4d-CT image reconstruction method

Abstract

A 4D-CT image reconstruction method. A respiratory curve is obtained from three-dimensional image body surface data acquired by a 3D scanning device, eliminating the need for an additional device to collect respiratory motion information. This makes the operation simpler and prevents patients from experiencing discomfort caused by wearing an additional respiratory device, thereby improving the patient experience, simplifying the doctor's workflow, and to some extent reducing operational errors. Moreover, for thoracic and abdominal radiotherapy, in the process of collecting respiratory data, collection can be performed according to the position where the patient requires radiotherapy. The selected region of interest (ROI) is directly placed in the radiotherapy region, such that the obtained respiratory phase information can more closely conform to the undulating motion of the region to be treated with radiotherapy, and the delineation of the target region can be more precise. This helps reduce the margins around the target region, increase the radiation dose delivered to the target region, and decrease toxic and side effects on normal tissues, thereby improving the precision of radiotherapy.

Description

A 4D-CT image reconstruction method Technical Field

[0001] This invention relates to the field of 4D-CT image technology, and more specifically, to a 4D-CT image reconstruction method. Background Technology

[0002] In computed tomography (CT) imaging, particularly during scans of the upper abdominal organs, respiratory motion artifacts are a common problem. Due to natural breathing, abdominal organs move periodically with each breath. This means that during a CT scan, the target organ may not be on the intended cross-section, resulting in the detector collecting data that is a mixture of information from multiple sections rather than a true projection of a single cross-section. This data confusion ultimately manifests as respiratory motion artifacts on CT images, which can interfere with the physician's diagnosis, especially in radiotherapy planning, where these artifacts can lead to incorrect target localization.

[0003] To address this issue, four-dimensional computed tomography (4D-CT) technology was developed. 4D-CT not only captures information in the spatial dimension but also incorporates the temporal dimension, enabling the recording and analysis of organ movement changes during the respiratory cycle, thus more accurately reflecting the true location and shape of organs. This technology simultaneously records the patient's respiratory signals during data acquisition. By analyzing these signals, data from different respiratory phases can be categorized, reconstructed separately, and ultimately synthesized into a continuous image sequence reflecting organ movement changes during respiration.

[0004] 4D-CT data acquisition faces two major challenges: minimizing the impact of respiratory motion during scanning and accurately analyzing and locating respiratory phases. To address the first challenge, two strategies are commonly used in clinical practice: respiratory restriction and free breathing. Respiratory restriction requires patients to maintain consistent breathing during scanning, but this is difficult for patients with poor respiratory control. In contrast, the free breathing strategy is more humane, allowing patients to breathe at their own pace, and then analyzing the respiratory curves to identify and classify data from different respiratory phases.

[0005] However, the key to a free-breathing strategy lies in accurately identifying and locating specific respiratory phases, which is crucial for the accuracy of 4D-CT reconstruction. Currently, 4D-CT systems primarily acquire patients' respiratory signals using the following methods: 1. Measuring respiratory volume using a spirometer; 2. Fixing infrared markers on the patient's body surface and using an infrared camera to measure the movement of these markers with respiration to obtain respiratory signals; 3. Fixing an abdominal pressure band on the patient's abdomen and measuring the pressure difference caused by respiratory motion, using the pressure difference signal to characterize respiratory motion. All of these methods require additional devices on the patient's body, which not only causes discomfort but also makes the operation more cumbersome, increases the number of steps, and is prone to operational errors, resulting in less accurate 4D-CT image reconstruction. Summary of the Invention

[0006] To overcome the shortcomings of the prior art, this invention provides a 4D-CT image reconstruction method that does not require additional devices to acquire respiratory signals. Instead, it directly acquires respiratory signals through the CT scanner, making the operation simpler and further improving the accuracy of the data.

[0007] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0008] A 4D-CT image reconstruction method includes the following steps:

[0009] S1. Use a 3D scanning device to acquire three-dimensional image surface data of the patient, render the three-dimensional image surface data on the display interface, use the mouse to obtain a three-dimensional point using the display buffer, and use the selected three-dimensional point as the reference point; set a cylinder to capture the three-dimensional surface point cloud within the cylinder range, and this three-dimensional surface point cloud region is the ROI of interest;

[0010] S2. Obtain the average coordinate values ​​of the three-dimensional points within the ROI region at time t along the Z-axis, and use this as the current breathing amplitude value a. t The respiratory amplitude values ​​at different times are acquired in real time, and a respiratory curve is formed with time as the horizontal axis and respiratory amplitude values ​​as the vertical axis.

