# Wave-Encoded Model-based Deep Learning for Highly Accelerated Imaging with Joint Reconstruction

arXiv: https://arxiv.org/abs/2202.02814 · HTML: https://mlsanity.com/paper/2202.02814/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 48 references that are in the map.
- First posted on arXiv 6 Feb 2022.

## Builds on (most cited first, 8 of 8)

- [MoDL: Model Based Deep Learning Architecture for Inverse Problems](https://mlsanity.com/paper/1712.02862/) (2017)
- [Learning a Variational Network for Reconstruction of Accelerated MRI Data](https://mlsanity.com/paper/1704.00447/) (2017)
- [LORAKI: Autocalibrated Recurrent Neural Networks for Autoregressive MRI Reconstruction in k-Space](https://mlsanity.com/paper/1904.09390/) (2019)
- [Joint multi-contrast Variational Network reconstruction (jVN) with application to rapid 2D and 3D imaging](https://mlsanity.com/paper/1910.03273/) (2019)
- [MoDL-MUSSELS: Model-Based Deep Learning for Multi-Shot Sensitivity Encoded Diffusion MRI](https://mlsanity.com/paper/1812.08115/) (2018)
- [Off-the-Grid Recovery of Piecewise Constant Images from Few Fourier Samples](https://mlsanity.com/paper/1510.00384/) (2015)
- [Scan-specific, Parameter-free Artifact Reduction in K-space (SPARK)](https://mlsanity.com/paper/1911.07219/) (2019)
- [Highly Accelerated EPI with Wave Encoding and Multi-shot Simultaneous Multi-Slice Imaging](https://mlsanity.com/paper/2106.01918/) (2021)

## Abstract (arXiv)

Purpose: To propose a wave-encoded model-based deep learning (wave-MoDL) strategy for highly accelerated 3D imaging and joint multi-contrast image reconstruction, and further extend this to enable rapid quantitative imaging using an interleaved look-locker acquisition sequence with T2 preparation pulse (3D-QALAS). Method: Recently introduced MoDL technique successfully incorporates convolutional neural network (CNN)-based regularizers into physics-based parallel imaging reconstruction using a small number of network parameters. Wave-CAIPI is an emerging parallel imaging method that accelerates the imaging speed by employing sinusoidal gradients in the phase- and slice-encoding directions during the readout to take better advantage of 3D coil sensitivity profiles. In wave-MoDL, we propose to combine the wave-encoding strategy with unrolled network constraints to accelerate the acquisition speed while enforcing wave-encoded data consistency. We further extend wave-MoDL to reconstruct multi-contrast data with controlled aliasing in parallel imaging (CAIPI) sampling patterns to leverage similarity between multiple images to improve the reconstruction quality. Result: Wave-MoDL enables a 47-second MPRAGE acquisition at 1 mm resolution at 16-fold acceleration. For quantitative imaging, wave-MoDL permits a 2-minute acquisition for T1, T2, and proton density mapping at 1 mm resolution at 12-fold acceleration, from which contrast weighted images can be synthesized as well. Conclusion: Wave-MoDL allows rapid MR acquisition and high-fidelity image reconstruction and may facilitate clinical and neuroscientific applications by incorporating unrolled neural networks into wave-CAIPI reconstruction.