Multi-Volume Sequence Image Data Processing Method, Computer Device, and Readable Storage Medium

Abstract

The present invention provides a method for processing multi-volume sequence image data, a computer device, and a readable storage medium, including: First, obtaining the multi-volume sequence image data to be processed, where the multi-volume sequence image data includes a first number of medical image data; Second, performing a first classification process on the multi-volume sequence image data to obtain the second multi-volume sequence image data to be classified; Third, performing a position calculation process on the second multi-volume sequence image data to obtain position sequence data; Fourth, performing a transformation process and a calculation process on the second multi-volume sequence image data according to the position sequence data to obtain the number of periods, the number of steps, and frequency domain image data; Finally, classifying the multi-volume sequence image data or the second multi-volume sequence image data into a second number of first volume sequence image data according to the number of periods and the number of steps. This method can accurately split the multi-volume sequence into multiple subsequences and has high robustness.

Description

Technical Field

[0001] The present invention relates to the field of image data processing, and in particular to a multi-volume sequence image data processing method, a computer device and a readable storage medium. Background Art

[0002] Digital Imaging and Communications in Medicine (DICOM) is an international standard for the storage and communication of medical image information and is widely used in the field of medical imaging. A DICOM image sequence consists of a series of images. Each DICOM image contains several DICOM tags. Different images in the same sequence have the same SeriesInstanceUID tag.

[0003] Generally speaking, a DICOM sequence shows a 3D scan. When all DICOM images are superimposed in a certain order, a spatial stereoscopic image will be presented, which is the stereoscopic observation result of the scanned body. We call it Volume (which means both stereo and volume). If a sequence contains multiple Volume images, we call this sequence a multi-volume sequence.

[0004] In DICOM multi-volume sequence images, multiple sequence files that should be separated are often placed in the same sequence due to the settings of scanning device parameters, or human reasons and habits. For example, a DWI sequence contains DWI subsequences with multiple b values. This mixture of subsequences brings splitting costs to downstream image processing. For example, in scientific research and artificial intelligence training, it is necessary to split the subsequences with different b values into separate volumes, because a single volume is required in the data analysis and artificial intelligence model construction process.

[0005] The challenges faced in subsequence splitting of multi-volume sequences are mainly in three aspects:

[0006] First, there are different ways of interleaving multi-volume sequences. Taking DWI multi-volume sequences as an example, some DWI multi-volume sequences put images with the same b value next to each other, while others put images with different b values at the same time next to each other.

[0007] Second, some multi-volume sequences are formed by superimposing the results of multiple scans, but the starting and ending points of these repetitive scans are not strictly the same, which means that they will show a deviated periodicity.

[0008] Third, some multi-volume sequences may have a missing frame or an extra frame due to artificial reasons, which destroys the uniformity of the distance between two adjacent images.

[0009] At present, the existing technology uses the following methods to deal with multi-volume sequence splitting, including:

[0010] One is a splitting method that relies on the image's own logic. Taking the DWI sequence as an example, this method knows that the b value is stored in a DICOM tag. It only needs to obtain the b value of the tag to distinguish each volume. This method is the most accurate method, but the disadvantages of this method are: on the one hand, many manufacturers do not support fixed tags, or different models of the same manufacturer have different parameters; on the other hand, the splitting of many sequences, such as the splitting of CTP, does not have similar iconic parameters.

[0011] Another common method is to split the DICOM images according to the interval of their spatial position. This method is the simplest method, but it is not suitable for multi-volume sequences where two sub-sequences are interspersed alternately, and it has weak anti-interference performance. When the spatial position of an image is disturbed, it is very easy to fail.

[0012] Another method is to use machine learning to decompose, which is to divide each frame of the image into several categories. This method is highly adaptable, but the cost is high, and different training processes are required for different multi-volume sequences. Summary of the invention

[0013] The purpose of the embodiments of the present invention is to provide a multi-volume sequence image data processing method based on the defects in the multi-volume sequence splitting method in the prior art, which quantifies the overall periodicity and step characteristics of the entire sequence based on the frequency domain characteristics of the spatial position of the image sequence, and accurately and robustly splits the multi-volume sequence on the basis of the quantification.

