Abstract #0404

M4Raw: A Multi-Contrast Multi-Repetition Multi-Channel Raw K-space Dataset for Low-Field MRI Reconstruction

Mengye Lyu¹, Lifeng Mei¹, Sixing Liu¹, Shoujin Huang¹, Yi Li¹, Kexin Yang¹, Yilong Liu², and Ed X. Wu^3,4

¹College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China, ²Guangdong-Hongkong-Macau Institute of CNS Regeneration, Key Laboratory of CNS Regeneration (Ministry of Education), Jinan University, Guangzhou, China, ³Laboratory of Biomedical Imaging and Signal Processing, the University of Hong Kong, Hong Kong, China, ⁴Department of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong, China

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Low-Field MRI, Open dataset

We release a new raw k-space dataset M4Raw acquired by the low-field MRI. Currently, it contains multi-channel brain data of 180 subjects each with 18 slices x 3 contrasts (T1w, T2w, and FLAIR). Moreover, each contrast consists of two or three repetitions (a.k.a. NEXs), leading to more than 25k trainable slices in total, which can be used in various ways by the low-field MRI community. It can be accessed via http://github.com/mylyu/M4Raw.

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