# SANTIS: Sampling-Augmented Neural neTwork with Incoherent Structure for MR image reconstruction

arXiv: https://arxiv.org/abs/1812.03278 · HTML: https://mlsanity.com/paper/1812.03278/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2018.

## Built on by (most cited first, 10 of 10)

- [AI-based Reconstruction for Fast MRI -- A Systematic Review and Meta-analysis](https://mlsanity.com/paper/2112.12744/) (2021)
- [Data augmentation for deep learning based accelerated MRI reconstruction with limited data](https://mlsanity.com/paper/2106.14947/) (2021)
- [Learning Fourier-Constrained Diffusion Bridges for MRI Reconstruction](https://mlsanity.com/paper/2308.01096/) (2023)
- [A review and experimental evaluation of deep learning methods for MRI reconstruction](https://mlsanity.com/paper/2109.08618/) (2021)
- [On Real-time Image Reconstruction with Neural Networks for MRI-guided Radiotherapy](https://mlsanity.com/paper/2202.05267/) (2022)
- [Paired Conditional Generative Adversarial Network for Highly Accelerated Liver 4D MRI](https://mlsanity.com/paper/2405.12357/) (2024)
- [A Brief Overview of Optimization-Based Algorithms for MRI Reconstruction Using Deep Learning](https://mlsanity.com/paper/2406.02626/) (2024)
- [Highly accelerated MR parametric mapping by undersampling the k-space and reducing the contrast number simultaneously with deep learning](https://mlsanity.com/paper/2112.00730/) (2021)
- [Generalized Deep Learning-based Proximal Gradient Descent for MR Reconstruction](https://mlsanity.com/paper/2211.16881/) (2022)
- [LSST: Learned Single-Shot Trajectory and Reconstruction Network for MR Imaging](https://mlsanity.com/paper/2409.07457/) (2024)

## Abstract (arXiv)

Deep learning holds great promise in the reconstruction of undersampled Magnetic Resonance Imaging (MRI) data, providing new opportunities to escalate the performance of rapid MRI. In existing deep learning-based reconstruction methods, supervised training is performed using artifact-free reference images and their corresponding undersampled pairs. The undersampled images are generated by a fixed undersampling pattern in the training, and the trained network is then applied to reconstruct new images acquired with the same pattern in the inference. While such a training strategy can maintain a favorable reconstruction for a pre-selected undersampling pattern, the robustness of the trained network against any discrepancy of undersampling schemes is typically poor. We developed a novel deep learning-based reconstruction framework called SANTIS for efficient MR image reconstruction with improved robustness against sampling pattern discrepancy. SANTIS uses a data cycle-consistent adversarial network combining efficient end-to-end convolutional neural network mapping, data fidelity enforcement and adversarial training for reconstructing accelerated MR images more faithfully. A training strategy employing sampling augmentation with extensive variation of undersampling patterns was further introduced to promote the robustness of the trained network. Compared to conventional reconstruction and standard deep learning methods, SANTIS achieved consistent better reconstruction performance, with lower errors, greater image sharpness and higher similarity with respect to the reference regardless of the undersampling patterns during inference. This novel concept behind SANTIS can particularly be useful towards improving the robustness of deep learning-based image reconstruction against discrepancy between training and evaluation, which is currently an important but less studied open question.