# Multi-Scale Learned Iterative Reconstruction

arXiv: https://arxiv.org/abs/1908.00936 · HTML: https://mlsanity.com/paper/1908.00936/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2019.

## Built on by (most cited first, 18 of 18)

- [DEFT: Efficient Fine-Tuning of Diffusion Models by Learning the Generalised h-transform](https://mlsanity.com/paper/2406.01781/) (2024)
- [Learning the optimal Tikhonov regularizer for inverse problems](https://mlsanity.com/paper/2106.06513/) (2021)
- [Learning Regularization Functionals for Inverse Problems: A Comparative Study](https://mlsanity.com/paper/2510.01755/) (2025)
- [Geometry-Aware Attenuation Learning for Sparse-View CBCT Reconstruction](https://mlsanity.com/paper/2303.14739/) (2023)
- [Graph Convolutional Networks for Model-Based Learning in Nonlinear Inverse Problems](https://mlsanity.com/paper/2103.15138/) (2021)
- [Model-corrected learned primal-dual models for fast limited-view photoacoustic tomography](https://mlsanity.com/paper/2304.01963/) (2023)
- [Glimpse: Generalized Locality for Scalable and Robust CT](https://mlsanity.com/paper/2401.00816/) (2024)
- [3D helical CT Reconstruction with a Memory Efficient Learned Primal-Dual Architecture](https://mlsanity.com/paper/2205.11952/) (2022)
- [Learned Cone-Beam CT Reconstruction Using Neural Ordinary Differential Equations](https://mlsanity.com/paper/2201.07562/) (2022)
- [Deep Learning for Material Decomposition in Photon-Counting CT](https://mlsanity.com/paper/2208.03360/) (2022)
- [Unsupervised denoising for sparse multi-spectral computed tomography](https://mlsanity.com/paper/2211.01159/) (2022)
- [Learned Interferometric Imaging for the SPIDER Instrument](https://mlsanity.com/paper/2301.10260/) (2023)
- [Retrospective Motion Correction in Gradient Echo MRI by Explicit Motion Estimation Using Deep CNNs](https://mlsanity.com/paper/2303.17239/) (2023)
- [Learned reconstruction methods for inverse problems: sample error estimates](https://mlsanity.com/paper/2312.14078/) (2023)
- [Regularizing Instabilities in Image Reconstruction Arising from Learned Denoisers](https://mlsanity.com/paper/2108.13551/) (2021)
- [Enhanced artificial intelligence-based diagnosis using CBCT with internal denoising: Clinical validation for discrimination of fungal ball, sinusitis, and normal cases in the maxillary sinus](https://mlsanity.com/paper/2211.15950/) (2022)
- [Equivariant Multiscale Learned Invertible Reconstruction for Cone Beam CT](https://mlsanity.com/paper/2401.11256/) (2024)
- [Equivariant Multiscale Learned Invertible Reconstruction for Cone Beam CT: From Simulated to Real Data](https://mlsanity.com/paper/2512.21180/) (2025)

## Abstract (arXiv)

Model-based learned iterative reconstruction methods have recently been shown to outperform classical reconstruction algorithms. Applicability of these methods to large scale inverse problems is however limited by the available memory for training and extensive training times, the latter due to computationally expensive forward models. As a possible solution to these restrictions we propose a multi-scale learned iterative reconstruction scheme that computes iterates on discretisations of increasing resolution. This procedure does not only reduce memory requirements, it also considerably speeds up reconstruction and training times, but most importantly is scalable to large scale inverse problems with non-trivial forward operators, such as those that arise in many 3D tomographic applications. In particular, we propose a hybrid network that combines the multi-scale iterative approach with a particularly expressive network architecture which in combination exhibits excellent scalability in 3D. Applicability of the algorithm is demonstrated for 3D cone beam computed tomography from real measurement data of an organic phantom. Additionally, we examine scalability and reconstruction quality in comparison to established learned reconstruction methods in two dimensions for low dose computed tomography on human phantoms.