| BackgroundKnee motion in vivo and prosthesis stability study are difficult topics in sports medicine and clinical research, for the bones of knee and implanted prosthesis are in the deeper structure, so it is not appropriate and realistic to substitute the information of the surface movement for the bones motion and analyze the stability of the prosthesis by surface information. Since X-ray are highly penetrating, and with its medical radiation dosage decreasing constantly, a variety of imaging equipments have become the mainstream tools to diagnose and study the internal human body, and it's absolutely a good way to utilize X-ray images in knee motion study.The internal structure of the human body can be observed by X-ray equipment, but it only shows two-dimension superimposed projection image, not three-dimension. While CT volume data can completely record all structural three-dimension information in vivo, but it is some kind of static situation, unable to display motion state. Therefore,2D/3D medical image registration which can combine the advantages of above two has become a popular research in recent years, and is widely applied in the fields as sports medicine, location radio surgery, surgical navigation, postoperative evaluation, and so on.2D/3D registration refers to a method that can obtain registration parameters of 2D and 3D data by comparing of DRR and real X-ray images. First, generate DRR images by simulating X-ray fluoroscopy in computer with the research objects CT or MR data acquired before; second, with the translation and rotation of the CT data, different DRR and X-ray images are compared to get maximum similarity, and obtain the location parameters of the 3D data in this state. Due to the improvement of the X-ray and CT equipment, and developing of medical image processing hardware and software,2D/3D registration results are more and more accuracy.Based on the research methods and outcomes internal and abroad, and aimed at own objectives, we developed a 2D/3D registration system for single X-ray image to spiral CT data with the hardware GPGPU and framework CUDA. Main process is as follows:first, obtain the condition of X-ray image-shooting by Zhang Zhengyou calibration method (DR shooting conditions), and set the parameters in the registration procedure. Second, with the support of the hardware GPGPU, generate different DRR images by translating and rotating CT data. Compare DRR and X-ray images using SSD(Sum-of-Squares difference) as the similarity measure object and locate the CT data when SSD is minimum. Complete description of the three-dimensional motion is outlined through the transform of the coordinate system. Third,2D/3D registration results are validated with the 3D laser scanning systems and software Raindrop Geomagic8.0. In the experiment as knee an integer, in 6 degrees of freedom the average translation errors were as follows (x,y,z axis,unit:mm, the same below):0.84,0.33,0.62; the average rotation errors were (x,y,z axis, unit: degree, the same below):0.32,0.53,0.80. In the experiment simulating knee motion, for the femur part, average translation errors were:1.55,1.25,0.98, and average rotation errors were:0.99,1.08,1.16; and for the tibia part, average translation errors were:1.65,1.27,0.89, and average rotation errors were:0.86,1.30,0.83. The results showed that the 2D/3D registration system achieved our goal, and it could be a good platform for future experiments. Objectives1. To establish X-ray image-shooting calibration system based on the Zhang Zhengyou method.2. To establish 2D/3D medical image registration system based on GPGPU and CUDA framework.3. To develop a method of checking 2D/3D registration results by using 3D laser scanner and software.4. To establish methods to study knee motion with this system.Materials and methods1. Establishment of the X-ray image-shooting calibration system. The hardware was a calibration plane with a printed circuit board as core part. We printed 0.254mm wide bronze lines with spacing of 10mm on a 0.4mm thickness PCB board, and lines were 11×11 arraying on direct cross and composed 10×10mm square grids. The printed circuit board was retained by two pieces of plexiglass boards, so it could be a standard plane. When used, Take some X-ray images of the calibration plane in different views, and carry out procedure based on Zhang Zhengyou calibration principle with the images to get the condition of the X-ray image-shooting, that is the location parameters of the light source and image receiver in the procedure.2. Establishment of the 2D/3D registration system based on GPGPU. We established a 2D/3D registration system based on GPGPU and CUDA framework. The procedure can build DRR images with CT data by the method of ray-tracing, then compare DRR and true X-ray images with SSD, by transforming the location and posture of the CT data, position parameters of CT data were obtained when SSD reached to extreme. 3. The study of the method of checking 2D/3D registration results with 3D laser scanner and software. The hardware is a 3D laser scanner (type:3DD RealScan USB 200), and its point cloud density is 512x1000. The software is Raindrop Geomagic8.0. At the moment of taking X-ray images of research objects, we recorded their 3D point clouds and saved them corresponding to X-ray images. In the software Geomagic, carried out 3D/3D registration for the point clouds after they were imported and transformed to a new coordinate system, and finally obtained the location transform parameters of the whole objects or some parts of them. As gold standards, point cloud registration parameters were used to check the 2D/3D registration procedure results.4. The experiment as knee an integer. A human knee specimen was scanned in CT equipment, and got 582 images which the parameters is:DICOM format,512×512pixel size,0.3515625^2mm/pixle and 0.75mm thickness. Kept the knee specimen frozen, and took 14 X-ray images, in which the former 6 pieces were calibration board images, while the latter 8 ones were frozen knee images, and all files were saved as DICOM format. The specimen had different position and posture in the X-ray images, and it was