Spiral CT reconstruction method and device, computer equipment, storage medium and computer program product

Abstract

The application relates to a spiral CT reconstruction method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: performing filtering processing on projection data to obtain filtered data; performing back projection processing on the filtered data to obtain a back projection image, and extracting bone tissue data from the back projection image; performing orthographic projection on the bone tissue data to obtain bone projection data; taking the difference between the projection data and the bone projection data as bone-free tissue data; and obtaining a CT reconstruction image according to the bone-free tissue data and the back projection image. The method can improve the CT reconstruction precision and efficiency.

Description

Technical Field

[0001] This application relates to the field of medical image processing technology, and in particular to a spiral CT reconstruction method, apparatus, computer equipment, storage medium, and computer program product. Background Technology

[0002] In the current field of spiral CT, the reconstruction algorithm mainly adopts the WFBP (Weighted Filtered Back Projection) algorithm, which is an improvement on the traditional Filtered Back Projection (FBP) algorithm.

[0003] FBP reconstructs an image by backprojecting projection data onto the image plane, but it often produces artifacts and noise. The WFBP algorithm reduces noise and artifacts by applying weighted filtering. The weights can be adjusted according to different angles or positions to adapt to image features, thereby improving image quality.

[0004] However, the WFBP algorithm often uses the method of converting helical scan to axial scan during orthographic projection. This method requires orthographic projection of the original data once, resulting in a large amount of computation for CT reconstruction and low reconstruction efficiency. Summary of the Invention

[0005] Therefore, it is necessary to provide a spiral CT reconstruction method, apparatus, computer equipment, computer-readable storage medium, and computer program product that can improve the efficiency and accuracy of CT reconstruction, addressing the aforementioned technical problems.

[0006] Firstly, this application provides a spiral CT reconstruction method, including:

[0007] The projected data is filtered to obtain filtered data;

[0008] The filtered data is back-projected to obtain a back-projected image, and bone tissue data is extracted from the back-projected image.

[0009] Orthographic projection of the bone tissue data yields bone projection data;

[0010] The difference between the projection data and the bone projection data is used as the deostomized tissue data;

[0011] Based on the deossified tissue data and the back-projection image, a CT reconstructed image is obtained.

[0012] In one embodiment, extracting bone tissue data from the back-projected image includes:

[0013] Extract bone tissue pixels from the back-projected image, wherein the CT value corresponding to the bone tissue pixel is within the range of bone tissue CT values;

[0014] For any bone tissue pixel, the bone tissue segment corresponding to the bone tissue pixel is determined based on the CT value of the bone tissue pixel, and the normalization coefficient of the bone tissue pixel is determined based on the upper limit CT value and lower limit CT value of the bone tissue segment, the normalization coefficient corresponding to the upper limit CT value and lower limit CT value, and the CT value of the bone tissue pixel. The range of bone tissue CT values ​​is composed of multiple bone tissue segments.

[0015] Bone tissue data is obtained based on the normalization coefficients and CT values ​​of each bone tissue pixel.

[0016] In one embodiment, the orthographic projection of the bone tissue data to obtain bone projection data includes:

[0017] Based on each pixel in the axial image corresponding to the data of rays passing through bone tissue, and the ray length weight corresponding to each pixel, the CT values ​​of each pixel are weighted and superimposed to obtain bone projection data.

[0018] The ray length weight is negatively correlated with the length of the ray within the voxel.

[0019] In one embodiment, obtaining the CT reconstructed image based on the deossified tissue data and the back-projection image includes:

[0020] The ratio of the deboned tissue data to the water baseline is used as the equivalent length of each projected pixel in the deboned tissue data relative to the water.

[0021] The bone projection data is weighted and corrected using the artifact correction coefficient corresponding to the equivalent length to obtain the corrected bone projection data.

[0022] After reconstructing the corrected bone projection data, the reconstruction result is superimposed on the back-projection image to obtain a CT reconstructed image.

[0023] In one embodiment, the filtering process of the projection data to obtain filtered data includes:

[0024] The projection data is rearranged using a pre-established initial virtual coordinate system, which has the detector illuminated by the central source as the origin, the spiral as the horizontal axis, and the direction of the horizontal displacement bed as the vertical axis.

[0025] The rearranged projection data is filtered based on the windowed filtering check to obtain filtered data.

[0026] In one embodiment, rearranging the projected data using a pre-established initial virtual coordinate system includes:

[0027] By rearranging the direction angle of the projection data, the projection data of the fan beam is rearranged into the projection data of the parallel beam.

[0028] Based on the amount of upsampled data and the interval of the rearrangement of the orientation angles, the projection data of the parallel beam is subjected to three Hermite interpolation to obtain a three-dimensional surface. The three-dimensional surface is then subjected to coordinate interpolation perpendicular to the initial virtual coordinate system to obtain the rearranged projection data.

[0029] In one embodiment, the step of performing coordinate interpolation processing on the three-dimensional surface perpendicular to the initial virtual coordinate system to obtain rearranged projection data includes:

[0030] Determine the grid points in the three-dimensional surface and calculate the rotation matrix for each grid point;

[0031] The rotation matrix is ​​used to transform the coordinates of each grid point from the initial virtual coordinate system to the target virtual coordinate system, where the horizontal axis of the target virtual coordinate system is horizontal.

[0032] Interpolation is performed on each grid point in the target virtual coordinate system to obtain rearranged projection data.

[0033] In one embodiment, the interpolation process performed on each of the grid points in the target virtual coordinate system to obtain rearranged projection data includes:

[0034] Interpolation is performed on each of the grid points in the target virtual coordinate system to obtain the interpolation result;

[0035] The interpolation results are filtered according to the initial virtual coordinate system to obtain rearranged projection data.

[0036] In one embodiment, the windowed filter includes an all-pass frequency and a cutoff frequency, and the windowed filter kernel is:

[0037]

[0038] Wherein, w represents the frequency component of the signal, wl represents the full-pass frequency, and wh represents the cutoff frequency.

[0039] In one embodiment, before filtering the projection data to obtain filtered data, the method further includes:

[0040] Slope correction is performed on the projection data;

[0041] The step of overlaying the reconstruction result with the back-projection image to obtain a CT reconstructed image includes:

[0042] The reconstruction result is then superimposed on the back-projected image to obtain the superimposed result;

[0043] The overlay result is subjected to intercept correction, and the correction result is subjected to slice thickness processing to obtain the CT reconstructed image.

[0044] In one embodiment, the back-projection processing of the filtered data to obtain a back-projected image includes:

[0045] The extension range is determined based on the extension collimation width, pitch, and reconstruction interval. The starting position of the horizontal motor is then shifted to the left and right based on the extension range to obtain the virtual horizontal motor position.

[0046] Interpolation is performed based on the virtual horizontal motor position and the filtered data to obtain extended projection data. Then, weighted back projection is performed based on the extended projection data and the filtered data to obtain a back projection image.

[0047] In one embodiment, the step of performing weighted backprojection processing based on the extended projection data and the filtered data to obtain a backprojected image includes:

[0048] Weighted back projection processing is performed based on the extended projection data and the filtered data to obtain an initial back projection image;

[0049] For any pixel in the initial back-projected image, calculate the distance between the coordinate value of the pixel on the depth axis and the position of the horizontal motor corresponding to the central ray passing through the pixel. If the distance is less than a preset threshold, the pixel is determined to be valid.

[0050] The rearranged data corresponding to the effective pixels are weighted and summed according to the Q-value weight curve to obtain the back-projected image.

[0051] Secondly, this application also provides a CT reconstruction device, comprising:

[0052] The filtering module is used to filter the projection data to obtain filtered data.

[0053] The back projection module is used to perform back projection processing on the filtered data to obtain a back projection image, and extract bone tissue data from the back projection image.

[0054] An orthographic projection module is used to orthographically project the bone tissue data to obtain bone projection data;

[0055] The processing module is used to take the difference between the projection data and the bone projection data as the debonded tissue data;

[0056] The reconstruction module is used to obtain a CT reconstructed image based on the deossified tissue data and the back-projection image.

[0057] In one embodiment, the back-projection module is specifically used for:

[0058] Extract bone tissue pixels from the back-projected image, wherein the CT value corresponding to the bone tissue pixel is within the range of bone tissue CT values;

[0059] For any bone tissue pixel, the bone tissue segment corresponding to the bone tissue pixel is determined based on the CT value of the bone tissue pixel, and the normalization coefficient of the bone tissue pixel is determined based on the upper limit CT value and lower limit CT value of the bone tissue segment, the normalization coefficient corresponding to the upper limit CT value and lower limit CT value, and the CT value of the bone tissue pixel. The range of bone tissue CT values ​​is composed of multiple bone tissue segments.

[0060] Bone tissue data is obtained based on the normalization coefficients and CT values ​​of each bone tissue pixel.

[0061] In one embodiment, the orthographic projection module is specifically used for:

[0062] Based on each pixel in the axial image corresponding to the data of rays passing through bone tissue, and the ray length weight corresponding to each pixel, the CT values ​​of each pixel are weighted and superimposed to obtain bone projection data.

[0063] The ray length weight is negatively correlated with the length of the ray within the voxel.

[0064] In one embodiment, the reconstruction module is specifically used for:

[0065] The ratio of the deboned tissue data to the water baseline is used as the equivalent length of each projected pixel in the deboned tissue data relative to the water.

[0066] The bone projection data is weighted and corrected using the artifact correction coefficient corresponding to the equivalent length to obtain the corrected bone projection data.

[0067] After reconstructing the corrected bone projection data, the reconstruction result is superimposed on the back-projection image to obtain a CT reconstructed image.

[0068] In one embodiment, the filtering module is specifically used for:

[0069] The projection data is rearranged using a pre-established initial virtual coordinate system, which has the detector illuminated by the central source as the origin, the spiral as the horizontal axis, and the direction of the horizontal displacement bed as the vertical axis.

[0070] The rearranged projection data is filtered based on the windowed filtering check to obtain filtered data.

[0071] In one embodiment, the filtering module is further configured to:

[0072] By rearranging the direction angle of the projection data, the projection data of the fan beam is rearranged into the projection data of the parallel beam.

[0073] Based on the amount of upsampled data and the interval of the rearrangement of the orientation angles, the projection data of the parallel beam is subjected to three Hermite interpolation to obtain a three-dimensional surface. The three-dimensional surface is then subjected to coordinate interpolation perpendicular to the initial virtual coordinate system to obtain the rearranged projection data.

[0074] In one embodiment, the filtering module is further configured to:

[0075] Determine the grid points in the three-dimensional surface and calculate the rotation matrix for each grid point;

[0076] The rotation matrix is ​​used to transform the coordinates of each grid point from the initial virtual coordinate system to the target virtual coordinate system, where the horizontal axis of the target virtual coordinate system is horizontal.

[0077] Interpolation is performed on each grid point in the target virtual coordinate system to obtain rearranged projection data.

[0078] In one embodiment, the filtering module is further configured to:

[0079] Interpolation is performed on each of the grid points in the target virtual coordinate system to obtain the interpolation result;

[0080] The interpolation results are filtered according to the initial virtual coordinate system to obtain rearranged projection data.

[0081] In one embodiment, the windowed filter includes an all-pass frequency and a cutoff frequency, and the windowed filter kernel is:

[0082]

[0083] Wherein, w represents the frequency component of the signal, wl represents the full-pass frequency, and wh represents the cutoff frequency.

[0084] In one embodiment, the device further includes:

[0085] A correction module is used to perform slope correction on the projection data;

[0086] The reconstruction module is further used for:

[0087] The reconstruction result is then superimposed on the back-projected image to obtain the superimposed result;

[0088] The overlay result is subjected to intercept correction, and the correction result is subjected to slice thickness processing to obtain the CT reconstructed image.

