Lin, H;
Zhou, Y;
Slator, P;
Alexander, D;
(2021)
Generalised Super Resolution for Quantitative MRI Using Self-supervised Mixture of Experts.
In: De Bruijne, M and Cattin, PC and Cotin, S and Padoy, N and Speidel, S and Zheng, Y and Essert, C, (eds.)
Medical Image Computing and Computer Assisted Intervention – MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part VI.
(pp. pp. 44-54).
Springer: Cham, Switzerland.
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Abstract
Multi-modal and multi-contrast imaging datasets have diverse voxel-wise intensities. For example, quantitative MRI acquisition protocols are designed specifically to yield multiple images with widely-varying contrast that inform models relating MR signals to tissue characteristics. The large variance across images in such data prevents the use of standard normalisation techniques, making super resolution highly challenging. We propose a novel self-supervised mixture-of-experts (SS-MoE) paradigm for deep neural networks, and hence present a method enabling improved super resolution of data where image intensities are diverse and have large variance. Unlike the conventional MoE that automatically aggregates expert results for each input, we explicitly assign an input to the corresponding expert based on the predictive pseudo error labels in a self-supervised fashion. A new gater module is trained to discriminate the error levels of inputs estimated by Multiscale Quantile Segmentation. We show that our new paradigm reduces the error and improves the robustness when super resolving combined diffusion-relaxometry MRI data from the Super MUDI dataset. Our approach is suitable for a wide range of quantitative MRI techniques, and multi-contrast or multi-modal imaging techniques in general. It could be applied to super resolve images with inadequate resolution, or reduce the scanning time needed to acquire images of the required resolution. The source code and the trained models are available at https://github.com/hongxiangharry/SS-MoE.
| Type: | Proceedings paper |
|---|---|
| Title: | Generalised Super Resolution for Quantitative MRI Using Self-supervised Mixture of Experts |
| Event: | Medical Image Computing and Computer Assisted Intervention – MICCAI 2021: 24th International Conference |
| ISBN-13: | 978-3-030-87230-4 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-030-87231-1_5 |
| Publisher version: | https://doi.org/10.1007/978-3-030-87231-1_5 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Self supervision, Mixture of experts, Quantitative MRI, Generalised super resolution, Pseudo labels |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10135778 |
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