Skip to main content
SpringerLink
Log in

Künstliche Intelligenz und Radiomics

Stellenwert in der kardialen MRT

Artificial intelligence and radiomics

Value in cardiac MRI

  • Leitthema
  • Published:
Die Radiologie Aims and scope Submit manuscript

Zusammenfassung

Klinisches/methodisches Problem

Kardiale Erkrankungen sind weltweit die führende Todesursache. Viele Erkrankungen können gezielt behandelt werden, sobald eine valide Diagnose gestellt wurde. Die kardiale Magnetresonanztomographie (MRT) hat in der Abklärung zahlreicher kardialer Pathologien einen hohen Stellenwert. Jedoch sind sowohl die Bildakquisition als auch die Befundung und damit zusammenhängende sekundäre Bildauswertungen zeitaufwändig und komplex.

Radiologische Standardverfahren

In den internationalen Leitlinien etabliert sich die kardiale MRT zunehmend in der Evaluation der Herzfunktion und der Differenzialdiagnostik verschiedenster kardialer Erkrankungen.

Methodische Innovationen

Die kardiale MRT besitzt aufgrund der Aufnahmetechnik und der Befundung mit aufwändigen Sekundärmessungen eine eingeschränkte Reproduzierbarkeit. Techniken künstlicher Intelligenz (KI) und Radiomics bieten das Potenzial, die Akquisition, Befundung und Reproduzierbarkeit der kardialen MRT zu verbessern.

Leistungsfähigkeit

Studien zeigen, dass KI und Radiomics-Analysen die kardiale MRT hinsichtlich Bildakquisition, diagnostischer und prognostischer Wertigkeit verbessern können. Zudem konnten mit dieser Herangehensweise neue Biomarker identifiziert werden.

Bewertung und Empfehlung für die Praxis

In der Anwendung von KI in der kardialen MRT liegt großes Potenzial. Die aktuelle Datenlage ist in einigen Aspekten noch zu gering, vor allem liegen zu wenige prospektive und große multizentrische Studien vor. Dadurch sind die entwickelten Algorithmen häufig wissenschaftlich nicht ausreichend validiert und finden in der klinischen Routine noch keine Anwendung.

Abstract

Clinical/methodical issue

Cardiac diseases are the leading cause of death. Many diseases can be specifically treated once a valid diagnosis is established. Cardiac magnetic resonance imaging (MRI) plays a central role in the workup of many cardiac pathologies. However, image acquisition as well as interpretation and related secondary image evaluation are time-consuming and complex.

Standard radiological methods

Cardiac MRI is becoming increasingly established in international guidelines for the evaluation of cardiac function and differential diagnosis of a wide variety of cardiac diseases.

Methodological innovations

Cardiac MRI has limited reproducibility due to the acquisition technique and interpretation of findings with complex secondary measurements. Artificial intelligence techniques and radiomics offer the potential to improve the acquisition, interpretation, and reproducibility of cardiac MRI.

Performance

Research suggests that artificial intelligence and radiomic analysis can improve cardiac MRI in terms of image acquisition and also diagnostic and prognostic value. Furthermore, the implementation of artificial intelligence and radiomics may result in the identification of new biomarkers.

Achievements and practical recommendations

The implementation of artificial intelligence in cardiac MRI has great potential. However, the current level of evidence is still limited in some aspects; in particular there are too few prospective and large multicenter studies available. As a result, the algorithms developed are often not sufficiently validated scientifically and are not yet applied in clinical routine.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Abb. 1
Abb. 2
Abb. 3
Abb. 4

Literatur

  1. Knuuti J, Wijns W, Saraste A et al (2020) 2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes. Eur Heart J 41:407–477. https://doi.org/10.1093/eurheartj/ehz425

    Article  PubMed  Google Scholar 

  2. McDonagh TA, Metra M, Adamo M et al (2021) 2021 ESC guidelines for the diagnosis and treatment of acute and chronic heart failure. Eur Heart J 42:3599–3726. https://doi.org/10.1093/eurheartj/ehab368

    Article  CAS  PubMed  Google Scholar 

  3. Petersen SE, Aung N, Sanghvi MM et al (2017) Reference ranges for cardiac structure and function using cardiovascular magnetic resonance (CMR) in Caucasians from the UK Biobank population cohort. J Cardiovasc Magn Reson 19:18. https://doi.org/10.1186/s12968-017-0327-9

    Article  PubMed  PubMed Central  Google Scholar 

  4. Choy G, Khalilzadeh O, Michalski M et al (2018) Current applications and future impact of machine learning in radiology. Radiology 288:318–328. https://doi.org/10.1148/radiol.2018171820

    Article  PubMed  Google Scholar 

  5. Aerts HJWL, Velazquez ER, Leijenaar RTH et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006. https://doi.org/10.1038/ncomms5006