[0089] In one embodiment, the back-projection module is specifically used for:

[0090] The extension range is determined based on the extension collimation width, pitch, and reconstruction interval. The starting position of the horizontal motor is then shifted to the left and right based on the extension range to obtain the virtual horizontal motor position.

[0091] Interpolation is performed based on the virtual horizontal motor position and the filtered data to obtain extended projection data. Then, weighted back projection is performed based on the extended projection data and the filtered data to obtain a back projection image.

[0092] In one embodiment, the back-projection module is further configured to:

[0093] Weighted back projection processing is performed based on the extended projection data and the filtered data to obtain an initial back projection image;

[0094] For any pixel in the initial back-projected image, calculate the distance between the coordinate value of the pixel on the depth axis and the position of the horizontal motor corresponding to the central ray passing through the pixel. If the distance is less than a preset threshold, the pixel is determined to be valid.

[0095] The rearranged data corresponding to the effective pixels are weighted and summed according to the Q-value weight curve to obtain the back-projected image.

[0096] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement any of the above spiral CT reconstruction methods.

[0097] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above spiral CT reconstruction methods.

[0098] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements any of the above spiral CT reconstruction methods.

[0099] The spiral CT reconstruction method, apparatus, computer equipment, storage medium, and computer program product described above, and the spiral CT reconstruction method provided in this application embodiment, can filter the projection data to obtain filtered data, perform back-projection processing on the filtered data to obtain a back-projected image, and then extract bone tissue data from the back-projected image. Orthographic projection is then performed on the bone tissue data to obtain bone projection data, and the difference between the projection data and the bone projection data is used as the de-bone tissue data. Finally, based on the de-bone tissue data and the back-projected image, a CT reconstructed image is obtained. Using the spiral CT reconstruction method, apparatus, computer equipment, storage medium, and computer program product provided in this application embodiment, in the post-reconstruction process, it is unnecessary to perform an orthographic projection on the original data; the reconstructed projection data can be used directly, which can effectively reduce the computational load and improve the efficiency and accuracy of CT reconstruction. Attached Figure Description

[0100] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0101] Figure 1 This is a flowchart illustrating a spiral CT reconstruction method in one embodiment;

[0102] Figure 2 This is a schematic diagram of the orthographic projection path in one embodiment;

[0103] Figure 3 This is a flowchart illustrating step 104 in one embodiment;

[0104] Figure 4a This shows the relationship between the rays and the pixels in the axial map during orthographic projection;

[0105] Figure 4b The relationship between the ray within a voxel of the reconstructed object and the voxel diagonal is shown.

[0106] Figure 5 This is a flowchart illustrating step 108 in one embodiment;

[0107] Figure 6 This is a flowchart illustrating step 102 in one embodiment;

[0108] Figure 7 This is a schematic diagram of a spiral CT reconstruction method in one embodiment;

[0109] Figure 8This is a flowchart illustrating step 602 in one embodiment;

[0110] Figure 9 This is a schematic diagram of a spiral CT reconstruction method in one embodiment;

[0111] Figure 10 This is a schematic diagram of the channel spacing in one embodiment;

[0112] Figure 11a This is a schematic diagram of a three-dimensional surface in one embodiment;

[0113] Figure 11b This is a schematic diagram of the UV direction plane in one embodiment;

[0114] Figure 12 This is a schematic diagram of a spiral CT reconstruction method in one embodiment;

[0115] Figure 13 This is a schematic diagram of step 702 in one embodiment;

[0116] Figure 14 This is a schematic diagram of step 1206 in one embodiment;

[0117] Figure 15 This is a schematic diagram of a spiral CT reconstruction method in one embodiment;

[0118] Figure 16 This is a flowchart illustrating step 506 in one embodiment;

[0119] Figure 17a This is a schematic diagram of a spiral CT reconstruction method in one embodiment;

[0120] Figure 17b This is another schematic diagram of the spiral CT reconstruction method in one embodiment;

[0121] Figure 18 This is a flowchart illustrating step 104 in one embodiment;

[0122] Figure 19 This is a schematic diagram of the virtual horizontal motor position in one embodiment;

[0123] Figure 20 This is a flowchart illustrating step 1704 in one embodiment;

[0124] Figure 21 This is a schematic diagram of the Q-value weighting curve in one embodiment;

[0125] Figure 22a This is a schematic diagram of a spiral CT reconstruction method in one embodiment;

[0126] Figure 22bThis is another schematic diagram of the spiral CT reconstruction method in one embodiment;

[0127] Figure 23 This is a structural block diagram of a spiral CT reconstruction device in one embodiment;

[0128] Figure 24 This is a schematic diagram of a spiral CT reconstruction method in one embodiment;

[0129] Figure 25 This is a schematic diagram of a spiral CT reconstruction method in one embodiment;

[0130] Figure 26 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0131] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0132] In one embodiment, such as Figure 1 As shown, a spiral CT reconstruction method is provided. This embodiment illustrates the application of this method to a terminal. It is understood that this method can also be applied to a server, and to a system including both a terminal and a server, and implemented through interaction between the terminal and the server. In this embodiment, the method includes steps 102 to 110, wherein:

[0133] Step 102: Filter the projection data to obtain filtered data.

[0134] In this embodiment of the application, during the CT reconstruction process, the projection data can be filtered to remove noise and improve the CT reconstruction effect. For example, filtering algorithms such as Laplace filtering and Hamming window filtering can be used to filter the projection data.

[0135] For example, projection data can be obtained from CT scanners. This projection data consists of intensity information of X-rays penetrating an object at multiple angles. First, a filter is selected, such as a Hamming window filter. The Hamming window filter can smooth the data in the frequency domain and reduce artifacts. Then, a Fourier transform can be performed on the projection data to convert it to the frequency domain. The Fourier transform can decompose the signal into different frequency components, facilitating filtering. Applying the selected filter in the frequency domain includes multiplying the Fourier transform result of the projection data with the frequency response of the filter, thereby suppressing unwanted frequency components (such as noise and artifacts), selectively retaining low-frequency components, and removing high-frequency components. Finally, an inverse Fourier transform is performed on the filtered frequency domain data to convert it back to the time domain (or spatial domain), thus obtaining the filtered data (the filtered projection data). For example, after obtaining the filtered data, further processing can be performed, such as normalization and smoothing, to improve image quality and usability.

[0136] The above filtering process not only removes noise and artifacts, but also improves image contrast and edge features, providing higher quality input data for subsequent CT image reconstruction.

[0137] Step 104: Perform backprojection processing on the filtered data to obtain a backprojected image, and extract bone tissue data from the backprojected image.

[0138] Backprojection is the process of projecting each piece of projection data back into the image space according to its imaging angle. Specifically, each piece of projection data is projected back into the two-dimensional image along the angle from which it was acquired. For example, for filtered data at any angle, the pixel value of each pixel in the filtered data can be accumulated along that angle to progressively construct a backprojected image. The filtered data from all angles accumulates during the backprojection process to form a single backprojected image, which typically contains mixed information from various tissues. In practice, weighted backprojection methods (such as normalizing the filtered values) can also be used during the accumulation process to improve the backprojection quality.

[0139] After obtaining the back projection data, clear bone tissue data can be extracted from the back projection image. For example, bone tissue data can be extracted from the back projection image by using the CT value range corresponding to the bone tissue. The extraction process of bone tissue data will be described in the following embodiments of this application. It will not be described in detail here.

[0140] Step 106: Perform orthographic projection on the bone tissue data to obtain bone projection data.

[0141] In this embodiment of the application, when orthographically projecting bone tissue data, the orthographic projection path can be viewed as a ray following the scanning path at a conical angle (i.e., the entire path follows a spiral). For example, see [link to example]. Figure 2 As shown, Figure 2 The diagram illustrates the orthographic projection path of a single image frame, with the scanning path following a spiral trajectory. This process simulates image imaging in three-dimensional space. This scanning method ensures that each original image frame has corresponding projection data, thus eliminating the rearrangement step in the reconstruction process. Eliminating the rearrangement step significantly improves data processing speed, making the imaging process more efficient.

[0142] Furthermore, the orthographic projection process can also downsample the projection lines, thereby reducing the amount of data processing. For example, the image resolution can be reduced to half (or a quarter) of the original image. While maintaining image quality, this reduces computational load and improves orthographic projection efficiency. In this embodiment, when downsampling to extract the original image values, the x-coordinate can be simply shifted one position to the left, allowing for quick acquisition of the corresponding projection value without complex processing.

[0143] In an exemplary embodiment, step 106, which involves orthographically projecting the bone tissue data to obtain bone projection data, may include the following steps:

[0144] Based on the pixels in the axial image corresponding to the data of rays passing through bone tissue, and the ray length weights corresponding to each pixel, the CT values ​​of each pixel are weighted and superimposed to obtain bone projection data; among them, the ray length weights are negatively correlated with the length of the rays within the voxel.

[0145] In this embodiment, the ray length weight can be determined based on the length of the ray within the voxel. The ray length weight is inversely proportional to the ray length within the voxel; that is, the longer the ray within the voxel, the smaller the influence of the point on that axial plane (orthogonal projection weight is the largest). The pixels in the axial image corresponding to the bone tissue data through which the ray passes are determined, and the pixel values ​​corresponding to each pixel are weighted and superimposed with the ray length weight to obtain the orthogonal projection value of each ray in the orthogonal projection, thus obtaining the bone projection data. For example, Figure 4a This shows the relationship between the rays and the pixels in the axial map during orthographic projection; Figure 4b The relationship between the ray within a voxel of the target reconstructed object and the voxel diagonal is shown.

[0146] Step 108: Use the difference between the projection data and the bone projection data as the deostomized tissue data.

[0147] In this embodiment of the application, after obtaining the bone projection data, the difference between the projection data and the bone projection data can be determined. This difference represents the signal of other tissues (such as soft tissue or fluid) other than bone tissue at that pixel position. This difference is used as the de-osteoscopic tissue data, which is an image of other tissues other than bone tissue.

[0148] Step 110: Obtain CT reconstructed images based on the deossified tissue data and back-projection images.

[0149] In this embodiment, bone projection data can be corrected using de-osteoscopic tissue data, and the corrected bone projection can be used for reconstruction to obtain a new bone reconstruction image. This new bone reconstruction image is then superimposed on the back-projection image to obtain a CT reconstruction image.

[0150] The spiral CT reconstruction method provided in this application can filter the projection data to obtain filtered data, and then perform back-projection processing on the filtered data to obtain a back-projected image. Bone tissue data is then extracted from the back-projected image. Orthographic projection is performed on the bone tissue data to obtain bone projection data, and the difference between the projection data and the bone projection data is used as the de-bone tissue data. Finally, a CT reconstructed image is obtained based on the de-bone tissue data and the back-projected image. Using the spiral CT reconstruction method provided in this application, during the post-reconstruction bone sclerosis correction process, it is unnecessary to perform an orthographic projection on the original data; the reconstructed projection data can be used directly, which can reduce the computational load and improve CT reconstruction efficiency.

[0151] In one exemplary embodiment, reference is made to Figure 3 As shown, in step 104, extracting bone tissue data from the back-projection image may include steps 302 to 306, wherein:

[0152] Step 302: Extract bone tissue pixels from the back-projected image. The CT values ​​corresponding to the bone tissue pixels are within the range of bone tissue CT values.

