A densely interconnected network for deep learning accelerated MRI

Objective To improve accelerated MRI reconstruction through a densely connected cascading deep learning reconstruction framework. Materials and methods A cascading deep learning reconstruction framework (reference model) was modified by applying three architectural modifications: input-level dense c...

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Vydáno v:Magma (New York, N.Y.) Ročník 36; číslo 1; s. 65 - 77
Hlavní autoři: Ottesen, Jon André, Caan, Matthan W. A., Groote, Inge Rasmus, Bjørnerud, Atle
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cham Springer International Publishing 01.02.2023
Témata:
Acceleration
Basic Science - Reconstruction algorithms and artificial intelligence
Biomedical Engineering and Bioengineering
Computer Appl. in Life Sciences
Deep Learning
Health Informatics
Imaging
Magnetic Resonance Imaging
Medicine
Medicine & Public Health
Radiology
Research Article
Signal-To-Noise Ratio
Solid State Physics
Deep learning
MRI
Image reconstruction
ISSN:1352-8661, 0968-5243, 1352-8661
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Shrnutí:Objective To improve accelerated MRI reconstruction through a densely connected cascading deep learning reconstruction framework. Materials and methods A cascading deep learning reconstruction framework (reference model) was modified by applying three architectural modifications: input-level dense connections between cascade inputs and outputs, an improved deep learning sub-network, and long-range skip-connections between subsequent deep learning networks. An ablation study was performed, where five model configurations were trained on the NYU fastMRI neuro dataset with an end-to-end scheme conjunct on four- and eightfold acceleration. The trained models were evaluated by comparing their respective structural similarity index measure (SSIM), normalized mean square error (NMSE), and peak signal to noise ratio (PSNR). Results The proposed densely interconnected residual cascading network (DIRCN), utilizing all three suggested modifications achieved a SSIM improvement of 8% and 11%, a NMSE improvement of 14% and 23%, and a PSNR improvement of 2% and 3% for four- and eightfold acceleration, respectively. In an ablation study, the individual architectural modifications all contributed to this improvement for both acceleration factors, by improving the SSIM, NMSE, and PSNR with approximately 2–4%, 4–9%, and 0.5–1%, respectively. Conclusion The proposed architectural modifications allow for simple adjustments on an already existing cascading framework to further improve the resulting reconstructions.
Bibliografie:ObjectType-Article-1
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ISSN:1352-8661
0968-5243
1352-8661
DOI:10.1007/s10334-022-01041-3