[0011] S3. Use a smoothing algorithm to smooth the breathing curve;

[0012] S4. Identify the peaks and troughs of the respiratory curve to obtain the patient's respiratory phase information;

[0013] S5. Add respiratory phase information to the patient's three-dimensional image surface data obtained in step S1;

[0014] S6. Group all three-dimensional image body surface data according to respiratory phase;

[0015] S7. Perform 3D reconstruction on the surface data of the 3D image in each phase using the selected reconstruction algorithm to generate a 3D CT image in that phase; stitch the 3D CT images in different phases in chronological order to obtain a 4D-CT sequence image.

[0016] Based on the above technical means, the 4D-CT image reconstruction method provided by this invention obtains the respiratory curve using three-dimensional image surface data from a 3D scanning device, eliminating the need for additional equipment to collect respiratory motion information. This simplifies the operation, prevents patient discomfort from wearing additional respiratory equipment, improves the patient experience, simplifies the doctor's workflow, and reduces operational errors to some extent. Moreover, for radiotherapy of the chest and abdomen, during the acquisition of respiratory data, data can be collected based on the patient's desired radiotherapy location, with the selected ROI directly chosen within the radiotherapy area. This allows the acquired respiratory phase information to better match the undulating motion of the radiotherapy area, enabling more precise target area setting. This helps reduce the outer boundary of the target area, increases the radiation dose to the target area, reduces toxic side effects on normal tissues, and thus improves the accuracy of radiotherapy.

[0017] Further, step S1 specifically includes:

[0018] S11. In the scanned 3D image model display interface, adjust the optimal rendering angle of the selected respiratory monitoring area and the scaling size of the 3D image model;

[0019] S12. Obtain the XY coordinates a(xa,xb) of the mouse cursor and obtain the view projection matrix M of the 3D image model;

[0020] S13. Traverse the three-dimensional point B in the three-dimensional point cloud data and project it onto the two-dimensional point b(xb,yb) on the two-dimensional image of the display interface size according to the view projection matrix M of the model. The three-dimensional point B corresponds one-to-one with the projected two-dimensional point, and the correspondence formula is: b=M×B; calculate the distance d between the two-dimensional coordinate point a(xa,xb) of the cursor and each projected two-dimensional point b(xb,yb) to form a distance set.

[0021] S14. Find the two-dimensional point that is closest to the projection. The three-dimensional point corresponding to this two-dimensional point is the three-dimensional center O of the selected respiratory monitoring area.

[0022] S15. Using the three-dimensional center O of the respiratory monitoring area as the center, set the radius R and the height H to set a cylindrical three-dimensional spatial region, i.e., the ROI of interest;

[0023] S16. Traverse the 3D points in the 3D point cloud data, determine whether the 3D point falls within the cylindrical 3D space region. If it does, determine that the 3D point is within the respiratory monitoring region and add the point to the respiratory monitoring region point set to obtain the ROI region 3D point set; otherwise, if it does not, do not add it.

[0024] Further, step S2 includes: calculating the average coordinates of all three-dimensional points in the ROI region at the current time t in the Z-axis direction, as the respiratory amplitude value at the current time t; acquiring the three-dimensional point set of the ROI region at different times in real time, and calculating the average coordinates in the Z-axis direction at each time to obtain the respiratory amplitude value at different times; forming a respiratory curve with time as the horizontal axis and respiratory amplitude value as the vertical axis.

[0025] Further, step S3 includes, assuming y0, y1, y2, y3, y4 are the latest five consecutive respiratory amplitude values, obtaining the smoothed five respiratory amplitude values ​​y'0, y'1, y'2, y'3, y'4 using the following calculation formulas: y'0=(3.0*y0+2.0*y1+y2-y4) / 5.0 y'1=(4.0*y0+3.0*y1+2*y2+y3) / 10.0 y'2=(y0+y1+y2+y3+y4) / 5.0 y'3=(4.0*y4+3.0*y3+2*y2+y1) / 10.0 y'4=(3.0*y4+2.0*y3+y2-y0) / 5.0

[0026] Furthermore, in step S4, starting from the acquisition of the breathing curve, the first peak is identified first, and the first trough is identified only after the first peak is identified; as the breathing curve is continuously updated, the i-th trough is identified only after the i-th peak is identified, and the (i+1)-th peak is identified only after the i-th trough is identified, where i≥0.