[0153] Step 304: For any bone tissue pixel, determine the bone tissue segment corresponding to the bone tissue pixel based on the CT value of the bone tissue pixel, and determine the normalization coefficient of the bone tissue pixel based on the upper limit CT value and lower limit CT value of the bone tissue segment, the normalization coefficient corresponding to the upper limit CT value and lower limit CT value, and the CT value of the bone tissue pixel. The range of bone tissue CT value is composed of multiple bone tissue segments.

[0154] Step 306: Obtain bone tissue data based on the normalization coefficients and CT values ​​of each bone tissue pixel.

[0155] In this embodiment, a pre-defined range of CT values ​​for bone tissue is established. This range can be composed of multiple bone tissue segments. For example, the range of CT values ​​for bone tissue is hu1~hu2, comprising three segments: the first segment hu1~hua (e.g., 850~1150), which represents CT values ​​between brain tissue and bone tissue; the second segment hua~hub (e.g., 1150~2075), which represents the CT value range for bone tissue; and the third segment hub~hu2 (e.g., 2075~3000), which represents CT values ​​between bone tissue and high-density bodies. The upper and lower limits of the CT values ​​for each segment have preset normalization coefficients. For example, hu1 corresponds to s1 (e.g., 0), hua corresponds to s2 (e.g., 0.4), hub corresponds to s3 (e.g., 0.7), and hu2 corresponds to s4 (e.g., 1).

[0156] For each pixel in the back-projected image, pixels whose corresponding CT values ​​are within the range of bone tissue CT values ​​can be identified as bone tissue pixels. For example, if the CT value hui of pixel i is between hu1 and hu2, then pixel i can be identified as a bone tissue pixel.

[0157] After obtaining the pixels of each bone tissue, the CT value of each bone tissue pixel can be acquired. Based on the CT value of each bone tissue pixel, the corresponding bone tissue segment can be determined. Then, based on the upper and lower CT values ​​of the corresponding bone tissue segment, the normalization coefficients corresponding to the upper and lower CT values, and the CT value of the bone tissue pixel, the normalization coefficient of each bone tissue pixel can be determined. For example, the process of determining the normalization coefficient can refer to the following formula (I):

[0158] Formula (1)

[0159] in, This represents the normalization coefficient corresponding to the pixel in the bone tissue. It is the normalization coefficient corresponding to the lower limit CT value of the bone tissue segment where the bone tissue pixel is located. is the normalization coefficient corresponding to the upper limit CT value of the bone tissue segment where the bone tissue pixel is located, and hu is the CT value of the bone tissue image point. This represents the lower limit CT value of the bone tissue segment where the bone tissue image point is located. This is the upper limit CT value of the bone tissue segment where the bone tissue image point is located.

[0160] After obtaining the normalization coefficients corresponding to the bone tissue pixels, the CT value of each bone tissue pixel is multiplied by the normalization coefficient corresponding to that bone tissue pixel to obtain the segmented and extracted bone tissue data.

[0161] The spiral CT reconstruction method provided in this application can improve the accuracy of bone tissue data extraction by setting multiple bone tissue segments, and further improve the accuracy of CT reconstruction by performing bone sclerosis correction based on the extracted bone tissue data.

[0162] In one exemplary embodiment, reference is made to Figure 5 As shown, in step 108, obtaining the CT reconstructed image based on the debone tissue data and the backprojection image may include the following steps 502 to 506, wherein:

[0163] Step 502: The ratio of the deboned tissue data to the water baseline is used as the equivalent length of each projected pixel in the deboned tissue data relative to the water.

[0164] Step 504: The bone projection data is weighted and corrected using the artifact correction coefficient corresponding to the equivalent length to obtain the corrected bone projection data.

[0165] Step 506: After reconstructing the corrected bone projection data, the reconstruction result is superimposed on the back projection image to obtain the CT reconstructed image.

[0166] In this embodiment, the de-osteoscopic tissue data is first divided by the water reference, and this ratio is used as the equivalent length of each projected pixel in the de-osteoscopic tissue data relative to the water. Then, based on the equivalent length, the corresponding artifact correction coefficient is looked up in a pre-set coefficient table, and the bone projection data is then subjected to polynomial weighted correction using this artifact correction coefficient to obtain the corrected bone projection data. The correction process can be referred to as shown in the following formula (II):

[0167] Formula (II)

[0168] in, This represents the bone projection data before correction. This represents the corrected bone projection data, where N is the number of polynomials. This represents the artifact correction coefficient.

[0169] After obtaining the corrected bone projection data, reconstruction can be performed based on the corrected bone projection data to obtain the reconstruction result. The specific reconstruction process depends on the selected reconstruction algorithm. By overlaying the reconstruction result with the backprojection image, the CT reconstructed image can be obtained.

[0170] Since the scanning path of the bone projection data in this embodiment is a rearranged path, there is no need to rearrange it again, which can greatly improve the efficiency of bone sclerosis correction.

[0171] In one exemplary embodiment, reference is made to Figure 6As shown, in step 102, the projection data is filtered to obtain filtered data, which may include the following steps 602 to 604, wherein:

[0172] Step 602: The projection data is rearranged using a pre-established initial virtual coordinate system. The initial virtual coordinate system has the detector illuminated by the central source as the origin, the spiral as the horizontal axis, and the direction of the horizontal displacement bed as the vertical axis.

[0173] Step 604: Filter the rearranged projection data according to the windowed filter check to obtain filtered data.

[0174] In this embodiment, the detector position irradiated by the center of the radiation source irradiation range (cone beam) can first be used as the origin of the UV coordinate system (initial virtual coordinate system). Then, the horizontal axis (U-axis) of the coordinate system is defined as the direction along the helix, which is usually the rotation trajectory around the scanning center, used to represent the movement of the detector during the scanning process. The vertical axis (V-axis) of the coordinate system is defined as the direction parallel to the horizontal displacement bed direction, which is used to represent the direction of the patient's movement along the bed during the scanning process. Figure 7 As shown.

[0175] For example, a two-dimensional UV coordinate system is established with the center point of the detector (the origin being the point corresponding to the radiation source; it should be noted that if the detector is not precisely placed in the center, this center point will not coincide with the point corresponding to the radiation source). Based on a preset sampling size, ΔU (the change along the U-axis) and ΔV (the change along the V-axis) are calculated in the coordinate system. The U-axis is called the virtual coordinate channel, and its characteristic is that it is not a straight line but a curve, meaning that the values ​​of the U-axis in this coordinate system are not arranged according to a conventional linear relationship.

[0176] In each frame, the position of the detector illuminated by the center ray of the source's irradiation range (cone beam) is taken as the origin, the helix is ​​taken as the U-axis, and the direction in the same direction as the horizontal displacement bed is taken as the V-axis. The UV-enclosed region then becomes an irregular image, see... Figure 7 The midpoint lattice takes values ​​at equal intervals according to the package distance in the V direction, and takes values ​​at equal curve distances according to the spiral direction in the U direction.

[0177] Raw projection data is acquired from the CT detector. This data is typically recorded at different angles and positions, and each projection data point usually includes information such as the projection angle, detector position, and corresponding X-ray intensity. The acquired projection data is rearranged using a pre-established UV coordinate system. For example, transformation parameters of the projection data can be calculated, and based on the definition of the initial virtual coordinate system and the transformation parameters, the position of each projection data point in the new coordinate system can be calculated. Each projection data point is then transformed from the original coordinate system to the new virtual UV coordinate system. This process often needs to consider information such as the detector's angle, position, and spiral trajectory.

[0178] In one exemplary embodiment, reference is made to Figure 8 As shown, in step 602, the projection data is rearranged using a pre-established initial virtual coordinate system, which may include steps 802 to 804, wherein:

[0179] Step 802: By rearranging the direction angle of the projection data, the projection data of the fan beam is rearranged into the projection data of the parallel beam.

[0180] Step 804: Based on the upsampled data volume and the interval of orientation angle rearrangement, perform three Hermite interpolation on the projection data of the parallel beam to obtain a three-dimensional surface, and perform coordinate interpolation processing on the three-dimensional surface perpendicular to the initial virtual coordinate system to obtain rearranged projection data.

[0181] In this embodiment, the projection data is first rearranged by orientation angle to rearrange the projection data of the fan-shaped beam into a parallel beam: the non-central rays of the fan-shaped beam are rearranged into a parallel beam pattern, such that these rays are parallel to the central ray at the specified orientation angle. Exemplarily, orientation angle rearrangement may include orientation angle adjustment and geometric transformation, wherein orientation angle adjustment includes adjusting the fan-shaped beam ray data at different orientation angles to align with the target parallel beam direction; geometric transformation includes performing a geometric transformation on the data to recalculate and adjust each ray in the fan-shaped beam to the form of a parallel beam, this process may include rotation and scaling to match the spacing of the parallel beams.

[0182] For example, refer to Figure 9 As shown, firstly, the orientation angles are rearranged to rearrange the fan-shaped bundles into parallel bundles. This is related to the orientation angle IV (…). Figure 9 A ray parallel to the central ray at the direction angle (set as 0 degrees) is rearranged onto a non-central ray at the same direction angle. This means the non-central ray is now parallel to the central ray, and the rearranged ray has a smaller spacing than the original. This is because any non-central ray, such as one hitting a ray at coordinates (iu, iv) on the detector at an angle of 0 degrees, will rotate through the rotation center to become a ray parallel to the central ray at direction angle IV. Figure 9The example in the image shows the image closer to the center after being rotated 20.35 degrees: AI <DJ。

[0183] It should be noted that the channel spacing is not equal after the azimuth angles are rearranged; the channel spacing is related to the detector installation pattern. For example, assuming there are 384 channels, refer to... Figure 10 As shown, the spacing between channels after rearranging the directional angles of the 384 channels is illustrated (the horizontal axis represents the channel spacing, and the vertical axis represents the spacing distance in mm; from left to right, these are the spacings between the 1st and 0th channels, the 2nd and 1st channels, and so on).

[0184] After the orientation angles are rearranged, the spacing between the rearranged channels can be obtained (hereinafter referred to as the orientation angle rearrangement spacing in this embodiment). Based on the upsampled data volume and the orientation angle rearrangement spacing, the projection data of the parallel beam is subjected to three Hermite interpolation. The result after interpolation is a three-dimensional surface, as shown in the reference. Figure 11a As shown. Since the rearranged single-view detector data is not a standard two-dimensional matrix, but a curved array related to the spiral orbit, the cubic interpolation method used in this embodiment of the application has better interpolation results than the general linear interpolation method.

[0185] Since the interpolation results in a three-dimensional surface, it approximates a parallelogram in two dimensions. That is, after the Hermite interpolation in the previous step, the V-axis is evenly spaced upwards. However, because the U-axis is not horizontal, directly using the data from this three-dimensional surface during back projection will result in inappropriate interpolation data, especially noticeable when the pitch is low. For example, refer to... Figure 12 As shown (it should be noted that, Figure 12 (The actual display is a curved surface, not a plane). The points used for bilinear interpolation of point G1 in the virtual UV coordinate system are G, M, L, and H. As can be seen from the figure, point H is obviously unsuitable. The most suitable data points should be G, M, L, and R.

[0186] Therefore, after performing three Hermite interpolations, the resulting 3D surface can be subjected to coordinate interpolation perpendicular to the initial virtual coordinate system. The purpose of this interpolation is to make the U-axis horizontal.

[0187] In one exemplary embodiment, reference is made to Figure 13 As shown, in step 802, the coordinate interpolation process of the three-dimensional surface perpendicular to the initial virtual coordinate system is performed to obtain the rearranged projection data, which may include steps 1302 to 1306, wherein:

[0188] Step 1302: Determine the mesh points in the 3D surface and calculate the rotation matrix for each mesh point;

[0189] Step 1304: Use a rotation matrix to transform the coordinates of each grid point from the initial virtual coordinate system to the target virtual coordinate system. The horizontal axis of the target virtual coordinate system is horizontal.

