Schlereth M, Schillinger M, Breininger K (2026)
Publication Type: Conference contribution
Publication year: 2026
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 15962 LNCS
Pages Range: 172-182
Conference Proceedings Title: Lecture Notes in Computer Science
Event location: Daejeon, KOR
ISBN: 9783032049469
DOI: 10.1007/978-3-032-04947-6_17
Acquiring images in high resolution is often a challenging task. Especially in the medical sector, image quality has to be balanced with acquisition time and patient comfort. To strike a compromise between scan time and quality for Magnetic Resonance (MR) imaging, two anisotropic scans with different low-resolution (LR) orientations can be acquired. Typically, LR scans are analyzed individually by radiologists, which is time consuming and can lead to inaccurate interpretation. To tackle this, we propose tripleSR, a novel approach for fusing two orthogonal anisotropic LR MR images, to reconstruct anatomical details in a unified representation. Our multi-view neural network is trained in a self-supervised manner, without requiring corresponding high-resolution (HR) data. To optimize the model, we introduce a sparse coordinate-based loss, enabling the integration of LR images with arbitrary scaling. We evaluate our method on MR images from two independent cohorts. Our results demonstrate comparable or even improved super-resolution (SR) performance compared to state-of-the-art (SOTA) self-supervised SR methods for different upsampling scales. By combining a patient-agnostic offline and a patient-specific online phase, we achieve a substantial speed-up of up to ten times for patient-specific reconstruction while achieving similar or better SR quality. Code is available at https://github.com/MajaSchle/tripleSR.
APA:
Schlereth, M., Schillinger, M., & Breininger, K. (2026). Faster, Self-supervised Super-Resolution for Anisotropic Multi-view MRI Using a Sparse Coordinate Loss. In James C. Gee, Jaesung Hong, Carole H. Sudre, Polina Golland, Daniel C. Alexander, Juan Eugenio Iglesias, Archana Venkataraman, Jong Hyo Kim (Eds.), Lecture Notes in Computer Science (pp. 172-182). Daejeon, KOR: Springer Science and Business Media Deutschland GmbH.
MLA:
Schlereth, Maja, Moritz Schillinger, and Katharina Breininger. "Faster, Self-supervised Super-Resolution for Anisotropic Multi-view MRI Using a Sparse Coordinate Loss." Proceedings of the 28th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025, Daejeon, KOR Ed. James C. Gee, Jaesung Hong, Carole H. Sudre, Polina Golland, Daniel C. Alexander, Juan Eugenio Iglesias, Archana Venkataraman, Jong Hyo Kim, Springer Science and Business Media Deutschland GmbH, 2026. 172-182.
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