# JoJoNet: Joint-contrast and Joint-sampling-and-reconstruction Network for Multi-contrast MRI arXiv: https://arxiv.org/abs/2210.12548 ยท HTML: https://mlsanity.com/paper/2210.12548/ ## Key facts - Cited by 3 papers in the map (data through 14 Oct 2026). - Builds on 31 of its 49 references that are in the map. - First posted on arXiv 22 Oct 2022. ## Builds on (most cited first, 30 of 31) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019) - [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015) - [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014) - [fastMRI: An Open Dataset and Benchmarks for Accelerated MRI](https://mlsanity.com/paper/1811.08839/) (2018) - [Learning a Variational Network for Reconstruction of Accelerated MRI Data](https://mlsanity.com/paper/1704.00447/) (2017) - [Compressed Sensing using Generative Models](https://mlsanity.com/paper/1703.03208/) (2017) - [A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction](https://mlsanity.com/paper/1704.02422/) (2017) - [Image reconstruction by domain transform manifold learning](https://mlsanity.com/paper/1704.08841/) (2017) - [End-to-End Variational Networks for Accelerated MRI Reconstruction](https://mlsanity.com/paper/2004.06688/) (2020) - [Deep learning for undersampled MRI reconstruction](https://mlsanity.com/paper/1709.02576/) (2017) - [Compressed Sensing MRI Reconstruction using a Generative Adversarial Network with a Cyclic Loss](https://mlsanity.com/paper/1709.00753/) (2017) - [DuDoRNet: Learning a Dual-Domain Recurrent Network for Fast MRI Reconstruction with Deep T1 Prior](https://mlsanity.com/paper/2001.03799/) (2020) - [Reducing Uncertainty in Undersampled MRI Reconstruction with Active Acquisition](https://mlsanity.com/paper/1902.03051/) (2019) - [Active MR k-space Sampling with Reinforcement Learning](https://mlsanity.com/paper/2007.10469/) (2020) - [GrappaNet: Combining Parallel Imaging with Deep Learning for Multi-Coil MRI Reconstruction](https://mlsanity.com/paper/1910.12325/) (2019) - [Experimental design for MRI by greedy policy search](https://mlsanity.com/paper/2010.16262/) (2020) - [Learning-based Optimization of the Under-sampling Pattern in MRI](https://mlsanity.com/paper/1901.01960/) (2019) - [Recurrent U-Net for Resource-Constrained Segmentation](https://mlsanity.com/paper/1906.04913/) (2019) - [Self-Supervised Deep Active Accelerated MRI](https://mlsanity.com/paper/1901.04547/) (2019) - [Learning the Sampling Pattern for MRI](https://mlsanity.com/paper/1906.08754/) (2019) - [Coupled Dictionary Learning for Multi-contrast MRI Reconstruction](https://mlsanity.com/paper/1806.09930/) (2018) - [Multi-Modal MRI Reconstruction Assisted with Spatial Alignment Network](https://mlsanity.com/paper/2108.05603/) (2021) - [Deep Generative Adversarial Networks for Compressed Sensing Automates MRI](https://mlsanity.com/paper/1706.00051/) (2017) - [Joint learning of cartesian undersampling and reconstruction for accelerated MRI](https://mlsanity.com/paper/1905.09324/) (2019) - [Wasserstein GANs for MR Imaging: from Paired to Unpaired Training](https://mlsanity.com/paper/1910.07048/) (2019) - [Adversarial and Perceptual Refinement for Compressed Sensing MRI Reconstruction](https://mlsanity.com/paper/1806.11216/) (2018) - [End-to-End Sequential Sampling and Reconstruction for MRI](https://mlsanity.com/paper/2105.06460/) (2021) - [Multi-Contrast MRI Reconstruction with Structure-Guided Total Variation](https://mlsanity.com/paper/1511.06631/) (2015) - [Scalable Learning-Based Sampling Optimization for Compressive Dynamic MRI](https://mlsanity.com/paper/1902.00386/) (2019) ## Built on by (most cited first, 3 of 3) - [Learning Task-Specific Strategies for Accelerated MRI](https://mlsanity.com/paper/2304.12507/) (2023) - [SGSR: Structure-Guided Multi-Contrast MRI Super-Resolution via Spatio-Frequency Co-Query Attention](https://mlsanity.com/paper/2408.03194/) (2024) - [Dual-Domain Multi-Contrast MRI Reconstruction with Synthesis-based Fusion Network](https://mlsanity.com/paper/2312.00661/) (2023) ## Abstract (arXiv) Multi-contrast Magnetic Resonance Imaging (MRI) generates multiple medical images with rich and complementary information for routine clinical use; however, it suffers from a long acquisition time. Recent works for accelerating MRI, mainly designed for single contrast, may not be optimal for multi-contrast scenario since the inherent correlations among the multi-contrast images are not exploited. In addition, independent reconstruction of each contrast usually does not translate to optimal performance of downstream tasks. Motivated by these aspects, in this paper we design an end-to-end framework for accelerating multi-contrast MRI which simultaneously optimizes the entire MR imaging workflow including sampling, reconstruction and downstream tasks to achieve the best overall outcomes. The proposed framework consists of a sampling mask generator for each image contrast and a reconstructor exploiting the inter-contrast correlations with a recurrent structure which enables the information sharing in a holistic way. The sampling mask generator and the reconstructor are trained jointly across the multiple image contrasts. The acceleration ratio of each image contrast is also learnable and can be driven by a downstream task performance. We validate our approach on a multi-contrast brain dataset and a multi-contrast knee dataset. Experiments show that (1) our framework consistently outperforms the baselines designed for single contrast on both datasets; (2) our newly designed recurrent reconstruction network effectively improves the reconstruction quality for multi-contrast images; (3) the learnable acceleration ratio improves the downstream task performance significantly. Overall, this work has potentials to open up new avenues for optimizing the entire multi-contrast MR imaging workflow.