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 / Zhaolin Chen, Sanuwani Dayarathna, Kh Tohidul Islam, Himashi Peiris, Parisa Zakavi, Shenjun Zhong, editors

Format:

Book

Conference/Event

Contributor:

Chen, Zhaolin, editor.

Dayarathna, Sanuwani, editor.

Islam, Kh Tohidul, editor.

Peiris, Himashi, editor.

Zakavi, Parisa, editor.

Zhong, Shenjun, editor.

Conference Name:

ULF-EnC (Challenge) (1st : 2025 : Taejŏn-si, Korea)

International Conference on Medical Image Computing and Computer-Assisted Intervention (28th : 2025 : Taejŏn-si, Korea)

Series:

Lecture notes in computer science ; 1611-3349 16293.

Lecture notes in computer science, 1611-3349 ; 16293

Language:

English

Subjects (All):

Brain--Magnetic resonance imaging--Congresses.

Brain.

Brain--Magnetic resonance imaging.

Genre:

proceedings (reports)

Conference papers and proceedings

Conference papers and proceedings.

Physical Description:

1 online resource : illustrations

Other Title:

ULF-EnC 2025

Place of Publication:

Cham, Switzerland : Springer, [2026]

Summary:

"This book constitutes the proceedings of the First ULF-EnC 2025 Challenge on Enhancing Ultra-Low-Field MRI with Paired High-Field MRI Comparisons for Brain Imaging, held in conjunction with MICCAI 2025. The 19 papers included in this book were carefully reviewed and selected from 21 short papers submitted to the proceedings. The challenge addressed a timely and clinically significant problem: bridging the quality gap between ultra-low-field (64 mT) and high-field (3T) brain MRI through algorithmic enhancement using paired imaging data"-- Springer Nature Link

Contents:

Robust and lightweight low-to-high field MRI synthesis guided by semantic prior / Yiyang Lin, Byungjun Kim, Yixuan Yuan, and Jinglei Lv

Ultra low-field MRI enhancement via conditional diffusion model / Haowen Pang, Xueqi Li, Tianyi Yan, and Chuyang Ye

Utilizing an ensemble of 3D U-Nets to predict high-field MRI T1-, T2-, and FLAIR-images from ultra-low-field MRI images / Jan Nikolas Morshuis, Matthias Hein, and Christian F. Baumgartner

Augment to augment : diverse augmentations enable competitive ultra-low-field MRI enhancement / Felix F. Zimmermann

From 64mT to 3T : multi-sequence low-field MRI enhancement via 3D U-Net / Zhicheng Lu, Hussain Mohammed Dipu Kabir, Toufique Soomro, and Mohammad Ali Moni

Multi-view fusion-guided Brownian bridge diffusion model for ultra-low-field MRI enhancement / Mengfei Wang, Ruipeng Zhang, Yidong Jin, and Yuehua Li

Bridged denoising diffusion in ultra-low field MRI enhancement challenge / Baris Imre, Chinmay Rao, Aram Salehi, Marius Staring, and Efe Ilicak

LowDM : a diffusion-based deep learning framework for generating high-field quality images from portable low-field MRI / Alfredo Lucas, Chetan Vadali, and Joel M. Stein

Super-resolution of ultra-low-field MRI using a GAN-based visual transformer network / Aram Salehi, Tavia E. Evans, Chloé Najac, Beatrice Lena, Yiming Dong, Ruben van den Broek, Hieab H. H. Adams, and Andrew Webb

UltraMR-enforce : a unified ensemble framework for enhancement of ultra-low-field MRI / Xiaoyu Bai, Yueyue Zhu, Haotian Jiang, Rongqing Cai, Yi Liu, and Geng Chen

NAF-GAN : anatomically constrained GAN for ultra-low-field MRI enhancement / Zhenyu Xiang, Yicheng Wu, Ziyang Chen, Zaiyuan Liu, Yongsheng Pan, and Yong Xia

Enhancing ultra-low-field MRI with segmentation-guided adversarial learning / James Grover, Andrew Phair, Michael Ferraro, and David E. J. Waddington

Ultra-low-field brain MRI enhancement using resfusion and residual artifact suppression network / Youngmin Kim, Jeongchan Kim, Taehoon Lee, Jaeyun Shin, Suhyeon Lee, and Jongchul Ye

Stable and generalizable acceleration of conditional score-SDEs for multi-contrast MRI enhancement / Qiwei Fan

Ultra-low-field MRI image enhancement using transformer models with latent space exploitation / Seonghyuk Kim, Sojeong Kim, Hye-Ryeong Choi, HyoSeok Lee, and Sung-Hong Park

A 3D vision transformer trained with synthetic and real MRI data for enhancing ULF-MRI / Peter Hsu, Jeongsol Kim, Daniel Sodickson, Jong Chul Ye, Patricia Johnson, and Jelle Veraart

Ultra-low-field brain MRI enhancement using a slice-based vision transformer / Peter Hsu, Jeongsol Kim, Daniel Sodickson, Jong Chul Ye, Patricia Johnson, and Jelle Veraart

Multi-input generalised-Hilbert mamba for super-resolution of ultra-low-field MRI / Levente Baljer, Niall Bourke, Zhenshan Xie, Emma Robinson, and František Váša

Enhancing ultra-low-field to high-field MRI using multi-cycle GAN and flow matching models / Jinghang Li, Bruno de Almeida, and Tamer S. Ibrahim

Notes:

Includes bibliographical references and index

Online resource; title from PDF title page (Springer Nature Link, viewed July 10, 2026)

Other Format:

Print version: ULF-EnC (Challenge) (1st : 2025 : Taejŏn-si, Korea) Enhancing ultra-low-field MRI with paired high-field MRI comparisons for brain imaging

ISBN:

9783032233448

3032233445

OCLC:

1602485276

Access Restriction:

Restricted for use by site license