A method and device for reconstructing images of lung breathing process based on 4D-CT of patients

Abstract

The present invention discloses a method for reconstructing images of the lung breathing process based on the patient's 4D-CT, including: 1. Acquire a 4D-CT data set of the lungs before surgery; 2. Based on the X-ray images of the lungs taken during surgery, calculate the image feature quantity of the diaphragm area in each X-ray image, and divide the period according to the range of change of the image feature quantity to obtain a curve representing the breathing; 3. Based on the image feature quantity representing the breathing curve and the current X-ray image during surgery, obtain the current respiratory cycle phase corresponding to the current X-ray image; 4. According to the current respiratory cycle phase, the 4D-CT data set is matched to obtain the corresponding CT data and posture information; 5. Based on the CT data and posture information, construct a simulated X-ray image. The present invention also discloses a device for reconstructing images of the lung breathing process based on the above method. The method provided by the present invention obtains high-quality simulated X-ray images through reconstruction, which can improve the success rate of doctors' puncture operations and ensure the safety of patients during surgery.

Description

Technical Field

[0001] The present invention relates to the technical field of medical image processing, and in particular to a method and device for reconstructing images of the lung breathing process based on a patient's 4D-CT. Background Art

[0002] With the development of medical imaging and computer technology, computers are more widely used in medical treatment. At present, the lung breathing process is dynamically displayed, and 4D CT data is used to facilitate doctors to observe during intraoperative puncture.

[0003] In the current percutaneous lung puncture surgery, doctors need to have a specific and accurate understanding of the lung breathing process and the location of lung tumors before the operation. If the tumor location is misjudged during the puncture, it will lead to repeated needle insertions, prolong the operation time, and in severe cases, cause complications such as pneumothorax, hemothorax, and bleeding. Therefore, image guidance and other assistance are needed to provide real-time spatial location information.

[0004] Although X-ray images are relatively stable, they can only provide one-dimensional information, which is insufficient. Although CT provides three-dimensional information, it does not show the specific changes during the breathing process, which is not conducive to the observation of the breathing process and is not convenient for surgery. 4DCT provides us with such a real-time observation method.

[0005] Since the position of the tumor changes greatly during the respiratory movement, it is impossible to accurately determine the actual position of the tumor and other tissues using only a single CT or X-ray. In practice, the breath-holding method is mainly used to keep the tumor position consistent with the respiratory phase planned before surgery, but this method is only suitable for patients with good respiratory conditions. In addition, there is a method that uses the correlation between the movement of tumors on the body surface and inside the body to determine the current tumor position and respiratory phase, but this correlation has deviations and is not strictly related. There is also a phase judgment method that borrows real-time respiratory motion signals, which also has the above problems. Phase judgment with the help of implanted markers requires implantation in advance, which brings physical burden to the patient and cumbersome operation.

[0006] Patent document CN114176726A discloses a puncture method based on phase configuration, including: selecting any 3D image in a 4D image as a planning image for planning; obtaining the patient's breathing curve; obtaining the position of the lesion feature in the 4D image to obtain the lesion feature motion curve; performing phase matching on the breathing curve and the lesion feature motion curve to obtain a mapping relationship between the breathing curve and the lesion feature motion curve; obtaining the corresponding amplitude of the planning image in the breathing curve according to the phase point of the planning image on the lesion feature motion curve and the mapping relationship, recorded as the target amplitude; when the patient's breathing amplitude reaches the target amplitude, executing the planning result. This method needs to be based on many monitoring devices. Although the puncture positioning effect is good, the investment cost and if there is a problem with the equipment may affect the positioning accuracy.

[0007] Patent document CN109330683A discloses a method for assessing the safety of interventional puncture paths based on 4D-CT of the patient's lungs, including: performing a 4D-CT scan on the patient, and obtaining a 4D-CT image sequence in each respiratory phase; marking the center point of normal lung tissue; assigning values ​​to the probability index M, risk index R, and severity S of puncture complications caused by damage to normal lung tissue; when performing CT-guided puncture, selecting the patient's body surface puncture point and angle based on the real-time collected CT image; aligning the real-time collected CT image with the 4D-CT image sequence of outlined tissues and organs; determining the puncture crisis and area, thereby realizing risk estimation and adjustment of the puncture path. This method requires marking the center point of normal lung tissue before surgery, and the marking method is prone to unclear display of tissue edge contours, which affects the judgment of puncture path safety. Summary of the invention

[0008] In order to solve the above problems, the present invention provides a method for reconstructing images of the lung breathing process based on the patient's 4D-CT. The method constructs a respiratory curve according to the changes in image feature quantities of the diaphragm area of ​​the lung X-ray image, obtains CT data corresponding to the current X-ray image during the operation through predictive fitting and matching, and obtains simulated X-ray images for real-time feedback of lung puncture navigation positioning through digital image reconstruction, thereby improving the success rate of doctors' puncture operations, while reducing puncture time and avoiding the occurrence of complications.