[0190] Step 1306: Interpolate each grid point in the target virtual coordinate system to obtain rearranged projection data.

[0191] In this embodiment, after obtaining the three-dimensional surface, the UV coordinates need to be interpolated to ensure the U-axis is horizontal. For example, uniformly distributed grid points are selected on the three-dimensional surface as needed. Typically, these points are distributed based on the object's geometric features to ensure coverage of the entire area within the effective range. For example, parameters u and v can be set in three-dimensional space to form a UV grid, and these grid points will be interpolated in subsequent processes.

[0192] The target direction of the U-axis can be determined, and the rotation angle can be calculated based on the initial UV coordinates of the grid points and the target direction. Construct the rotation matrix R, for example: Construct the rotation matrix .

[0193] Furthermore, this rotation matrix is ​​applied to the UV coordinates of each grid point to adjust the orientation of the U-axis, for example: ,in The original coordinates in the standard initial virtual coordinate system This represents the new coordinates after the original coordinates are transformed to the target virtual coordinate system. In this way, the coordinates of each grid point can be transformed from the virtual coordinate system to a virtual coordinate system with the U-axis horizontal.

[0194] After coordinate transformation, interpolation can be performed on each grid point in the target virtual coordinate system. For example, bilinear interpolation or bicubic interpolation can be used to interpolate and obtain smoother projection data.

[0195] In one exemplary embodiment, reference is made to Figure 14 As shown, in step 1306, interpolation is performed on each grid point in the target virtual coordinate system to obtain rearranged projection data, which may include the following steps 1402 to 1404, wherein:

[0196] Step 1402: Perform interpolation on each grid point in the target virtual coordinate system to obtain the interpolation result;

[0197] Step 1404: Filter the interpolation results according to the initial virtual coordinate system to obtain the rearranged projection data.

[0198] In this embodiment, interpolation processing can be performed on each grid point in the target virtual coordinate system to obtain interpolation results. The interpolation method can include bilinear interpolation or bicubic interpolation, etc., and this embodiment does not specifically limit the interpolation method. After obtaining the interpolation results, data within the boundary range of the original coordinate system can be saved as valid data, while data outside the boundary range is invalid data. For example, the boundary range of the three-dimensional surface in the initial virtual coordinate system can be determined, and data exceeding the boundary range in the interpolation results can be marked as invalid data to avoid affecting the results during the reconstruction process. Only data within the boundary range is extracted as valid data to form the final rearranged projection data for subsequent CT reconstruction.

[0199] Since the initial virtual coordinate system provided in this application embodiment is based on a spiral as the horizontal axis and the horizontal displacement bed direction as the vertical axis, which is consistent with the scanning path, curve fitting interpolation was performed according to the spiral U-axis during rearrangement, and grid node interpolation was performed according to the UV coordinate system with an approximately horizontal U-axis after rearrangement, which can greatly improve the accuracy of back projection.

[0200] In an exemplary embodiment, windowed filtering includes an all-pass frequency and a cutoff frequency, and the windowed filter core... Refer to the following formula (III):

[0201] Formula (3)

[0202] Where w represents the frequency component of the signal, wl represents the full-pass frequency, and wh represents the cutoff frequency.

[0203] In one example, a sinc window can be used, which provides clearer details at the same cutoff frequency compared to a hanning window, which is too smooth. Two limits are set: low frequency (full-pass frequency) and high frequency (cutoff frequency). When the frequency component of the projected data is less than or equal to the full-pass frequency, the window value of the corresponding windowed filter kernel can be determined to be 1; or, when the frequency component of the projected data is greater than the cutoff frequency, the window value of the corresponding windowed filter kernel can be determined to be 0; or, when the frequency component of the projected data is greater than the full-pass frequency but less than the cutoff frequency, the window value of the corresponding windowed filter kernel is... .

[0204] In one example, refer to Figure 15 As shown, the positions of the full-pass frequency and the cutoff frequency are displayed. The horizontal axis represents the sampling point (normalized to positive and negative, i.e., the center is the center of the image sampling width), and the vertical axis represents the window function value. Figure 15 In the diagram, WL is the full pass frequency (e.g., it can take values ​​of 1, 1 / 16, 1 / 32), WH is the cutoff frequency (e.g., it can take values ​​of 1, 2 / 3, 1 / 2), and WL <= WH; the horizontal axis is the sampling width of the convolution (0 represents the sampling center), and the vertical axis is the normalized window width value.

[0205] In one example, depending on the requirements of the reconstructed image, the full pass frequency and cutoff frequency can take different values, and are divided into multiple groups, such as 7 groups, each corresponding to different sharpening and smoothing effects. A group can be selected from multiple groups to calculate the windowed filter kernel and perform windowed filtering on the projection data.

[0206] In an exemplary embodiment, before filtering the projection data to obtain filtered data, the above method may further include the step of: performing slope correction on the projection data;

[0207] In this embodiment, refer to Figure 16 As shown, in step 506, the reconstruction result is superimposed on the backprojection image to obtain the CT reconstructed image, which may include steps 1602 to 1604, wherein:

[0208] Step 1602: Overlay the reconstruction result with the backprojection image to obtain the overlay result;

[0209] Step 1604: Perform intercept correction on the superposition result and slice thickness processing on the correction result to obtain the CT reconstructed image.

[0210] In related technologies, slope correction and intercept correction are performed during post-reconstruction to dynamically stretch the reconstruction results, making air reach -1000 and water reach 0, i.e., linear correction, to obtain slope and intercept for correction of each pixel. In this embodiment, slope correction is performed on the projection data before backprojection. For example, for the projection data, the projection data can be corrected based on the air average value. During the correction process, the difference between the air average value under the initial angular period of the projection data and the air average value under the initial angular period of the air can be considered. The specific process is described in the following formula (iv):

[0211] Formula (IV)

[0212] in, , This refers to the value of the initial angle of the projection data within one cycle (for example, one cycle is 360 degrees, and the projection data is at the 30-degree position). This refers to the value of the initial air angle over one cycle (for example, one cycle is 360 degrees, and the air data is at 70 degrees). This indicates the deviation in angle. This represents the projection data before correction. that is The corresponding air data, among which This indicates the current angular position, which is the value obtained from the projection data. Given this angular position data, the air is then taken as... Location data, This represents the corrected projection data.

[0213] After air correction, further water hardening correction can be performed on the air-corrected projection data. Slope correction is then applied to the water-hardened projection data, and intercept correction is performed on the reconstruction results during the subsequent reconstruction process. Since projection values ​​are the superposition of pixels, correcting the slope portion of the CT values ​​before reconstruction allows for equal scaling, effectively expanding the dynamic range of the projection data and improving data accuracy and reconstruction precision.

[0214] In the post-reconstruction process, the reconstruction results and the back-projection image can be superimposed to obtain the superimposed result. The intercept of the superimposed result is then corrected to obtain the corresponding correction result. Further slice thickness processing is performed on the correction result to obtain the CT reconstructed image.

[0215] The process of layer thickness processing includes: performing layer thickness overlay calculations; determining the effective layer thickness based on the reconstruction interval, displayed layer spacing, and preset layer thickness; performing layer thickness overlay calculations; and referring to... Figure 17a and Figure 17b The diagrams shown illustrate the effects of layer thickness treatment and layer thickness treatment, respectively.

[0216] In the embodiments of this application, reference is made to Figure 18 As shown, in step 104, the filtered data is back-projected to obtain a back-projected image, which may include steps 1802 to 1804, wherein:

[0217] Step 1802: Determine the extension range based on the extension collimation width, pitch and reconstruction interval, and shift the starting position of the horizontal motor to the left and right based on the extension range to obtain the virtual horizontal motor position;

[0218] Step 1804: Interpolation is performed based on the virtual horizontal motor position and filtered data to obtain extended projection data, and weighted back projection is performed based on the extended projection data and filtered data to obtain a back projection image.

[0219] In this embodiment, the horizontal motor drives the displacement bed, enabling relative movement between the patient and the CT equipment in the horizontal direction. During helical scanning, the CT equipment rotates, and driven by the horizontal motor, the patient moves relative to the CT equipment along the axis of rotation, i.e., the horizontal direction, thus realizing the helical scanning process.

[0220] During CT reconstruction, the projection data can also be extended in the Z-axis field of view, including setting the virtual horizontal motor position. For example, the extension range can be determined based on the extension collimation width, pitch, and reconstruction interval. Then, the starting position of the horizontal motor can be shifted left and right based on the extension range to obtain the virtual horizontal motor position. (Refer to...) Figure 19 The diagram shows the virtual horizontal motor position (i.e., the position of the spiral sweep extending upwards and downwards). The process for determining the extension range can be referenced in the following formula (V):

[0221] Formula (5)

[0222] in, Indicates the extended range, Indicates the extended collimation width, Indicates the reconstruction interval. Indicates the pitch.

[0223] In this embodiment, interpolation can be performed based on the virtual motor's horizontal motor position and projection data to obtain extended projection data. Then, CT-weighted backprojection is performed using the original projection data and the extended projection data, thereby increasing the number of reconstructed frames and reducing artifacts from incomplete projection.

[0224] For example, by traversing each pixel point and each rearrangement viewpoint in the XY plane of the target coordinate system (each axial view), the starting and ending positions of the horizontal motor corresponding to the rearrangement viewpoint are determined. Based on the previously determined expansion range, the starting and ending positions are shifted to the left and right, respectively, to determine the upper and lower boundaries of the effective Z plane under the rearrangement viewpoint. The pixels on the XY plane are transformed to the initial virtual coordinate system to determine the UV coordinate values. The channel position is determined based on the U coordinate value, i.e., the position of the horizontal channel for collecting rearranged data (only the effective horizontal channel is calculated). Based on the V-direction position of the ray, the V coordinate value determined by the pixels on the XY plane transformed to the initial virtual coordinate system, and the V position (i.e., the Z position) of the desired image plane, the row number is determined, i.e., the position of the vertical channel for collecting rearranged data (only the effective vertical channel is calculated). Based on the position of the effective vertical channel for collecting rearranged data, the weight W is calculated according to the following formula (VI). Q (q):

[0225] Formula (VI)

[0226] Among them, W Q (q) represents the Q-value weight, where q is the normalized corresponding value for the layer direction (row number), and Q is a threshold (within 1). For example, Figure 21 A schematic diagram of the Q-value weighting curve is shown. Assuming Q is 0.6, the curve is constructed using... Figure 21 It can be seen that W Q(q) The weight is on the vertical axis, and q is on the horizontal axis. q is the normalized value corresponding to the row direction, which is the V direction. For example, if there are 32 rows in the V direction, then the center is the point corresponding to row 16.5. Figure 21 The horizontal zero point is the position where q=0, and rows 1 and 32 are the positions where q=-1 and q-1, respectively.

[0227] Iterate through the rearranged data corresponding to the XYZ values ​​and multiply by the corresponding weight W. Q (q). A weighted average is applied to the weights along the same direction, i.e., interpolation reconstruction is performed using a 180-degree interpolation method. In this embodiment, an extended image can be obtained through extension, and the proportion of rays can be increased and artifacts of incomplete projection reduced by adjusting the weights at the location of the extended image (setting virtual horizontal motor parameters and weighting the effective weights W obtained from the extension). Finally, the superimposed axial image is multiplied by the angular weights to obtain the back-projected image.

