Name
Abstract | ||
|---|---|---|
In this paper, we propose a kernel backprojection method for computed tomography. The classical backprojection method estimates an unknown pixel value by the summation of the projection values with linear weights, while our kernel backprojection is a generalized version of the classic approach, in which we compute the weights from a kernel (weight) function. The generalization reveals that the performance of the backprojection operation strongly depends on the choice of the kernel, and a good choice of the kernels effectively suppresses both noise and streak artifacts while preserving major structures of the unknown phantom. The proposed method is a two-step procedure where we first compute a preliminary estimate of the phantom (a "pilot"), from which we compute the kernel weights. From these kernel weights we then re-estimate the phantom, arriving at a much improved result. The experimental results show that our approach significantly enhances the backprojection operation not only numerically but also visually. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICASSP.2010.5495184 | ICASSP |
Keywords | Field | DocType |
computerised tomography,image reconstruction,computed tomography,kernel weight function,linear weights,nonlinear kernel backprojection,phantom,projection values,unknown pixel value,filtered backprojection,kernel regression,nonlinear filters,projection reconstruction,tomography | Kernel (linear algebra),Iterative reconstruction,Mathematical optimization,Radial basis function kernel,Iterative method,Imaging phantom,Variable kernel density estimation,Compressed sensing,Mathematics,Kernel regression | Conference |
ISSN | Citations | PageRank |
1520-6149 | 1 | 0.36 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hiroyuki Takeda | 1 | 226 | 8.63 |
| Peyman Milanfar | 2 | 3284 | 155.61 |