A deep learning- based reconstruction approach for accelerated magnetic resonance image of the knee with compressed sense: evaluation in healthy volunteers

Objectives: To evaluate the feasibility of combining compressed sense (CS) with a newly developed deep learning -based algorithm (CS -AI) using convolutional neural networks to accelerate 2D MRI of the knee.Methods: In this prospective study, 20 healthy volun-teers were scanned with a 3T MRI scanner. All subjects received a fat-saturated sagittal 2D proton density refer-ence sequence without acceleration and four additional acquisitions with different acceleration levels: 2, 3, 4 and 6. All sequences were reconstructed with the conven-tional CS and a new CS -AI algorithm. Two independent, blinded readers rated all images by seven criteria (overall image impression, visible artifacts, delineation of ante-rior ligament, posterior ligament, menisci, cartilage, and bone) using a 5 -point Likert scale. Signal-and contrast -to -noise ratios were calculated. Subjective ratings and quantitative metrics were compared between CS and CS -AI with similar acceleration levels and between all CS/CS- AI images and the non-accelerated reference sequence. Friedman and Dunn ' s multiple comparison tests were used for subjective, ANOVA and the Tukey Kramer test for quantitative metrics.Results: Conventional CS images at the lowest acceler-ation level (CS2) were already rated significantly lower than reference for 6/7 criteria. CS -AI images maintained similar image quality to the reference up to CS -AI three for all criteria, which would allow for a reduction in scan time of 64% with unchanged image quality compared to the unaccelerated sequence. SNR and CNR were signifi-cantly higher for all CS -AI reconstructions compared to CS (all p < 0.05).Conclusions AI -based image reconstruction showed higher image quality than CS for 2D knee imaging. Its implementation in the clinical routine yields the poten-tial for faster MRI acquisition but needs further valida-tion in non-healthy study subjects.Advances in knowledge Combining compressed SENSE with a newly developed deep learning -based algorithm using convolutional neural networks allows a 64% reduction in scan time for 2D imaging of the knee. Implementation of the new deep learning -based algo-rithm in clinical routine in near future should enable better image quality/resolution with constant scan time, or reduced acquisition times while maintaining diagnostic quality.