Generative modeling and inverse imaging of cardiac transmembrane potential

Sandesh Ghimire, Jwala Dhamala, Prashnna Kumar Gyawali, John L. Sapp, Milan Horacek, Linwei Wang

Producción científica: Capítulo en Libro/Reporte/Acta de conferenciaContribución a la conferencia

20 Citas (Scopus)

Resumen

Noninvasive reconstruction of cardiac transmembrane potential (TMP) from surface electrocardiograms (ECG) involves an ill-posed inverse problem. Model-constrained regularization is powerful for incorporating rich physiological knowledge about spatiotemporal TMP dynamics. These models are controlled by high-dimensional physical parameters which, if fixed, can introduce model errors and reduce the accuracy of TMP reconstruction. Simultaneous adaptation of these parameters during TMP reconstruction, however, is difficult due to their high dimensionality. We introduce a novel model-constrained inference framework that replaces conventional physiological models with a deep generative model trained to generate TMP sequences from low-dimensional generative factors. Using a variational auto-encoder (VAE) with long short-term memory (LSTM) networks, we train the VAE decoder to learn the conditional likelihood of TMP, while the encoder learns the prior distribution of generative factors. These two components allow us to develop an efficient algorithm to simultaneously infer the generative factors and TMP signals from ECG data. Synthetic and real-data experiments demonstrate that the presented method significantly improve the accuracy of TMP reconstruction compared with methods constrained by conventional physiological models or without physiological constraints.

Idioma originalEnglish
Título de la publicación alojadaMedical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings
EditoresGabor Fichtinger, Christos Davatzikos, Carlos Alberola-López, Alejandro F. Frangi, Julia A. Schnabel
EditorialSpringer Verlag
Páginas508-516
Número de páginas9
ISBN (versión impresa)9783030009335
DOI
EstadoPublished - 2018
Evento21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 - Granada, Spain
Duración: sep. 16 2018sep. 20 2018

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen11071 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conference

Conference21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
País/TerritorioSpain
CiudadGranada
Período9/16/189/20/18

Nota bibliográfica

Funding Information:
Acknowledgement. This work is supported by the National Science Foundation under CAREER Award ACI-1350374.

Publisher Copyright:
© Springer Nature Switzerland AG 2018.

ASJC Scopus Subject Areas

  • Theoretical Computer Science
  • General Computer Science

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Citar esto

Ghimire, S., Dhamala, J., Gyawali, P. K., Sapp, J. L., Horacek, M., & Wang, L. (2018). Generative modeling and inverse imaging of cardiac transmembrane potential. En G. Fichtinger, C. Davatzikos, C. Alberola-López, A. F. Frangi, & J. A. Schnabel (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings (pp. 508-516). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11071 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-00934-2_57