Fast optimal decomposed modification of FDK with potential decreasing of memory consuming

2017/08/24 by Andriiashen, Vladyslav, Kozhevnikov, Danila
#FOS: Physical sciences #Medical Physics (physics.med-ph)

paper · doi:10.48550/arxiv.1708.07515

Abstract

We present a new algorithm for 3D cone-beam tomography. The algorithm is based on decomposition of the cone-beam backprojection operation and angular decimation. It has computational complexity of O(N^3.5) and allows considerable reduction of peak memory usage in comparison with conventional algorithms. Tests with real data demonstrate the acceleration, achieved by using our algorithm instead of FDK, with 20-fold speedup for a 2000 × 2000 × 720 image. The algorithm is compared with other fast FDK algorithms.

Related

• A computationally efficient reconstruction algorithm for circular\n cone-beam computed tomography using shallow neural networks
• Learning Wavelet-Sparse FDK for 3D Cone-Beam CT Reconstruction
• Deep Learning Computed Tomography based on the Defrise and Clack Algorithm
• The Non-uniform Fast Fourier Transform in Computed Tomography
• Swap-Net: A Memory-Efficient 2.5D Network for Sparse-View 3D Cone Beam CT Reconstruction