Zhai, R. et al. Intelligent Incorporation of AI with Model Constraints for MRI Acceleration.
Liu, H. et al. AI-assisted compressed sensing and parallel imaging sequences for MRI of patients with nasopharyngeal carcinoma: comparison of their capabilities in terms of examination time and image quality. Eur Radiol (2023) doi:10.1007/s00330-023-09742-6.
Hou, Y. et al. Application value of T2 fluid-attenuated inversion recovery sequence based on deep learning in static lacunar infarction. Acta Radiol 64, 1650–1658 (2023).
Sui, H. et al. Comparison of Artificial Intelligence-Assisted Compressed Sensing (ACS) and Routine Two-Dimensional Sequences on Lumbar Spine Imaging. J Pain Res 16, 257–267 (2023).
Yan, X. et al. Dark blood T2-weighted imaging of the human heart with AI-assisted compressed sensing: a patient cohort study. Quant Imaging Med Surg 13, 1699–1710 (2023).
Wang, Q. et al. Feasibility of AI-assisted compressed sensing protocols in knee MR imaging: a prospective multi-reader study. Eur Radiol (2023) doi:10.1007/s00330-023-09823-6.
Liu, J. et al. Magnetic resonance shoulder imaging using deep learning-based algorithm. Eur Radiol 33, 4864–4874 (2023).
- 应用ACS进行单次屏气肝脏T2W成像 vs 常规多次屏气成像
Sheng, R.-F. et al. Single-breath-hold T2WI liver MRI with deep learning-based reconstruction: A clinical feasibility study in comparison to conventional multi-breath-hold T2WI liver MRI. Magn Reson Imaging 81, 75–81 (2021).
- 应用ACS进行单次屏气肝脏T2W成像 vs 常规呼吸触发成像
Li, H. et al. Single-breath-hold T2WI MRI with artificial intelligence-assisted technique in liver imaging: As compared with conventional respiratory-triggered T2WI. Magn Reson Imaging 93, 175–180 (2022).
- 不配合患者进行ACS SS FLAIR vs 常规T2 FLAIR
Liu, K. et al. The clinical feasibility of artificial intelligence-assisted compressed sensing single-shot fluid-attenuated inversion recovery (ACS-SS-FLAIR) for evaluation of uncooperative patients with brain diseases: comparison with the conventional T2-FLAIR with parallel imaging. Acta Radiol 64, 1943–1949 (2023).
Zhao, Y., Peng, C., Wang, S., Liang, X. & Meng, X. The feasibility investigation of AI -assisted compressed sensing in kidney MR imaging: an ultra-fast T2WI imaging technology. BMC Med Imaging 22, 119 (2022).