Evidence map›Paper›PMID 39964601›Full record

ArticleMagma (New York, N.Y.)2025

Exploring the potential performance of 0.2 T low-field unshielded MRI scanner using deep learning techniques.

Lei Li, Qingyuan He, Shufeng Wei, Huixian Wang, Zheng Wang, Wenhui Yang

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In one paragraph

Article in Magma (New York, N.Y.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Lei LiInstitute of Electrical Engineering, Chinese Academy of Sciences, Beijing, China.ORCID http://orcid.org/0000-0002-7910-5723
Qingyuan HeDepartment of Radiology, Peking University Third Hospital, Beijing, China.
Shufeng WeiInstitute of Electrical Engineering, Chinese Academy of Sciences, Beijing, China.
Huixian WangInstitute of Electrical Engineering, Chinese Academy of Sciences, Beijing, China.
Zheng WangInstitute of Electrical Engineering, Chinese Academy of Sciences, Beijing, China.
Wenhui YangInstitute of Electrical Engineering, Chinese Academy of Sciences, Beijing, China. yangwenh@mail.iee.ac.cn.

Funding

National Natural Science Foundation of China 32427802National Natural Science Foundation of China 52293424
6 · The paper itself

Abstract

objectiveUsing deep learning-based techniques to overcome physical limitations and explore the potential performance of 0.2 T low-field unshielded MRI in terms of imaging quality and speed.

methodsFirst, fast and high-quality unshielded imaging is achieved using active electromagnetic shielding and basic super-resolution. Then, the speed of basic super-resolution imaging is further improved by reducing the number of excitations. Next, the feasibility of using cross-field super-resolution to map low-field low-resolution images to high-field ultra-high-resolution images is analyzed. Finally, by cascading basic and cross-field super-resolution, the quality of the low-field low-resolution image is improved to the level of the high-field ultra-high-resolution image.

resultsUnder unshielded conditions, our 0.2 T scanner can achieve image quality comparable to that of a 1.5 T scanner (acquisition resolution of 512 × 512, spatial resolution of 0.45 mm DISCUSSION: The proposed strategy overcomes the physical limitations of the hardware and rapidly acquires images close to the high-field level on a low-field unshielded MRI scanner. These findings have significant practical implications for the advances in MRI technology, supporting the shift from conventional scanners to point-of-care imaging systems.

Indexed as

Deep LearningImage Processing, Computer-AssistedMagnetic Resonance ImagingAlgorithmsEquipment DesignHumansPhantoms, ImagingSignal-To-Noise RatioCross-fieldDeep learningImage processingLow-field MRISuper-resolution

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.