[0228] In one exemplary embodiment, reference is made to Figure 20 As shown, in step 1804, a weighted backprojection process is performed based on the extended projection data and filtered data to obtain the backprojected image, including steps 2002 and 2006, wherein:

[0229] Step 2002: Perform weighted backprojection processing based on the extended projection data and filtered data to obtain the initial backprojection image;

[0230] Step 2004: For any pixel in the initial back-projection image, calculate the distance between the vertical coordinate value of the pixel and the position of the horizontal motor corresponding to the central ray passing through the pixel. If the distance is less than a preset threshold, the pixel is determined to be valid.

[0231] Step 2006: The rearranged data corresponding to the effective pixels are weighted and summed according to the Q-value weight curve to obtain the back-projected image.

[0232] In this embodiment, a weighted backprojection process is performed based on the extended projection data and filtered data to obtain an initial backprojection image. During the reconstruction process, valid reconstruction points can be selected from this initial backprojection image. For example, the distance between the V-coordinate value (coordinate value on the vertical axis) of each coordinate point (reconstruction point) and the position of the horizontal axis of the center ray passing through it can be calculated. If this distance is greater than or equal to a preset threshold value, the pixel is considered invalid; otherwise, the pixel is considered valid.

[0233] Wherein, the V coordinate value = frame number * inc + startPos, where the frame number indicates the layer in which the reconstruction point is located, which is the frame number, inc indicates the layer interval, that is, the distance between two frames, and startPos indicates the starting reconstruction distance (that is, the starting position of the horizontal motor movement), representing the distance from the 0th image to the starting position of the horizontal motor; the position of the horizontal motor of the center ray passing through the reconstruction point is the position value corresponding to the frame number of the center ray read from the horizontal motor parameters, representing the distance from that frame to the initial horizontal motor position; the difference between the V coordinate value of the reconstruction point and the position of the horizontal motor of the center ray passing through it is the distance between the V coordinate value of the reconstruction point and the position of the horizontal motor of the center ray passing through it.

[0234] In fact, referring to Figure 22a As shown, the reconstructed target field of view is a sphere for each frame of ray (in one example, it could also be an ellipsoid). If a reconstructed point is inside the sphere, it is considered valid and can participate in the reconstruction. If a reconstructed point is outside the sphere, it can be considered that the reconstructed point is too far from the source and is an edge ray. Due to errors, the accuracy of this reconstructed point is poor, and it can be considered invalid and not included in the superposition calculation (including when calculating the weights). (Refer to...) Figure 22b As shown, the same reconstruction point for both spheres is a valid point relative to one ray and is included in the reconstruction; however, it is an invalid point relative to the other ray and is not included in the reconstruction. This is equivalent to an effective 3D simulated collimator, which can effectively improve cone artifacts and windmill artifacts in images.

[0235] To enable those skilled in the art to better understand the embodiments of this disclosure, the embodiments of this application are described below through specific examples. The spiral CT reconstruction method in this example includes four stages: pre-reconstruction, coarse reconstruction, fine reconstruction, and post-reconstruction. Specifically:

[0236] 1. The pre-reconstruction stage includes the calculation of the number of reconstruction layers, the calculation of the starting position of the horizontal motor movement based on the position selected by the positioning piece, the allocation of video memory, the advance reading of data from various parameter files, the establishment of a virtual coordinate system, the customization of missing parameters, the texture binding of weights, and the determination of windowed filter kernels.

[0237] Pre-reconstruction can be performed before data acquisition, without consuming acquisition and reconstruction time. Since storage space needs to be allocated beforehand (allocating video memory is time-consuming), the number of reconstruction layers needs to be known first. The reconstruction interval and reconstruction layer thickness are data used during the reconstruction process, and they differ between coarse and fine reconstruction. The number of reconstruction layers can include both fine and coarse reconstruction layers. The fine reconstruction layer number can be calculated based on reconstruction parameters, including: scan length (scanlenth), collimation angle (colangle), collimation width (colwidth), extended collimation width (ecol), pitch (pitch), reconstruction interval (inc), and display layer spacing (thk), resulting in the fine reconstruction layer number Num_bp. The coarse reconstruction interval is pre-designed, while the fine reconstruction interval is selected by the user interface during shooting (or by the user-selected protocol, which has a default setting). The collimation angle, collimation width, and extended collimation width are set before image acquisition. The collimation angle and collimation width are determined by the machine's mechanics and are related to its design. The extended collimation width is set based on empirical values.

[0238] The process for determining the number of fine reconstruction layers is shown in the following formula (VII):

[0239] Formula (VII)

[0240] Since there is a certain proportional relationship between the number of fine reconstruction layers and the number of coarse reconstruction layers, the number of coarse reconstruction layers can be further determined based on this proportional relationship. The process of determining the number of coarse reconstruction layers num_rough is shown in the following formula (VIII):

[0241] Formula (8)

[0242] Where 5 represents the coarse reconstruction interval of 5mm, it can be replaced with other values ​​when other reconstruction intervals are used in the coarse reconstruction.

[0243] Furthermore, based on the number of fine reconstruction layers, the reconstruction interval, and the display layer spacing, the number of images reconstructed after fine reconstruction layer thickness processing can be further determined, as shown in Formula (IX):

[0244] Formula (IX).

[0245] The calculation of the starting position of the horizontal motor movement based on the selected position on the positioning plate includes: the user selects the shooting area through the positioning plate, and during helical scanning, the starting point of the scan needs to be moved to the starting point selected by the user. The scanning distance is the distance specified by the user in the positioning plate. The initial horizontal motor position startcouphpos, the distance from the selected initial position on the positioning plate to the initial position on the positioning plate scoutdis, the collimation angle colangle, the collimation width colwidth, the extended collimation width ecol, and the pitch pitch can be obtained, and the starting position of the horizontal motor movement startpos can be calculated. The initial horizontal motor position is the mechanical position of the displacement bed, that is, the 0 point position of the displacement bed. The starting position of the horizontal motor movement is the position of the horizontal motor when scanning starts from the starting position selected on the positioning plate, and the starting position of the horizontal motor movement is calculated using formula (x) or formula (xi).

[0246] Formula (10)

[0247] Formula (XI)

[0248] In the scheme corresponding to formula (x), the predefined direction of movement of the horizontal motor is opposite to the V-axis of the initial virtual coordinate system, and the negative value reflects the opposite concept; in the scheme corresponding to formula (xi), the predefined direction of movement of the horizontal motor is consistent with the V-axis of the initial virtual coordinate system.

[0249] In this process, the user selects the imaging area using a positioning film. During helical scanning, the starting point of the scan needs to be moved to the user-selected starting point. Therefore, the horizontal motor movement starting position can be determined based on the position selected on the positioning film (the positioning film typically displays the geometry of the entire scanning area, helping operators and doctors determine the specific location and extent of the scan. A starting position for the scan is predefined on the positioning film; this position is called the initial position on the positioning film. The operator will then use a mouse or other tools to select an area on the positioning film. This selected area is usually used to determine the Region of Interest (ROI) for further CT scanning. The selected position helps ensure the scanner acquires data at the correct position and angle. The selected position also includes a starting position for the scan, called the initial position selected on the positioning film. For example, when scanning the chest, the positioning film may show the outline of the entire chest; the radiologist will select the lung area so that the CT scanner can focus on acquiring lung data), and the displacement table is moved accordingly.

[0250] During the process of allocating video memory, the video memory is allocated and initialized in advance, avoiding the allocation and release of video memory during the reconstruction process, which greatly improves the reconstruction time. Specifically, the allocated video memory includes storage for the following data: tomographic images reconstructed after layer thickness stacking; tomographic images after back projection; total projection data images; detector geometric position parameter index table (X, Y, Z of each detector point in a virtual coordinate system); actual horizontal motor correction parameter index table (the position of the patient support, i.e., the bed, in mm values ​​in the Z direction for each scan frame); uniform horizontal motor correction parameter index table (used for coarse reconstruction, calculated as an average based on pitch, collimation width, etc.); air data images (storing air images after dark field correction); angle data of each ray relative to the central ray (in the UV coordinate system); coordinate distance data of each ray in the V direction (in the UV coordinate system); directional angle interpolation data (coordinate distance data of each ray in the V direction after directional angle rearrangement); rearranged images; filter kernel data; filtered images; water homogeneity correction parameter index table; water CT value correction parameter index table; bone sclerosis correction parameter index table; detector odd-even frame correction ratio parameter index table; segmented projection data images, etc.

[0251] Pre-reading of various parameter files, including air correction data, detector position data, inconsistency parameters, water correction parameters, and bone sclerosis parameters, and pre-reading of parameter data into the GPU, avoids reading from the hard drive and switching from the CPU to the GPU during the reconstruction process, which significantly improves reconstruction time.

[0252] During the establishment of the virtual coordinate system, a two-dimensional UV coordinate system is established with the center point of the detector (the point corresponding to the radiation source; if the detector is offset and not at the center point) as the origin. ΔU and ΔV are calculated based on the preset sampling size, and the virtual coordinate U-channel size is determined. The U-axis of the virtual coordinate system is not a linear axis but a curved axis. Below each frame, the position of the detector irradiated by the central ray of the radiation source's illumination range (cone beam) is taken as the origin, with the helix as the U-axis and the same direction as the horizontal displacement bed as the V-axis. The UV-enclosed area is an irregular image, see [link to image description]. Figure 7 The midpoint lattice takes values ​​at equal intervals according to the package distance in the V direction, and takes values ​​at equal curve distances according to the spiral direction in the U direction.

[0253] Similar to registration and fusion in CT scans, CT also requires coordinate system transformation. However, CT coordinate systems are simpler, consisting of two: the target coordinate system (which can be considered the world coordinate system, representing the x, y, z coordinates of the reconstructed image) and the acquired geometric coordinate system (the coordinate system of the detector at a fixed position within a single frame of the image). The detector's length and width directions (u, v) represent the x and y coordinates, while the direction perpendicular to the detector from the source focal point is z. This coordinate system can be considered a virtual coordinate system. Back projection includes the transformation from the virtual coordinate system to the target coordinate system. The back projection process involves superimposing the target positions in each virtual coordinate system, hence the transformation. The purpose of rearrangement is twofold: firstly, to facilitate back projection interpolation; and secondly, the rearrangement process itself is an interpolation process. For example, rearranging 384 points into 512 points effectively densifies the data, improving image quality.

[0254] Reference for 3D geometric position display after spiral CT rearrangement Figure 11a The UV-axis plane of the detector after spiral CT rearrangement is displayed as a reference. Figure 11b As shown in the diagram, taking a planar display as an example, the coordinate system U-axis is the horizontal axis (but it is actually a curved surface, the surface where the helix lies, or in other words, the helix is ​​attached to the outer surface of the cylinder; this U-axis is the curve axis of the outer surface of the cylinder (this curve axis is perpendicular to the central axis of the cylinder), and the coordinate system V-axis is the vertical axis (parallel to the central axis of the cylinder). The advantage of doing this is that the interpolation is more accurate; that is, the points rearranged to the detector are not perfectly evenly spaced straight lines, but points on an oblique curved surface. Interpolating according to this position is more consistent with the actual position changes than interpolating points in an approximate square lattice.

[0255] Customization of missing parameters includes: customizing horizontal motor parameters based on the pitch, and position parameters (i.e., the mm value of the horizontal motor encoder for each frame of image, and the number of values ​​based on the number of frames captured). This is used for coarse reconstruction, i.e., approximating the horizontal motor parameters.

[0256] During the texture binding process of weights, the specific calculation formula for weights is shown in Formula (VI).