[0009] A method for reconstructing an image of a lung breathing process based on a patient's 4D-CT, comprising:

[0010] Step 1, obtaining a 4D-CT data set of the lungs before surgery, wherein the 4D-CT data set includes at least one set of CT data of the lungs within a respiratory cycle and corresponding phase numbers;

[0011] Step 2: Based on the X-ray images of the lungs taken during the operation, the image feature quantity of the diaphragm region in each X-ray image is calculated, and the period is divided according to the variation range of the image feature quantity to obtain a respiratory curve representing the respiratory cycle phase corresponding to the X-ray image;

[0012] Step 3: Based on the image feature quantity representing the respiratory curve obtained in step 2 and the current X-ray image during the operation, the current respiratory cycle phase corresponding to the current X-ray image is obtained through predictive fitting analysis;

[0013] Step 4: According to step 3, the current respiratory cycle phase is obtained, and the corresponding CT data and posture information are obtained by matching from the 4D-CT data set in step 1;

[0014] Step 5: Based on the CT data and posture information obtained in step 4, a simulated X-ray image is constructed for real-time feedback of lung puncture navigation positioning.

[0015] Specifically, the image feature quantity in the diaphragm area in each of the X-ray images in step 2 is obtained by calculating the median of the pixel grayscale in the diaphragm area. Since the diaphragm is one of the force-generating parts during respiratory movement, obvious volume changes will appear in the X-ray image.

[0016] Preferably, the diaphragm region is obtained by cutting off the junction of the lowest rib and the diaphragm. The movement state of the lungs and diaphragm at this position is most obvious, so the pixel grayscale change in this area is most obvious.

[0017] Specifically, the period division according to the variation range of the image characteristic quantity in step 2 is based on the division according to a set of time intervals corresponding to the phases of the respiratory cycle within the interval between the minimum value and the maximum value of the image characteristic quantity.

[0018] Specifically, the prediction and fitting analysis in step 3 includes predicting the cycle of the respiratory process and the variation range of the image feature quantity based on the ARMA model and interpolating and fitting the image feature quantity using Fourier series.

[0019] Specifically, the specific process of step 3 is as follows:

[0020] Step 3.1, based on the respiratory curve obtained in step 2, predict the image feature change amount and the respiratory cycle size of the current respiratory cycle through the ARMA model;

[0021] Step 3.2, according to the prediction result of step 3.1 and the characterizing breathing curve obtained in step 2, the image feature quantity is interpolated and fitted by Fourier series to obtain the current characterizing breathing curve of the current breathing cycle;

[0022] Step 3.3, select the characterizing respiratory data corresponding to the respiratory cycle phase in the current characterizing respiratory curve, and make a judgment based on the Euclidean distance between the image feature of the current X-ray image and the characterizing respiratory data. If the calculated distance is less than the threshold, output the corresponding respiratory cycle phase as the current respiratory cycle phase of the current X-ray image.

[0023] Preferably, the matching in step 4 is based on the grayscale gradient histogram features to perform global rigid registration of the CT data and the bone projection information in the X-ray image. This method can suppress the influence of translation and selection on the matching process, and can ignore the influence of illumination on the image, thereby reducing the dimension of the data required to represent the image.

[0024] Specifically, the simulated X-ray image for real-time feedback of lung puncture navigation positioning in step 5 is constructed by ray tracing and posture transformation matrix.

[0025] The present invention also provides a device for reconstructing images of the lung breathing process, comprising: a computer memory, a computer processor, and a computer program stored in the computer memory and executable on the computer processor, wherein the computer memory adopts the above-mentioned method for reconstructing images of the lung breathing process based on the patient's 4D-CT; when the computer processor executes the computer program, the following steps are implemented: inputting the current X-ray image during the operation, and constructing a high-quality simulated X-ray image for real-time feedback of lung puncture navigation positioning through predictive fitting analysis and image reconstruction.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] (1) A respiratory curve is constructed based on the image features of the diaphragm region in the X-ray image and the respiratory cycle phase, thereby achieving rapid conversion from the X-ray image to the corresponding respiratory cycle phase.

