A photon counting CT data compression method

Abstract

The present invention discloses a photon counting CT data compression method, which stores the best matching data bits for data in different energy levels and different regions. The specific steps are as follows: S1, energy spectrum partitioning: collecting projection data, extracting it through a data processing unit, and then dividing the energy level of the projection data into multiple regions; S2, dynamic threshold decision: dividing the energy spectrum data according to the scanning area and calculating its regional division threshold; S3, projection data regional division, automatic bit width allocation: calculating the maximum storage bit number corresponding to the regional data; S4, raw data encapsulation: storing data according to the maximum storage bit number, recording regional position information and storage bit number information. The present invention is used to losslessly compress the amount of raw data collected by photon counting CT, effectively reducing the amount of data that needs to be transmitted without losing data accuracy; the algorithm consumes little computing resources and has good real-time performance.

Description

Technical Field

[0001] The present invention belongs to the technical field of lossless data compression, and in particular relates to a photon counting CT data compression method. Background Art

[0002] Photon-counting CT, a new generation of CT, offers superior performance compared to conventional CT. Its core principle is to use photon-counting detectors to count X-ray photons attenuated by the human body and distinguish between a range of energy levels, typically 4 to 8. Furthermore, the pixel size of photon-counting detectors is typically an order of magnitude smaller than that of conventional CT detectors. These characteristics offer important clinical benefits, such as higher spatial resolution, improved low-contrast resolution, metal artifact removal, and the ability to decompose materials, i.e., clinical differential diagnosis. However, the smaller physical size of detector pixels generates more raw data. In particular, to achieve better energy resolution, data acquisition at multiple energy levels is often required. This results in transmission bandwidth requirements of hundreds of GB / s for projection data acquired with photon-counting CT, compared to conventional CT. This poses significant challenges to both data transmission and storage in CT systems, especially in clinical applications where information loss due to data compression or processing is undesirable.

[0003] However, X-rays will be greatly attenuated after passing through the scanned object; spectra at different energy levels are attenuated to different degrees by the object, and the lower the energy level, the greater the attenuation; the distribution of the incident spectrum at different energy levels is different, that is, the number of incident photons at different energy levels is different.

[0004] Currently, there are several papers on compression processing of CT raw data:

[0005] US20210057084A1 MEDICAL DATA PROCESSING APPARATUS, MEDICAL DATAPROCESSING METHOD, AND MEDICAL IMAGE DIAGNOSTIC proposes a multi-channel data compression algorithm whose data organization is similar to photon counting CT. The core idea of ​​this patent is to first convert the raw data into frequency domain data through data conversion, then compress and convert it using AI methods, and finally perform quantization encoding. This method requires a high-performance processor to execute complex machine learning algorithms and data processing.

[0006] PichaShunhavanich2016 Lossless Compression of Projection Data fromPhoton Counting Detectors proposed an estimation algorithm that estimates the current value by taking a weighted average of adjacent samples (adjacent view angles and channels) and energy level estimates, and encodes and records the residual error. A local gradient-based context model is also used to mark boundary information. Data compression is achieved by compressing the estimated value and residual. This method has the advantage of being able to perform both lossy and lossless compression. However, its disadvantages are that the validity of the estimation algorithm needs further confirmation, the implementation process is relatively complex, and it has limitations in high-speed transmission.

[0007] Sjo ·· lin2017 Compression of CT sinogram data by decimation in the view direction proposed a data compression method that achieves 10-10 decimation in the view direction, while improving high-contrast resolution (compared to a low-view sampling rate) without increasing the amount of data collected. The core concept is to first increase the sampling rate and then perform filtering and downsampling. This method has the advantage of combining practical applications for improving high-contrast resolution. However, the effectiveness of increasing the sampling rate and then downsampling depends on the specific application. Furthermore, downsampling can produce lossy images, and its effectiveness also depends on the specific clinical application.

[0008] Kimura2021 Data traffic compression in spectral photon-counting CTimaging based on human visual characteristics A data compression method for photon counting CT is proposed, the purpose of which is to compress the data volume while maintaining the spatial resolution, while the final image remains visually consistent. The core idea is to maintain high-resolution acquisition of data at a certain energy level, and then perform low-resolution acquisition (interval acquisition or merged acquisition) of the data used to maintain energy spectrum information, and then merge the images reconstructed from the two sets of data to achieve an image that takes into account both energy spectrum information and resolution information. The advantage of this method is that it comprehensively considers the retention of energy spectrum information and spatial resolution information. The disadvantage is that there are differences between the spatial resolution information of different energy levels, so there will be differences between the final synthesized image effect and the original image. Its effectiveness depends on the specific clinical application.

[0009] YirongYang2023 proposed an energy-weighted photon counting CT data compression algorithm based on energy-weighted optimization of energy bin weights for compressing measurements with realistic photon counting x-ray detectors. The core idea is to weightedly combine data at different energy levels while maintaining the effects of virtual monochrome and virtual plain scan images, retaining data at two or three energy levels (depending on the type of material decomposition), thereby reducing the amount of data. This method has the advantage of fully incorporating the image category information of photon counting CT. However, its disadvantages are high complexity and lossy performance. Summary of the Invention

[0010] In order to address the defects and shortcomings of the above-mentioned prior art, the present invention provides a photon counting CT data compression method for losslessly compressing the original data collected by photon counting CT, effectively reducing the amount of data to be transmitted without losing data accuracy; the algorithm is simple and consumes little computing resources.

