# Multi-Channel Convolutional Analysis Operator Learning for Dual-Energy CT Reconstruction

arXiv: https://arxiv.org/abs/2203.05968 · HTML: https://mlsanity.com/paper/2203.05968/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 51 references that are in the map.
- First posted on arXiv 10 Mar 2022.

## Builds on (most cited first, 10 of 10)

- [On instabilities of deep learning in image reconstruction - Does AI come at a cost?](https://mlsanity.com/paper/1902.05300/) (2019)
- [Image Reconstruction: From Sparsity to Data-adaptive Methods and Machine Learning](https://mlsanity.com/paper/1904.02816/) (2019)
- [Coupled Dictionary Learning for Multi-contrast MRI Reconstruction](https://mlsanity.com/paper/1806.09930/) (2018)
- [PWLS-ULTRA: An Efficient Clustering and Learning-Based Approach for Low-Dose 3D CT Image Reconstruction](https://mlsanity.com/paper/1703.09165/) (2017)
- [Convolutional Analysis Operator Learning: Acceleration and Convergence](https://mlsanity.com/paper/1802.05584/) (2018)
- [Low-dose spectral CT reconstruction using L0 image gradient and tensor dictionary](https://mlsanity.com/paper/1801.01452/) (2017)
- [Convolutional Dictionary Learning: Acceleration and Convergence](https://mlsanity.com/paper/1707.00389/) (2017)
- [Efficient Sum of Outer Products Dictionary Learning (SOUP-DIL) and Its Application to Inverse Problems](https://mlsanity.com/paper/1511.06333/) (2015)
- [Synergistic Multi-spectral CT Reconstruction with Directional Total Variation](https://mlsanity.com/paper/2101.01834/) (2021)
- [Online Convolutional Dictionary Learning for Multimodal Imaging](https://mlsanity.com/paper/1706.04256/) (2017)

## Built on by (most cited first, 1 of 1)

- [Multibranch Generative Models for Multichannel Imaging with an Application to PET/CT Synergistic Reconstruction](https://mlsanity.com/paper/2404.08748/) (2024)

## Abstract (arXiv)

Objective. Dual-energy computed tomography (DECT) has the potential to improve contrast, reduce artifacts and the ability to perform material decomposition in advanced imaging applications. The increased number or measurements results with a higher radiation dose and it is therefore essential to reduce either number of projections per energy or the source X-ray intensity, but this makes tomographic reconstruction more ill-posed. Approach. We developed the multi-channel convolutional analysis operator learning (MCAOL) method to exploit common spatial features within attenuation images at different energies and we propose an optimization method which jointly reconstructs the attenuation images at low and high energies with a mixed norm regularization on the sparse features obtained by pre-trained convolutional filters through the convolutional analysis operator learning (CAOL) algorithm. Main results. Extensive experiments with simulated and real computed tomography (CT) data were performed to validate the effectiveness of the proposed methods and we reported increased reconstruction accuracy compared to CAOL and iterative methods with single and joint total-variation (TV) regularization. Significance. Qualitative and quantitative results on sparse-views and low-dose DECT demonstrate that the proposed MCAOL method outperforms both CAOL applied on each energy independently and several existing state-of-the-art model-based iterative reconstruction (MBIR) techniques, thus paving the way for dose reduction.