[0014] A first aspect of the present invention provides a method for processing multi-volume sequence image data, the method comprising:

[0015] Acquire multi-volume sequence image data to be processed, wherein the multi-volume sequence image data includes a first number of medical image data; wherein the medical image data is medical digital imaging or communication image data;

[0016] performing a first classification process on the multi-volume sequence image data to obtain second multi-volume sequence image data to be classified;

[0017] performing position calculation processing on the second multi-volume sequence image data to obtain position sequence data;

[0018] transforming the second multi-volume sequence image data according to the position sequence data to obtain a frequency domain sequence and frequency domain image data;

[0019] Perform amplitude zeroing processing on the frequency-domain image data according to the frequency-domain sequence to obtain amplitude-zeroed frequency-domain image data;

[0020] Calculate the number of periods according to the amplitude-zeroed frequency-domain image data;

[0021] Perform step number calculation processing on the amplitude-zeroed frequency-domain image data according to the number of periods to obtain the step number of the amplitude-zeroed frequency-domain image data;

[0022] Classify the multi-volume sequence image data or the second multi-volume sequence image data into a second number of first volume sequence image data according to the number of periods and the number of steps.

[0023] Preferably, the first classification processing includes:

[0024] Traverse each medical image data in the multi-volume sequence image data; wherein, the medical image data includes ImagePatientOrientation tag, ImagePatientPosition tag and InstanceNumber tag;

[0025] Add an availability tag to each of the medical image data, and assign the value of disable or enable to the availability tag according to the ImagePatientOrientation tag, ImagePatientPosition tag and / or InstanceNumber tag of each of the medical image data;

[0026] Obtain the second multi-volume sequence image data according to the medical image data after adding the availability tag.

[0027] Further preferably, the performing position calculation processing on the second multi-volume sequence image data to obtain position sequence data is specifically:

[0028] For each of the medical image data with the availability tag value of enable in the second multi-volume sequence image data, calculate the relative position data of the medical image data;

[0029] Sort the medical image data in ascending order according to the InstanceNumber value of the medical image data, and obtain the position sequence data according to the relative position data of the medical image data.

[0030] Further preferably, the calculating the relative position data of the medical image data specifically includes:

[0031] Split the ImagePatientOrientation value of the medical image data into a first three-dimensional vector and a second three-dimensional vector;

[0032] Perform a cross product operation on the first three-dimensional vector and the second three-dimensional vector to obtain a third three-dimensional vector;

[0033] Perform a dot product operation on the third three-dimensional vector and the three-dimensional vector represented by the ImagePatientPosition value to obtain the relative position data.

[0034] Preferably, the performing a transformation process on the second multi-volume sequence image data according to the position sequence data to obtain frequency domain image data specifically includes:

[0035] Perform a discrete Fourier transform process on the position sequence data to obtain a frequency domain sequence;

[0036] Obtain the frequency domain image data according to the frequency domain sequence and the second multi-volume sequence image data; wherein,

[0037] The frequency domain sequence is expressed as (f i , v i ) i = 0, 1,...., m,

[0038] where f represents frequency, v represents amplitude, and m represents the sequence length of the position sequence data.

[0039] According to the multi-volume sequence image data processing method described in claim 1, wherein the performing an amplitude zeroing process on the frequency domain image data according to the frequency domain sequence to obtain an amplitude-zeroed frequency domain image data specifically is:

[0040] Perform a judgment process on each amplitude value in the frequency domain sequence according to a first amplitude threshold;

[0041] Set the amplitude values less than the first amplitude threshold to 0 to obtain the amplitude-zeroed frequency domain image data.

[0042] Preferably, the calculating the number of steps of the amplitude-zeroed frequency domain image data according to the number of periods to obtain the number of steps of the amplitude-zeroed frequency domain image data specifically includes:

[0043] Judge the value of the number of periods:

[0044] When the number of periods = 1, determine the number of terms k where the first amplitude value v k is 0, and determine the number of steps as k;

[0045] When the number of periods > 1, find the first frequency value f that satisfies the amplitude value of 0 according to the span of n × the number of periods k , and determine that the number of steps is f k ; if the amplitude value of 0 is not found, assign the number of steps as None.

[0046] Preferably, the classifying the multi-volume sequence image data or the second multi-volume sequence image data into the second number of first volume sequence image data according to the number of periods and the number of steps is specifically:

[0047] Make a judgment according to the number of periods and the number of steps:

[0048] When the number of periods = 1 and the number of steps is not None, determine that the second number = the first number / the number of steps, and extract the number of steps of medical image data from the multi-volume sequence image data at the extraction interval of the second number to generate the second number of first volume sequence image data respectively;

[0049] When the number of periods = 1 and the number of steps is None, determine that the second number = 1, and the multi-volume sequence image data is one first volume sequence image data;

[0050] When the number of periods > 1 and the number of steps is None, determine that the second number = the number of periods, and divide the second multi-volume sequence image data according to the sequence arranged in ascending order of the InstanceNumber value to obtain the second number of first volume sequence image data;

[0051] When the number of periods > 1 and the number of steps is not None, determine that the second number = the first number / the number of steps × the number of periods, and divide the frequency domain image data into the number of periods of subsequences according to the position sequence data, and extract the number of steps of medical image data from each subsequence at the extraction interval of the first number / the number of steps to generate the second number / the number of periods of first volume sequence image data respectively, and obtain the second number of first volume sequence image data from the number of periods of the second number / the number of periods of first volume sequence image data.

