Detecting respiratory motion artefacts for cardiovascular MRIs to ensure high-quality segmentation

TL;DR

This paper introduces a workflow that predicts respiratory motion severity in cardiovascular MRI scans, enabling immediate quality assessment and reducing the need for re-acquisition, thereby improving diagnostic reliability.

Contribution

The study presents a novel method for real-time respiratory motion severity prediction in CMR, integrated with segmentation, to ensure high-quality images for accurate diagnosis.

Findings

Effective motion severity prediction during MRI acquisition

Improved image quality assurance before diagnosis

Facilitates immediate re-acquisition of poor-quality images

Abstract

While machine learning approaches perform well on their training domain, they generally tend to fail in a real-world application. In cardiovascular magnetic resonance imaging (CMR), respiratory motion represents a major challenge in terms of acquisition quality and therefore subsequent analysis and final diagnosis. We present a workflow which predicts a severity score for respiratory motion in CMR for the CMRxMotion challenge 2022. This is an important tool for technicians to immediately provide feedback on the CMR quality during acquisition, as poor-quality images can directly be re-acquired while the patient is still available in the vicinity. Thus, our method ensures that the acquired CMR holds up to a specific quality standard before it is used for further diagnosis. Therefore, it enables an efficient base for proper diagnosis without having time and cost-intensive re-acquisitions in cases of severe motion artefacts. Combined with our segmentation model, this can help cardiologists and technicians in their daily routine by providing a complete pipeline to guarantee proper quality assessment and genuine segmentations for cardiovascular scans. The code base is available at https://github.com/MECLabTUDA/QA_med_data/tree/dev_QA_CMRxMotion.