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Purpose: To investigate the potential of combining Compressed Sensing (CS) and a newly developed AI-based super resolution reconstruction prototype consisting of a series of convolutional neural networks (CNN) for a complete five-minute 2D knee MRI protocol., Methods: In this prospective study, 20 volunteers were examined using a 3T-MRI-scanner (Ingenia Elition X, Philips). Similar to clinical practice, the protocol consists of a fat-saturated 2D-proton-density-sequence in coronal, sagittal and transversal orientation as well as a sagittal T1-weighted sequence. The sequences were acquired with two different resolutions (standard and low resolution) and the raw data reconstructed with two different reconstruction algorithms: a conventional Compressed SENSE (CS) and a new CNN-based algorithm for denoising and subsequently to interpolate and therewith increase the sharpness of the image (CS-SuperRes). Subjective image quality was evaluated by two blinded radiologists reviewing 8 criteria on a 5-point Likert scale and signal-to-noise ratio calculated as an objective parameter., Results: The protocol reconstructed with CS-SuperRes received higher ratings than the time-equivalent CS reconstructions, statistically significant especially for low resolution acquisitions (e.g., overall image impression: 4.3 ± 0.4 vs. 3.4 ± 0.4, p < 0.05). CS-SuperRes reconstructions for the low resolution acquisition were comparable to traditional CS reconstructions with standard resolution for all parameters, achieving a scan time reduction from 11:01 min to 4:46 min (57 %) for the complete protocol (e.g. overall image impression: 4.3 ± 0.4 vs. 4.0 ± 0.5, p < 0.05)., Conclusion: The newly-developed AI-based reconstruction algorithm CS-SuperRes allows to reduce scan time by 57% while maintaining unchanged image quality compared to the conventional CS reconstruction., Competing Interests: Declaration of competing interest David Maintz is part of the speakers bureau at Philips Healthcare; all other authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Purpose: To evaluate potential clinical acceleration factors of Compressed SENSE (CS) 1 in direct comparison with SENSE for fat saturated (fs) 2 , proton density-weighted (PD) 3 2D and 3D sequences of the knee., Method: Twenty healthy volunteers were scanned with a 3 T scanner, all receiving a standard, fs 2D PD, three CS (CS 2, CS 3, CS 5) as well as time-equivalent SENSE accelerations (S 2, S 3, S 5). The fs 3D PD sequence was acquired with four CS (CS 6, CS 8, CS 10, CS 15) and equivalent SENSE (S 5.72, S 7.69, S 9.57, S 14) factors. Three independent readers rated the images. Signal-to-noise, contrast-to-noise, root-mean-square error and structural similarity index were analyzed for objective evaluation., Results: Scan time decreased with increasing CS factor (2D CS 2: 145 s, 2D CS 3: 95 s, 2D CS 5: 57 s, 3D CS 6: 293 s, 3D CS 8: 220 s, 3D CS 10: 176 s, 3D CS 15: 119 s). The 2D standard sequence was rated best for diagnostic certainty and overall image impression with an average of 4.97 ± 0.10 and 4.80 ± 0.24 (all p < 0.05), except for 2D CS 2 and 2D S 2. For the 3D sequences, the standard sequence performed better for both parameters for CS 15, S 9.57 and S 4, as well as S 7.69 for overall image impression while CS 8 was non-inferior for all tested criteria and CS 10 only inferior for delineation of the anterior cruciate ligament, both outperforming the time-equivalent SENSE accelerations., Conclusion: Compressed SENSE can significantly decrease (34.39 % for 2D CS 2 and 54.17 % for 3D CS 10) scan time in knee imaging with unchanged diagnostic certainty and overall image impression compared to the clinical reference., (Copyright © 2020 Elsevier B.V. All rights reserved.)