Accelerated FBP for computed tomography image reconstruction
This work addresses computational bottlenecks in medical imaging for faster reconstruction, but it is incremental as it builds on existing FBP methods.
The paper tackles the high computational complexity of Filtered Back Projection (FBP) for computed tomography image reconstruction by proposing a novel approach that reduces operations to Θ(N²log N) additions, avoiding Fourier space and using recursive filters and fast discrete Hough transform, with experimental results showing efficiency on simulated data.
Filtered back projection (FBP) is a commonly used technique in tomographic image reconstruction demonstrating acceptable quality. The classical direct implementations of this algorithm require the execution of $Θ(N^3)$ operations, where $N$ is the linear size of the 2D slice. Recent approaches including reconstruction via the Fourier slice theorem require $Θ(N^2\log N)$ multiplication operations. In this paper, we propose a novel approach that reduces the computational complexity of the algorithm to $Θ(N^2\log N)$ addition operations avoiding Fourier space. For speeding up the convolution, ramp filter is approximated by a pair of causal and anticausal recursive filters, also known as Infinite Impulse Response filters. The back projection is performed with the fast discrete Hough transform. Experimental results on simulated data demonstrate the efficiency of the proposed approach.