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ORCIDULF-EnC@MICCAI 2025: Daejeon, South Korea
- Zhaolin Chen
, Sanuwani Dayarathna, Kh Tohidul Islam, Himashi Peiris, Parisa Zakavi, Shenjun Zhong:
Enhancing Ultra-Low-Field MRI with Paired High-Field MRI Comparisons for Brain Imaging - First International Challenge, ULF-EnC 2025, Held in Conjunction with MICCAI 2025, Daejeon, South Korea, September 23, 2025, Proceedings. Lecture Notes in Computer Science 16293, Springer 2026, ISBN 978-3-032-23343-1 - Yiyang Lin, Byungjun Kim, Yixuan Yuan, Jinglei Lv:
Robust and Lightweight Low-to-High Field MRI Synthesis Guided by Semantic Prior. 1-8 - Haowen Pang, Xueqi Li, Tianyi Yan, Chuyang Ye:
Ultra Low-Field MRI Enhancement via Conditional Diffusion Model. 9-17 - Jan Nikolas Morshuis, Matthias Hein, Christian F. Baumgartner:
Utilizing an Ensemble of 3D U-Nets to Predict High-Field MRI T1-, T2-, and FLAIR-Images from Ultra-Low-Field MRI Images. 18-27 - Felix F. Zimmermann:
Augment to Augment: Diverse Augmentations Enable Competitive Ultra-Low-Field MRI Enhancement. 28-36 - Zhicheng Lu, Hussain Mohammed Dipu Kabir, Toufique Ahmed Soomro, Mohammad Ali Moni:
From 64mT to 3T: Multi-sequence Low-Field MRI Enhancement via 3D U-Net. 37-46 - Mengfei Wang, Ruipeng Zhang, Yidong Jin, Yuehua Li:
Multi-view Fusion-Guided Brownian Bridge Diffusion Model for Ultra-Low-Field MRI Enhancement. 47-56 - Baris Imre, Chinmay Rao, Aram Salehi, Marius Staring, Efe Ilicak:
Bridged Denoising Diffusion in Ultra-Low Field MRI Enhancement Challenge. 57-66 - Alfredo Lucas, Chetan Vadali, Joel M. Stein:
LowDM: A Diffusion-Based Deep Learning Framework for Generating High-Field Quality Images from Portable Low-Field MRI. 67-75 - Aram Salehi, Tavia E. Evans, Chloé Najac, Beatrice Lena, Yiming Dong, Ruben van den Broek, Hieab H. H. Adams, Andrew G. Webb:
Super-Resolution of Ultra-Low-Field MRI Using a GAN-Based Visual Transformer Network. 76-83 - Xiaoyu Bai, Yueyue Zhu, Haotian Jiang, Rongqing Cai, Yi Liu, Geng Chen:
UltraMR-Enforce: A Unified Ensemble Framework for Enhancement of Ultra-Low-Field MRI. 84-93 - Zhenyu Xiang, Yicheng Wu, Ziyang Chen, Zaiyuan Liu, Yongsheng Pan, Yong Xia:
NAF-GAN: Anatomically Constrained GAN for Ultra-Low-Field MRI Enhancement. 94-103 - James Grover, Andrew Phair, Michael Ferraro, David E. J. Waddington:
Enhancing Ultra-Low-Field MRI with Segmentation-Guided Adversarial Learning. 104-114 - Youngmin Kim, Jeongchan Kim, Taehoon Lee, Jaeyun Shin, Suhyeon Lee, Jong Chul Ye:
Ultra-Low-Field Brain MRI Enhancement using Resfusion and Residual Artifact Suppression Network. 115-124 - Qiwei Fan:
Stable and Generalizable Acceleration of Conditional Score-SDEs for Multi-contrast MRI Enhancement. 125-132 - Seonghyuk Kim, Sojeong Kim, Hye-Ryeong Choi, HyoSeok Lee, Sung-Hong Park:
Ultra-Low-Field MRI Image Enhancement Using Transformer Models with Latent Space Exploitation. 133-140 - Peter Hsu, Jeongsol Kim, Daniel K. Sodickson, Jong Chul Ye, Patricia Johnson, Jelle Veraart:
A 3D Vision Transformer Trained with Synthetic and Real MRI Data for Enhancing ULF-MRI. 141-151 - Peter Hsu, Jeongsol Kim, Daniel K. Sodickson, Jong Chul Ye, Patricia Johnson, Jelle Veraart:
Ultra-Low-Field Brain MRI Enhancement Using a Slice-Based Vision Transformer. 152-159 - Levente Baljer, Niall J. Bourke, Zhenshan Xie, Emma C. Robinson, Frantisek Vása:
Multi-input Generalised-Hilbert Mamba for Super-Resolution of Ultra-Low-Field MRI. 160-172 - Jinghang Li, Bruno de Almeida, Tamer S. Ibrahim:
Enhancing Ultra-Low-Field to High-Field MRI Using Multi-cycle GAN and Flow Matching Models. 173-182
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