CMRxRecon2026: Towards Clinical Adoption of Ultra-Fast 4D Flow MRI
Creators: name: Li, Hao
name: Wang, Chengyan
name: Markl, Michael
name: Prieto, Claudia
name: Wang, He
name: Wang, Bingyi
name: Chen, Zhensen
name: Guo, Xin
name: Qin, Chen
name: Qu, Xiaobo
name: Zhang, Haoyu
name: Huang, Mingkai
name: Yang, Guang
name: Wang, Zi
name: Wang, Fanwen
name: Young, Alistair
name: Li, Yan
name: Ouyang, Cheng
name: Qi, Haikun
name: Zhang, Yajing
name: Qin, Jing
name: Zhuang, Xiahai
name: Zhu, Jiayu
name: Hu, Junpu
name: Hu, Chenxi
name: Shi, Zhang
name: Zhang, Yizhe
name: Hua, Sha
name: Wu, Lianming
name: Xu, Ziqiang
name: Wang, Shuo
Publisher: name: Zenodo
Year: 2025
Subjects: 4D flow
MRI reconstruction
high acceleration
computational efficiency
hemodynamic
MICCAI 2026 challenge

description: Four-dimensional (4D) flow MR imaging is a cutting-edge technique for visualizing and quantifying cardiovascular hemodynamics, providing high-resolution spatiotemporal information on blood flow patterns.It allows for the assessment of critical hemodynamic parameters, such as flow velocity, flow rate, wall shear stress, and vorticity, providing critical insights for cardiovascular diagnosis, treatment evaluation, and hemodynamic research. MR 4D flow has broad potential applications, including assessing aortic stenosis, aneurysms, and ventricular function, as well as guiding surgical planning and monitoring postoperative recovery.

Currently, the clinical application of 4D flow MR is constrained by prolonged acquisition times and computational complexity. Without acceleration techniques, a full cardiac cycle typically requires 30~60 minutes to capture, making it impractical for routine clinical workflows. Even with existing acceleration methods, such as parallel imaging and compressed sensing, scan times are often reduced to 10~20 minutes—still longer than the clinically desired range of within 5 minutes.

Furthermore, reconstruction of undersampled 4D flow datasets remains highly computationally intensive. The large data volumes and high acceleration factors demand substantial resources, limiting the feasibility of routine clinical use, especially in hardware-constrained environments. Developing resource-efficient reconstruction methods is critical to overcome this barrier and enable broader clinical adoption of 4D flow technology. In addition to these challenges, there remains a significant gap in large-scale, standardized public datasets and challenges focused on 4D flow reconstruction. To our knowledge, there is currently no open challenge relating to 4D flow imaging, as most existing 4D flow reconstruction studies primarily rely on in-house data from either healthy individuals or simulations. This limitation hinders the validation of algorithms in real-world clinical scenarios, particularly for cases with diverse and complex flow patterns. Establishing an open dataset with cases of different diseases, such as stenosis, regurgitation, aneurysms, and dissections, is critical for testing algorithm robustness and adaptability.

This challenge builds on the success of previous efforts (CMRxRecon2023, CMRxRecon2024, and CMRxRecon2025) in advancing multi-contrast cardiac imaging reconstruction. Unlike prior challenges that focused on conventional 2D and 3D cardiac imaging, the proposed challenge shifts its focus to the unique and complex domain of 4D flow imaging. With its inclusion of both 3D spatial and 1D temporal dimensions, 4D flow presents significantly higher data dimensionality and computational demands.Moreover, unlike conventional structural imaging, which primarily emphasizes magnitude accuracy, 4D flow critically depends on precise phase reconstruction for calculating blood velocities. The focus on both magnitude and phase reconstruction accuracy, combined with the greater acceleration potential of 4D flow, presents new opportunities and challenges for advancing cutting-edge reconstruction techniques.

The dataset will be collected from 2~3 clinical centers and will comprise more than 200 cases, including a diverse range of diseases, such as aortic stenosis, regurgitation, aneurysms, coarctation, and dissections. This diversity ensures the simulation of real-world scenarios for robust algorithm evaluation. Participants of this challenge will need to address two key objectives:



Accurate reconstruction under ultra-high acceleration factors: Develop algorithms capable of restoring high-quality images (including magnitude and phase) from highly undersampled data while ensuring the reliability of hemodynamic parameters.

Fast and accurate reconstruction with limited computational resources: Develop resource-efficient solutions that enable rapid reconstruction in hardware-constrained environments, making the technology viable for real-world clinical practice.