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. 2025 Jul 1;25(1):224.
doi: 10.1186/s12880-025-01763-5.

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 #  1 , Huan Ma #  1 , Shaoxin Xiang  2 , Zhichao Feng  3 , Chuanjiang Guan  4 , Xiang Li  5
Affiliations

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 et al. BMC Med Imaging. .

Abstract

Background: T2-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.

Methods: The 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.

Results: The 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).

Conclusions: The 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.

Keywords: Artificial intelligence–assisted compressed sensing; Brain; Compressed sensing; Deep learning; Magnetic resonance imaging.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: This study was approved by the ethics committee of Chongqing University Three Gorges Hospital (KS-2024157). All patient-related procedures performed in this study were in accordance with the local legislation. Informed consent was obtained from all participants. Consent for publication: All data have been anonymized to adhere to ethical standards, with explicit consent obtained from the participants, and all authors have provided their consent for this manuscript’s publication. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
A case example from a 60-year-old woman with acute ischemic stroke. a, T2WI-ACS; b, T2WI-ACS-DLR; c, T2WI-PI. The noise in T2WI-ACS (a) is quite noticeable. After applying DLR technology (b), image quality improvement is comparable to that of PI (c)
Fig. 2
Fig. 2
A case example from a 64-year-old man with glioblastoma. a, T2WI-ACS; b, T2WI-ACS-DLR; c, T2WI-PI. T2WI-ACS-DLR (b) and T2WI-PI (c) provide a clearer representation of the internal details of the tumor, as well as the distinct boundaries and contrasts of the lesions compared to T2WI-ACS (a)
Fig. 3
Fig. 3
A case example from a 77-year-old man with glioblastoma. a, T2WI-ACS; b, T2WI-ACS-DLR; c, T2WI-PI. T2WI-ACS (a) has a tumor visibility score of 4, while T2WI-ACS-DLR (b) and T2WI-PI (c) both have tumor visibility scores of 5 with richer internal details
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