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

TL;DR

SANTIS is a deep learning framework for MRI reconstruction that enhances robustness to different undersampling patterns through sampling augmentation and adversarial training, leading to more accurate and consistent images.

Contribution

Introduces SANTIS, a novel adversarial network with sampling augmentation, improving robustness of MRI reconstruction across varying undersampling schemes.

Findings

SANTIS outperforms traditional methods with lower errors.

Achieves higher image sharpness and similarity to reference images.

Demonstrates robustness against sampling pattern discrepancies.

Abstract

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.