[0052] In a second aspect of the present invention, there is provided a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it implements the multi-volume sequence image data processing method according to any one of claims 1-8.

[0053] In a third aspect of the present invention, there is provided a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the multi-volume sequence image data processing method according to any one of claims 1-8.

[0054] The present invention provides a multi-volume sequence image data processing method, which quantifies the overall periodicity and step characteristics of the entire multi-volume sequence based on the frequency domain characteristics of the spatial positions of the multi-volume sequence images, and on the basis of the quantification, splits the multi-volume sequence into multi-volume sequence subsequences. This method has accurate splitting and high robustness. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 is a flowchart of a multi-volume sequence image data processing method according to the present invention;

[0056] Figure 2 is a flowchart of a method for calculating position sequence data of multi-volume sequence images according to the present invention;

[0057] Figure 3 is a flowchart of a method for calculating relative position data of medical image data according to the present invention;

[0058] Figure 4 is a flowchart of a method for calculating frequency domain image data and parameters according to the present invention;

[0059] Figure 5 is a schematic diagram of the relationship between InstanceNumber and Location of a DualEcho image according to the present invention;

[0060] Figure 6a is a schematic diagram of the relationship between InstanceNumber and Location of a CTP image according to the present invention;

[0061] Figure 6b Viewed from the frequency domain according to the present invention Figure 6a is a schematic diagram of the CTP frequency domain data relationship;

[0062] Figure 7 is a schematic diagram of a computer device according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0063] The present application will be further described in detail below with reference to the drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the related invention and not to limit the invention. In addition, it should be noted that for the sake of description, only parts related to the relevant invention are shown in the drawings.

[0064] It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The following will detail the present application with reference to the drawings and in combination with the embodiments.

[0065] The first and second in the following text are only for distinction and have no other meanings.

[0066] The embodiments of the present invention will be introduced below in a manner combined with the drawings.

[0067] In the first aspect of the present invention, a method for processing multi-volume sequence image data is provided, and the method will be introduced in detail below. Figure 1 As shown in the flowchart of a method for processing multi-volume sequence image data according to the present invention, the method includes the following steps:

[0068] Step 110, obtain the multi-volume sequence image data to be processed, where the multi-volume sequence image data includes a first number of medical imaging image data.

[0069] Among them, the medical imaging image data is medical digital imaging or communication image data.

[0070] Specifically, after receiving an external processing instruction, the method for processing multi-volume sequence image data provided by the present invention is started. In the present invention, the first number is denoted as m. In this step, according to the processing instruction, the multi-volume sequence image data to be processed is obtained, and the multi-volume sequence image data includes m medical imaging image data which are medical digital imaging or communication image data. In the present invention, these medical imaging image data which are medical digital imaging or communication image data are collectively referred to as medical imaging image data.

[0071] The multi-volume sequence image data to be processed obtained by the present invention is received from the outside, and each medical imaging image data included therein is formed by a medical imaging device through scanning, and different multiple tags are automatically added according to established standards and rules during the scanning process.

[0072] The purpose of the present invention is to separate multiple sub-sequence image data from the multi-volume sequence image data to be processed, that is, the second number of first volume sequence image data finally obtained by the present invention.

[0073] Step 120, perform a first classification process on the multi-volume sequence image data to obtain the second multi-volume sequence image data to be classified.

[0074] Specifically, in the embodiments of the present invention, each medical image data includes an ImagePatientOrientation tag, an ImagePatientPosition tag, and an InstanceNumber tag. Among them, the value of the ImagePatientOrientation tag is a tuple with a length of 6, representing the orientation of the image plane: it indicates the direction cosine of the first row and the first column of the image relative to the patient. The direction of the coordinate axes is determined according to the patient's orientation (LPS system: the x-axis points to the patient's left hand side, the y-axis points to the patient's back, and the Z-axis points to the patient's head.

[0075] The value of the ImagePatientPosition tag is a tuple with a length of 3, representing the position of the first pixel of the pixel matrix in the currently represented image. And the value of the InstanceNumber tag is the unique number of the image in the entire sequence, which is an integer starting from 1.