scanned by 3D laser scanner at the moment of image-shooting. X-ray images and CT data were imported into the 2D/3D images registration procedure and calculated. Finally, the laser point cloud data registration results were obtained in software and used to validate the 2D/3D registration results, and errors and causes were analyzed.5. Experiment of motion simulation registration. CT data is the same one. The defrosted specimen was bent to different angles to simulate the knee motion, and 16 X-ray images were taken at the same time. The former 8 images were for calibration board, while the latter 8 ones were for specimen.3D laser point cloud was captured at the moment of taking X-ray images. CT data was segmented to two parts:femur part and tibia part, and they were registered to X-ray images separately. The results were dealt to calculate the parameters of knee motion, and were validated by the method of point cloud registration. Finally, we analyzed the errors and probed the feasibility of this method.Results and discussion1. Developed a 2D/3D image registration procedure based on hardware GPGPU, and an X-ray image-shooting calibration procedure, and developed a method that could validate the results of 2D/3D registration by the 3D laser scanner and software.1.1 Calibration procedure for X-ray image-shooting. We designed and made a calibration board fitting the X-ray and Zhang Zhengyou calibration method, then improved the procedure to fit the board. Accuracy results could be calculated and the procedure was suitable for the X-ray image-shooting calibration.1.2 Developed a method which could validate the accuracy of the 2D/3D registration results by making use of the 3D laser scanner and Raindrop Geomagic8.0. We analyzed the errors of this method based on the accuracy of the hardware and software. The accuracy of the 3D laser scanner could be 0.01mm at the maximum distance of 20-30cm, while the errors of the point cloud registration could be 0.08mm as a maximum.1.3 Developed a 2D/3D registration system based on the GPGPU and CUDA framework. Compared with the traditional method, DRR images can be updated in real time, and the registration process can be great accelerated.2. Two experiments with a human knee specimen were done separately. Obtained CT data and two series of X-ray images, one was for the movement as knee specimen integer, and the other was for simulation of knee motion. The former were 14 images in which 6 images were for the calibration board, and the latter were 16 images in which 8 were for the calibration board.2D/3D registration of the two groups images to CT data were carried out separately.2.1 Experiment as knee a integer.6 calibration board images were divided to 3 groups to calculate the parameters of the X-ray shooting conditions, and the average value of the 3 group results was set in the procedure as the parameters of the source and image receiver. CT data and X-ray images were imported into the procedure and got 8 groups of registration parameters including 6 degrees of freedom, responded to the absolute locations of the knee in the images. Refer to the first image, the follow-up images were calculated to get the location transform parameters, and the results were valuated by the method of the point cloud registration. As knee a integrated body, the registration errors of the 6 degree of freedom were as follows:the average translation errors were(x,y,z axis, unit:mm): 0.84,0.33,0.62; the rotation errors were(x,y,z axis, unit:degree):0.32,0.53,0.80, and the maximum was 1.25mm(translation) along x-axis and 1.54 degree(rotation) by y-axis. We believed that the main error were from the processes of DRR generation and comparison of two kinds of images. First, DRR generation process was a virtual X-ray image shooting, but CT data was different with the real object and the ray-tracing algorithm was also quite different with the process of X-ray penetrating, so DRR images were not possible same as real X-ray images. What's more, the detail texture difference of the two kinds of images would cause unavoidable errors in the similarity measurement calculation. Second, when DRR and X-ray images were compared, small changes in the depth of field had little impact on the DRR images. Although the location parameters were changed, the similarity measurement might has no change, and the error was hard to remove.2.2 Motion simulation registration. CT data was segmented to two parts, one was the upper part of the knee (including the soft tissue) that the main bones were femur and patella, the other was the lower part that the main bones were tibia and fibula. The two parts of CT data were registered to X-ray images separately, and the results were valuated with the method of point cloud registration. The results showed that the femur part errors of 6 degrees of freedom of were as follows: the average translation errors (x,y,z axis, unit:mm, same below):1.55,1.25,0.98; the average rotation errors (x,y,z axis, unit:degree, same below):0.99,1.08,1.16; and as part of the tibia, the average translation errors were:1.65,1.27,0.89; and the average rotation errors were:0.86,1.30,0.83. Finally, the errors were analyzed. First, the error factors appeared in earlier experiment still existed. Second, when knee flexion was changed, the soft tissue around the joints had some deformation, but CT data remained the same state, so the soft tissue information caused more interference when DRR and X-ray images were compared. Third, the segment of the CT data reduced the information and affected the results to some extent.Conclusion1.2D/3D image registration system has achieved the practical requirement and it can be used to test the knee motion and prosthesis location.2. The method based on the 3D laser scanner and Raindrop Geomagic8.0 can be used to detect the whole objects movement, and also calculate the relative movement of the different parts (such as bones), so it could be a better choice to valuate the accuracy of such systems.3. The method used in the registration study of the knee motion simulation can be extended to test the joint motion in vivo.
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