Towards Accurate MRF T2 in Structured Material at 0.55T Using MT-suppressed Excitations
Magnetic Resonance Imaging(2025)
Abstract
Purpose To develop a 0.55 T FISP-MRF approach that provides more accurate T2 maps in structured materials (e.g. white matter). Method Non-selective low-bandwidth excitation strategies that reduce on-resonance MT effects were implemented. Dictionaries were simulated using a conventional single pool model. Estimated MRF T2 maps using the non-selective approach with 2 pulse durations were compared against MRF T2 maps using the conventional slab-selective approach, and against conventional but slow reference measurements. Results The proposed approach substantially reduces T2 underestimation in white matter from ∼40 % to <10 % without compromising precision. Conclusion The use of non-selective low-bandwidth excitations substantially reduces MT effects in 0.55T FISP-MRF, enabling use of a single pool model. This is particularly important for MRF at low field strengths and in structured materials such as white matter.
MoreTranslated text
Key words
FISP-MRF,Magnetization transfer effects,0.55T MRI
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
Summary is being generated by the instructions you defined