[0076] In the embodiments of the present invention, the first classification process is a marking process, according to

[0077] the ImagePatientOrientation tag, the ImagePatientPosition tag, and the InstanceNumber tag, each medical image data in the multi-volume sequence image data is marked. The purpose is to classify them into usable image data and unusable image data after marking each medical image. What is actually completed is to divide each medical image data of the multi-volume sequence picture data into available or unavailable.

[0078] The marking method provided by the optional solution of the embodiment of the present invention is as follows: traverse each medical image data in the multi-volume sequence image data; add an availability tag to each medical image data, for example, add AvailabilityTag, and judge the ImagePatientOrientation tag, ImagePatientPosition tag, and InstanceNumber tag of each medical image data. Assign the availability tag with all of the ImagePatientOrientation tag, ImagePatientPosition tag, and / or InstanceNumber tag available as enable, that is, AvailabilityTag = enable, indicating that the medical image data is available; if any of the ImagePatientOrientation tag, ImagePatientPosition tag, and InstanceNumber tag of the medical image data is unavailable, then we assign the availability tag of the medical image data as disable, that is, AvailabilityTag = disable, indicating that the medical image data is unavailable.

[0079] After completing a full traversal of the multi-volume sequence image data, the second multi-volume sequence image data is composed of the medical image data with the availability tag AvailabilityTag added. That is to say, by adding the availability tag AvailabilityTag to each medical image data of the multi-volume sequence image data, the second multi-volume sequence image data is formed.

[0080] It should be noted that the availability tag AvailabilityTag is added to each medical image data of the multi-volume sequence image data. Therefore, it can be considered that the multi-volume sequence picture data after adding the availability tag AvailabilityTag is the second multi-volume sequence image data.

[0081] Step 130, perform position calculation processing on the second multi-volume sequence image data to obtain position sequence data.

[0082] Specifically, this step includes two sub-steps, step 1301 and step 1302, and the purpose is to obtain the position sequence data of the second multi-volume sequence image data through calculation. In the embodiment of the present invention, LocationSeries is used to represent the position sequence data. Figure 2 As shown in the flowchart of the position sequence data calculation method for the multi-volume sequence image data according to the present invention, the method specifically includes:

[0083] Step 1301, calculate the relative position data Location of the medical image data. Specifically, for each piece of medical image data with an availability label value of enable in the second multi-volume sequence image data, calculate the relative position data Location of the medical image data.

[0084] Step 1302, calculate the position sequence data of the second multi-volume sequence image data. Specifically, after calculating the relative position data, sort the medical image data in ascending order according to the InstanceNumber value of the medical image data, and obtain the position sequence data LocationSeries based on the relative position data Location of the medical image data. Specifically, sorting in ascending order according to the InstanceNumber value rearranges the position sequence of each piece of medical image data in the second volume sequence image data. Since each piece of medical image data has its corresponding relative position data Location, therefore, as the sequence of the medical image data is rearranged, the relative position data Location of the medical image data will be rearranged accordingly to form the position sequence data LocationSeries.

[0085] In an alternative solution of the embodiment of the present invention, for the calculation of the relative position data Location of the medical image data in step 1301, the method as Figure 3 shown is adopted. The following will explain this method in detail in combination with Figure 3 this.

[0086] Figure 3 As shown in the flowchart of the method for calculating the relative position data of the medical image data according to the present invention, the method for calculating the relative position data of the medical image data includes steps 13011 to 13013:

[0087] Step 13011, split the ImagePatientOrientation label of the medical image data into a first three-dimensional vector and a second three-dimensional vector. Specifically, in the embodiment of the present invention, the value of the ImagePatientOrientation label of the medical image data is a tuple with a length of 6. In this step, this tuple with a length of 6 needs to be split into 2 three-dimensional vectors, namely the first three-dimensional vector and the second three-dimensional vector.

[0088] Step 13012: Perform a cross product operation on the first three-dimensional vector and the second three-dimensional vector to obtain the first three-dimensional vector. Specifically, the first three-dimensional vector and the second three-dimensional vector are the two three-dimensional vectors obtained by splitting in Step 13011. By performing a cross product calculation on the two vectors, the third three-dimensional vector is obtained.

[0089] Step 13013: Perform a dot product operation on the third three-dimensional vector and the three-dimensional vector represented by the ImagePatientPosition tag to obtain relative position data. Specifically, in the present invention, the value of the ImagePatientPosition tag is a tuple of length 3. By performing a dot product of the third three-dimensional vector and the value of the ImagePatientPosition tag, the relative position data of the medical image data is obtained.

