Detecting Respiratory Motion Artefacts for Cardiovascular MRIs to Ensure High-Quality Segmentation

Amin Ranem; John Kalkhof; Caner Özer; Anirban Mukhopadhyay; Ilkay Oksuz

doi:10.1007/978-3-031-23443-9_42

Detecting Respiratory Motion Artefacts for Cardiovascular MRIs to Ensure High-Quality Segmentation

Amin Ranem^*, John Kalkhof, Caner Özer, Anirban Mukhopadhyay, Ilkay Oksuz

^*Corresponding author for this work

Department of Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Statistical Atlases and Computational Models of the Heart. Regular and CMRxMotion Challenge Papers - 13th International Workshop, STACOM 2022, Held in Conjunction with MICCAI 2022, Revised Selected Papers
Editors	Oscar Camara, Esther Puyol-Antón, Avan Suinesiaputra, Alistair Young, Chen Qin, Maxime Sermesant, Shuo Wang
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	447-456
Number of pages	10
ISBN (Print)	9783031234422
DOIs	https://doi.org/10.1007/978-3-031-23443-9_42
Publication status	Published - 2022
Event	13th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2022, held in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 - Singapore, Singapore Duration: 18 Sept 2022 → 18 Sept 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13593 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2022, held in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022
Country/Territory	Singapore
City	Singapore
Period	18/09/22 → 18/09/22

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Funding

Acknowledgements. This paper has been produced benefiting from the 2232 International Fellowship for Outstanding Researchers Program of TUBITAK (Project No: 118C353). However, the entire responsibility of the publication/paper belongs to the owner of the paper. The financial support received from TUBITAK does not mean that the content of the publication is approved in a scientific sense by TUBITAK.

Funders	Funder number
Türkiye Bilimsel ve Teknolojik Araştırma Kurumu	118C353

Keywords

Cardiovascular MRI
Image quality assessment
Respiratory motion artefact detection
Semantic segmentation

Access to Document

10.1007/978-3-031-23443-9_42

Cite this

Ranem, A., Kalkhof, J., Özer, C., Mukhopadhyay, A., & Oksuz, I. (2022). Detecting Respiratory Motion Artefacts for Cardiovascular MRIs to Ensure High-Quality Segmentation. In O. Camara, E. Puyol-Antón, A. Suinesiaputra, A. Young, C. Qin, M. Sermesant, & S. Wang (Eds.), Statistical Atlases and Computational Models of the Heart. Regular and CMRxMotion Challenge Papers - 13th International Workshop, STACOM 2022, Held in Conjunction with MICCAI 2022, Revised Selected Papers (pp. 447-456). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13593 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-23443-9_42

Ranem, Amin ; Kalkhof, John ; Özer, Caner et al. / Detecting Respiratory Motion Artefacts for Cardiovascular MRIs to Ensure High-Quality Segmentation. Statistical Atlases and Computational Models of the Heart. Regular and CMRxMotion Challenge Papers - 13th International Workshop, STACOM 2022, Held in Conjunction with MICCAI 2022, Revised Selected Papers. editor / Oscar Camara ; Esther Puyol-Antón ; Avan Suinesiaputra ; Alistair Young ; Chen Qin ; Maxime Sermesant ; Shuo Wang. Springer Science and Business Media Deutschland GmbH, 2022. pp. 447-456 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{a55133a1f3f8428b8546c12fb338fa91,

title = "Detecting Respiratory Motion Artefacts for Cardiovascular MRIs to Ensure High-Quality Segmentation",

