Magnetization Transfer Imaging Using Non-Balanced SSFP at Ultra-Low Field.
Magnetic resonance in medicine(2025)
Hyperfine Inc. | Department of Neuroimaging | Department of Psychiatry and Psychology | Medicine (Neurology) | Department of Radiology
Abstract
PURPOSE:Ultra-low field MRI scanners have the potential to improve health care delivery, both through improved access in areas where there are few MRI scanners and allowing more frequent monitoring of disease progression and treatment response. This may be particularly true in white matter disorders, including leukodystrophies and multiple sclerosis, in which frequent myelin-sensitive imaging, such as magnetization transfer (MT) imaging, might improve clinical care and patient outcomes. METHODS:We implemented an on-resonance approach to MT imaging on a commercial point-of-care 64 mT scanner using a non-balanced steady-state free precession sequence. Phantom and in vivo experiments were used to evaluate and optimize the sequence sensitivity and reproducibility, and to demonstrate in vivo performance and inter-site reproducibility. RESULTS:From phantom experiments, T1 and T2 effects were determined to have a negligible effect on the differential MT weighting. MT ratio (MTR) values in white matter were 23.1 ± 1.0% from 10 healthy volunteers, with an average reproducibility coefficient of variation of 1.04%. Normal-appearing white matter MTR values in a multiple sclerosis participant (21.5 ± 6.2%) were lower, but with a similar spread of values, compared to an age-matched healthy volunteer (23.3 ± 6.2%). CONCLUSION:An on-resonance MT imaging approach was developed at 64 mT that can be performed in as little as 4 min. A semi-quantitative myelin-sensitive imaging biomarker at this field strength is available for assessing both myelination and demyelination.
MoreTranslated text
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper