Evidence map›Paper›PMID 40596927›Full record

ArticleBMC medical imaging2025

Accelerating brain T2-weighted imaging using artificial intelligence-assisted compressed sensing combined with deep learning-based reconstruction: a feasibility study at 5.0T MRI.

Yun Wen, Huan Ma, Shaoxin Xiang, Zhichao Feng, Chuanjiang Guan, Xiang Li

Abstract readComparative Study
In one paragraph

Article in BMC medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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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.

Yun Wen *Department of Radiology, Chongqing University Three Gorges Hospital, Chongqing, 404000, China.
Huan Ma *Department of Radiology, Chongqing University Three Gorges Hospital, Chongqing, 404000, China.
Shaoxin XiangMR Collaboration, United Imaging Research Institute of Intelligent Imaging, Beijing, 100089, China.
Zhichao FengUnited Imaging Healthcare Group, Shanghai, 201807, China.
Chuanjiang GuanDepartment of Radiology, Chongqing University Three Gorges Hospital, Chongqing, 404000, China. 843081898@qq.com.
Xiang LiDepartment of Radiology, Chongqing University Three Gorges Hospital, Chongqing, 404000, China. 578867343@qq.com.

Funding

Chongqing Key Medical Discipline zdxk202116Science-Health Joint Medical Scientific Research Project of Chongqing 2025MSXM157
6 · The paper itself

Abstract

backgroundT2-weighted imaging (T2WI), renowned for its sensitivity to edema and lesions, faces clinical limitations due to prolonged scanning time, increasing patient discomfort, and motion artifacts. The individual applications of artificial intelligence-assisted compressed sensing (ACS) and deep learning-based reconstruction (DLR) technologies have demonstrated effectiveness in accelerated scanning. However, the synergistic potential of ACS combined with DLR at 5.0T remains unexplored. This study systematically evaluates the diagnostic efficacy of the integrated ACS-DLR technique for T2WI at 5.0T, comparing it to conventional parallel imaging (PI) protocols.

methodsThe prospective analysis was performed on 98 participants who underwent brain T2WI scans using ACS, DLR, and PI techniques. Two observers evaluated the overall image quality, truncation artifacts, motion artifacts, cerebrospinal fluid flow artifacts, vascular pulsation artifacts, and the significance of lesions. Subjective rating differences among the three sequences were compared. Objective assessment involved the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in gray matter, white matter, and cerebrospinal fluid for each sequence. The SNR, CNR, and acquisition time of each sequence were compared.

resultsThe acquisition time for ACS and DLR was reduced by 78%. The overall image quality of DLR is higher than that of ACS (P < 0.001) and equivalent to PI (P > 0.05). The SNR of the DLR sequence is the highest, and the CNR of DLR is higher than that of the ACS sequence (P < 0.001) and equivalent to PI (P > 0.05).

conclusionsThe integration of ACS and DLR enables the ultrafast acquisition of brain T2WI while maintaining superior SNR and comparable CNR compared to PI sequences. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

Artificial IntelligenceBrainDeep LearningImage Interpretation, Computer-AssistedMagnetic Resonance ImagingAdultAgedArtifactsBrain EdemaCerebrospinal FluidFeasibility StudiesFemaleGray MatterHumansMaleMiddle AgedArtificial intelligence–assisted compressed sensingBrainCompressed sensingDeep learningMagnetic resonance imaging

Identifiers

PMID40596927
PMCPMC12211377

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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.