Local Implicit Neural Representations for Multi-Sequence MRI Translation

Conference Paper (2023)
Authors

Yunjie Chen (Leiden University Medical Center)

Marius Staring (TU Delft - Pattern Recognition and Bioinformatics, Leiden University Medical Center)

Jelmer M. Wolterink (University of Twente)

Qian Tao (Leiden University Medical Center, TU Delft - ImPhys/Tao group)

Research Group
Pattern Recognition and Bioinformatics
Copyright
© 2023 Yunjie Chen, M. Staring, Jelmer M. Wolterink, Q. Tao
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Publication Year
2023
Language
English
Copyright
© 2023 Yunjie Chen, M. Staring, Jelmer M. Wolterink, Q. Tao
Research Group
Pattern Recognition and Bioinformatics
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. @en
ISBN (electronic)
9781665473583
DOI:
https://doi.org/10.1109/ISBI53787.2023.10230409
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Abstract

In radiological practice, multi-sequence MRI is routinely acquired to characterize anatomy and tissue. However, due to the heterogeneity of imaging protocols and contraindications to contrast agents, some MRI sequences, e.g. contrast-enhanced T1-weighted image (T1ce), may not be acquired. This creates difficulties for large-scale clinical studies for which heterogeneous datasets are aggregated. Modern deep learning techniques have demonstrated the capability of synthesizing missing sequences from existing sequences, through learning from an extensive multi-sequence MRI dataset. In this paper, we propose a novel MR image translation solution based on local implicit neural representations. We split the available MRI sequences into local patches and assign to each patch a local multi-layer perceptron (MLP) that represents a patch in the T1ce. The parameters of these local MLPs are generated by a hypernetwork based on image features. Experimental results and ablation studies on the BraTS challenge dataset showed that the local MLPs are critical for recovering fine image and tumor details, as they allow for local specialization that is highly important for accurate image translation. Compared to a classical pix2pix model, the proposed method demonstrated visual improvement and significantly improved quantitative scores (MSE 0.86 × 10-3 vs. 1.02 × 10-3 and SSIM 94.9 vs 94.3).

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