[0028] (2) The respiratory cycle phase is used as a "bridge" to quickly match the X-ray image with the 4D-CT data, thereby avoiding the impact of conventional labeling or implantation methods on the patient's body. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 A schematic flow chart of a method for reconstructing an image of a patient's lung breathing process based on 4D-CT provided by the present invention;

[0030] Figure 2 is a schematic diagram of the diaphragm region selected in this embodiment;

[0031] Figure 3 This is a schematic diagram of the tumor location in the simulated X-ray image in this embodiment. DETAILED DESCRIPTION

[0032] The treatment of lung tumors is mainly solved by puncture, but using only a single CT or X-ray cannot accurately determine the actual location of the tumor and other tissues. Common methods include breath-holding to keep the tumor position consistent with the respiratory phase planned before surgery, but this method is only suitable for patients with good respiratory conditions; or using the correlation between surface and internal tumor movement to determine the current tumor position and respiratory phase, but this correlation has deviations; there is also a marking method to record respiratory signals or chest movement trajectories, combined with the patient's own intraoperative X-ray images for simulation, but this method requires the marker to be implanted into the patient's body surface in advance or coated with a certain amount of radioactive paint as a marker, which poses a safety hazard and is complicated to operate.

[0033] Therefore, this embodiment provides a method for reconstructing images of the lung breathing process based on a patient's 4D-CT. Figure 1 As shown, including:

[0034] Step 1: Obtain a 4D-CT data set of the lungs before surgery, wherein the 4D-CT data set includes a set of CT data of the lungs within a respiratory cycle and corresponding phase numbers:

[0035] A set of lung CT data within a respiratory cycle is obtained through CT machine sampling, and the CT data at each moment is assigned a corresponding phase number, with a total of ten respiratory phases.

[0036] Step 2: Based on the X-ray images of the lungs taken during the operation, the image feature quantity of the diaphragm region in each X-ray image is calculated, and the period is divided according to the variation range of the image feature quantity to obtain a respiratory curve representing the respiratory cycle phase corresponding to the X-ray image:

[0037] Obtain 3 to 5 sets of X-ray images taken during the respiratory cycle, such as Figure 2 As shown, the junction area between the diaphragm and the lowest rib of each X-ray image is selected, and the median of the pixel grayscale in the area is calculated as the image feature of the X-ray image;

[0038] Since the diaphragm is one of the force generating tissues for chest movement during breathing, the pixel grayscale of the diaphragm in the X-ray image changes positively correlated with the respiratory movement within a certain range.

[0039] According to the statistically obtained pixel grayscale interval of the X-ray image, the breathing cycle time T0, T1...T j The pixel grayscale interval is divided to obtain a respiratory curve representing the respiratory cycle phase corresponding to the X-ray image.

[0040] Step 3: Based on the image feature quantity characterizing the respiratory curve obtained in step 2 and the current X-ray image during the operation, the current respiratory cycle phase corresponding to the current X-ray image is obtained through predictive fitting analysis. The specific steps are as follows:

[0041] Step 3.1, based on the respiratory curve obtained in step 2, predict the image feature change amount and the respiratory cycle size of the current respiratory cycle through the ARMA model;

[0042] Step 3.2, according to the prediction result of step 3.1 and the characterizing breathing curve obtained in step 2, the image feature quantity is interpolated and fitted by Fourier series to obtain the current characterizing breathing curve of the current breathing cycle;

[0043] Step 3.3, select the characterizing respiratory data corresponding to the respiratory cycle phase in the current characterizing respiratory curve, and make a judgment based on the Euclidean distance between the image feature of the current X-ray image and the characterizing respiratory data. If the calculated distance is less than the threshold, output the corresponding respiratory cycle phase as the current respiratory cycle phase of the current X-ray image.

[0044] Step 4: According to step 3, the current respiratory cycle phase is obtained, and the corresponding CT data and posture information are matched from the 4D-CT data set in step 1:

[0045] After obtaining the current respiratory cycle phase, multiple posture CT data corresponding to the phase are obtained from the 4D-CT data set. Based on the current X-ray image and the bone projection information of all CT data in this phase, the corresponding CT data and posture information are obtained through global rigid registration of the histogram of grayscale gradients (HOG).

[0046] Step 5: Based on the CT data and posture information obtained in step 4, a ray tracing method and posture transformation matrix are used to construct a simulated X-ray image for real-time feedback of lung puncture navigation positioning:

[0047] The ray tracing method simulates the process of X-ray imaging, projects the CT image, and calculates the pixel value of each pixel on the projected image when the geometric parameters are known, because each voxel of the CT image represents the attenuation performance of its corresponding position. Since the process of CT projection can be converted into the integration of the CT voxels passed by the virtual light path, and the CT voxels are all small rectangular blocks, the integral solution can be converted into a summation:

[0048]

[0049] Among them, p(u,v) is the pixel value of the corresponding point of the plane reconstructed X-ray image after projection, L(u,v) is the light path from the ray source to the corresponding projection plane point, T(x,y,z) is the voxel value of the corresponding point (x,y,z) on the CT image, l(x,y,z) is the length of the virtual light path in the CT image, and μ is the linear attenuation coefficient;

[0050] The pixel value of each pixel is calculated according to the above formula, and converted into the corresponding coordinate parameters through the posture transformation matrix to obtain the simulated X-ray image under the posture.

