No, the dataset is only available to participants during the challenge to ensure enough submissions.

Each team can consist of up to 6 people. The authors in the submitted paper should be the same as the team member list.

No, only the challenge dataset is allowed for developing the reconstruction algorithms.

Data augmentation based on the training dataset is allowed.

No, any manual interventions are not allowed, including manual annotation of unlabeled images.

No, any manual interventions are not allowed, including manual selection of high-quality pseudo labels for unlabeled images.

No, the validation submission does not affect the final ranking.

Yes, modifications can be made before the testing submission, but not after.

 You can choose to calculate and save results for both multi-channel and single-channel data in their respective folders, or you can run only one of them. Our final evaluation score will be based on the higher value between the two.

Your model should output results for all acceleration factors (4x, 8x, and 10x). We will evaluate the results for each acceleration factor and use the average value as the final result.

Our original data does not provide a sensitivity map, but the central 24 lines of our k-space are fully sampled and can be used as calibration lines for GRAPPA reconstruction. Alternatively, you can use the "myESP_SENSEmap.m" code provided at https://github.com/CmrxRecon/CMRxRecon/tree/main/CMRxReconDemo .

We recommend zero-padding the k-space data to a consistent size for reconstruction, and then cropping them back to its original dimensions.

For cine, the evaluations should be performed on the entire image. For mapping, we will provide segmentation labels for the myocardium, and only the mapping parameter values within the segmented region should be used to calculate the RMSE as the ranking metric.

Please follow the Submission example we provided and save the results in .mat format, with the data dimensions consistent with the original data (without the coil dimension). During the validation phase, to reduce file size, please use the "run4Ranking.m" code provided to crop the image data and save it with the same directory structure. All file names should match the original data names. For more details, please refer to the SubmissionFormat.txt file.

We do not require teams to upload the training code. Only the inference code's Docker is needed for participation.

It is not required.

You can use different models, but the code should automatically determine the model to use. During the testing phase, we only allow reading the root directory path at once.

You can use different models, but the code should automatically determine the model to use. During the testing phase, we only allow reading the root directory path at once.

You can use different models, but the code should automatically determine the model to use. During the testing phase, we only allow reading the root directory path at once.

We do not restrict any data preprocessing, and we only evaluate the final reconstruction effect. However, the total running time for all data in each task cannot exceed 4 hours.

No, it can be done but is not mandatory.

It can be done but is not mandatory.. We only evaluate the test results and do not have specific requirements for the process.

Both options are acceptable.

It is not mandatory.

No, there is no need to upload them. We will perform the fitting calculations during the evaluation.