Effect of MR head coil geometry on deep-learning-based MR image reconstruction.

Autor: Dubljevic N; Department of Biomedical Engineering, University of Calgary, Calgary, Alberta, Canada.; Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, Alberta, Canada.; Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada., Moore S; Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, Alberta, Canada.; Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada.; O'Brien Centre for the Health Sciences, Cumming School of Medicine, Calgary, Alberta, Canada., Lauzon ML; Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, Alberta, Canada.; Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada.; Radiology and Clinical Neuroscience, University of Calgary, Calgary, Alberta, Canada., Souza R; Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada.; Department of Electrical and Software Engineering, University of Calgary, Calgary, Alberta, Canada., Frayne R; Department of Biomedical Engineering, University of Calgary, Calgary, Alberta, Canada.; Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, Alberta, Canada.; Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada.; Radiology and Clinical Neuroscience, University of Calgary, Calgary, Alberta, Canada.
Jazyk: angličtina
Zdroj: Magnetic resonance in medicine [Magn Reson Med] 2024 Oct; Vol. 92 (4), pp. 1404-1420. Date of Electronic Publication: 2024 Apr 22.
DOI: 10.1002/mrm.30130
Abstrakt: Purpose: To investigate whether parallel imaging-imposed geometric coil constraints can be relaxed when using a deep learning (DL)-based image reconstruction method as opposed to a traditional non-DL method.
Theory and Methods: Traditional and DL-based MR image reconstruction approaches operate in fundamentally different ways: Traditional methods solve a system of equations derived from the image data whereas DL methods use data/target pairs to learn a generalizable reconstruction model. Two sets of head coil profiles were evaluated: (1) 8-channel and (2) 32-channel geometries. A DL model was compared to conjugate gradient SENSE (CG-SENSE) and L1-wavelet compressed sensing (CS) through quantitative metrics and visual assessment as coil overlap was increased.
Results: Results were generally consistent between experiments. As coil overlap increased, there was a significant (p < 0.001) decrease in performance in most cases for all methods. The decrease was most pronounced for CG-SENSE, and the DL models significantly outperformed (p < 0.001) their non-DL counterparts in all scenarios. CS showed improved robustness to coil overlap and signal-to-noise ratio (SNR) versus CG-SENSE, but had quantitatively and visually poorer reconstructions characterized by blurriness as compared to DL. DL showed virtually no change in performance across SNR and very small changes across coil overlap.
Conclusion: The DL image reconstruction method produced images that were robust to coil overlap and of higher quality than CG-SENSE and CS. This suggests that geometric coil design constraints can be relaxed when using DL reconstruction methods.
(© 2024 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)
Databáze: MEDLINE
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