[0051] This embodiment also provides a lung breathing process image reconstruction device, comprising: a computer memory, a computer processor, and a computer program stored in the computer memory and executable on the computer processor, wherein the above-mentioned lung breathing process image reconstruction method based on the patient's 4D-CT is adopted in the computer memory;

[0052] When the computer processor executes the computer program, the following steps are implemented: inputting the current intraoperative X-ray image, and constructing a high-quality simulated X-ray image for real-time feedback of lung puncture navigation positioning through predictive fitting analysis and image reconstruction.

[0053] like Figure 3 As shown, it is a simulated X-ray image generated by the image reconstruction device of the lung breathing process, and the framed part in the figure is the location of the target tumor.

Claims

1. A method for reconstructing images of the lung breathing process based on 4D-CT of a patient, characterized in that: include: Step 1, obtaining a 4D-CT data set of the lungs before surgery, wherein the 4D-CT data set includes at least one set of CT data of the lungs within a respiratory cycle and corresponding phase numbers; Step 2: Based on the X-ray images of the lungs taken during the operation, the image feature quantity of the diaphragm region in each X-ray image is calculated, and the period is divided according to the variation range of the image feature quantity to obtain a respiratory curve representing the respiratory cycle phase corresponding to the X-ray image; Step 3: Based on the image feature quantity characterizing the respiratory curve obtained in step 2 and the current intraoperative X-ray image, the current respiratory cycle phase corresponding to the current X-ray image is obtained through predictive fitting analysis. The predictive fitting analysis in step 3 includes predicting the cycle of the respiratory process and the variation range of the image feature quantity based on the ARMA model and interpolating and fitting the image feature quantity using the Fourier series. The specific process is as follows: Step 3.1, based on the respiratory curve obtained in step 2, predict the image feature change amount and the respiratory cycle size of the current respiratory cycle through the ARMA model; Step 3.2, according to the prediction result of step 3.1 and the characterizing breathing curve obtained in step 2, the image feature quantity is interpolated and fitted by Fourier series to obtain the current characterizing breathing curve of the current breathing cycle; Step 3.3, selecting the respiratory data corresponding to the respiratory cycle phase in the current respiratory curve, and judging according to the Euclidean distance between the image feature of the current X-ray image and the respiratory data, if the calculated distance is less than a threshold, outputting the corresponding respiratory cycle phase as the current respiratory cycle phase of the current X-ray image; Step 4: According to step 3, the current respiratory cycle phase is obtained, and the corresponding CT data and posture information are obtained by matching from the 4D-CT data set in step 1; Step 5: Based on the CT data and posture information obtained in step 4, a simulated X-ray image is constructed for real-time feedback of lung puncture navigation positioning.

2. The method for reconstructing the image of the lung breathing process based on the patient's 4D-CT according to claim 1, characterized in that: The image feature quantity in the diaphragm region of each X-ray image in step 2 is obtained by calculating the median of the pixel grayscale in the diaphragm region.

3. The method for reconstructing the image of the lung breathing process based on the patient's 4D-CT according to claim 2, characterized in that: The diaphragm region is obtained by cutting the junction between the lowest rib and the diaphragm.

4. The method for reconstructing the image of the lung breathing process based on the patient's 4D-CT according to claim 1, characterized in that: The period division according to the variation range of the image characteristic quantity in step 2 is based on the minimum value and the maximum value of the image characteristic quantity, and is divided according to a set of time intervals corresponding to the phases of the respiratory cycle.

5. The method for reconstructing the image of the lung breathing process based on the patient's 4D-CT according to claim 1, characterized in that: The matching in step 4 is to perform global rigid registration of the CT data and the bone projection information in the X-ray image based on the grayscale gradient histogram feature.

6. The method for reconstructing the image of the lung breathing process based on the patient's 4D-CT according to claim 1, characterized in that: The construction of the simulated X-ray image for real-time feedback of lung puncture navigation positioning in step 5 is constructed by ray tracing and posture transformation matrix.

7. A device for reconstructing images of a lung breathing process, comprising a computer memory, a computer processor, and a computer program stored in the computer memory and executable on the computer processor, characterized in that: When the computer processor runs the computer program, it executes the steps of the method for reconstructing the image of the lung breathing process based on the patient's 4D-CT as described in any one of claims 1 to 6, and the steps are as follows: inputting the current X-ray image during the operation, and constructing a high-quality simulated X-ray image for real-time feedback of lung puncture navigation positioning through predictive fitting analysis and image reconstruction.

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