ArticleMagma (New York, N.Y.)2023
A densely interconnected network for deep learning accelerated MRI.
Article in Magma (New York, N.Y.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Optimized reconstruction of undersampled Dixon sequences using new memory-efficient unrolled deep neural networks: HalfVarNet and HalfDIRCN.Magnetic resonance in medicine · 2026Article
- Deep learning-based Intraoperative MRI reconstruction.European radiology experimental · 2025Article
- Accelerated MRI reconstructions via variational network and feature domain learning.Scientific reports · 2024Article
- Emerging Trends in Fast MRI Using Deep-Learning Reconstruction on Undersampled k-Space Data: A Systematic Review.Bioengineering (Basel, Switzerland) · 2023Review
- De-Aliasing and Accelerated Sparse Magnetic Resonance Image Reconstruction Using Fully Dense CNN with Attention Gates.Bioengineering (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
objectiveTo improve accelerated MRI reconstruction through a densely connected cascading deep learning reconstruction framework. MATERIALS AND
methodsA 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).
resultsThe 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.
conclusionThe proposed architectural modifications allow for simple adjustments on an already existing cascading framework to further improve the resulting reconstructions.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.