〔Abstract〕 Objective To explore the value of deep learning reconstruction（DLR）in liver magnetic resonance im ‑ aging（MRI）by comparing the single-shot half-fourier rapid spin-echo sequence with DLR（HASTEDLR）and diffusion-weighted imaging sequence with DLR（DWIDLR）against the conventional BLADE and conventional DWI sequences. Methods 70 patients underwent MRI examinations. Two observers independently evaluated the image quality of each sequence（including liver edge，blood vessels，lesion clarity，etc. ）. Additionally，quantitative evaluation was conducted by measuring the signal to noise ratio（SNR）of liver parenchyma and lesions，contrast to noise ratio（CNR）of lesions，as well as apparent diffusion coefficient（ADC）values from conventional DWI and DWIDLR. Intraclass correlation coefficient（ICC）was used to evaluate the consistency between the two observers. Results The inter-observer consistency was high（ICC：0. 84-0. 97）. The scanning time was reduced by 92. 63% for HASTEDLR and 50% for DWIDLR sequences，respectively. The lesion clarity score of the HASTEDLR group was sig‑ nificantly better than that of the BLADE group（P<0. 001）， with artifacts reduced in both DLR sequences（P< 0. 05）. The HASTEDLR group demonstrated higher SNR and CNR，while the DWIDLR group showed higher SNR and ADC values（all P<0. 05）. Conclusion The DLR technology can enhance the efficiency of liver MRI scans，im‑ prove image quality，and reduce artifacts，demonstrating promising application prospects.