[0027] Further, in step S4, the following steps are included: S41. Obtain at least 5 of the latest respiratory amplitude values ​​of the respiratory curve in chronological order, and record them as: p1, p2, p3, p4, p5 respectively; and record the type T(peak, valley, nan) of the nearest neighbor of the identified peak and valley points to these 5 respiratory amplitude values ​​in time, and identify the peak or valley according to formula (1), which is as follows:

[0028] In the formula, R(t) = 1 represents p3 as a peak, R(t) = -1 represents p3 as a trough, and R(t) = 0 represents p3 as neither a peak nor a trough.

[0029] Furthermore, during the peak identification process, the peak identified at the current moment is taken as a temporary peak p.m (t1), as the respiratory curve is continuously updated, continue to identify peaks and troughs according to step S41; if a new peak p n If (t2) is identified first, then compare p. m (t1) and p n The magnitude of (t2) is determined, and the final peak is determined according to the following formula:

[0030] If the trough is identified first, then p(t) = p m (t1).

[0031] Furthermore, in the process of identifying troughs, the trough identified at the current moment is used as a temporary trough v. m (t1), as the breathing curve is continuously updated, continue to identify peaks and troughs according to step S41. If a new trough v n (t2) If it is identified first, then compare v. m (t1) and v n The magnitude of (t2) is determined, and the final trough is determined according to the following formula:

[0032] If the wave peak is identified first, then v(t) = v m (t1).

[0033] Furthermore, in step S6, the acquired three-dimensional image body surface data are divided according to respiratory phase information, and the three-dimensional image body surface data under the same phase are grouped together.

[0034] Furthermore, the reconstruction algorithm includes a filtered back-projection reconstruction algorithm and an iterative reconstruction algorithm.

[0035] Compared with existing technologies, the beneficial effects are as follows: The 4D-CT image reconstruction method provided by this invention eliminates the need for additional equipment to collect respiratory motion information, making the operation simpler and eliminating discomfort for patients due to wearing additional respiratory equipment, thus improving the patient experience and simplifying the doctor's operating procedures, reducing operational errors to a certain extent. Moreover, it allows the acquired respiratory phase information to better match the undulating motion of the area to be radiotreated, ensuring more accurate target area setting, helping to reduce the outer boundary of the target area, increase the radiation dose to the target area, reduce toxic side effects on normal tissues, and thereby improve the accuracy of radiotherapy. Attached Figure Description

[0036] Figure 1 is a schematic diagram of the method flow of the present invention.

[0037] Figure 2 is a schematic diagram of the selection of respiratory monitoring points in Example 2.

[0038] Figure 3 is a schematic diagram of the effect of smoothing the breathing curve in Example 3.

[0039] Figure 4 is a schematic diagram of the breathing curve phase in Example 4. Detailed Implementation

[0040] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. The present invention will be described in one embodiment below with reference to specific embodiments. The accompanying drawings are for illustrative purposes only and represent schematic diagrams, not actual pictures, and should not be construed as limiting the present invention. In order to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged, or reduced, and do not represent the actual product size. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0041] In the description of this invention, it should be understood that if terms such as "upper," "lower," "left," and "right" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, they are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms describing positional relationships in the accompanying drawings are only for illustrative purposes and should not be construed as limiting the invention. Those skilled in the art can understand the specific meaning of the above terms according to the specific circumstances. In addition, if the embodiments of this invention involve descriptions of "first," "second," etc., such descriptions are only for descriptive purposes and should not be construed as indicating or implying their relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include at least one of those features. Furthermore, the meaning of "and / or" throughout the text is to include three parallel solutions. Taking "A and / or B" as an example, it includes solution A, or solution B, or a solution that simultaneously satisfies A and B.

[0042] Example 1:

[0043] As shown in Figure 1, a 4D-CT image reconstruction method includes the following steps:

[0044] S1. Use a 3D scanning device to acquire three-dimensional image surface data of the patient, render the three-dimensional image surface data on the display interface, use the mouse to obtain a three-dimensional point using the display buffer, and use the selected three-dimensional point as the reference point; set a cylinder to capture the three-dimensional surface point cloud within the cylinder range, and this three-dimensional surface point cloud region is the ROI of interest;

[0045] S2. Obtain the average coordinate value of the three-dimensional points in the ROI region at the current time t in the Z-axis direction, and use it as the current breathing amplitude value at. Obtain the breathing amplitude value at different times in real time, and form a breathing curve with time as the horizontal axis and breathing amplitude value as the vertical axis.

