# Highly accelerated MR parametric mapping by undersampling the k-space and reducing the contrast number simultaneously with deep learning

arXiv: https://arxiv.org/abs/2112.00730 · HTML: https://mlsanity.com/paper/2112.00730/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 82 references that are in the map.
- First posted on arXiv 1 Dec 2021.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://mlsanity.com/paper/1707.02921/) (2017)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [Blind Super-Resolution With Iterative Kernel Correction](https://mlsanity.com/paper/1904.03377/) (2019)
- [DIFFnet: Diffusion parameter mapping network generalized for input diffusion gradient schemes and bvalues](https://mlsanity.com/paper/2102.02463/) (2021)
- [SANTIS: Sampling-Augmented Neural neTwork with Incoherent Structure for MR image reconstruction](https://mlsanity.com/paper/1812.03278/) (2018)
- [Spatially Regularized Parametric Map Reconstruction for Fast Magnetic Resonance Fingerprinting](https://mlsanity.com/paper/1911.03786/) (2019)

## Built on by (most cited first, 2 of 2)

- [An Uncertainty Aided Framework for Learning based Liver T1ρ Mapping and Analysis](https://mlsanity.com/paper/2307.02736/) (2023)
- [Advancing MRI Reconstruction: A Systematic Review of Deep Learning and Compressed Sensing Integration](https://mlsanity.com/paper/2501.14158/) (2025)

## Abstract (arXiv)

Purpose: To propose a novel deep learning-based method called RG-Net (reconstruction and generation network) for highly accelerated MR parametric mapping by undersampling k-space and reducing the acquired contrast number simultaneously. Methods: The proposed framework consists of a reconstruction module and a generative module. The reconstruction module reconstructs MR images from the acquired few undersampled k-space data with the help of a data prior. The generative module then synthesizes the remaining multi-contrast images from the reconstructed images, where the exponential model is implicitly incorporated into the image generation through the supervision of fully sampled labels. The RG-Net was evaluated on the T1rho mapping data of knee and brain at different acceleration rates. Regional T1rho analysis for cartilage and the brain was performed to access the performance of RG-Net. Results: RG-Net yields a high-quality T1rho map at a high acceleration rate of 17. Compared with the competing methods that only undersample k-space, our framework achieves better performance in T1rho value analysis. Our method also improves quality of T1rho maps on patient with glioma. Conclusion: The proposed RG-Net that adopted a new strategy by undersampling k-space and reducing the contrast number simultaneously for fast MR parametric mapping, can achieve a high acceleration rate while maintaining good reconstruction quality. The generative module of our framework can also be used as an insert module in other fast MR parametric mapping methods. Keywords: Deep learning, convolutional neural network, fast MR parametric mapping