[0257] In determining the windowed filter kernel, a sinc window is used, which provides clearer details at the same cutoff frequency compared to the Hanning window, which is overly smooth. Two limits are set: low frequency and high frequency. For frequencies below the low frequency, the window value is 1, meaning the limit is fully open; this is called the full-pass frequency. For frequencies above the high frequency, the window value is 0, meaning the high-frequency limit is the cutoff frequency. Based on the requirements of the reconstructed image, the full-pass and cutoff frequencies are set to different values, resulting in 7 groups, each corresponding to a different sharpening and smoothing effect.

[0258] The positions of the full-pass and cutoff frequencies are shown on the x-axis as sampling points (normalized to positive and negative, i.e., centered at the image sampling width), and the y-axis as window function values. (Refer to...) Figure 15 As shown, WL is the full-pass frequency (values ​​1, 1 / 16, 1 / 32), and WH is the cutoff frequency (values ​​1, 2 / 3, 1 / 2), with WL <= WH. The horizontal axis represents the sampling width of the convolution (0 represents the sampling center), and the vertical axis represents the normalized window width. The formula for the windowed filter kernel is shown in Formula (III). In this way, the filter kernel is prepared during preprocessing, that is, before acquisition. The texture binding mentioned above is also prepared before acquisition, so it does not occupy the acquisition and reconstruction time.

[0259] 2. In the coarse reconstruction stage, coarse reconstruction is performed to display the reconstruction results in real time and facilitate doctors' judgment of the acquisition situation. The coarse reconstruction interval, display slice spacing, and slice thickness are all fixed at 5mm. This includes processes such as logarithmic calculation, water hardening correction, CT value correction, rearrangement, FFT, fast back projection, and HU value calculation.

[0260] The coarse reconstruction physically segments the original data based on the acquisition time, ensuring that the reconstruction time is less than the acquisition time (acquisition and reconstruction run independently in two threads). The horizontal motor speed (bed stepper speed) is calculated by averaging the user-set parameters such as pitch, without using real-time speed (this eliminates the need to read the horizontal motor file, resulting in faster reconstruction).

[0261] When performing logarithmic calculations, the initial angle of the air correction parameters and the initial angle of the collected data are taken into account, and an offset value is added to make the angles correspond. The specific process is shown in formula (IV), and will not be repeated here.

[0262] Furthermore, considering that the radiation is not always stable after it is turned on, for example, it may show a gradual attenuation trend. Although the amplitude is small and meets the requirements for radiation source stability, it still has a certain impact on the image brightness level correction. Therefore, an influence factor is added. That is, the difference between the average air value under the initial angular period of prj and the average air value under the initial angular period of prj.

[0263] In the water hardening correction process, polynomial fitting correction is used, and the coefficients of the polynomial are determined based on multiple water model images.

[0264] In the CT value correction process, placing slope correction before back projection has the advantage of expanding the dynamic range of the prj value and improving data accuracy (when compared to float type). Generally, CT value correction is placed after reconstruction, dynamically stretching the reconstruction results to -1000 for air and 0 for water (i.e., linear correction), obtaining the slope and intercept for individual pixel correction. In this embodiment, a two-step method is used: before reconstruction, the slope portion k of the CT value is corrected first. Because the projected value is a superposition of pixels, it can be scaled equally, and the slope method effectively expands the dynamic range of the data, improving reconstruction accuracy. After reconstruction, the intercept portion b=1000 is processed. All CT value corrections involve linear stretching.

[0265] The CT value correction formula is as follows: .

[0266] During the rearrangement process, firstly: directional rearrangement: the fan-shaped bundles are rearranged into parallel bundles. This is related to the directional angle IV (refer to...). Figure 9 A ray parallel to the central ray at an angle of 0 degrees (directly upwards) is rearranged onto a non-central ray at the same angle. This means the non-central ray is now parallel to the central ray, and the rearranged ray has a smaller spacing than the original. This is because any non-central ray at any position, such as when it hits the detector at coordinates (iu, iv), is angled. The ray rotates through the rotation center to become a ray parallel to the central ray at angle IV. Figure 9 The example shown is closer to the center after rotating 20.35 degrees: AI <DJ。

[0267] After the orientation angles are rearranged, the channel spacing is not equal; the 384 channel spacings are as follows: Figure 10 As shown, this is related to the installation pattern of the detector. The spacing between each channel after the orientation angle is rearranged (the horizontal axis is the channel spacing, the vertical axis is the spacing distance in mm, and from left to right are the spacing between the 1st and 0th channels, the spacing between the 2nd and 1st channels, and so on).

[0268] Secondly, based on the upsampled data volume and the rearranged azimuth angle interval, cubic Hermite interpolation is performed (because the rearranged single-view detector data is not a standard two-dimensional matrix, but a surface array related to the helical orbit, cubic spline interpolation is more effective than general linear interpolation). After the azimuth angle rearrangement, the detector orientation is a three-dimensional surface, which approximates a parallelogram in two dimensions. That is, after the previous Hermite interpolation, the V-axis interval is the same, but because the U-axis is not horizontal, directly using this data during back projection will result in inappropriate interpolation data. This is especially noticeable when the pitch is low. Figure 12This is an approximate illustration (because the actual surface is curved, not planar). In this coordinate system, point G1 uses GMLH for bilinear interpolation; point H is clearly unsuitable, and GMLR is preferable. Therefore, an additional step is added to perform vertical UV coordinate interpolation, making the U-axis horizontal. This involves setting a horizontal grid based on the sampling density. Furthermore, data within the original coordinate system boundary is saved as valid data, while data outside the boundary is considered invalid.

[0269] In the FFT process, first, a power-padding zero-padding extension is performed; second, an FFT transform is performed; third, frequency domain filtering is performed (using a pre-reconstruction filter kernel, as described in the previous embodiment); then, an inverse FFT transform is performed; and finally, the result is multiplied by a coefficient Δ.

[0270] During the assignment phase, the results of the projection processing are stored in the overall pre-backprojection data, so that these processing steps do not need to be considered during fine reconstruction, and only backprojection is required.

[0271] In the fast back-projection process, since the purpose of coarse reconstruction is only to display the approximate shooting position, fast back-projection is used in this example, and the image accuracy requirement is not high. Fast back-projection is achieved through the following methods: using a thicker reconstruction interval for back-projection; using 360-degree interpolation instead of 180-degree interpolation to reduce weight calculation by half; the position of the horizontal motor corresponding to each frame can be replaced by a uniform average value calculated based on the pitch, i.e., the intervals are all equal, and the frame number can be directly multiplied by the horizontal interval; there is no layer thickness superposition calculation, and the display result is directly output (layer thickness equals reconstruction interval).

[0272] During the HU value correction process, refer to the aforementioned CT value correction formula and perform a subtraction of 1000, or add 1000 to the display window level.

[0273] 3. In the fine reconstruction stage, the interlayer spacing and layer thickness are reassigned according to the preset values ​​in the user interface. (The coarse reconstruction set it to 5mm; if the layer thickness in the user agreement is relatively small and the calculation workload is large, the fine reconstruction will follow the user settings). The specific process of fine reconstruction includes:

[0274] Read the horizontal motor parameters to determine the physical position of the horizontal displacement bed corresponding to each frame of the acquired image. Coarse reconstruction provides an approximate set of horizontal motor parameters, based on the average pitch interval. However, the actual horizontal displacement in each frame is not equal. Fine reconstruction is based on the actual position, resulting in higher accuracy.

[0275] The reconstruction of the Z-axis field of view is extended to improve dose utilization. The extended half-amplitude range is calculated using formula (V). For example, the positions of the upper and lower extensions in the helical scan are referenced... Figure 19As shown. By adjusting the weights at the locations of the extended image (setting virtual horizontal motor parameters, and weighting the effective weights W values ​​obtained from the extension), the proportion of the ray is increased, reducing artifacts from incomplete projection. The weights to be calculated for the extension are the weights w values ​​at positions such as -10, considering the extension of the horizontal motor, and adding the weighted weights to the denominator when superimposing the projection.

[0276] The horizontal position corresponding to the center line of the superimposed ray is determined based on the pitch and collimation width. This process is equivalent to the reconstructed target field of view being a sphere (which could be an ellipsoid) for each frame of rays. A reconstructed point inside the sphere is considered valid; a point outside the sphere is invalid and not included in the superposition calculation (and is also not considered when calculating weights). This is equivalent to an effective 3D simulated collimator, which can effectively improve cone and windmill artifacts in images. The main reason for this is that if the reconstructed point is too far from the source and is an edge ray, its quality is inherently low due to errors, and therefore it is not included in the superposition calculation. Figure 22b For the same reconstruction point of two spheres, if one ray is a valid point and enters the reconstruction, the other ray is a non-valid point and does not enter the reconstruction.

[0277] During the back projection process, each pixel in the XY plane of the target coordinate system (each axial view) and each acquisition rearrangement view are traversed to determine the start and end positions of the horizontal motors under the rearrangement view. The upper and lower boundaries of the effective Z plane under the acquisition rearrangement view are determined according to the semi-extended range. The pixels on XY are transformed to the virtual coordinate system to determine the UV coordinate values. The channel position is determined according to the U coordinate value, that is, the position of the horizontal channel of the acquisition rearrangement data (only the effective horizontal channel is calculated). According to the V position of the ray, the V coordinate value of the pixels on XY transformed to the virtual coordinate system and the V position (i.e., Z position) of the image plane are used to determine the row number, that is, the position of the vertical channel of the acquisition rearrangement data (only the effective vertical channel is calculated). And according to the position of the effective vertical channel of the acquisition rearrangement data, the Q value weight W is calculated according to the following formula (VI). Q (q).

[0278] Iterate through the rearranged data corresponding to the XYZ values ​​and multiply them by the corresponding weights. Then, perform a weighted average of the weights in the same direction, i.e., use 180-degree interpolation (compared to the 360-degree interpolation of coarse reconstruction, 180-degree interpolation has more features). Figure 24 (Reconstruction of the dashed line portion). Finally, multiply the superimposed axial image by the angular weights to obtain the back-projected image.

[0279] The post-reconstruction phase includes bone sclerosis correction, CT value correction, and slice thickness processing.

[0280] Bone sclerosis correction, essentially high-density volume artifact optimization, generally falls into two categories: preprocessing and post-processing. Preprocessing typically requires modeling based on a pre-defined model. However, since each patient's bone density and morphological distribution differ, some deviations can occur, limiting its application. Post-processing also broadly falls into two categories: one is the orthographic reconstruction of remaining tissue after bone removal, and the other is the orthographic reconstruction of bone tissue after extraction. In this embodiment, firstly, segmented bone tissue segmentation with correction is used; secondly, the orthographic projection is not a full rotation projection according to axial scanning, but rather a helical scanning projection method. The steps are as follows:

[0281] First: Segment the bone tissue (Note that this step does not yet include CT value correction to -1000, so these values ​​are 1000 higher than normal CT values). The bone images are segmented. Because CT values ​​in bone images vary across different axes, locations, and individuals, segmentation using a single threshold is difficult. Therefore, a two-step method is used: First, extract the bone tissue portion, i.e., the portion with CT values ​​between hu1 and hu2 (e.g., 850-3000). This portion should ideally encompass all cases and have a broad range. The CT values ​​for other parts are set to 0. That is, all bone locations are included within hu1-hu2. Then, the CT values ​​between hu1 and hu2 are segmented; we divide them into three segments here, but multiple segments are also possible. The first segment, hu1~hua (e.g., 850~1150), represents CT values ​​between brain and bone tissue. The second segment, hua~hub (e.g., 1150~2075), represents the CT value range for bone tissue. The third segment, hub~hu2 (e.g., 2075~3000), represents CT values ​​between bone tissue and high-density bodies. Normalization coefficients are preset for each node; for example, hu1 corresponds to s1 (e.g., 0), hua to s2 (e.g., 0.4), hub to s3 (e.g., 0.7), and hu2 to s4 (e.g., 1). Then, for each point in the segmented bone tissue portion of hu1~hu2, coefficient s is calculated. si is the low-end coefficient value of the segment, sj is the high-end coefficient value, hu is the CT value of that point, hui is the low-end CT value of the segment, and huj is the high-end CT value of the segment. Multiplying the CT value of the bone tissue portion by this coefficient yields the final segmented and extracted bone tissue.

