Background <p>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.</p> Materials and methods <p>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.</p> Results <p>All sequences showed comparable lesion-to-liver contrast ratios (<i>p</i> = 0.139), detection rates (<i>p</i> = 0.106), and lesion conspicuity scores (<i>p</i> = 0.990). RG DL-T2WI showed higher overall image quality compared to BH DL-T2WI (<i>p</i> = 0.027), and similar scores to ARMS-T2WI (<i>p</i> = 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 (SD_amp) was an independent factor for BH DL-T2WI image quality (validation set, odds ratio 0.297, <i>p</i> = 0.049). For patients with high SD_amp, RG DL-T2WI provided better image quality compared to BH DL-T2WI (68.6% <i>versus</i> 14.3%, <i>p</i> &lt; 0.001).</p> Conclusion <p>Both RG and BH DL-T2WI offer image quality comparable to ARMS-T2WI. Respiratory metrics derived from breathing curves may facilitate personalized liver imaging.</p> Relevance statement <p>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.</p> Key Points <p><UnorderedList Mark="Bullet"> <ItemContent> <p>Respiratory-gated and breath-hold deep learning T2WI exhibited satisfactory image quality.</p> </ItemContent> <ItemContent> <p>Respiratory curve traits variably impact T2WI quality, guiding personalized imaging workflows.‌</p> </ItemContent> <ItemContent> <p>Respiratory-gated deep learning-reconstructed T2WI benefits patients with breath-holding difficulties in liver MRI.</p> </ItemContent> </UnorderedList></p> Graphical Abstract <p></p>