[0090] Each medical image data of the second multi-volume sequence image data obtained through the processing of this step includes its relative position data, and the position sequence data corresponding to the second multi-volume sequence image data has been obtained after sorting in ascending order according to the InstanceNumber value of each medical image data.

[0091] Step 140: Perform transformation processing and calculation processing on the second multi-volume sequence image data according to the position sequence data to obtain the number of periods, the number of steps, and the frequency domain image data.

[0092] Specifically, the position sequence data LocationSeries obtained in Step 130 is a finite discrete sequence. After performing a discrete Fourier transform on this finite discrete sequence data, a frequency domain sequence can be obtained. The purpose of the present invention is to classify or split the multi-volume sequence image data by leveraging the characteristics of the frequency domain sequence. The images in the frequency domain have the following characteristics:

[0093] First, for a uniformly linearly varying original sequence, starting from a frequency of 1, it successively shows a decrease of v n = v1 / n.

[0094] Second, if the original sequence is periodic with a period of n, then the amplitude is non-zero only at frequencies that are multiples of n, v kn ≠0, k ∈ N.

[0095] Third, if the original sequence shows continuous identical values, which we call a step, in a periodic image, if the total number of steps is n, then the amplitude at a frequency = n is exactly 0.

[0096] The purpose of this step is to obtain the number of periods, the number of steps, and the frequency domain image data of the second multi-volume sequence image data through calculation and transformation processing. The optional solution of the embodiment of the present invention adoptsFigure 4 Perform calculations using the method shown below in conjunction with Figure 4 A detailed introduction to the calculation method for obtaining frequency-domain image data and parameters in this step will be given. Figure 4 The flowchart of the calculation method for frequency-domain image data and parameters according to the present invention is shown in the figure. As shown, the method includes the following steps:

[0097] Step 1401: Perform transformation processing on the second multi-volume sequence image data according to the position sequence data to obtain a frequency-domain sequence and frequency-domain image data.

[0098] Specifically, in an alternative embodiment of the present invention, a discrete Fourier transform is directly performed on the position sequence data LocationSeries corresponding to the second multi-volume sequence image data to obtain a frequency-domain sequence. After obtaining the frequency-domain sequence, it is mapped to each medical image data of the second multi-volume sequence image data, thus forming the second multi-volume sequence image data with the frequency-domain sequence, denoted as frequency-domain image data. In the embodiment of the present invention, the frequency-domain sequence is expressed as:

[0099] (f i ,v i ) i = 0, 1,...., m, (Equation 1)

[0100] where f represents frequency, v represents amplitude, m represents the sequence length of the position sequence data, and the value range of i is an integer in [0, m).

[0101] Step 1402: Perform amplitude zeroing processing on the frequency-domain image data according to the frequency-domain sequence to obtain amplitude-zeroed frequency-domain image data.

[0102] Specifically, in an alternative embodiment of the present invention, each amplitude value v in the frequency-domain sequence is judged according to the first amplitude threshold, and the amplitude value v less than the first amplitude threshold is j set to 0, that is, v j = 0, to obtain amplitude-zeroed frequency-domain image data. In the embodiment of the present invention, the first amplitude threshold can be considered according to the actual situation of the frequency-domain image data to set a reasonable value. In a preferred embodiment of the present invention, 5% of v1 is used as the first amplitude threshold. After this step of processing, the amplitude values of some terms in the frequency-domain sequence corresponding to the frequency-domain image data are set to 0, and the processed frequency-domain image data is denoted as amplitude-zeroed frequency-domain image data.

[0103] Step 1403: Calculate the number of cycles according to the amplitude-zeroed frequency-domain image data.

[0104] Specifically, in the embodiments of the present invention, the number of periods refers to the quantity of periods, denoted as period. The calculation of the number of periods is specifically as follows: Judging according to the frequency domain sequence corresponding to the amplitude-zeroed frequency domain image data, and finding all frequencies f ln = 1 in the frequency domain sequence, and the amplitude value v ln , judging according to v ln :

[0105] If v ln > 1, it indicates that the frequency domain sequence is a non-periodic sequence, and it is confirmed that the number of periods period = 1.

[0106] If only v ln is not 0, then period = n. Where, ln, l ∈ N.

[0107] Step 1404, according to the number of periods, perform step number calculation processing on the amplitude-zeroed frequency domain image data to obtain the number of steps.

[0108] Specifically, the number of steps is denoted as foot_step. In the embodiments of the present invention, the following method is used to calculate the number of steps foot_step. Judge the value of the number of periods period:

[0109] When the number of periods period = 1, determine the number of terms k that first satisfies the amplitude value v k = 0, and determine the number of steps as k, that is, foot_step = k.

