# Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning

arXiv: https://arxiv.org/abs/1810.12185 · HTML: https://mlsanity.com/paper/1810.12185/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2018.

## Built on by (most cited first, 22 of 22)

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- [An efficient semi-supervised quality control system trained using physics-based MRI-artefact generators and adversarial training](https://mlsanity.com/paper/2206.03359/) (2022)
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- [Systematic Review and Meta-analysis of AI-driven MRI Motion Artifact Detection and Correction](https://mlsanity.com/paper/2509.05071/) (2025)
- [Detecting respiratory motion artefacts for cardiovascular MRIs to ensure high-quality segmentation](https://mlsanity.com/paper/2209.09678/) (2022)
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- [Motion-related Artefact Classification Using Patch-based Ensemble and Transfer Learning in Cardiac MRI](https://mlsanity.com/paper/2210.07717/) (2022)
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- [Beyond Pixels: Medical Image Quality Assessment with Implicit Neural Representations](https://mlsanity.com/paper/2508.05168/) (2025)
- [A Deep Learning-based Integrated Framework for Quality-aware Undersampled Cine Cardiac MRI Reconstruction and Analysis](https://mlsanity.com/paper/2205.01673/) (2022)
- [X-DECODE: EXtreme Deblurring with Curriculum Optimization and Domain Equalization](https://mlsanity.com/paper/2504.08072/) (2025)
- [Bridging Single Distortion Artifacts and Multifactorial Clinical Quality: Few-shot Biparametric MRI Quality Assessment via Distortion-trained Prototypical Networks](https://mlsanity.com/paper/2606.18872/) (2026)

## Abstract (arXiv)

Good quality of medical images is a prerequisite for the success of subsequent image analysis pipelines. Quality assessment of medical images is therefore an essential activity and for large population studies such as the UK Biobank (UKBB), manual identification of artefacts such as those caused by unanticipated motion is tedious and time-consuming. Therefore, there is an urgent need for automatic image quality assessment techniques. In this paper, we propose a method to automatically detect the presence of motion-related artefacts in cardiac magnetic resonance (CMR) cine images. We compare two deep learning architectures to classify poor quality CMR images: 1) 3D spatio-temporal Convolutional Neural Networks (3D-CNN), 2) Long-term Recurrent Convolutional Network (LRCN). Though in real clinical setup motion artefacts are common, high-quality imaging of UKBB, which comprises cross-sectional population data of volunteers who do not necessarily have health problems creates a highly imbalanced classification problem. Due to the high number of good quality images compared to the relatively low number of images with motion artefacts, we propose a novel data augmentation scheme based on synthetic artefact creation in k-space. We also investigate a learning approach using a predetermined curriculum based on synthetic artefact severity. We evaluate our pipeline on a subset of the UK Biobank data set consisting of 3510 CMR images. The LRCN architecture outperformed the 3D-CNN architecture and was able to detect 2D+time short axis images with motion artefacts in less than 1ms with high recall. We compare our approach to a range of state-of-the-art quality assessment methods. The novel data augmentation and curriculum learning approaches both improved classification performance achieving overall area under the ROC curve of 0.89.