    Article  CAS  PubMed  Google Scholar 

  6. Hauptmann A, Arridge S, Lucka F et al (2019) Real-time cardiovascular MR with spatio-temporal artifact suppression using deep learning-proof of concept in congenital heart disease. Magn Reson Med 81:1143–1156. https://doi.org/10.1002/mrm.27480

    Article  PubMed  Google Scholar 

  7. Blansit K, Retson T, Masutani E et al (2019) Deep learning-based prescription of cardiac MRI planes. Radiol Artif Intell 1:e180069. https://doi.org/10.1148/ryai.2019180069

    Article  PubMed  PubMed Central  Google Scholar 

  8. Schuppert C, von Krüchten R, Hirsch JG et al (2022) Whole-body magnetic resonance imaging in the large population-based German national cohort study: predictive capability of automated image quality assessment for protocol repetitions. Invest Radiol 57:478–487. https://doi.org/10.1097/RLI.0000000000000861

    Article  PubMed  Google Scholar 

  9. Zhang N, Yang G, Gao Z et al (2019) Deep learning for diagnosis of chronic myocardial infarction on nonenhanced cardiac cine MRI. Radiology 291:606–617. https://doi.org/10.1148/radiol.2019182304

    Article  PubMed  Google Scholar 

  10. Xia Y, Ravikumar N, Greenwood JP et al (2021) Super-resolution of cardiac MR cine imaging using conditional GANs and unsupervised transfer learning. Med Image Anal 71:102037. https://doi.org/10.1016/j.media.2021.102037

    Article  PubMed  Google Scholar 

  11. Bernard O, Lalande A, Zotti C et al (2018) Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: Is the problem solved? IEEE Trans Med Imaging 37:2514–2525. https://doi.org/10.1109/TMI.2018.2837502

    Article  PubMed  Google Scholar 

  12. Bai W, Sinclair M, Tarroni G et al (2018) Automated cardiovascular magnetic resonance image analysis with fully convolutional networks. J Cardiovasc Magn Reson 20:65. https://doi.org/10.1186/s12968-018-0471-x

    Article  PubMed  PubMed Central  Google Scholar 

  13. Davies RH, Augusto JB, Bhuva A et al (2022) Precision measurement of cardiac structure and function in cardiovascular magnetic resonance using machine learning. J Cardiovasc Magn Reson 24:16. https://doi.org/10.1186/s12968-022-00846-4

    Article  PubMed  PubMed Central  Google Scholar 

  14. Zheng Q, Delingette H, Duchateau N, Ayache N (2018) 3‑D consistent and robust segmentation of cardiac images by deep learning with spatial propagation. IEEE Trans Med Imaging 37:2137–2148. https://doi.org/10.1109/TMI.2018.2820742

    Article  PubMed  Google Scholar 

  15. Chen D, Bhopalwala H, Dewaswala N et al (2022) Deep neural network for cardiac magnetic resonance image segmentation. J Imaging. https://doi.org/10.3390/jimaging8050149

    Article  PubMed  PubMed Central  Google Scholar 

  16. Peng P, Lekadir K, Gooya A et al (2016) A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging. Magma 29:155–195. https://doi.org/10.1007/s10334-015-0521-4

    Article  PubMed  PubMed Central  Google Scholar 

  17. Winther HB, Hundt C, Schmidt B et al (2018) ν‑net: deep learning for generalized biventricular mass and function parameters using multicenter cardiac MRI data. JACC Cardiovasc Imaging 11:1036–1038. https://doi.org/10.1016/j.jcmg.2017.11.013

    Article  PubMed  Google Scholar 

  18. Fahmy AS, Rausch J, Neisius U et al (2018) Automated cardiac MR scar quantification in hypertrophic cardiomyopathy using deep convolutional neural networks. JACC Cardiovasc Imaging 11:1917–1918. https://doi.org/10.1016/j.jcmg.2018.04.030

    Article  PubMed  PubMed Central  Google Scholar 

  19. Fahmy AS, Rowin EJ, Chan RH et al (2021) Improved quantification of myocardium scar in late gadolinium enhancement images: deep learning based image fusion approach. J Magn Reson Imaging 54:303–312. https://doi.org/10.1002/jmri.27555

    Article  PubMed  PubMed Central  Google Scholar 

  20. Neisius U, El-Rewaidy H, Nakamori S et al (2019) Radiomic analysis of myocardial native T1 imaging discriminates between hypertensive heart disease and hypertrophic cardiomyopathy. JACC Cardiovasc Imaging 12:1946–1954. https://doi.org/10.1016/j.jcmg.2018.11.024