[0110] When the number of periods period > 1, search for the first frequency value f k = 0 that satisfies the amplitude value v k at the span of n × period, and determine the number of steps as f k , that is, foot_step = f k ; if no term with the amplitude value v k = 0 is found, then assign the number of steps as None, that is, foot_step = None.

[0111] After the processing in this step 140, the frequency domain image data with a frequency domain sequence, as well as the number of periods period and the number of steps foot_step of the frequency domain image data, are obtained.

[0112] Step 150, classify the multi-volume sequence image data or the frequency domain image data into the second number of first volume sequence image data according to the number of periods and the number of steps.

[0113] Specifically, in the embodiments of the present invention, judge the number of periods period and the number of steps foot_step of the frequency domain image data, and split the multi-volume sequence image data into the second number Nvolume A first volume sequence image data, including the following situations:

[0114] Situation 1: When period = 1 and foot_step ≠ None, determine N volume = m / foot_step, with N volume as the extraction interval, extract foot_step medical image data from the multi-volume sequence image data to generate N volume first volume sequence image data respectively. Specifically, the original multi-volume sequence image data with this characteristic is each frame of medical image data of multiple first volume sequence image data at the same position. By alternately obtaining medical image data from the multi-volume sequence image data with N volume as the extraction interval, N volume first volume sequence image data can be formed. A detailed introduction to this situation is given with an example. For example, taking a DualEcho image as an example, Figure 5 is a schematic diagram of the relationship between the InstanceNumber and Location of a DualEcho according to the present invention. As Figure 5 shown, the DualEcho image is multi-volume sequence image data with a length of 176. At the same position in space, there are two medical image data with different Echo parameters. The relationship between InstanceNumber and Location presents a fine step. By frequency calculation, foot_step = 88, so N volume = 2. Extract the 1st, 3rd, 5th... 175th from the DualEcho multi-volume sequence image data and merge them into one volume as a first multi-volume sequence image data, and extract the 2nd, 4th, 6th... 176th and merge them into a second volume as another first multi-volume sequence image data. That is to say, for the DualEcho multi-volume sequence image data, through the method provided by the present invention, it is split into two first multi-volume sequence image data.

[0115] Situation 2: When the number of periods period = 1 and foot_step = None, determine N volume = 1, and the multi-volume sequence image data is a first volume sequence image data. That is to say, in this case, the original multi-volume sequence image data to be processed only contains one multi-volume sequence image data and does not need to be split.

[0116] Situation 3: When the number of periods period > 1 and foot_step = None, determine N volume= period, partition the sequence of the second most volume sequence image data arranged in ascending order according to the InstanceNumber value to obtain N volume first volume sequence image data containing m / N volume medical image data. Specifically, taking a CTP image as an example, Figure 6a is a schematic diagram of the relationship between the InstanceNumber and Location of a CTP according to the present invention, Figure 6b from the perspective of the frequency domain according to the present invention Figure 6a schematic diagram of the CTP frequency domain data relationship in, as Figure 6a shown, it can be seen from the relationship between the InstanceNumber and Location of the CTP that the scanning positions of each medical image data in the CTP multi-volume sequence image data cycle, as Figure 6b shown, looking at the same CTP image in the frequency domain, there is a value at the position of 20, and it can be concluded that this frequency domain is 20 times the frequency, that is, the number of periods period = 20. Therefore, according to the rule N volume = period = 20, the CTP multi-volume sequence image data can be split into 20 first multi-volume sequence image data. According to the calculated second quantity N volume sort the entire CTP multi-volume sequence image data in ascending order according to InstanceNumber, and split it into 20 subsequences. Each subsequence is a volume, that is, each subsequence is a first multi-volume sequence image data. As Figure 6a shown. The CTP multi-volume sequence image data includes 4400 medical image data, and it can be determined that each volume contains 4400 / 20 medical image data. The first volume is obtained from the sequence formed by the 1st, 2nd, 3rd... 220th medical image data in order, and the second volume is obtained from the sequence formed by the 221st, 222nd... 440th medical image data in order, and so on. Finally, the CTP multi-volume sequence image data is split into 20 first multi-volume sequence image data.

[0117] Case 4: When the number of periods period > 1 and foot_step ≠ None, determine N volume= m / foot_step × period. The second most volumetric sequence image data is partitioned according to the sequence obtained by arranging the InstanceNumber values in ascending order, resulting in period first subsequences. With m / foot_step as the extraction interval, m / foot_step medical image data is extracted from each of the obtained period first subsequences in order to generate m / foot_step first volumetric sequence image data respectively. From the m / foot_step first volumetric sequence image data extracted from each of the period first subsequences, m / foot_step × period first volumetric sequence image data is obtained. Here, the method of splitting each first subsequence into m / foot_step first volumetric sequence image data is the same as the splitting method in Case 1 in Step 150, and will not be elaborated here.