[0046] S3. Use a smoothing algorithm to smooth the breathing curve;

[0047] S4. Identify the peaks and troughs of the respiratory curve to obtain the patient's respiratory phase information;

[0048] S5. Add respiratory phase information to the patient's three-dimensional image surface data obtained in step S1;

[0049] S6. Group all three-dimensional image body surface data according to respiratory phase;

[0050] S7. Perform 3D reconstruction on the surface data of the 3D image in each phase using the selected reconstruction algorithm to generate a 3D CT image in that phase; stitch the 3D CT images in different phases in chronological order to obtain a 4D-CT sequence image.

[0051] In step S6, the acquired three-dimensional image surface data is divided according to respiratory phase information, and the three-dimensional image surface data under the same phase are grouped together. Based on the respiratory waveform data, the respiratory cycle is divided into multiple phases, such as the end-exhalation phase, the end-inspiratory phase, and several intermediate phases. Each phase represents a specific stage in the respiratory process.

[0052] In step S7, a suitable reconstruction algorithm can be selected based on the specific scanning conditions and requirements. Examples include filtered back-projection reconstruction algorithms and iterative reconstruction algorithms. These are all existing reconstruction algorithms, and since they are not the focus of this invention, they will not be described in detail in this embodiment.

[0053] A 3D reconstruction algorithm was used to reconstruct the projected data for each phase, generating 3D CT images for that phase. These images demonstrate the morphology of the thoracic and abdominal organs at different respiratory phases. The 3D CT images from different phases were stitched together chronologically to generate a complete 4D-CT sequence, which shows the dynamic changes of the thoracic and abdominal organs throughout the respiratory cycle. Furthermore, the generated 4D-CT sequence underwent quality control to check for artifacts, noise, and other issues, and necessary post-processing was performed to improve image quality.

[0054] In this embodiment, the respiratory curve is obtained using three-dimensional image surface data from a 3D scanning device, eliminating the need for additional equipment to collect respiratory motion information. This simplifies the operation, prevents patient discomfort from wearing additional respiratory equipment, improves the patient experience, simplifies the doctor's workflow, and reduces operational errors to some extent. Furthermore, for radiotherapy to the chest and abdomen, respiratory data can be collected based on the patient's desired radiotherapy location, with the selected Region of Interest (ROI) directly chosen within the radiotherapy area. This allows the acquired respiratory phase information to better match the undulating motion of the radiotherapy area, enabling more precise target area setting. This helps reduce the outer boundary of the target area, increases the radiation dose to the target area, reduces toxic side effects on normal tissues, and thus improves the accuracy of radiotherapy.

[0055] During the operation, 3D scanning equipment is used to acquire three-dimensional image surface data and obtain respiratory curves, thereby obtaining respiratory phase information. In the subsequent 4D-CT image reconstruction process, 3D scanning equipment is also used to acquire three-dimensional image surface data and reconstruct three-dimensional images. Using data acquired by the same equipment to obtain respiratory phase information and reconstruct three-dimensional images not only simplifies the operation process but also avoids systematic errors between different devices and improves the accuracy of the data.

[0056] Example 2:

[0057] In this embodiment, the other steps are the same as in Embodiment 1. This embodiment provides a method for obtaining a respiratory curve; specifically, it includes the following steps:

[0058] S11. In the scanned 3D image model display interface, adjust the optimal rendering angle of the selected respiratory monitoring area and the scaling size of the 3D image model.

[0059] S12. Obtain the XY coordinates a(xa,xb) of the mouse cursor and the view projection matrix M of the 3D image model.

[0060] S13. Traverse the three-dimensional point B in the three-dimensional point cloud data and project it onto the two-dimensional point b(xb,yb) on the two-dimensional image of the display interface size according to the view projection matrix M of the model. The three-dimensional point B corresponds one-to-one with the projected two-dimensional point, and the correspondence formula is: b = M × B; calculate the distance d between the two-dimensional coordinate point a(xa,xb) of the cursor and each projected two-dimensional point b(xb,yb) to form a distance set.

