Evidence map›Paper›PMID 38922525›Full record

ReviewMagma (New York, N.Y.)2024

Artificial intelligence for neuro MRI acquisition: a review.

Hongjia Yang, Guanhua Wang, Ziyu Li, Haoxiang Li, Jialan Zheng, Yuxin Hu, Xiaozhi Cao, Congyu Liao, Huihui Ye, Qiyuan Tian

Abstract readReview
PubMed Publisher
In one paragraph

Review in Magma (New York, N.Y.), 2024. 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

10 authors.

Hongjia Yang *School of Biomedical Engineering, Tsinghua University, Beijing, China.
Guanhua Wang *Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.
Ziyu LiWellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
Haoxiang LiSchool of Biomedical Engineering, Tsinghua University, Beijing, China.
Jialan ZhengSchool of Biomedical Engineering, Tsinghua University, Beijing, China.
Yuxin HuDepartment of Electrical Engineering, Stanford University, Stanford, CA, USA.
Xiaozhi CaoDepartment of Electrical Engineering, Stanford University, Stanford, CA, USA.
Congyu LiaoDepartment of Electrical Engineering, Stanford University, Stanford, CA, USA.
Huihui YeState Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, China.
Qiyuan TianSchool of Biomedical Engineering, Tsinghua University, Beijing, China. qiyuantian@tsinghua.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectTo review recent advances of artificial intelligence (AI) in enhancing the efficiency and throughput of the MRI acquisition workflow in neuroimaging, including planning, sequence design, and correction of acquisition artifacts. MATERIALS AND

methodsA comprehensive analysis was conducted on recent AI-based methods in neuro MRI acquisition. The study focused on key technological advances, their impact on clinical practice, and potential risks associated with these methods.

resultsThe findings indicate that AI-based algorithms have a substantial positive impact on the MRI acquisition process, improving both efficiency and throughput. Specific algorithms were identified as particularly effective in optimizing acquisition steps, with reported improvements in workflow efficiency. DISCUSSION: The review highlights the transformative potential of AI in neuro MRI acquisition, emphasizing the technological advances and clinical benefits. However, it also discusses potential risks and challenges, suggesting areas for future research to mitigate these concerns and further enhance AI integration in MRI acquisition.

Indexed as

AlgorithmsArtificial IntelligenceImage Processing, Computer-AssistedMagnetic Resonance ImagingNeuroimagingArtifactsBrainHumansImage Interpretation, Computer-AssistedWorkflowArtifacts correctionArtificial intelligenceExperimental designMachine learningMRI acquisition

Identifiers

PMID38922525

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.