[0118] The above is a method for processing multi-volumetric sequence image data provided by the first aspect of the present invention. Based on the frequency domain characteristics of the spatial positions of the multi-volumetric sequence images, the overall periodicity and step characteristics of the entire multi-volumetric sequence are quantified. On the basis of the quantification, the multi-volumetric sequence is split into multi-volumetric sequence subsequences. This method has accurate splitting and high robustness.

[0119] Each step in the above method for processing multi-volumetric sequence image data of the present invention can be implemented in whole or in part by software, hardware, and their combination. The above steps can be embedded in the processor of the computer device in hardware form or be independent of it, or can be stored in the memory of the computer device in software form, so as to facilitate the processor to call and execute the operations corresponding to the above steps.

[0120] To solve the above technical problems, the embodiments of the present application also provide a computer device. For details, please refer to Figure 7 , Figure 7 which is the basic structural block diagram of the computer device in this embodiment.

[0121] The computer device 7 includes a memory 71, a processor 72, and a network interface 73 that are interconnected and communicate with each other through a system bus. It should be noted that the figure only shows a computer device 7 with components connected to the memory 71, the processor 72, and the network interface 73, but it should be understood that it is not required to implement all the components shown, and more or fewer components may be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), digital processing

[0122] Devices (Digital Signal Processor, DSP), embedded devices, etc.

[0123] Computer devices can be computing devices such as desktop computers, notebooks, PDAs, and cloud servers. Computer devices can interact with users through keyboards, mice, remote controls, touch pads, or voice control devices.

[0124] The memory 71 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or D interface display memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 71 can be an internal storage unit of the computer device 7, such as a hard disk or memory of the computer device 7. In other embodiments, the memory 71 can also be an external storage device of the computer device 7, such as a plug-in hard disk equipped on the computer device 7, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. Of course, the memory 71 can also include both the internal storage unit of the computer device 7 and its external storage device. In this embodiment, the memory 71 is generally used to store the operating system and various application software installed on the computer device 7, such as the program code for controlling electronic files, etc. In addition, the memory 71 can also be used to temporarily store various types of data that have been output or are to be output.

[0125] In some embodiments, the processor 72 may be a Central Processing Unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips. The processor 72 is generally used to control the overall operation of the computer device 7. In this embodiment, the processor 72 is used to run the program code stored in the memory 71 or process data, such as running the program code for controlling electronic files.

[0126] The network interface 73 may include a wireless network interface or a wired network interface. The network interface 73 is generally used to establish a communication connection between the computer device 7 and other electronic devices.

[0127] The present application also provides another implementation manner, that is, to provide a computer-readable storage medium storing an interface display program, which can be executed by at least one processor to enable the at least one processor to execute the steps of the control method of the electronic file as described above.

[0128] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-described embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation manner. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disc) and includes several instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods of the various embodiments of the present application.

[0129] Obviously, the above-described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The accompanying drawings show the preferred embodiments of the present application, but do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosed content of the present application more thorough and comprehensive. Although the present application has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions described in the foregoing specific embodiments, or perform equivalent replacements for some of the technical features. Any equivalent structure directly or indirectly using the content of the specification and drawings of the present application in other related technical fields shall be within the scope of the patent protection of the present application by the same token.

[0130] The above specific embodiments have further elaborated in detail the object, technical solution and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for processing multi-volume sequence image data, characterized in that The method includes: Obtaining multi-volume sequence image data to be processed, where the multi-volume sequence image data includes a first number of medical imaging image data; wherein, the medical imaging image data is medical digital imaging or communication image data; Performing a first classification process on the multi-volume sequence image data to obtain second multi-volume sequence image data to be classified; Performing a position calculation process on the second multi-volume sequence image data to obtain position sequence data; Performing a transformation process on the second multi-volume sequence image data according to the position sequence data to obtain a frequency domain sequence and frequency domain image data; Performing an amplitude zeroing process on the frequency domain image data according to the frequency domain sequence to obtain amplitude-zeroed frequency domain image data; Calculating the number of periods according to the amplitude-zeroed frequency domain image data; Performing a step number calculation process on the amplitude-zeroed frequency domain image data according to the number of periods to obtain the step number of the amplitude-zeroed frequency domain image data; Classifying the multi-volume sequence image data or the second multi-volume sequence image data into a second number of first volume sequence image data according to the number of periods and the step number; Wherein, the performing a step number calculation process on the amplitude-zeroed frequency domain image data according to the number of periods to obtain the step number of the amplitude-zeroed frequency domain image data specifically includes: Judging the value of the number of periods: When the number of cycles = 1, determine the first number of terms k that satisfies the amplitude value v k to be 0, and determine the number of steps to be k; When the number of periods > 1, search for the first frequency value f that satisfies an amplitude value of 0 at a span of n × the number of periods k , and determine that the number of steps is f k ; if no amplitude value of 0 is found, assign the number of steps as None.