[0061] In this process, the 3D point B in the 3D point cloud data is traversed and projected onto the model's viewpoint projection matrix M. The viewpoint projection matrix M refers to the product of the current viewpoint matrix and the projection matrix. Since the coordinates a(xa,xb) obtained in step S12 and the current viewpoint projection matrix M are used, the purpose of step S13 is to project the 3D point onto the currently displayed 2D image. This ensures that the projected 3D point and the coordinates a(xa,xb) selected by the mouse cursor are in the same coordinate system, thus establishing a distance relationship. Projecting a 3D point cloud onto the current display screen requires considering the 3D point cloud model's perspective and scaling (reflected in the view projection matrix). Projecting the 3D point cloud model onto the current screen image involves traversing the 3D point B in the 3D point cloud and, according to the S12 view projection matrix M, projecting B onto the 2D point b(xb, yb) on the 2D screen image of the display window. Here, the 3D point B and the 2D point b correspond one-to-one: the correspondence is b = M × B. Finally, the distance between the 2D point a and b is calculated. After traversing, the set of distances between the projected point and point a is obtained. This set of distances is used in step S14 to find the 3D center point O of the selected breathing region.

[0062] S14. Find the two-dimensional point of the nearest projection. The three-dimensional point corresponding to this two-dimensional point is the three-dimensional center O of the selected respiratory monitoring area.

[0063] S15. Using the three-dimensional center O of the respiratory monitoring area as the center, set the radius R and the height H to set a cylindrical three-dimensional spatial region, i.e., ROI of interest; as shown in Figure 2, the respiratory monitoring point selection effect is shown, and the red area is the selected small ROI.

[0064] S16. Traverse the 3D points in the 3D point cloud data, determine whether the 3D point falls within the cylindrical 3D space region. If it does, determine that the 3D point is within the respiratory monitoring region and add the point to the respiratory monitoring region point set to obtain the ROI region 3D point set; otherwise, if it does not, do not add it.

[0065] To determine whether a three-dimensional point is within the respiratory monitoring area, the following formula can be used: Assuming the coordinates of the three-dimensional point are (xb, yb, zb), and the three-dimensional center of the cylinder is O(xo, yo, zo), the three-dimensional cylindrical region is represented as: (x-xo)×(x-xo)+(y-yo)×(y-yo)<=R×R, and |z-zo|<=H.

[0066] S17. Calculate the average coordinates of all three-dimensional points in the ROI region at time t in the Z-axis direction, and use it as the respiratory amplitude value at time t; acquire the three-dimensional point set of the ROI region at different times in real time, and calculate the average coordinates in the Z-axis direction at each time to obtain the respiratory amplitude value at different times; form a respiratory curve with time as the horizontal axis and respiratory amplitude value as the vertical axis.

[0067] After obtaining the set of three-dimensional points (n three-dimensional points) within the ROI region, calculate the average value of the sum of the Z coordinates of all three-dimensional points, average_z = (z1 + z2 + ... + zn) / n. The value of Average_z is the current breathing amplitude value.

[0068] Example 3:

[0069] In this embodiment, the other steps are the same as in Embodiment 1. This embodiment provides a method for smoothing the breathing curve. Specifically, assuming that y0, y1, y2, y3, and y4 are the latest five consecutive breathing amplitude values, the smoothed five breathing amplitude values ​​y'0, y'1, y'2, y'3, and y'4 are obtained using the following calculation formulas: y'0 = (3.0 * y0 + 2.0 * y1 + y2 - y4) / 5.0 y'1 = (4.0 * y0 + 3.0 * y1 + 2 * y2 + y3) / 10.0 y'2 = (y0 + y1 + y2 + y3 + y4) / 5.0 y'3 = (4.0 * y4 + 3.0 * y3 + 2 * y2 + y1) / 10.0 y'4 = (3.0 * y4 + 2.0 * y3 + y2 - y0) / 5.0.

[0070] As shown in Figure 3, the thinner color represents the actual breathing curve, and the thicker color represents the smoothed breathing curve.

[0071] Example 4:

[0072] In this embodiment, a method for identifying peaks and troughs in a respiratory curve is provided. Starting from the acquisition of the respiratory curve, the first peak is identified first, and only after the first peak is identified is the first trough identified. As the respiratory curve is continuously updated, only after the i-th peak is identified is the i-th trough identified, and only after the i-th trough is identified is the (i+1)-th peak identified, where i ≥ 0. Peaks and troughs in the continuously updated respiratory curve are identified according to this rule. The respiratory curves of real patients are not very smooth and exhibit curve fluctuations. The currently identified peaks or troughs are considered temporary peaks or troughs. If a higher peak or lower trough appears in the future within the same cycle, the currently identified temporary peak or trough is updated.

