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Author Spotlight: Optimized Lung MRI Protocol with Computationally Efficient Reconstruction Methods
Published on: September 6, 2024
Comparison of respiratory-gated and breath‑hold accelerated T2-weighted sequences for liver MRI with deep learning
Hualing Li1, Chenglin Hu1, Qiuxia Wang1
1Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Background:
T2-weighted imaging (T2WI) of the liver suffers from prolonged scan times and respiratory motion artifacts. Deep learning (DL)-based reconstruction can accelerate acquisition while maintaining diagnostic quality. We compared respiratory-gated (RG) and breath-hold (BH) DL-T2WI to radial k-space sampling acquisition and reconstruction with motion suppression (ARMS)-T2WI and evaluated how respiratory characteristics affect image quality.
Materials And Methods:
We prospectively enrolled 120 participants who underwent 3-T RG DL-, BH DL-, and ARMS-T2WI. Three radiologists evaluated image quality and lesion conspicuity using a 5-point scale. Respiratory characteristics were extracted from breathing curves.
Results:
All sequences showed comparable lesion-to-liver contrast ratios (p = 0.139), detection rates (p = 0.106), and lesion conspicuity scores (p = 0.990). RG DL-T2WI showed higher overall image quality compared to BH DL-T2WI (p = 0.027), and similar scores to ARMS-T2WI (p = 0.106). A respiratory score calculated using four parameters predicted ARMS-T2WI image quality with an area under the receiver operating characteristic curve (AUROC) of 0.836 (95% confidence interval 0.638-0.968) in the validation set. For RG DL-T2WI, a respiratory score using seven parameters achieved an AUROC of 0.831 (0.652-0.967) in the validation set. Standard deviation of the respiratory amplitude (SDamp) was an independent factor for BH DL-T2WI image quality (validation set, odds ratio 0.297, p = 0.049). For patients with high SDamp, RG DL-T2WI provided better image quality compared to BH DL-T2WI (68.6% versus 14.3%, p < 0.001).
Conclusion:
Both RG and BH DL-T2WI offer image quality comparable to ARMS-T2WI. Respiratory metrics derived from breathing curves may facilitate personalized liver imaging.
Relevance Statement:
Both respiratory-gated and breath-hold T2WI with deep learning reconstruction showed comparable image quality to T2WI based on radial k-space sampling strategies. Respiratory parameters enable personalized magnetic resonance liver imaging workflows.
Key Points:
Respiratory-gated and breath-hold deep learning T2WI exhibited satisfactory image quality. Respiratory curve traits variably impact T2WI quality, guiding personalized imaging workflows. Respiratory-gated deep learning-reconstructed T2WI benefits patients with breath-holding difficulties in liver MRI.