[0282] Secondly, orthographic projection is performed on the bone tissue. The projection path follows the path shown below. Figure 2As shown, it can be viewed as a parallel beam of light with a conical angle. Its scanning path follows a spiral. The advantages of this approach are: firstly, a pair of original images have a projection at that position (equivalent to the result of rearranging the projection image), allowing for direct correspondence; secondly, it eliminates the rearrangement step in reconstruction, improving speed; and thirdly, the orthographic projection at that position is downsampled according to half of the rearrangement (sampling can be reduced based on computational needs), reducing computational load and eliminating the need to process the projection of the original image—simply shifting the x-coordinate one position to the left when corresponding to the original image value.

[0283] The total value of each ray in the orthographic projection is calculated by iterating through the pixels along the axial path and the ray length weight. The ray length weight is inversely proportional to the ray length; that is, the longer the ray within a voxel, the smaller the influence of the point on that axial plane (the orthographic weight is the largest). The weight is AG / IJ (e.g., ...). Figure 4b , where AG is the diagonal length of the voxel, and IJ is the path length of one of the rays passing through the voxel.

[0284] Next, bone sclerosis parameter correction. Weighted correction is applied to the bone projection map. First, the original image projection is subtracted from the above orthographic projection result (with a left shift of one bit, i.e., division by 2). The result is the projection of the tissue after removing bone tissue. This projection is divided by the water reference to obtain the equivalent length of each projected pixel relative to water. The equivalent length is used to determine the artifact correction coefficient, and this value is used to correct the above orthographic projection result.

[0285] Finally: The corrected bone projection map is reconstructed (the reconstruction step does not require rearranging the previous steps, as the scanning path of the projection map is always the rearranged path), resulting in a new bone reconstruction image. The original image and the new bone reconstruction image are then overlaid to obtain the final corrected image.

[0286] During CT value correction, a 1000 offset correction is applied, which means subtracting 1000 from the value of all pixels.

[0287] During the layer thickness processing, layer thickness overlay calculations are performed. The effective layer thickness is determined based on the reconstruction interval, displayed layer spacing, and preset layer thickness, and then the layer thickness overlay calculations are performed. Figure 17a and Figure 17b As shown. Data is stored in the specified memory (space pre-allocated during pre-reconstruction).

[0288] Repeat the above steps until all collected data has been processed. The time for one round of acquisition is set to 1 or 2 seconds depending on the user, while the time for one round of coarse reconstruction processing is no more than 0.2 seconds (for ordinary computer configurations, and much less than 0.2 seconds for high-end computer configurations). The reconstruction time is much shorter than the acquisition time, so the axial reconstruction image of the current shooting position can be displayed almost in real time, thereby achieving real-time reconstruction display. This allows doctors to understand the shooting situation in a timely manner. If problems occur with the shooting position, etc., the image can be canceled in time without having to confirm after the shooting is completed.

[0289] The overall reconstruction process includes coarse reconstruction, fine reconstruction, and post-reconstruction, which are displayed simultaneously and include the following steps:

[0290] After pressing the X-ray acquisition button, the first step is delayed exposure. Refer to the interface for delayed exposure acquisition. Figure 25 As shown, the left image is of the positioning plate, and the right image is of the spiral scan. In this case, the right image is completely black.

[0291] Pre-reconstruction can be completed before data acquisition, either during this delay or synchronously in a multi-threaded manner beforehand. This process takes approximately 4 seconds. If the delay is too short, it's recommended to place it earlier to ensure this time doesn't interfere with subsequent data acquisition and reconstruction. In this example, it's actually placed after the device preparation button is clicked. After device preparation, some doctors need a check period, such as confirming the patient hasn't moved. The delayed exposure interface pops up after device preparation, and the delay only begins after clicking to capture the image.

[0292] After the positioning image is captured and the helical scanning parameters are set, you can click "Prepare Device". Pre-reconstruction is performed during this process, meaning it's completed before data acquisition, so it doesn't take up acquisition and reconstruction time and is imperceptible to the user.

[0293] The results are displayed on the right image after one scan is completed, meaning coarse reconstruction is performed simultaneously with data acquisition. This example is set to coarse reconstruction after one scan, adjusted according to the acquisition time. If one scan takes too long, the time can be shortened accordingly (e.g., half a scan). In this example, one scan takes 1-2 seconds, and coarse reconstruction takes only 0.1+ seconds, which is more than sufficient.

[0294] The number of frames per acquisition cycle depends on the computer configuration. This example uses approximately 2000 frames (1440 frames per cycle). Higher-performance computers can be set more, while lower-performance computers can be set less, ensuring real-time performance. This can be viewed as segmenting the image to achieve simultaneous acquisition and reconstruction. A callback function can be used to inform the system software how many images have been reconstructed. In coarse reconstruction, the callback function can return the total number of coarse reconstruction axes and the number of axes already reconstructed in real time. Because coarse reconstruction also needs to consider acquisition time (which is negligible compared to acquisition time), the number of reconstructed axes needs to be displayed based on a 2-second acquisition time. For example, if two new axes are reconstructed, one axis is displayed per second. The coarse reconstruction time interval is set to -1 because the main time is acquisition time, not reconstruction time (coarse reconstruction truncates the reconstruction time to be less than the acquisition time).

[0295] During the post-reconstruction process, the post-reconstruction module includes bone sclerosis, CT value intercept correction, and slice thickness calculation. The post-reconstruction time is synchronized within the callback of the fine reconstruction. This means that once the fine reconstruction time interval is displayed, the post-reconstruction is already fully completed. Therefore, it is recommended to refresh the displayed data after the fine reconstruction progress bar is fully completed.

[0296] The spiral CT reconstruction method provided in this embodiment addresses the common practice of using faster processors (high-spec reconstruction computers), uploading data to cloud servers, or employing new reconstruction algorithms to achieve real-time reconstruction (simultaneous acquisition and reconstruction) in spiral CT. This example segments the reconstruction process, using low-precision coarse reconstruction for display during acquisition and high-precision fine reconstruction for saving the results. This approach ensures both real-time acquisition and reconstruction while maintaining final accuracy. Furthermore, a pre-reconstruction stage is added, completing part of the reconstruction before acquisition. The spiral CT reconstruction method provided in this example allows for real-time reconstruction even on low-end computers without affecting the doctor's real-time monitoring of the imaging process. Since unexpected situations such as patient movement may occur during imaging, real-time monitoring of the scan position is necessary to ensure its normality.

[0297] Before coarse reconstruction, a judgment is made, and the operator can choose to proceed to the coarse reconstruction step: depending on the parameters selected by the operator, it is determined whether coarse reconstruction is necessary. When the reconstruction computation is small, it directly proceeds to fine reconstruction. Furthermore, coarse reconstruction includes correction; that is, scanning, acquisition, correction, reconstruction, and display are all completed in real time.

[0298] The UV virtual coordinate system is set as follows: the entire coordinate system is an approximately rectangular curved surface, with the U-axis being the curve axis. This coordinate system can be viewed as two separate systems: one is the pre-UV virtual coordinate system, where curve fitting interpolation was performed along the U-axis during rearrangement (not the final U-axis, but the U-axis of the pre-UV virtual coordinate system, i.e., the axis of the spiral), not linear interpolation. The other is the final UV virtual coordinate system, where the re-projected points are interpolated using grid nodes according to the final UV coordinate system, effectively straightening slanted points and improving the accuracy of the backprojection.

[0299] The fast backprojection of coarse reconstruction and the backprojection of fine reconstruction significantly improve speed. Furthermore, the coarse reconstruction algorithm can be adjusted based on the acquisition time. For example, if the acquisition time is 1-2 seconds, which is much longer than the time for one full rotation of coarse reconstruction, the coarse reconstruction can be divided into multiple rotations, allowing it to be completed entirely within the acquisition time. If the acquisition time is further increased—for example, the fastest spiral CT in the industry is 0.28 seconds—the first six steps of coarse reconstruction can be divided into shorter frames, such as half a rotation, or even just one frame, with only the backprojection stage completing a full rotation. This also ensures that coarse reconstruction and acquisition occur simultaneously, without consuming separate time.

[0300] The spiral CT reconstruction provided in this example effectively reduces cone-beam artifacts and also improves windmill artifacts. Collimation is usually added by hardware, but here we use software simulation to increase collimation to achieve the same effect of eliminating cone-beam artifacts.

[0301] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0302] Based on the same inventive concept, this application also provides a spiral CT reconstruction apparatus for implementing the spiral CT reconstruction method described above. The solution provided by this apparatus is similar to the implementation described in the above method; therefore, the specific limitations in one or more spiral CT reconstruction apparatus embodiments provided below can be found in the limitations of the spiral CT reconstruction method described above, and will not be repeated here.

[0303] In one exemplary embodiment, such as Figure 23As shown, a spiral CT reconstruction device 2300 is provided, including: a filtering module 2302, a back projection module 2304, a front projection module 2306, a processing module 2308, and a reconstruction module 2310, wherein:

[0304] Filtering module 2302 is used to filter the projection data to obtain filtered data;

[0305] The back projection module 2304 is used to perform back projection processing on the filtered data to obtain a back projection image, and extract bone tissue data from the back projection image.

[0306] Orthographic projection module 2306 is used to orthographically project the bone tissue data to obtain bone projection data;

[0307] Processing module 2308 is used to take the difference between the projection data and the bone projection data as bone removal tissue data;

[0308] The reconstruction module 2310 is used to obtain a CT reconstructed image based on the deossified tissue data and the back-projection image.

[0309] The aforementioned spiral CT reconstruction device can filter the projection data to obtain filtered data, and then perform back-projection processing on the filtered data to obtain a back-projected image. Bone tissue data is then extracted from the back-projected image. Orthographic projection is performed on the bone tissue data to obtain bone projection data, and the difference between the projection data and the bone projection data is used as the de-bone tissue data. Finally, based on the de-bone tissue data and the back-projected image, a CT reconstructed image is obtained. Using the spiral CT reconstruction device provided in this application embodiment, in the post-reconstruction process, it is unnecessary to perform an orthographic projection on the original data; the reconstructed projection data can be used directly, which can effectively reduce the computational load and improve the efficiency and accuracy of CT reconstruction.

[0310] In one embodiment, the back-projection module 2304 is specifically used for:

[0311] Extract bone tissue pixels from the back-projected image, wherein the CT value corresponding to the bone tissue pixel is within the range of bone tissue CT values;

[0312] For any bone tissue pixel, the bone tissue segment corresponding to the bone tissue pixel is determined based on the CT value of the bone tissue pixel, and the normalization coefficient of the bone tissue pixel is determined based on the upper limit CT value and lower limit CT value of the bone tissue segment, the normalization coefficient corresponding to the upper limit CT value and lower limit CT value, and the CT value of the bone tissue pixel. The range of bone tissue CT values ​​is composed of multiple bone tissue segments.