[0073] Specifically, it includes the following steps:

[0074] S41. Obtain the latest 5 respiratory amplitude values ​​of the respiratory curve in chronological order, and record them as: p1, p2, p3, p4, p5 respectively; and record the type T(peak, valley, nan) of the peak and valley points that are the closest to these 5 respiratory amplitude values ​​in time. Identify the peak or valley according to formula (1), which is as follows:

[0075] In the formula, R(t) = 1 represents p3 as a peak, R(t) = -1 represents p3 as a trough, and R(t) = 0 represents p3 as neither a peak nor a trough.

[0076] It should be noted that during the identification process, the first peak needs to be identified first, and then the first trough needs to be identified. The identification of the first peak and the first trough are both carried out in step S41, but the identification of the first peak and the first trough does not require the comparison of the nearest neighbor type.

[0077] S42. During the peak identification process, the peak identified at the current moment is taken as a temporary peak p. m (t1), as the respiratory curve is continuously updated, continue to identify peaks and troughs according to step S41; if a new peak p n If (t2) is identified first, then compare p. m (t1) and p n The magnitude of (t2) is determined, and the final peak is determined according to the following formula:

[0078] If the trough is identified first, then p(t) = p m (t1).

[0079] S43. In the process of identifying troughs, the trough identified at the current moment is taken as a temporary trough v. m (t1), as the breathing curve is continuously updated, continue to identify peaks and troughs according to step S41. If a new trough v n (t2) If it is identified first, then compare v. m (t1) and v n The magnitude of (t2) is determined, and the final trough is determined according to the following formula:

[0080] If the wave peak is identified first, then v(t) = v m (t1).

[0081] When a peak or trough point is identified in real time, the software immediately sends a signal through the communication interface to mark and classify the CT image sequence of this scan phase for subsequent 4D-CT reconstruction.

[0082] This embodiment can identify the peaks and troughs of the respiratory curve in real time from the complex pattern of free breathing, thereby extracting clear respiratory phase markers. The peak and trough identification method can identify the beginning and end of breathing, and accurately determine key phases such as the end of expiration (trough) and the end of inspiration (peak), so that the data corresponding to these phases can be classified for subsequent accurate reconstruction.

[0083] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0084] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. Those skilled in the art can make other variations or modifications based on the above description. It is neither necessary nor possible to exhaustively describe all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A 4D-CT image reconstruction method, characterized in that, Includes the following steps: S1. Use a 3D scanning device to acquire three-dimensional image surface data of the patient, render the three-dimensional image surface data on the display interface, use the mouse to obtain a three-dimensional point using the display buffer, and use the selected three-dimensional point as the reference point; set a cylinder to capture the three-dimensional surface point cloud within the cylinder range, and this three-dimensional surface point cloud region is the ROI of interest; S2. Obtain the average coordinate values ​​of the three-dimensional points within the ROI region at time t along the Z-axis, and use this as the current breathing amplitude value a. t The respiratory amplitude values ​​at different times are acquired in real time, and a respiratory curve is formed with time as the horizontal axis and respiratory amplitude values ​​as the vertical axis. S3. Use a smoothing algorithm to smooth the breathing curve; S4. Identify the peaks and troughs of the respiratory curve to obtain the patient's respiratory phase information; S5. Add respiratory phase information to the patient's three-dimensional image surface data obtained in step S1; S6. Group all three-dimensional image body surface data according to respiratory phase; S7. Perform 3D reconstruction on the surface data of the 3D image in each phase using the selected reconstruction algorithm to generate a 3D CT image in that phase; stitch the 3D CT images in different phases in chronological order to obtain a 4D-CT sequence image.

