned is updated, the dynamic index changes, and the initial value of the dynamic index is a preset value, in this embodiment, the initial value of the dynamic index is preset to 1.

The expansion coefficient can affect the frequency of expansion or contraction of the current region to be scanned, and affect the increase amplitude of the dynamic index, the value of the expansion coefficient should be greater than 1, and in this embodiment, the expansion coefficient is preset to be 1.2.

And S532, determining an expansion updating index based on the expansion coefficient and the dynamic index.

Wherein, the product of the expansion coefficient and the dynamic index is calculated and determined as the expansion updating index.

In this embodiment, before triggering the reset, the dynamic index may be multiplied by the expansion coefficient to increase the dynamic index, or may be multiplied by the contraction coefficient to decrease the dynamic index, so that the expansion coefficient and the contraction coefficient jointly affect the variation range of the dynamic index.

S533, judging whether the expansion updating index is less than or equal to the expansion threshold value, if so, executing the step S534; if not, go to step S535.

The dilation threshold is a preset value, and when the initial value of the dynamic index is greater than or equal to 1, the dilation threshold should be greater than or equal to the dilation coefficient, and in this embodiment, the dilation threshold is preset to 1.4.

The expansion threshold is larger than the initial value of the dynamic index, the numerical value of the dynamic index is gradually increased along with the multiplication of the dynamic index and the expansion coefficient, and when the dynamic index is larger than the expansion threshold, the dynamic index meets the comprehensive expansion condition. The multiplication process of the dynamic index and the expansion coefficient is equivalent to the accumulation of the deviation value, the more times of the multiplication of the dynamic index and the expansion coefficient, the greater the trend of the range shrinkage of the area to be scanned is, before the trend does not meet the comprehensive expansion condition, the area to be scanned is equivalent to the state of receiving the error influence, and the range adjustment is not performed temporarily; before the trend meets the comprehensive expansion condition, the range of the area to be scanned is adjusted.

On the other hand, because the expansion coefficient and the contraction coefficient jointly affect the change range of the dynamic index, if the dynamic index does not meet the comprehensive expansion condition, the expansion trend and the contraction trend are equivalent to equal, and the region to be scanned cannot be directly expanded or contracted; if the dynamic index meets the comprehensive expansion condition, the expansion trend exceeds the contraction trend, so that the region to be scanned can be expanded reasonably; on the contrary, if the dynamic index meets the range contraction condition, the contraction trend exceeds the expansion trend, so that the region to be scanned can be contracted reasonably.

In summary, in step S53, there are two ways to update the to-be-scanned area, which are corresponding dynamic index update and corresponding area range update, respectively, where after the scanning of each group of scanning sequences is completed, the dynamic index of the to-be-scanned area is updated; and the area range of the area to be scanned is updated only when the dynamic index reaches the threshold condition.

S534, local expansion: and determining a point expansion region according to the position of each acquisition point in the effective point set, taking the point expansion region as an expansion region, performing range expansion on the current region to be scanned based on the expansion region, replacing the dynamic index corresponding to the current region to be scanned with the expansion updating index, and returning to the step S1.

The point expansion area comprises a peripheral area of the effective point centralized collection point, the point expansion area is an area which takes the collection point as a circle center and takes the expansion distance as a radius, and the distance from each point in the expansion area to the circle center is less than or equal to the expansion distance. In the present embodiment, the expansion distance is preset to 1.

The specific method for determining the point spread region comprises the following steps:

1) and determining the expansion circle center based on the position of the effective point centralized acquisition point.

Wherein, the position and the number of the expansion circle centers are consistent with those of the acquisition points.

2) And determining a point expansion area based on the expansion circle center and the preset expansion distance.

In view of the distribution rule of cranial blood vessels, blood vessels may pass through a plurality of continuous acquisition points, so that the peripheral range of each acquisition point in the effective point set can be deduced, that is, acquisition points which can acquire blood flow signals but do not belong to the range of the current region to be scanned may exist in the expansion region, and therefore, acquisition points which are not subjected to over-scanning in the expansion region need to be added into the region to be scanned, so as to increase the range of the scanning region and reduce the occurrence of data missing.

