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NoSENSE: Learned Unrolled Cardiac MRI Reconstruction Without Explicit Sensitivity Maps

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Statistical Atlases and Computational Models of the Heart. Regular and CMRxRecon Challenge Papers (STACOM 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14507))

Abstract

We present a novel learned image reconstruction method for accelerated cardiac MRI with multiple receiver coils based on deep convolutional neural networks (CNNs) and algorithm unrolling. In contrast to many existing learned MR image reconstruction techniques that necessitate coil-sensitivity map (CSM) estimation as a distinct network component, our proposed approach avoids explicit CSM estimation. Instead, it implicitly captures and learns to exploit the inter-coil relationships of the images. Our method consists of a series of novel learned image and k-space blocks with shared latent information and adaptation to the acquisition parameters by feature-wise modulation (FiLM), as well as coil-wise data-consistency (DC) blocks.

Our method achieved PSNR values of 34.89 and 35.56 and SSIM values of 0.920 and 0.942 in the cine track and mapping track validation leaderboard of the MICCAI STACOM CMRxRecon Challenge, respectively, ranking 4th among different teams at the time of writing.

Code is available at https://github.com/fzimmermann89/CMRxRecon.

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Acknowledgments

This project has received funding from the European Partnership on Metrology, co-financed from the European Union’s Horizon Europe Research and Innovation Programme, and by the Participating States. This work was supported in part by the Metrology for Artificial Intelligence in Medicine (M4AIM) project, which is funded by the German Federal Ministry for Economic Affairs and Climate Action (BMWi) as part of the QIDigital initiative.

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Authors and Affiliations

  1. Physikalisch-Technische Bundesanstalt (PTB), Berlin, Germany

    Felix Frederik Zimmermann & Andreas Kofler

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  1. Felix Frederik Zimmermann
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  2. Andreas Kofler
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Correspondence to Felix Frederik Zimmermann .

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Editors and Affiliations

  1. Universitat Pompeu Fabra, Barcelona, Spain

    Oscar Camara

  2. King's College London, London, UK

    Esther Puyol-Antón

  3. Inria, Sophia Antipolis, France

    Maxime Sermesant

  4. King's College London, London, UK

    Avan Suinesiaputra

  5. Technische Universiteit Delft, Delft, The Netherlands

    Qian Tao

  6. Fudan University, Shanghai, China

    Chengyan Wang

  7. King's College London, London, UK

    Alistair Young

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Table 3. Challenge Participation Information
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Zimmermann, F.F., Kofler, A. (2024). NoSENSE: Learned Unrolled Cardiac MRI Reconstruction Without Explicit Sensitivity Maps. In: Camara, O., et al. Statistical Atlases and Computational Models of the Heart. Regular and CMRxRecon Challenge Papers. STACOM 2023. Lecture Notes in Computer Science, vol 14507. Springer, Cham. https://doi.org/10.1007/978-3-031-52448-6_43

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  • DOI: https://doi.org/10.1007/978-3-031-52448-6_43

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-52447-9

  • Online ISBN: 978-3-031-52448-6

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