biostudies-literatureZhu LNational Natural Science Foundation of ChinaShanghai Jiao Tong University2537-2548https://www.ebi.ac.uk/biostudies/studies/S-EPMC12343407biostudies-literatureUnknown38(4)Deep learning reconstruction (DLR) has exhibited potential in saving scan time. There is limited research on the evaluation of accelerated acquisition with DLR in staging rectal cancers. Our first objective was to explore the best DLR level in saving time through phantom experiments. Resolution and number of excitations (NEX) adjusted for different scan time, image quality of conventionally reconstructed T2W images were measured and compared with images reconstructed with different DLR level. The second objective was to explore the feasibility of accelerated T2W imaging with DLR in image quality and diagnostic performance for rectal cancer patients. 52 patients were prospectively enrolled to undergo accelerated acquisition reconstructed with highly-denoised DLR (DLR_H_40sec) and Journal of imaging informatics in medicineAccelerated T2W Imaging with Deep Learning Reconstruction in Staging Rectal Cancer: A Preliminary Study.PMC1234340782271934YG2021QN0882101986Xia YDing BWang BZhang HShen HWang KYuan FShi BDong HZhu LXu TFeng WDai JfalseAccelerated T2W Imaging with Deep Learning Reconstruction in Staging Rectal Cancer: A Preliminary Study.Deep learning reconstruction (DLR) has exhibited potential in saving scan time. There is limited research on the evaluation of accelerated acquisition with DLR in staging rectal cancers. Our first objective was to explore the best DLR level in saving time through phantom experiments. Resolution and number of excitations (NEX) adjusted for different scan time, image quality of conventionally reconstructed T2W images were measured and compared with images reconstructed with different DLR level. The second objective was to explore the feasibility of accelerated T2W imaging with DLR in image quality and diagnostic performance for rectal cancer patients. 52 patients were prospectively enrolled to undergo accelerated acquisition reconstructed with highly-denoised DLR (DLR_H_40sec) and 2025-01-01T00:00:00Z2025 Aug2026-04-18T13:08:33.1Z2026-04-07T14:36:02.501ZS-EPMC123434073966332010.1007/s10278-024-01345-x