  Cardiac Magnetic Resonance Imaging (MRI) plays an important role in the
analysis of cardiac function. However, the acquisition is often accompanied by
motion artefacts because of the difficulty of breath-hold, especially for acute
symptoms patients. Therefore, it is essential to assess the quality of cardiac
MRI for further analysis. Time-consuming manual-based classification is not
conducive to the construction of an end-to-end computer aided diagnostic
system. To overcome this problem, an automatic cardiac MRI quality estimation
framework using ensemble and transfer learning is proposed in this work.
Multiple pre-trained models were initialised and fine-tuned on 2-dimensional
image patches sampled from the training data. In the model inference process,
decisions from these models are aggregated to make a final prediction. The
framework has been evaluated on CMRxMotion grand challenge (MICCAI 2022)
dataset which is small, multi-class, and imbalanced. It achieved a
classification accuracy of 78.8% and 70.0% on the training set (5-fold
cross-validation) and a validation set, respectively. The final trained model
was also evaluated on an independent test set by the CMRxMotion organisers,
which achieved the classification accuracy of 72.5% and Cohen's Kappa of 0.6309
(ranked top 1 in this grand challenge). Our code is available on Github:
https://github.com/ruizhe-l/CMRxMotion.
