ReviewSheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi2023
[Application of generative adversarial network in magnetic resonance image reconstruction].
Review in Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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.
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Who cites it
3 citing papers in PubMed, 1 citations in OpenAlex.
- Impact of AIR™ Recon DL on magnetic resonance imaging-based quantitative brain structure measurements.Psychoradiology · 2026Article
- [Cross modal translation of magnetic resonance imaging and computed tomography images based on diffusion generative adversarial networks].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Article
- CS-MRI Reconstruction Using an Improved GAN with Dilated Residual Networks and Channel Attention Mechanism.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Magnetic resonance imaging (MRI) is an important medical imaging method, whose major limitation is its long scan time due to the imaging mechanism, increasing patients' cost and waiting time for the examination. Currently, parallel imaging (PI) and compress sensing (CS) together with other reconstruction technologies have been proposed to accelerate image acquisition. However, the image quality of PI and CS depends on the image reconstruction algorithms, which is far from satisfying in respect to both the image quality and the reconstruction speed. In recent years, image reconstruction based on generative adversarial network (GAN) has become a research hotspot in the field of magnetic resonance imaging because of its excellent performance. In this review, we summarized the recent development of application of GAN in MRI reconstruction in both single- and multi-modality acceleration, hoping to provide a useful reference for interested researchers. In addition, we analyzed the characteristics and limitations of existing technologies and forecasted some development trends in this field.
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Registered trials
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.