Deep Learning-reconstructed Parallel Accelerated Imaging for Knee MRI

Abstract

Background: Deep learning (DL) can improve image quality by removing noise from accelerated MRI.

Objective: To compare the quality of various accelerated imaging applications in knee MRI with and without DL.

Method: We analyzed 44 knee MRI scans from 38 adult patients using the DL-reconstructed parallel acquisition technique (PAT) between May 2021 and April 2022. The participants underwent sagittal fat-saturated T2-weighted turbo-spin-echo accelerated imaging without DL (PAT-2 [2-fold parallel accelerated imaging], PAT-3, and PAT-4) and with DL (DL with PAT-3 [PAT-3DL] and PAT-4 [PAT-4DL]). Two readers independently evaluated subjective image quality (diagnostic confidence of knee joint abnormalities, subjective noise and sharpness, and overall image quality) using a 4-point grading system (1-4, 4=best). Objective image quality was assessed based on noise (noise power) and sharpness (edge rise distance).

Results: The mean acquisition times for PAT-2, PAT-3, PAT-4, PAT-3DL, and PAT-4DL sequences were 2:55, 2:04, 1:33, 2:04, and 1:33 min, respectively. Regarding subjective image quality, PAT-3DL and PAT-4DL scored higher than PAT-2. Objectively, DL-reconstructed imaging had significantly lower noise than PAT-3 and PAT-4 (P <0.001), but the results were not significantly different from those for PAT-2 (P >0.988). Objective image sharpness did not differ significantly among the imaging combinations (P =0.470). The inter-reader reliability ranged from good to excellent (κ = 0.761–0.832).

Conclusion: PAT-4DL imaging in knee MRI exhibits similar subjective image quality, objective noise, and sharpness levels compared with conventional PAT-2 imaging, with an acquisition time reduction of 47%.

Keywords: Accelerated imaging; Deep learning; Image quality; Knee MRI; Noise; Parallel imaging; musculoskeletal radiology.