Because the current dynamic index does not meet the comprehensive expansion condition, the current region to be scanned is not adjusted in a large range, and only the point expansion region is taken as the expansion region, so that the region to be scanned extends around the effective point set.

And updating the expansion updating index into the current dynamic index of the area to be scanned while expanding the range of the area to be scanned through the expansion area so as to change the dynamic index under the influence of the deviation brought by the effective point set.

S535, overall expansion: determining a point expansion region based on the position of each acquisition point in the effective point set, determining an expansion region based on the point expansion region and the current low matching region, performing range expansion on the current region to be scanned based on the expansion region, resetting the dynamic index corresponding to the current region to be scanned, and returning to the step S1.

The candidate region refers to a region distributed around the current low-matching region, and the probability that the blood flow signal can be expected to be acquired by the acquisition points in the candidate region is less than or equal to the probability that the blood flow signal can be expected to be acquired by the acquisition points in the low-matching region.

When the range of the region to be scanned is shrunk, a part of acquisition points are rejected from the region to be scanned due to the fact that the probability that the blood flow signals can be acquired by the acquisition points is low; however, there are still collection points in this part of the collection points where a blood flow signal may be collected, and this assumption is confirmed if a full expansion condition is satisfied, and the purpose of the candidate region is to recall and rejoin this part of the collection points in this case into the region to be scanned.

In step S535, the method includes:

s5351, determining a point spread area based on the position of each acquisition point in the effective point set.

The point expansion area is an area which takes the acquisition point as a circle center and takes the expansion distance as a radius, and the distance from each point in the expansion area to the circle center is less than or equal to the expansion distance.

S5352, determining a candidate area based on the current low matching area.

Wherein, specifically include:

and acquiring a statistical comparison table corresponding to the total planning area.

Referring to fig. 6, in this embodiment, there are a total of 4 matching level regions partitioned based on the statistical model in the total planning region, including: zone A, zone B, zone C and zone D. Each matching grade area is recorded in the statistical comparison table, and the candidate area corresponding to the low matching area can be obtained by inquiring in the statistical comparison table according to the current low matching area. If the current low matching area is an area A, the candidate area is an area B; if the current low matching area is the area B, the candidate area is the area C; if the current low matching area is the area C, the candidate area is the area D; if the current low matching region is the region D, the candidate region is still the region D because there is no region with a probability lower than the region D or the region with a probability lower than the region D has no reference value. On the other hand, the statistical comparison table is established based on the statistical model, the statistical model is gradually optimized and perfected along with the updating iteration of a large amount of historical data on the statistical model, and the statistical comparison table is correspondingly updated and iterated, so that the statistical comparison table has reference value.

S5353, removing the area related to the scanned scanning sequence from the candidate area, and determining the expandable area.

The expandable area refers to an area that does not interfere with the scanned scan sequence, and specifically, the expandable area is an area left by excluding a row occupied by an executed area in the plan view.

Because each scanning sequence is executed from top to bottom in sequence, if an effective point set appears in the scanned scanning sequence, the scanned sequence may be repeatedly adjusted, therefore, the overall expansion step only expands the range of the line where the scanning sequence which is not scanned is located, the region outside the expandable region is not affected by the range expansion and the larger range brought by the candidate region, and is only affected by the range expansion and the smaller range brought by the point expansion region, so as to improve the accuracy of the expansion direction and achieve the better convergence effect.

S5354, determining an expanded region according to the point expanded region and the candidate region, performing range expansion on the expandable region based on the expanded region, resetting the dynamic index corresponding to the current region to be scanned, and returning to step S1.

And taking a collection of the point expansion region and the candidate region, determining an expansion region, and adding the expansion region into the region to be scanned so as to reasonably expand the range of the region to be scanned.

In this embodiment, after the area to be scanned is updated, the low matching area and the high matching area in the area to be scanned are adjusted, wherein the expanded area is used as a new low matching area, and the low matching area before updating is used as a new high matching area.

