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1000
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Abstract/Summary
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<jats:title>Abstract</jats:title><jats:sec>
<jats:title>Background</jats:title>
<jats:p>To investigate the potential of combining compressed sensing (CS) and deep learning (DL) for accelerated two-dimensional (2D) and three-dimensional (3D) magnetic resonance imaging (MRI) of the shoulder.</jats:p>
</jats:sec><jats:sec>
<jats:title>Methods</jats:title>
<jats:p>Twenty healthy volunteers were examined using at 3-T scanner with a fat-saturated, coronal, 2D proton density-weighted sequence with four acceleration levels (2.3, 4, 6, and 8) and a 3D sequence with three acceleration levels (8, 10, and 13), all accelerated with CS and reconstructed using the conventional algorithm and a new DL-based algorithm (CS-AI). Subjective image quality was evaluated by two blinded readers using 6 criteria on a 5-point Likert scale (overall impression, artifacts, and delineation of the subscapularis tendon, bone, acromioclavicular joint, and glenoid labrum). Objective image quality was measured by calculating signal-to-noise-ratio, contrast-to-noise-ratio, and a structural similarity index measure. All reconstructions were compared to the clinical standard (CS 2D acceleration factor 2.3; CS 3D acceleration factor 8). Additionally, subjective and objective image quality were compared between CS and CS-AI with the same acceleration levels.</jats:p>
</jats:sec><jats:sec>
<jats:title>Results</jats:title>
<jats:p>Both 2D and 3D sequences reconstructed with CS-AI achieved on average significantly better subjective and objective image quality compared to sequences reconstructed with CS with the same acceleration factor (<jats:italic>p</jats:italic> ≤ 0.011). Comparing CS-AI to the reference sequences showed that 4-fold acceleration for 2D sequences and 13-fold acceleration for 3D sequences without significant loss of quality (<jats:italic>p</jats:italic> ≥ 0.058).</jats:p>
</jats:sec><jats:sec>
<jats:title>Conclusions</jats:title>
<jats:p>For MRI of the shoulder at 3 T, a DL-based algorithm allowed additional acceleration of acquisition times compared to the conventional approach.</jats:p>
</jats:sec><jats:sec>
<jats:title>Relevance statement</jats:title>
<jats:p>The combination of deep-learning and compressed sensing hold the potential for further scan time reduction in 2D and 3D imaging of the shoulder while providing overall better objective and subjective image quality compared to the conventional approach.</jats:p>
</jats:sec><jats:sec>
<jats:title>Trial registration</jats:title>
<jats:p>DRKS00024156.</jats:p>
</jats:sec><jats:sec>
<jats:title>Key points</jats:title>
<jats:p>• Combination of compressed sensing and deep learning improved image quality and allows for significant acceleration of shoulder MRI.</jats:p>
<jats:p>• Deep learning-based algorithm achieved better subjective and objective image quality than conventional compressed sensing.</jats:p>
<jats:p>• For shoulder MRI at 3 T, 40% faster image acquisition for 2D sequences and 38% faster image acquisition for 3D sequences may be possible.</jats:p>
</jats:sec><jats:sec>
<jats:title>Graphical Abstract</jats:title>
</jats:sec>
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1000
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Hinweis
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DeepGreen-ID: 7539a1fafa584a419a1ddb223bfd5b8b ; metadata provieded by: DeepGreen (https://www.oa-deepgreen.de/api/v1/), LIVIVO search scope life sciences (http://z3950.zbmed.de:6210/livivo), Crossref Unified Resource API (https://api.crossref.org/swagger-ui/index.html), to.science.api (https://frl.publisso.de/), ZDB JSON-API (beta) (https://zeitschriftendatenbank.de/api/), lobid - Dateninfrastruktur für Bibliotheken (https://lobid.org/resources/search)
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