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The International Journal of Cardiovascular Imaging

, Volume 35, Issue 12, pp 2189–2196 | Cite as

Denoising and artefact removal for transthoracic echocardiographic imaging in congenital heart disease: utility of diagnosis specific deep learning algorithms

  • Gerhard-Paul DillerEmail author
  • Astrid E. Lammers
  • Sonya Babu-Narayan
  • Wei Li
  • Robert M. Radke
  • Helmut Baumgartner
  • Michael A. Gatzoulis
  • Stefan Orwat
Original Paper

Abstract

Deep learning (DL) algorithms are increasingly used in cardiac imaging. We aimed to investigate the utility of DL algorithms in de-noising transthoracic echocardiographic images and removing acoustic shadowing artefacts specifically in patients with congenital heart disease (CHD). In addition, the performance of DL algorithms trained on CHD samples was compared to models trained entirely on structurally normal hearts. Deep neural network based autoencoders were built for denoising and removal of acoustic shadowing artefacts based on routine echocardiographic apical 4-chamber views and performance was assessed by visual assessment and quantifying cross entropy. 267 subjects (94 TGA and atrial switch and 39 with ccTGA, 10 Ebstein anomaly, 9 with uncorrected AVSD and 115 normal controls; 56.9% male, age 38.9 ± 15.6 years) with routine transthoracic examinations were included. The autoencoders significantly enhanced image quality across diagnostic subgroups (p < 0.005 for all). Models trained on congenital heart samples performed significantly better when exposed to examples from congenital heart disease patients. Our study demonstrates the potential of autoencoders for denoising and artefact removal in patients with congenital heart disease and structurally normal hearts. While models trained entirely on samples from structurally normal hearts perform reasonably in CHD, our data illustrates the value of dedicated image augmentation systems trained specifically on CHD samples.

Keywords

Adult congenital heart disease Congenital heart disease Deep-learning Echocardiography De-noising Autoencoder 

Notes

Acknowledgement

This study was supported by a research Grant from the EMAH Stiftung Karla Voellm, Krefeld, Germany and by the German Competence Network for Congenital Heart Defects (Funded by the German Federal Ministry of Education and Research, BMBF -FKZ 01G10210, 01GI0601).

Author contribution

GPD, AEL and SO planned and conducted the study. GPD, AEL and SO prepared and analyzed the data using DL networks. SBN, RR, WL, MG and HB made substantial contributions in analysis, drafting the article and revising it critically for important intellectual content. All authors gave final approval of the version to be submitted and any revised version.

Funding

This study was supported by a research Grant from the EMAH Stiftung Karla Voellm, Krefeld, Germany. The Adult Congenital Heart Centre and Centre for Pulmonary Hypertension, Royal Brompton Hospital, London, UK have received support from Actelion UK, Pfizer UK, GSK UK, the British Heart Foundation and the NIHR Cardiovascular and Respiratory Biomedical Research Units.

Compliance with ethical standards

Conflict of interest

The authors have no conflicts of interest.

Supplementary material

10554_2019_1671_MOESM1_ESM.mp4 (279 kb)
Supplementary file1 (MP4 279 kb)
10554_2019_1671_MOESM2_ESM.mp4 (411 kb)
Supplementary file2 (MP4 411 kb)

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Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of Cardiology III – Adult Congenital and Valvular Heart DiseaseUniversity Hospital MuensterMünsterGermany
  2. 2.Adult Congenital Heart Disease ProgrammeRoyal Brompton HospitalLondonUK
  3. 3.Competence Network for Congenital Heart DefectsDZHK (German Centre for Cardiovascular Research)BerlinGermany
  4. 4.Division of Paediatric CardiologyUniversity Hospital MuensterMünsterGermany

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