The following is a specific example description of step S53:

referring to fig. 11 and 12, it is assumed that the scanning task in the specific example of step S52 described above is continued, where the low matching region is the region C and the high matching region is the region B.

And after all the acquisition points in the scanning sequence are scanned, if an acquisition point P1 capable of acquiring blood flow signals exists in the low matching area, the effective point set records an acquisition point P1, and a region expansion step is executed.

When the dynamic index I =1 and the expansion coefficient e =1.2, the expansion update index Ie =1.2, and since the expansion threshold Te =1.4, the expansion update index Ie is smaller than the expansion threshold Te, and the dynamic index does not satisfy the full expansion condition, the local expansion step is performed:

and determining a point expansion region by taking the acquisition point P1 as a circle center and the expansion distance m =1 as a radius, and taking the expansion region as an expansion region.

The acquisition point P2 in the expanded region that was not over-scanned is added to the region to be scanned.

Replacing the value of the dynamic index I by the value of the expansion update index Ie, the dynamic index I = 1.2.

Referring to fig. 13, the updated region to be scanned is shown, in which the low matching region includes the collection points that have not been scanned in the region C, and the high matching region is still the region B.

Assuming that the scanning task continues to start, the scanning sequence is the first row of the region to be scanned, i.e. the third row of the planning view, since the newly added acquisition point P2 is located in the third row of the planning view, but since the third row has already been scanned, it does not participate in the statistics of the valid point set whether or not the blood flow signal is acquired as a result of the scan at the acquisition point P2.

Referring to FIG. 14, after scanning acquisition Point P2, assume that the scan job continues to begin, with the low match region being region C and the high match region being region B.

And after all the acquisition points in the scanning sequence are scanned, if an acquisition point P3 capable of acquiring blood flow signals exists in the low matching area, the effective point set records an acquisition point P3, and a region expansion step is executed.

At this time, if the dynamic index I =1.2 and the expansion coefficient s =1.2, then the expansion update index Ie =1.44, and since the expansion threshold Te =1.4 and the expansion update index Ie is greater than the expansion threshold Te, the dynamic index satisfies the full expansion condition, then the full expansion step is executed:

and determining a point expansion region by taking the acquisition point P3 as a circle center and the expansion distance m =1 as a radius.

And determining a candidate region based on the current low matching region, wherein the candidate region is determined to be a D region because the current low matching region is the C region.

And eliminating the areas of the rows (the first four rows in the planning view) occupied by the executed areas in the candidate areas, and determining the expandable areas.

And combining the acquisition points obtained in all the point expansion areas and the acquisition points in the expandable area to determine the expanded area.

And adding the acquisition points which are not subjected to over-scanning in the expansion area into the area to be scanned, readjusting the high matching area into the area C, and readjusting the low matching area into the area D.

The dynamic index I is reset to 1.

Referring to FIG. 15, after the above steps 1-6, a new region to be scanned can be obtained, wherein under the influence of the expanded region and the expandable region, only acquisition point P4 and acquisition point P5 are added to the first four rows in the plan view.

Combining the step S52 and the step S53, in the process of changing the dynamic index, if the planning of the region to be scanned needs to have a faster contraction speed, a smaller contraction coefficient and a larger contraction threshold value need to be selected; if a faster dilation speed is required for the planning of the region to be scanned, a larger dilation coefficient and a smaller dilation threshold need to be taken. However, faster contraction/expansion speed may lead to faster range convergence/range expansion, and the planning adjustment of the region to be scanned may be more frequent; and the slower contraction/expansion speed brings slower range convergence/range expansion, which may cause the acquisition efficiency to be not obviously improved, and therefore, the acquisition efficiency needs to be set reasonably according to the actual situation.

In the range change process of the region to be scanned, the division of the high matching region and the low matching region determines whether the subsequent expansion logic or the contraction logic is executed, the effect of the whole method is obviously influenced, in order to enable the division of the high matching region and the low matching region to be more accurate, the high matching region and the low matching region are established on a statistical model based on a large amount of historical data, the influence caused by the wearing deviation of a head frame in the ultrasonic equipment is included, and better adaptability can be provided.

