# Spatially-Adaptive Reconstruction in Computed Tomography using Neural Networks

arXiv: https://arxiv.org/abs/1311.7251 · HTML: https://mlsanity.com/paper/1311.7251/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2013.

## Built on by (most cited first, 2 of 2)

- [LRIP-Net: Low-Resolution Image Prior based Network for Limited-Angle CT Reconstruction](https://mlsanity.com/paper/2208.00207/) (2022)
- [Uncertainty Estimation and Out-of-Distribution Detection for Deep Learning-Based Image Reconstruction using the Local Lipschitz](https://mlsanity.com/paper/2305.07618/) (2023)

## Abstract (arXiv)

We propose a supervised machine learning approach for boosting existing signal and image recovery methods and demonstrate its efficacy on example of image reconstruction in computed tomography. Our technique is based on a local nonlinear fusion of several image estimates, all obtained by applying a chosen reconstruction algorithm with different values of its control parameters. Usually such output images have different bias/variance trade-off. The fusion of the images is performed by feed-forward neural network trained on a set of known examples. Numerical experiments show an improvement in reconstruction quality relatively to existing direct and iterative reconstruction methods.