[0011] The technical solution of the present invention is as follows: a photon counting CT data compression method stores the best matching data bits of data at different energy levels and in different regions, and the steps are as follows:

[0012] S1. Energy spectrum partitioning: Collect projection data, extract it through the data processing unit, and then divide the energy level of the projection data into multiple regions;

[0013] S2, dynamic threshold decision: divide the energy spectrum data according to the scanning area and calculate its area division threshold;

[0014] S3. Projection data area division and automatic bit width allocation: Calculate the maximum number of storage bits corresponding to the regional data;

[0015] S4. Original data encapsulation: Store data according to the maximum storage bit number, and record the area location information and storage bit number information.

[0016] Preferably, the step S1 specifically refers to dividing the projection data according to viewing angles and rows, the processing unit uses a single row of data as the minimum unit, and the viewing angle and row currently being processed are marked with vid and sid.

[0017] Preferably, the step S2 specifically includes:

[0018] 1) Set the ratios of the scanned object and air areas according to their intensities: ratio1 and ratio2;

[0019] 2) Traverse to obtain the maximum projection data value of the current viewing angle and row:

[0020] val mx =max(data(1:N c ,1:N b ,sid,vid))

[0021] Among them, data is the energy spectrum data, N c is the number of channels, N b is the energy level number;

[0022] 3) Calculate the division threshold of the two regions:

[0023] trd1=val mx ×ratio1

[0024] trd2=val mx ×ratio2;

[0025] 4) Use trd1 and trd2 to divide the projection data into 5 regions in the channel direction:

[0026] rgn(1)=pos(1):pos(2),data(pos(1):pos(2),1:N b ,sid,vid)<trd1

[0027] rgn(2)=pos(2)+1:pos(3)-1

[0028] rgn(3)=pos(3):pos(4),data(pos(3):pos(4),1:N b ,sid,vid)<trd2

[0029] rgn(4)=pos(4)+1:pos(5)-1

[0030] rgn(5)=pos(5):pos(6),data(pos(5):pos(6),1:N b ,sid,vid)<trd1

[0031] Among them, pos(1)=1, pos(6)=N c .

[0032] Preferably, the specific operation of the projection data area division in step S3 is to count the maximum values ​​of each energy level in each area:

[0033] val mx (rid,bid)=max(data(rgn(rid),bid,sid,vid)

[0034] Among them, rid is the current region and bid is the current energy level.

[0035] Preferably, the specific operation of the automatic bit width allocation in step S3 is to calculate val mx The maximum number of storage digits corresponding to (rid, bid):

[0036] bits(rid,bid)=nbit,2 nbit <val mx (rid,bid)≤2 nbit+1 -1.

[0037] Preferably, the specific operation of step S4 is to store the data according to nbit, and additionally record pos and nbit information for data recovery.

[0038] The beneficial effects of the present invention are as follows: the present invention is used to losslessly compress the original data collected by photon counting CT, effectively reducing the amount of data that needs to be transmitted without losing data accuracy; the algorithm is simple and consumes little computing resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] To more clearly illustrate the technical solutions in the embodiments of this application, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be derived from these drawings without inventive effort.

[0040] Figure 1 Schematic diagram of the data compression process of the present invention;

[0041] Figure 2 A schematic diagram of data layout for a single perspective in the present invention;

[0042] Figure 3 Schematic diagram of the data motif used for compression in the present invention;

[0043] Figure 4 For attachment Figure 3 Schematic diagram of the data distribution of the 7-energy level collection of the medium data model, where each line represents the data of one energy level. DETAILED DESCRIPTION

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0045] Example 1

[0046] The present invention is based on the following three facts: 1. X-rays are significantly attenuated after passing through the scanned object; 2. Spectra at different energy levels are attenuated to different degrees by the object, with the lower the energy level, the greater the attenuation; and 3. The distribution of the incident spectrum at different energy levels is different, that is, the number of incident photons at different energy levels is different.

[0047] A data compression method based on the energy level distribution of the scanned object is provided, which stores the best matching data bits for data in different energy levels and regions. The basic process is as follows: Figure 1 The specific steps are as follows:

[0048] S1. Projection data acquisition and processing unit extraction:

[0049] Divide the projection data by viewing angle and row (see the attached data layout Figure 2 ), the processing unit uses a single row of data as the smallest unit, and the currently processed view angle and row are marked with vid and sid;

[0050] S2. Divide the energy spectrum data by scanning area:

[0051] 1) Set the ratios of the scanned object and air areas according to their intensities: ratio1 and ratio2;

[0052] 2) Traverse to obtain the maximum projection data value of the current viewing angle and row:

[0053] val mx =max(data(1:N c ,1:N b ,sid,vid))

