# CNN-based regularisation for CT image reconstructions

arXiv: https://arxiv.org/abs/2201.09132 · HTML: https://mlsanity.com/paper/2201.09132/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 38 references that are in the map.
- First posted on arXiv 22 Jan 2022.

## Builds on (most cited first, 12 of 12)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Deep Convolutional Neural Network for Inverse Problems in Imaging](https://mlsanity.com/paper/1611.03679/) (2016)
- [Lipschitz regularity of deep neural networks: analysis and efficient estimation](https://mlsanity.com/paper/1805.10965/) (2018)
- [Regularisation of Neural Networks by Enforcing Lipschitz Continuity](https://mlsanity.com/paper/1804.04368/) (2018)
- [Universality of Deep Convolutional Neural Networks](https://mlsanity.com/paper/1805.10769/) (2018)
- [Universal approximations of invariant maps by neural networks](https://mlsanity.com/paper/1804.10306/) (2018)
- [A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction](https://mlsanity.com/paper/1610.09736/) (2016)
- [A Review of Convolutional Neural Networks for Inverse Problems in Imaging](https://mlsanity.com/paper/1710.04011/) (2017)
- [CNN-Based Projected Gradient Descent for Consistent Image Reconstruction](https://mlsanity.com/paper/1709.01809/) (2017)
- [Deep Neural Networks with Trainable Activations and Controlled Lipschitz Constant](https://mlsanity.com/paper/2001.06263/) (2020)
- [Deep Residual Learning for Compressed Sensing CT Reconstruction via Persistent Homology Analysis](https://mlsanity.com/paper/1611.06391/) (2016)
- [Data Consistent Artifact Reduction for Limited Angle Tomography with Deep Learning Prior](https://mlsanity.com/paper/1908.06792/) (2019)

## Abstract (arXiv)

X-ray computed tomographic infrastructures are medical imaging modalities that rely on the acquisition of rays crossing examined objects while measuring their intensity decrease. Physical measurements are post-processed by mathematical reconstruction algorithms that may offer weaker or top-notch consistency guarantees on the computed volumetric field. Superior results are provided on the account of an abundance of low-noise measurements being supplied. Nonetheless, such a scanning process would expose the examined body to an undesirably large-intensity and long-lasting ionising radiation, imposing severe health risks. One main objective of the ongoing research is the reduction of the number of projections while keeping the quality performance stable. Due to the under-sampling, the noise occurring inherently because of photon-electron interactions is now supplemented by reconstruction artifacts. Recently, deep learning methods, especially fully convolutional networks have been extensively investigated and proven to be efficient in filtering such deviations. In this report algorithms are presented that take as input a slice of a low-quality reconstruction of the volume in question and aim to map it to the reconstruction that is considered ideal, the ground truth. Above that, the first system comprises two additional elements: firstly, it ensures the consistency with the measured sinogram, secondly it adheres to constraints proposed in classical compressive sampling theory. The second one, inspired by classical ways of solving the inverse problem of reconstruction, takes an iterative approach to regularise the hypothesis in the direction of the correct result.