  Background and Objectives: Cardiovascular magnetic resonance (CMR) imaging is
a powerful modality in functional and anatomical assessment for various
cardiovascular diseases. Sufficient image quality is essential to achieve
proper diagnosis and treatment. A large number of medical images, the variety
of imaging artefacts, and the workload of imaging centres are among the things
that reveal the necessity of automatic image quality assessment (IQA). However,
automated IQA requires access to bulk annotated datasets for training deep
learning (DL) models. Labelling medical images is a tedious, costly and
time-consuming process, which creates a fundamental challenge in proposing
DL-based methods for medical applications. This study aims to present a new
method for CMR IQA when there is limited access to annotated datasets. Methods:
The proposed generalised deep meta-learning model can evaluate the quality by
learning tasks in the prior stage and then fine-tuning the resulting model on a
small labelled dataset of the desired tasks. This model was evaluated on the
data of over 6,000 subjects from the UK Biobank for five defined tasks,
including detecting respiratory motion, cardiac motion, Aliasing and Gibbs
ringing artefacts and images without artefacts. Results: The results of
extensive experiments show the superiority of the proposed model. Besides,
comparing the model's accuracy with the domain adaptation model indicates a
significant difference by using only 64 annotated images related to the desired
tasks. Conclusion: The proposed model can identify unknown artefacts in images
with acceptable accuracy, which makes it suitable for medical applications and
quality assessment of large cohorts.