[0054] Among them, data is the energy spectrum data, N c is the number of channels, N b is the energy level number;

[0055] 3) Calculate the division threshold of the two regions:

[0056] trd1=val mx ×ratio1

[0057] trd2=val mx ×ratio2;

[0058] 4) Use trd1 and trd2 to divide the projection data into 5 regions in the channel direction:

[0059] rgn(1)=pos(1):pos(2),data(pos(1):pos(2),1:N b ,sid,vid)<trd1

[0060] rgn(2)=pos(2)+1:pos(3)-1

[0061] rgn(3)=pos(3):pos(4),data(pos(3):pos(4),1:N b ,sid,vid)<trd2

[0062] rgn(4)=pos(4)+1:pos(5)-1

[0063] rgn(5)=pos(5):pos(6),data(pos(5):pos(6),1:N b ,sid,vid)<trd1

[0064] Among them, pos(1)=1, pos(6)=N c ;

[0065] S3-S4, data compression:

[0066] S3. Count the maximum values ​​of each energy level in each region:

[0067] val mx (rid,bid)=max(data(rgn(rid),bid,sid,vid)

[0068] Among them, rid is the current region and bid is the current energy level;

[0069] Calculate val mx The maximum number of storage digits corresponding to (rid, bid):

[0070] bits(rid,bid)=nbit,2 nbit <val mx (rid,bid)≤2 nbit+1 -1;

[0071] S4. Store the data according to nbit and additionally record pos and nbit information for data recovery.

[0072] Example 2

[0073] An embodiment of the present invention is as follows:

[0074] 1. Using 7-level data acquisition, the storage bit of each data before compression is 16 bits;

[0075] 2. Scan at 120 kVp with energy levels set to [25 34 50 60 70 90 100];

[0076] 3. Attachment Figure 3 The data distribution of a single row of single viewing angles is shown in the attached figure. Figure 4 As shown;

[0077] 4. Setting: ratio1 = 0.3, ratio2 = 0.12, the calculated data range is: pos(2) = 357, pos(3) = 847, pos(4) = 1554, pos(5) = 2044

[0078] rgn(1)=1:357

[0079] rgn(2)=358:846

[0080] rgn(3)=847:1554

[0081] rgn(4)=1555:2043

[0082] rgn(5)=2044:2400;

[0083] 5. The maximum value of each energy level in each region is as follows (energy level x region):

[0084]

[0085] 6. The maximum number of storage bits corresponding to the maximum value of each energy level and each region is:

[0086]

[0087] 7. The compression ratio (after compression / before compression) corresponding to each energy level and region is:

[0088]

[0089] 8. The overall compression ratio of the current data is: 0.8392;

[0090] 9. Compression rate of each energy level: 0.5535 0.8521 0.9076 0.9076 0.9262 0.86370.8637;

[0092] 10. The compression ratio of each region is: 0.7857 0.9554 0.7321 0.9554 0.7857;

[0094] 11. The amount of additional information is:

[0095] 1) 4 POS information, each information is recorded using 12 bits, totaling 48 bits;

[0096] 2) The bits of information for each data segment at each energy level are recorded using 4 bits, which is: 5x7x4=140 bits;

[0097] In summary, each row of each viewing angle requires 188 byte of data for compressed information recording. Taking the current data as an example, the amount of data saved by compression is: 2400x7x16x0.8392=43223 byte ≈ 5.28 KB. The additional recorded information accounts for 0.43% of the saved amount.

[0098] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

[0099] In addition, it should be understood that although this specification is described in terms of implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

Claims

1. A photon counting CT data compression method stores the best matching data bits for data at different energy levels and in different regions, characterized by: Here are the steps: S1. Energy spectrum partitioning: Collect projection data, extract it through the data processing unit, and then divide the energy level of the projection data into multiple regions; S2, dynamic threshold decision: divide the energy spectrum data according to the scanning area and calculate its area division threshold; S3. Projection data area division and automatic bit width allocation: Calculate the maximum number of storage bits corresponding to the regional data; S4, original data packaging: store data according to the maximum storage bit number, record the area location information and storage bit number information; The step S1 specifically refers to dividing the projection data according to viewing angle and row, and the processing unit takes a single row of data as the smallest unit. The viewing angle and row currently being processed are used and to mark; The step S2 specifically includes: 1) Set the ratio of the scanned object and air areas according to the intensity: and ; 2) Traverse to obtain the maximum projection data value of the current viewing angle and row: ； in, is the energy spectrum data, is the number of channels, is the energy level number; 3) Calculate the division threshold of the two regions: ； ； 4) Use and The projection data is divided into 5 regions in the channel direction: ； ； ； ； ； in, , ; The projection data area division in step S3 is specifically as follows: Count the maximum values ​​of each energy level in each region: ； in, For the current region, is the current energy level.

2. The photon counting CT data compression method according to claim 1, characterized in that: The automatic bit width allocation in step S3 is specifically as follows: calculate The corresponding maximum number of storage bits: 。 3. The photon counting CT data compression method according to claim 1, characterized in that: The step S4 is specifically as follows: according to Store data and record additionally and Information for data recovery.

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