    Article  PubMed  PubMed Central  Google Scholar 

  21. Schofield R, Ganeshan B, Kozor R et al (2016) CMR myocardial texture analysis tracks different etiologies of left ventricular hypertrophy. J Cardiovasc Magn Reson 18:O82. https://doi.org/10.1186/1532-429X-18-S1-O82

    Article  PubMed Central  Google Scholar 

  22. Baessler B, Luecke C, Lurz J et al (2019) Cardiac MRI and texture analysis of myocardial T1 and T2 maps in myocarditis with acute versus chronic symptoms of heart failure. Radiology 292:608–617. https://doi.org/10.1148/radiol.2019190101

    Article  PubMed  Google Scholar 

  23. Chen B‑H, An D‑A, He J et al (2021) Myocardial extracellular volume fraction radiomics analysis for differentiation of reversible versus irreversible myocardial damage and prediction of left ventricular adverse remodeling after ST-elevation myocardial infarction. Eur Radiol 31:504–514. https://doi.org/10.1007/s00330-020-07117-9

    Article  PubMed  Google Scholar 

  24. Dawes TJW, de Marvao A, Shi W et al (2017) Machine learning of three-dimensional right ventricular motion enables outcome prediction in pulmonary hypertension: a cardiac MR imaging study. Radiology 283:381–390. https://doi.org/10.1148/radiol.2016161315

    Article  PubMed  Google Scholar 

  25. Kwak S, Everett RJ, Treibel TA et al (2021) Markers of myocardial damage predict mortality in patients with aortic stenosis. J Am Coll Cardiol 78:545–558. https://doi.org/10.1016/j.jacc.2021.05.047

    Article  CAS  PubMed  Google Scholar 

  26. Krebs J, Mansi T, Delingette H et al (2021) CinE caRdiac magneTic resonAnce to predIct veNTricular arrhYthmia (CERTAINTY). Sci Rep 11:22683. https://doi.org/10.1038/s41598-021-02111-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Raisi-Estabragh Z, Jaggi A, Gkontra P et al (2021) Cardiac magnetic resonance radiomics reveal differential impact of sex, age, and vascular risk factors on cardiac structure and myocardial tissue. Front Cardiovasc Med 8:763361. https://doi.org/10.3389/fcvm.2021.763361

    Article  PubMed  PubMed Central  Google Scholar 

  28. Ponsiglione A, Stanzione A, Cuocolo R et al (2022) Cardiac CT and MRI radiomics: systematic review of the literature and radiomics quality score assessment. Eur Radiol 32:2629–2638. https://doi.org/10.1007/s00330-021-08375-x

    Article  PubMed  Google Scholar 

  29. Bamberg F, Kauczor H‑U, Weckbach S et al (2015) Whole-body MR imaging in the German national cohort: rationale, design, and technical background. Radiology 277:206–220. https://doi.org/10.1148/radiol.2015142272

    Article  PubMed  Google Scholar 

  30. Puyol-Anton E, Ruijsink B, Gerber B et al (2019) Regional multi-view learning for cardiac motion analysis: application to identification of dilated cardiomyopathy patients. IEEE Trans Biomed Eng 66:956–966. https://doi.org/10.1109/TBME.2018.2865669

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Klinik für Diagnostische und Interventionelle Radiologie, Universitätsklinikum Freiburg, Medizinische Fakultät, Albert-Ludwigs-Universität Freiburg, Freiburg, Deutschland

    Alexander Rau, Martin Soschynski, Jana Taron, Christopher L. Schlett, Fabian Bamberg & Tobias Krauss

  2. Klinik für Klinik für Kardiologie und Angiologie, Universitäts-Herzzentrum Freiburg – Bad Krozingen, Universitätsklinikum Freiburg, Medizinische Fakultät, Albert-Ludwigs-Universität Freiburg, Bad Krozingen, Deutschland

    Philipp Ruile

Authors
  1. Alexander Rau
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Martin Soschynski
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jana Taron
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Philipp Ruile
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Christopher L. Schlett
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Fabian Bamberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Tobias Krauss
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alexander Rau.

Ethics declarations

Interessenkonflikt

A. Rau, M. Soschynski, J. Taron, P. Ruile, C.L. Schlett, F. Bamberg und T. Krauss geben an, dass kein Interessenkonflikt besteht.

Für diesen Beitrag wurden von den Autor/-innen keine Studien an Menschen oder Tieren durchgeführt. Für die aufgeführten Studien gelten die jeweils dort angegebenen ethischen Richtlinien.

Additional information

figure qr

QR-Code scannen & Beitrag online lesen

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rau, A., Soschynski, M., Taron, J. et al. Künstliche Intelligenz und Radiomics. Radiologie 62, 947–953 (2022). https://doi.org/10.1007/s00117-022-01060-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00117-022-01060-0

Schlüsselwörter

Keywords

Search

Navigation