The implementation principle of the first embodiment of the application is as follows: before executing the scanning task, the scanning mechanism refers to the path planning information of which the path planning is finished and scans based on the motion path in the path planning information. After scanning a scanning sequence, the system records the positions of acquisition points capable of acquiring blood flow signals in the scanning sequence, determines an effective point set, collects the blood flow signals acquired at all the positions and determines cerebral blood flow data information. When all the acquisition points in the area to be scanned have been scanned, the scanning task is finished, and based on the acquired cerebral blood flow data information, cerebral blood flow data of the cranium can be obtained.

By utilizing the mode of dynamically adjusting the region to be scanned according to the actual scanning result, the whole acquisition process is dynamically narrowed and the acquisition range is expanded, so that a final acquisition path is formed. This collection path collection point is greater than the collection point that covers in the minimum scanning area, but must be less than the total and traverse the collection point in the total planning area that covers, thereby it is extravagant to reduce the time that the collection point that can not gather blood flow signal carries out the scanning among the scanning process, improve three-dimensional cerebral blood flow data acquisition's whole scanning speed, realize the effect of quick scanning, and, because the scanning of each collection point is removed by the probe and is accomplished, consequently, can also reach the burden that reduces probe control drive module, the effect of extension probe control drive module's life.

The embodiment of the application also discloses a cerebral blood flow data acquisition system based on rapid scanning.

Referring to fig. 16, in an embodiment, a brain blood flow data acquisition system based on fast scanning is provided, which corresponds to the brain blood flow data acquisition method based on fast scanning in the first embodiment. The functional modules are explained in detail as follows:

the area selection module 1 is configured to determine an area to be scanned in the total planning area, update the current area to be scanned according to the area update information, and send information of the area to be scanned to the sequence determination module 2.

The sequence determining module 2 is configured to determine a scanning sequence in the current region to be scanned, send path planning information to the scanning mechanism based on the scanning sequence, so that the scanning mechanism scans all acquisition points in the current scanning sequence, and send scanning sequence information to the scanning analysis module 3.

The scanning mechanism comprises a probe for collecting data of each collecting point and a control driving module for controlling the movement of the probe. The control driving module receives the path planning information and drives the probe to move to the position of each acquisition point in the scanning sequence according to the path planning information; the probe moves to a corresponding position to acquire data, and after the data acquisition of a scanning sequence is completed, the scanning result is sent to the scanning analysis module 3.

And the scanning analysis module 3 is configured to acquire a scanning result sent by the scanning mechanism, determine cerebral blood flow data information and an effective point set based on the scanning result, and send the cerebral blood flow data information and the effective point set to the execution judgment module 4.

The execution judging module 4 is used for judging whether all the acquisition points in the current area to be scanned are over-scanned, if so, the scanning task of the cranial cerebral blood flow data is finished, and the execution judging module 4 sends cerebral blood flow data information and scanning end information to the result output module 6; if not, the task of scanning the cerebral blood flow data of the cranium is not completed, the execution judgment module 4 sends the cerebral blood flow data information to the result output module 6, and sends the effective point set to the path updating module 5.

And the result output module 6 is used for recording cerebral blood flow data information corresponding to all the scanning sequences, counting the cerebral blood flow data information obtained by scanning according to the scanning end information, and outputting a data acquisition result.

And the path updating module 5 is configured to determine an area updating policy based on the effective point set, update the current area to be scanned according to the area updating policy, and send area updating information to the area selecting module 1.

The cerebral blood flow data acquisition system based on fast scanning provided by this embodiment can achieve the same technical effects as the foregoing embodiment because of the functions of the modules themselves and the logical connections between the modules, and the principle analysis can be referred to the related description of the foregoing method steps, which will not be described herein again.

The embodiment of the application also discloses an intelligent terminal.

In one embodiment, an intelligent terminal is provided and includes a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the memory stores training data, algorithm formulas, filtering mechanisms, and the like in a training model. The processor is used for providing calculation and control capability, and the processor realizes the method steps of the cerebral blood flow data acquisition method when executing the computer program.