2. The multi-volume sequence image data processing method according to claim 1, wherein The first classification process includes: Traversing each medical imaging image data in the multi-volume sequence image data; wherein, the medical imaging image data includes ImagePatientOrientation tag, ImagePatientPosition tag and InstanceNumber tag; Adding an availability tag to each medical imaging image data, and assigning the value of disable or enable to the availability tag according to the ImagePatientOrientation tag, ImagePatientPosition tag and / or InstanceNumber tag of each medical imaging image data; Obtaining the second multi-volume sequence image data according to the medical imaging image data after adding the availability tag.

3. The multi-volume sequence image data processing method according to claim 2, wherein The performing a position calculation process on the second multi-volume sequence image data to obtain position sequence data is specifically: For each medical imaging image data with the availability tag value of enable in the second multi-volume sequence image data, calculating the relative position data of the medical imaging image data; Sorting the medical imaging image data in ascending order according to the InstanceNumber value of the medical imaging image data, and obtaining the position sequence data according to the relative position data of the medical imaging image data.

4. The multi-volume sequence image data processing method according to claim 3, characterized in that The calculating the relative position data of the medical imaging image data specifically includes: Split the ImagePatientOrientation value of the medical imaging image data into a first three-dimensional vector and a second three-dimensional vector; Perform a cross product operation on the first three-dimensional vector and the second three-dimensional vector to obtain a third three-dimensional vector; Perform a dot product operation on the third three-dimensional vector and the three-dimensional vector represented by the value of the ImagePatientPosition tag to obtain the relative position data.

5. The multi-volume sequence image data processing method according to claim 1, wherein The performing transformation processing on the second multi-volume sequence image data according to the position sequence data to obtain frequency domain image data specifically includes: Perform a discrete Fourier transform processing on the position sequence data to obtain a frequency domain sequence; Obtain the frequency domain image data according to the frequency domain sequence and the second multi-volume sequence image data; where The frequency domain sequence is represented as (f i , v i ), where i = 0, 1,...., m, where f represents frequency, v represents amplitude, and m represents the sequence length of the position sequence data.

6. The multi-volume sequence image data processing method according to claim 1, characterized in that The performing amplitude zeroing processing on the frequency domain image data according to the frequency domain sequence to obtain amplitude-zeroed frequency domain image data specifically is: Perform a judgment processing on each amplitude value in the frequency domain sequence according to a first amplitude threshold; Set the amplitude values less than the first amplitude threshold to 0 to obtain the amplitude-zeroed frequency domain image data.

7. The multi-volume sequence image data processing method according to claim 3, wherein The classifying the multi-volume sequence image data or the second multi-volume sequence image data into a second number of first volume sequence image data according to the number of periods and the number of steps specifically is: Make a judgment according to the number of periods and the number of steps: When the number of periods = 1 and the number of steps is not None, determine that the second number = the first number / the number of steps, and extract the number of steps of medical imaging image data from the multi-volume sequence image data at the extraction interval of the second number to generate the second number of first volume sequence image data respectively; When the number of periods = 1 and the number of steps is None, determine that the second number = 1, and the multi-volume sequence image data is one first volume sequence image data; When the number of periods > 1 and the number of steps is None, determine that the second number = the number of periods, and divide the sequence of the second multi-volume sequence image data arranged in ascending order according to the InstanceNumber value to obtain the second number of first volume sequence image data; When the number of periods > 1 and the number of steps is not None, determine that the second number = the first number / the number of steps × the number of periods, and divide the frequency domain image data into the number of periods of subsequences according to the position sequence data, and extract the number of steps of medical imaging image data from each subsequence at the extraction interval of the first number / the number of steps to generate the second number / the number of periods of first volume sequence image data respectively, and obtain the second number of first volume sequence image data from the number of periods of the second number / the number of periods of first volume sequence image data.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the multi-volume sequence image data processing method according to any one of claims 1-7.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the multi-volume sequence image data processing method according to any one of claims 1-7.

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