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Generating Short-Axis DENSE Images from 4D XCAT Phantoms: A Proof-of-Concept Study

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Functional Imaging and Modeling of the Heart (FIMH 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13958))

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Abstract

Displacement ENcoding with Stimulated Echoes (DENSE) is a CMR modality that can encode myocardial tissue displacement at a pixel level, enabling the characterization of cardiac disease at early stages. However, we do not currently have a way of evaluating the accuracy of derived results, since the ground truth is unknown. In this study, we developed a proof-of-concept pipeline to generate realistic DENSE images with a known ground truth. We leverage the XCAT tool to create body anatomies, along with associated myocardial tissue displacements, and generate DENSE images with a Bloch simulation based on the time-resolved positions. We generated 6 samples: an apical, a mid, and a basal short-axis slice for both male and female anatomy. We then extracted radial and circumferential strain components using DENSEanalysis, and compared them to the ground-truth strain obtained from the XCAT displacements. While the reproducibility of the strain calculations was similar to the inter-observer variability from previous studies, and the bias in circumferential strain was small (0.03 ± 0.02), the current methods for strain extraction resulted in a bias in radial strain of 0.19 ± 0.19. There is a need to develop better regularization strategies for DENSE analysis, for instance using Deep Learning, and this study provides initial groundwork for obtaining ground-truth strain to evaluate these methods.

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Acknowledgements

This work is funded by EPSRC Centre for Doctoral Training in Smart Medical Imaging (EP/S022104/1), by a Program Grant from the British Heart Foundation (RG/19/1/34160), and Siemens Healthineers. This work was supported by the National Institute of Health (NIH R01-HL131823).

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Authors and Affiliations

  1. School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK

    Hugo Barbaroux & Alistair A. Young

  2. Cardiovascular Magnetic Resonance Unit, The Royal Brompton Hospital (Guy’s and St Thomas’s NHS Foundation Trust), London, UK

    Hugo Barbaroux, Sonia Nielles-Vallespin & Andrew D. Scott

  3. Department of Radiology, Stanford University, Stanford, CA, USA

    Michael Loecher & Daniel B. Ennis

  4. MR Research Collaborations, Siemens Healthcare Limited, Camberley, UK

    Karl P. Kunze & Radhouene Neji

  5. Division of Radiology, Veterans Affairs Health Care System, Palo Alto, CA, USA

    Daniel B. Ennis

  6. National Heart and Lung Institute, Imperial College London, London, UK

    Sonia Nielles-Vallespin & Andrew D. Scott

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  1. Hugo Barbaroux
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  4. Radhouene Neji
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Corresponding author

Correspondence to Hugo Barbaroux .

Editor information

Editors and Affiliations

  1. CREATIS, INSA-Lyon, University of Lyon, Lyon, France

    Olivier Bernard

  2. CREATIS, CNRS, University of Lyon, Lyon, France

    Patrick Clarysse

  3. CREATIS, IUF, University of Lyon, Lyon, France

    Nicolas Duchateau

  4. TIMC, Savoie Mont Blanc University, Chambéry, France

    Jacques Ohayon

  5. CREATIS, Jean Monnet University, Saint-Etienne, France

    Magalie Viallon

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Barbaroux, H. et al. (2023). Generating Short-Axis DENSE Images from 4D XCAT Phantoms: A Proof-of-Concept Study. In: Bernard, O., Clarysse, P., Duchateau, N., Ohayon, J., Viallon, M. (eds) Functional Imaging and Modeling of the Heart. FIMH 2023. Lecture Notes in Computer Science, vol 13958. Springer, Cham. https://doi.org/10.1007/978-3-031-35302-4_43

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  • DOI: https://doi.org/10.1007/978-3-031-35302-4_43

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  • Print ISBN: 978-3-031-35301-7

  • Online ISBN: 978-3-031-35302-4

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