In the intelligent terminal provided by this embodiment, after the computer program in the memory of the intelligent terminal is run on the processor, the steps of the foregoing embodiment are implemented, so that the same technical effects as those of the foregoing embodiment can be achieved, and for principle analysis, reference may be made to the related description of the steps of the foregoing method, which will not be described herein again.

The embodiment of the application also discloses a computer readable storage medium.

In an embodiment, a computer readable storage medium is provided, which stores a computer program that can be loaded by a processor and executed to perform the above-mentioned fast scan based cerebral blood flow data acquisition method, the computer program, when being executed by the processor, implementing the method steps of the above-mentioned cerebral blood flow data acquisition method.

The readable storage medium provided by this embodiment may achieve the same technical effects as the foregoing embodiment because the computer program in the readable storage medium is loaded and executed on the processor to implement the steps of the foregoing embodiment, and for principle analysis, reference may be made to the related description of the foregoing method steps, which will not be described herein again.

The computer-readable storage medium includes, for example: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.

The embodiments are preferred embodiments of the present application, and the scope of the present application is not limited by the embodiments, so: all equivalent variations made according to the methods and principles of the present application should be covered by the protection scope of the present application.

Claims

1. A cerebral blood flow data acquisition method based on rapid scanning is characterized by comprising the following steps:

2. The method for acquiring cerebral blood flow data according to claim 1, wherein the specific method of the scanning analysis step includes:

acquiring a scanning result sent by a scanning mechanism;

3. The method for acquiring cerebral blood flow data according to claim 2, wherein the specific method of the route update step includes:

4. The cerebral blood flow data acquisition method according to claim 3, wherein the specific method of the region expansion step includes:

in a particular method of the zone shrinking step, comprising:

5. The method of claim 4, wherein the specific method of determining the point spread region based on the position of each acquisition point in the effective point set comprises:

6. The method for acquiring cerebral blood flow data according to claim 4, wherein the specific method of the global dilation step includes:

7. The method of claim 5, wherein the specific method for determining the candidate region based on the current low-matching region comprises:

8. The cerebral blood flow data acquisition method according to claim 4, wherein the specific method of the range narrowing step includes:

9. The method for acquiring cerebral blood flow data according to claim 8, wherein the specific method for determining the contraction region based on the low matching region corresponding to the current region to be scanned comprises:

determining a minimum scanning area in the total planning area;

and if not, determining the high matching area as the contraction area.

10. Brain blood flow data acquisition system based on quick scanning, its characterized in that includes:

the region selection module (1) is used for determining a region to be scanned in the total planning region;

the sequence determining module (2) is used for determining a scanning sequence in the current region to be scanned and sending path planning information to the scanning mechanism based on the scanning sequence so that the scanning mechanism scans all acquisition points in the current scanning sequence; the area to be scanned comprises at least one scanning sequence, the scanning sequence comprises at least one acquisition point which is not subjected to overscan, and all the scanning sequences sequentially participate in scanning according to a scanning sequence;

the scanning analysis module (3) is used for acquiring a scanning result sent by the scanning mechanism and determining cerebral blood flow data information and an effective point set based on the scanning result; wherein the cerebral blood flow data information comprises all blood flow signals acquired in the current scanning sequence, and the effective point set comprises acquisition points capable of acquiring the blood flow signals;

the execution judgment module (4) is used for judging whether all the acquisition points in the current region to be scanned are over-scanned or not, and if yes, outputting a data acquisition result based on cerebral blood flow data information; if not, executing a path updating step;

a path updating module (5) for determining an area updating strategy based on the effective point set, updating the current area to be scanned according to the area updating strategy, and returning to the area selecting step;

11. Intelligent terminal, characterized in that it comprises a memory and a processor, said memory having stored thereon a computer program that can be loaded by the processor and that executes the method according to any one of claims 1 to 9.

12. Computer-readable storage medium, characterized in that a computer program is stored which can be loaded by a processor and which executes the method according to any one of claims 1 to 9.