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Objective: To improve accelerated MRI reconstruction through a denselyconnected cascading deep learning reconstruction framework.  Materials and Methods: A cascading deep learning reconstruction framework(baseline model) was modified by applying three architectural modifications:Input-level dense connections between cascade inputs and outputs, an improveddeep learning sub-network, and long-range skip-connections between subsequentdeep learning networks. An ablation study was performed, where five modelconfigurations were trained on the NYU fastMRI neuro dataset with an end-to-endscheme conjunct on four- and eight-fold acceleration. The trained models wereevaluated 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 SSIMimprovement of 8% and 11% for four- and eight-fold acceleration, respectively.For eight-fold acceleration, the model achieved a 23% decrease in the NMSE whencompared to the baseline model. In an ablation study, the individualarchitectural modifications all contributed to this improvement, by reducingthe SSIM and NMSE with approximately 3% and 5% for four-fold acceleration,respectively.  Conclusion: The proposed architectural modifications allow for simpleadjustments on an already existing cascading framework to further improve theresulting reconstructions.

A Densely Interconnected Network for Deep Learning Accelerated MRI