2. The 4D-CT image reconstruction method according to claim 1, characterized in that, Step S1 specifically includes: S11. In the scanned 3D image model display interface, adjust the optimal rendering angle of the selected respiratory monitoring area and the scaling size of the 3D image model; S12. Obtain the XY coordinates a(xa,xb) of the mouse cursor and obtain the view projection matrix M of the 3D image model; S13. Traverse the three-dimensional point B in the three-dimensional point cloud data and project it onto the two-dimensional point b(xb,yb) on the two-dimensional image of the display interface size according to the view projection matrix M of the model. The three-dimensional point B corresponds one-to-one with the projected two-dimensional point, and the correspondence formula is: b=M×B; calculate the distance d between the two-dimensional coordinate point a(xa,xb) of the cursor and each projected two-dimensional point b(xb,yb) to form a distance set. S14. Find the two-dimensional point that is closest to the projection. The three-dimensional point corresponding to this two-dimensional point is the three-dimensional center O of the selected respiratory monitoring area. S15. Using the three-dimensional center O of the respiratory monitoring area as the center, set the radius R and the height H to set a cylindrical three-dimensional spatial region, i.e., the ROI of interest; S16. Traverse the 3D points in the 3D point cloud data, determine whether the 3D point falls within the cylindrical 3D space region. If it does, determine that the 3D point is within the respiratory monitoring region and add the point to the respiratory monitoring region point set to obtain the ROI region 3D point set; otherwise, if it does not, do not add it.

3. The 4D-CT image reconstruction method according to claim 2, characterized in that, Step S2 includes: calculating the average coordinates of all three-dimensional points in the ROI region at the current time t in the Z-axis direction, as the respiratory amplitude value at the current time t; acquiring the three-dimensional point set of the ROI region at different times in real time, and calculating the average coordinates in the Z-axis direction at each time to obtain the respiratory amplitude value at different times; forming a respiratory curve with time as the horizontal axis and respiratory amplitude value as the vertical axis.

4. The 4D-CT image reconstruction method according to claim 3, characterized in that, Step S3 includes, assuming y0, y1, y2, y3, y4 are the latest five consecutive respiratory amplitude values, and using the following calculation formulas to obtain the smoothed five respiratory amplitude values ​​y'0, y'1, y'2, y'3, y'4: y'0=(3.0*y0+2.0*y1+y2-y4) / 5.0 y'1=(4.0*y0+3.0*y1+2*y2+y3) / 10.0 y'2=(y0+y1+y2+y3+y4) / 5.0 y'3=(4.0*y4+3.0*y3+2*y2+y1) / 10.0 y'4=(3.0*y4+2.0*y3+y2-y0) / 5.

0.

5. The 4D-CT image reconstruction method according to claim 1, characterized in that, In step S4, starting from the acquisition of the breathing curve, the first peak is identified first, and the first trough is identified only after the first peak is identified. As the breathing curve is continuously updated, the i-th trough is identified only after the i-th peak is identified, and the (i+1)-th peak is identified only after the i-th trough is identified, where i≥0.

6. The 4D-CT image reconstruction method according to claim 5, characterized in that, Step S4 includes: S41. Obtain the latest 5 respiratory amplitude values ​​of the respiratory curve in chronological order, and record them as: p1, p2, p3, p4, p5 respectively; and record the type T(peak, valley, nan) of the peak and valley points that are the closest to these 5 respiratory amplitude values ​​in time. Identify the peak or valley according to formula (1), which is as follows: In the formula, R(t) = 1 represents p3 as a peak, R(t) = -1 represents p3 as a trough, and R(t) = 0 represents p3 as neither a peak nor a trough.

7. The 4D-CT image reconstruction method according to claim 6, characterized in that, During the peak identification process, the peak identified at the current moment is taken as a temporary peak p. m (t1), as the respiratory curve is continuously updated, continue to identify peaks and troughs according to step S41; if a new peak p n If (t2) is identified first, then compare p. m (t1) and p n The magnitude of (t2) is determined, and the final peak is determined according to the following formula: If the trough is identified first, then p(t) = p m (t1).

8. The 4D-CT image reconstruction method according to claim 6, characterized in that, During the trough identification process, the trough identified at the current moment is taken as a temporary trough v. m (t1), as the breathing curve is continuously updated, continue to identify peaks and troughs according to step S41. If a new trough v n (t2) If it is identified first, then compare v. m (t1) and v n The magnitude of (t2) is determined, and the final trough is determined according to the following formula: If the wave peak is identified first, then v(t) = v m (t1).

9. The 4D-CT image reconstruction method according to any one of claims 1 to 8, characterized in that, In step S6, the collected three-dimensional image body surface data are divided according to the respiratory phase information, and the three-dimensional image body surface data under the same phase are grouped together.

10. The 4D-CT image reconstruction method according to claim 9, characterized in that, The reconstruction algorithms include filtered backprojection reconstruction algorithm and iterative reconstruction algorithm.

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