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.",

keywords = "Cardiovascular MRI, Image quality assessment, Respiratory motion artefact detection, Semantic segmentation",

author = "Amin Ranem and John Kalkhof and Caner {\"O}zer and Anirban Mukhopadhyay and Ilkay Oksuz",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 13th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2022, held in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 ; Conference date: 18-09-2022 Through 18-09-2022",

year = "2022",

doi = "10.1007/978-3-031-23443-9_42",

language = "English",

isbn = "9783031234422",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "447--456",

editor = "Oscar Camara and Esther Puyol-Ant{\'o}n and Avan Suinesiaputra and Alistair Young and Chen Qin and Maxime Sermesant and Shuo Wang",

booktitle = "Statistical Atlases and Computational Models of the Heart. Regular and CMRxMotion Challenge Papers - 13th International Workshop, STACOM 2022, Held in Conjunction with MICCAI 2022, Revised Selected Papers",

address = "Germany",

}

Ranem, A, Kalkhof, J, Özer, C, Mukhopadhyay, A & Oksuz, I 2022, Detecting Respiratory Motion Artefacts for Cardiovascular MRIs to Ensure High-Quality Segmentation. in O Camara, E Puyol-Antón, A Suinesiaputra, A Young, C Qin, M Sermesant & S Wang (eds), Statistical Atlases and Computational Models of the Heart. Regular and CMRxMotion Challenge Papers - 13th International Workshop, STACOM 2022, Held in Conjunction with MICCAI 2022, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13593 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 447-456, 13th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2022, held in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, Singapore, Singapore, 18/09/22. https://doi.org/10.1007/978-3-031-23443-9_42

Detecting Respiratory Motion Artefacts for Cardiovascular MRIs to Ensure High-Quality Segmentation. / Ranem, Amin; Kalkhof, John; Özer, Caner et al.
Statistical Atlases and Computational Models of the Heart. Regular and CMRxMotion Challenge Papers - 13th International Workshop, STACOM 2022, Held in Conjunction with MICCAI 2022, Revised Selected Papers. ed. / Oscar Camara; Esther Puyol-Antón; Avan Suinesiaputra; Alistair Young; Chen Qin; Maxime Sermesant; Shuo Wang. Springer Science and Business Media Deutschland GmbH, 2022. p. 447-456 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13593 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Detecting Respiratory Motion Artefacts for Cardiovascular MRIs to Ensure High-Quality Segmentation

AU - Ranem, Amin

AU - Kalkhof, John

AU - Özer, Caner

AU - Mukhopadhyay, Anirban

AU - Oksuz, Ilkay

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - Cardiovascular MRI

KW - Image quality assessment

KW - Respiratory motion artefact detection

KW - Semantic segmentation

UR - http://www.scopus.com/inward/record.url?scp=85148013037&partnerID=8YFLogxK

U2 - 10.1007/978-3-031-23443-9_42

DO - 10.1007/978-3-031-23443-9_42

M3 - Conference contribution

AN - SCOPUS:85148013037

SN - 9783031234422

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 447

EP - 456

BT - Statistical Atlases and Computational Models of the Heart. Regular and CMRxMotion Challenge Papers - 13th International Workshop, STACOM 2022, Held in Conjunction with MICCAI 2022, Revised Selected Papers

A2 - Camara, Oscar

A2 - Puyol-Antón, Esther

A2 - Suinesiaputra, Avan

A2 - Young, Alistair

A2 - Qin, Chen

A2 - Sermesant, Maxime

A2 - Wang, Shuo

PB - Springer Science and Business Media Deutschland GmbH

T2 - 13th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2022, held in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022

Y2 - 18 September 2022 through 18 September 2022

ER -

Ranem A, Kalkhof J, Özer C, Mukhopadhyay A, Oksuz I. Detecting Respiratory Motion Artefacts for Cardiovascular MRIs to Ensure High-Quality Segmentation. In Camara O, Puyol-Antón E, Suinesiaputra A, Young A, Qin C, Sermesant M, Wang S, editors, Statistical Atlases and Computational Models of the Heart. Regular and CMRxMotion Challenge Papers - 13th International Workshop, STACOM 2022, Held in Conjunction with MICCAI 2022, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2022. p. 447-456. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-23443-9_42