Image interpolation is often required during medical image processing and analysis. Although interpolation method based on Gaussian radial basis function (GRBF) has high precision, the long calculation time still limits its application in field of image interpolation. To overcome this problem, a method of two-dimensional and three-dimensional medical image GRBF interpolation based on computing unified device architecture (CUDA) is proposed in this paper. According to single instruction multiple threads (SIMT) executive model of CUDA, various optimizing measures such as coalesced access and shared memory are adopted in this study. To eliminate the edge distortion of image interpolation, natural suture algorithm is utilized in overlapping regions while adopting data space strategy of separating 2D images into blocks or dividing 3D images into sub-volumes. Keeping a high interpolation precision, the 2D and 3D medical image GRBF interpolation achieved great acceleration in each basic computing step. The experiments showed that the operative efficiency of image GRBF interpolation based on CUDA platform was obviously improved compared with CPU calculation. The present method is of a considerable reference value in the application field of image interpolation.
The aim of this study was to propose an algorithm for three-dimensional projection onto convex sets (3D POCS) to achieve super resolution reconstruction of 3D lung computer tomography (CT) images, and to introduce multi-resolution mixed display mode to make 3D visualization of pulmonary nodules. Firstly, we built the low resolution 3D images which have spatial displacement in sub pixel level between each other and generate the reference image. Then, we mapped the low resolution images into the high resolution reference image using 3D motion estimation and revised the reference image based on the consistency constraint convex sets to reconstruct the 3D high resolution images iteratively. Finally, we displayed the different resolution images simultaneously. We then estimated the performance of provided method on 5 image sets and compared them with those of 3 interpolation reconstruction methods. The experiments showed that the performance of 3D POCS algorithm was better than that of 3 interpolation reconstruction methods in two aspects, i.e. subjective and objective aspects, and mixed display mode is suitable to the 3D visualization of high resolution of pulmonary nodules.
The selection of fiducial points has an important effect on electrocardiogram (ECG) denoise with cubic spline interpolation. An improved cubic spline interpolation algorithm for suppressing ECG baseline drift is presented in this paper. Firstly the first order derivative of original ECG signal is calculated, and the maximum and minimum points of each beat are obtained, which are treated as the position of fiducial points. And then the original ECG is fed into a high pass filter with 1.5 Hz cutoff frequency. The difference between the original and the filtered ECG at the fiducial points is taken as the amplitude of the fiducial points. Then cubic spline interpolation curve fitting is used to the fiducial points, and the fitting curve is the baseline drift curve. For the two simulated case test, the correlation coefficients between the fitting curve by the presented algorithm and the simulated curve were increased by 0.242 and 0.13 compared with that from traditional cubic spline interpolation algorithm. And for the case of clinical baseline drift data, the average correlation coefficient from the presented algorithm achieved 0.972.
Diffusion tensor imaging (DTI) is a rapid development technology in recent years of magnetic resonance imaging. The diffusion tensor interpolation is a very important procedure in DTI image processing. The traditional spectral quaternion interpolation method revises the direction of the interpolation tensor and can preserve tensors anisotropy, but the method does not revise the size of tensors. The present study puts forward an improved spectral quaternion interpolation method on the basis of traditional spectral quaternion interpolation. Firstly, we decomposed diffusion tensors with the direction of tensors being represented by quaternion. Then we revised the size and direction of the tensor respectively according to different situations. Finally, we acquired the tensor of interpolation point by calculating the weighted average. We compared the improved method with the spectral quaternion method and the Log-Euclidean method by the simulation data and the real data. The results showed that the improved method could not only keep the monotonicity of the fractional anisotropy (FA) and the determinant of tensors, but also preserve the tensor anisotropy at the same time. In conclusion, the improved method provides a kind of important interpolation method for diffusion tensor image processing.
The dose data produced by treatment plan system (TPS) in intensity-modulated radiation therapy (IMRT) has many gradient edge points. Considering this feature we proposed a new interpolation algorithm called treatment plan dose interpolation algorithm based on gradient feature in intensity-modulated radiation therapy (TDAGI), which improves the Canny algorithm to detect the gradient edge points and non-edge points by using the gradient information in the dose data plane. For each gradient edge point, the corresponding gradient profile was traced and the profile's sharpness was calculated, and for each non-edge point, the dispersion was calculated. With the sharpness or dispersion, the kernel coefficients of bi-cubic interpolation can be obtained and can be used as the central point to complete the bi-cubic interpolation calculation. Compared with bi-cubic interpolation and bilinear interpolation, the TDAGI algorithm is more accurate. Furthermore, the TDAGI algorithm has the advantage of gradient keeping. Therefore, TDAGI can be used as an alternative method in the dose interpolation of TPS in IMRT.
In recent years, the pollution problem of particulate matter, especially PM2.5, is becoming more and more serious, which has attracted many people’s attention from all over the world. In this paper, a Kalman prediction model combined with cubic spline interpolation is proposed, which is applied to predict the concentration of PM2.5 in the micro-regional environment of campus, and to realize interpolation simulation diagram of concentration of PM2.5 and simulate the spatial distribution of PM2.5. The experiment data are based on the environmental information monitoring system which has been set up by our laboratory. And the predicted and actual values of PM2.5 concentration data have been checked by the way of Wilcoxon signed-rank test. We find that the value of bilateral progressive significance probability was 0.527, which is much greater than the significant level α = 0.05. The mean absolute error (MEA) of Kalman prediction model was 1.8 μg/m3, the average relative error (MER) was 6%, and the correlation coefficient R was 0.87. Thus, the Kalman prediction model has a better effect on the prediction of concentration of PM2.5 than those of the back propagation (BP) prediction and support vector machine (SVM) prediction. In addition, with the combination of Kalman prediction model and the spline interpolation method, the spatial distribution and local pollution characteristics of PM2.5 can be simulated.