[0313] Bone tissue data is obtained based on the normalization coefficients and CT values ​​of each bone tissue pixel.

[0314] In one embodiment, the orthographic projection module 2306 is specifically used for:

[0315] Based on each pixel in the axial image corresponding to the data of rays passing through bone tissue, and the ray length weight corresponding to each pixel, the CT values ​​of each pixel are weighted and superimposed to obtain bone projection data.

[0316] The ray length weight is negatively correlated with the length of the ray within the voxel.

[0317] In one embodiment, the reconstruction module 2310 is specifically used for:

[0318] The ratio of the deboned tissue data to the water baseline is used as the equivalent length of each projected pixel in the deboned tissue data relative to the water.

[0319] The bone projection data is weighted and corrected using the artifact correction coefficient corresponding to the equivalent length to obtain the corrected bone projection data.

[0320] After reconstructing the corrected bone projection data, the reconstruction result is superimposed on the back-projection image to obtain a CT reconstructed image.

[0321] In one embodiment, the filtering module 2302 is specifically used for:

[0322] The projection data is rearranged using a pre-established initial virtual coordinate system, which has the detector illuminated by the central source as the origin, the spiral as the horizontal axis, and the direction of the horizontal displacement bed as the vertical axis.

[0323] The rearranged projection data is filtered based on the windowed filtering check to obtain filtered data.

[0324] In one embodiment, the filtering module 2302 is further configured to:

[0325] By rearranging the direction angle of the projection data, the projection data of the fan beam is rearranged into the projection data of the parallel beam.

[0326] Based on the amount of upsampled data and the interval of the rearrangement of the orientation angles, the projection data of the parallel beam is subjected to three Hermite interpolation to obtain a three-dimensional surface. The three-dimensional surface is then subjected to coordinate interpolation perpendicular to the initial virtual coordinate system to obtain the rearranged projection data.

[0327] In one embodiment, the filtering module 2302 is further configured to:

[0328] Determine the grid points in the three-dimensional surface and calculate the rotation matrix for each grid point;

[0329] The rotation matrix is ​​used to transform the coordinates of each grid point from the initial virtual coordinate system to the target virtual coordinate system, where the horizontal axis of the target virtual coordinate system is horizontal.

[0330] Interpolation is performed on each grid point in the target virtual coordinate system to obtain rearranged projection data.

[0331] In one embodiment, the filtering module 2302 is further configured to:

[0332] Interpolation is performed on each of the grid points in the target virtual coordinate system to obtain the interpolation result;

[0333] The interpolation results are filtered according to the initial virtual coordinate system to obtain rearranged projection data.

[0334] In one embodiment, the windowed filter includes an all-pass frequency and a cutoff frequency, and the windowed filter kernel is:

[0335]

[0336] Wherein, w represents the frequency component of the signal, wl represents the full-pass frequency, and wh represents the cutoff frequency.

[0337] In one embodiment, the device further includes:

[0338] A correction module is used to perform slope correction on the projection data;

[0339] The reconstruction module 2310 is further used for:

[0340] The reconstruction result is then superimposed on the back-projected image to obtain the superimposed result;

[0341] The overlay result is subjected to intercept correction, and the correction result is subjected to slice thickness processing to obtain the CT reconstructed image.

[0342] In one embodiment, the back-projection module 2304 is specifically used for:

[0343] The extension range is determined based on the extension collimation width, pitch, and reconstruction interval. The starting position of the horizontal motor is then shifted to the left and right based on the extension range to obtain the virtual horizontal motor position.

[0344] Interpolation is performed based on the virtual horizontal motor position and the filtered data to obtain extended projection data. Then, weighted back projection is performed based on the extended projection data and the filtered data to obtain a back projection image.

[0345] In one embodiment, the back projection module 2304 is further configured to:

[0346] Weighted back projection processing is performed based on the extended projection data and the filtered data to obtain an initial back projection image;

[0347] For any pixel in the initial back-projected image, calculate the distance between the coordinate value of the pixel on the depth axis and the position of the horizontal motor corresponding to the central ray passing through the pixel. If the distance is less than a preset threshold, the pixel is determined to be valid.

[0348] The rearranged data corresponding to the effective pixels are weighted and summed according to the Q-value weight curve to obtain the back-projected image.

[0349] Each module in the aforementioned spiral CT reconstruction device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0350] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 26 As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a spiral CT reconstruction method. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0351] Those skilled in the art will understand that Figure 26 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0352] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0353] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0354] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0355] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0356] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0357] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0358] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A spiral CT reconstruction method, characterized in that, The method includes: The projection data is rearranged using a pre-established initial virtual coordinate system. The initial virtual coordinate system has the position of the detector irradiated by the central ray as the origin, the spiral as the horizontal axis, and the direction of the horizontal displacement bed as the vertical axis. The horizontal axis is a virtual coordinate channel with a curved shape, and its direction is the rotation trajectory around the scanning center, which is used to represent the movement of the detector during the scanning process. The direction of the vertical axis is used to represent the direction of the scanned object moving along the bed during the scanning process. The rearranged projection data is filtered based on the windowed filter check to obtain the filtered data; The filtered data is back-projected to obtain a back-projected image, and bone tissue data is extracted from the back-projected image. Orthographic projection of the bone tissue data yields bone projection data; The difference between the projection data before rearrangement and the bone projection data is used as the deostomized tissue data; The bone projection data is corrected using the deossified tissue data, and the corrected bone projection data is used for reconstruction to obtain a new bone reconstruction image. The new bone reconstruction image is then superimposed on the back-projection image to obtain a CT reconstruction image.

2. The method according to claim 1, characterized in that, The extraction of bone tissue data from the back-projected image includes: Extract bone tissue pixels from the back-projected image, wherein the CT value corresponding to the bone tissue pixel is within the range of bone tissue CT values; For any bone tissue pixel, the bone tissue segment corresponding to the bone tissue pixel is determined based on the CT value of the bone tissue pixel, and the normalization coefficient of the bone tissue pixel is determined based on the upper limit CT value and lower limit CT value of the bone tissue segment, the normalization coefficient corresponding to the upper limit CT value and lower limit CT value, and the CT value of the bone tissue pixel. The range of bone tissue CT values ​​is composed of multiple bone tissue segments. Bone tissue data is obtained based on the normalization coefficients and CT values ​​of each bone tissue pixel.

3. The method according to claim 1 or 2, characterized in that, The process of orthographically projecting the bone tissue data to obtain bone projection data includes: Based on each pixel in the axial image corresponding to the data of rays passing through bone tissue, and the ray length weight corresponding to each pixel, the CT values ​​of each pixel are weighted and superimposed to obtain bone projection data. The ray length weight is negatively correlated with the length of the ray within the voxel.

4. The method according to claim 1 or 2, characterized in that, The process involves correcting the bone projection data using the de-osteoscopic tissue data, reconstructing the bone using the corrected bone projection data to obtain a new bone reconstruction image, and then overlaying the new bone reconstruction image with the back-projection image to obtain a CT reconstruction image, including: The ratio of the deboned tissue data to the water baseline is used as the equivalent length of each projected pixel in the deboned tissue data relative to the water. The bone projection data is weighted and corrected using the artifact correction coefficient corresponding to the equivalent length to obtain the corrected bone projection data. After reconstructing the corrected bone projection data, the reconstruction result is superimposed on the back-projection image to obtain a CT reconstructed image.

5. The method according to claim 1, characterized in that, The rearrangement of the projected data using a pre-established initial virtual coordinate system includes: By rearranging the direction angle of the projection data, the projection data of the fan beam is rearranged into the projection data of the parallel beam. Based on the amount of upsampled data and the interval of the rearrangement of the orientation angles, the projection data of the parallel beam is subjected to three Hermite interpolation to obtain a three-dimensional surface. The three-dimensional surface is then subjected to coordinate interpolation perpendicular to the initial virtual coordinate system to obtain the rearranged projection data.

6. The method according to claim 5, characterized in that, The process of interpolating the coordinates of the three-dimensional surface perpendicular to the initial virtual coordinate system to obtain rearranged projection data includes: Determine the grid points in the three-dimensional surface and calculate the rotation matrix for each grid point; The rotation matrix is ​​used to transform the coordinates of each grid point from the initial virtual coordinate system to the target virtual coordinate system, where the horizontal axis of the target virtual coordinate system is horizontal. Interpolation is performed on each grid point in the target virtual coordinate system to obtain rearranged projection data.

7. The method according to claim 6, characterized in that, The step of interpolating each of the grid points in the target virtual coordinate system to obtain rearranged projection data includes: Interpolation is performed on each of the grid points in the target virtual coordinate system to obtain the interpolation result; The interpolation results are filtered according to the initial virtual coordinate system to obtain rearranged projection data.

8. The method according to claim 1, characterized in that, Windowed filtering includes an all-pass frequency and a cutoff frequency, and the windowed filter kernel is: Wherein, w represents the frequency component of the signal, wl represents the full-pass frequency, and wh represents the cutoff frequency.

9. The method according to claim 4, characterized in that, The method further includes: Slope correction is performed on the projection data; The step of overlaying the reconstruction result with the back-projection image to obtain a CT reconstructed image includes: The reconstruction result is superimposed on the back-projected image to obtain the superimposed result; The overlay result is subjected to intercept correction, and the correction result is subjected to slice thickness processing to obtain the CT reconstructed image.

10. The method according to claim 1, characterized in that, The back-projection processing of the filtered data to obtain the back-projected image includes: The extension range is determined based on the extension collimation width, pitch, and reconstruction interval. The starting position of the horizontal motor is then shifted to the left and right based on the extension range to obtain the virtual horizontal motor position. Interpolation is performed based on the virtual horizontal motor position and the filtered data to obtain extended projection data. Then, weighted back projection is performed based on the extended projection data and the filtered data to obtain a back projection image.

11. The method according to claim 10, characterized in that, The weighted backprojection processing based on the extended projection data and the filtered data to obtain the backprojected image includes: Weighted back projection processing is performed based on the extended projection data and the filtered data to obtain an initial back projection image; For any pixel in the initial back-projected image, calculate the distance between the coordinate value of the pixel on the depth axis and the position of the horizontal motor corresponding to the central ray passing through the pixel. If the distance is less than a preset threshold, the pixel is determined to be valid. The rearranged data corresponding to the effective pixels are weighted and summed according to the Q-value weight curve to obtain the back-projected image.

12. A spiral CT reconstruction device, characterized in that, The device includes: The filtering module rearranges the projection data using a pre-established initial virtual coordinate system. This initial virtual coordinate system has its origin at the position of the detector illuminated by the central ray, its horizontal axis as a spiral, and its vertical axis as the direction of the horizontal displacement bed. The horizontal axis is a virtual coordinate channel, curved in shape, and its direction represents the rotation trajectory around the scanning center, used to represent the movement of the detector during the scanning process. The vertical axis represents the direction in which the scanned object moves along the bed during the scanning process. The rearranged projection data is then filtered according to a windowed filter to obtain filtered data. The back projection module is used to perform back projection processing on the filtered data to obtain a back projection image, and extract bone tissue data from the back projection image. An orthographic projection module is used to orthographically project the bone tissue data to obtain bone projection data; The processing module is used to take the difference between the projection data before rearrangement and the bone projection data as the de-osteoscopic tissue data; The reconstruction module is used to correct the bone projection data using the de-osteoscopic tissue data, and to reconstruct the bone using the corrected bone projection data to obtain a new bone reconstruction image. The new bone reconstruction image is then superimposed on the back-projection image to obtain a CT reconstruction image.

13. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 11.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 11.

15. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 11.

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