  Objective: To improve accelerated MRI reconstruction through a densely
connected 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 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 eight-fold 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% for four- and eight-fold acceleration, respectively.
For eight-fold acceleration, the model achieved a 23% decrease in the NMSE when
compared to the baseline model. In an ablation study, the individual
architectural modifications all contributed to this improvement, by reducing
the SSIM and NMSE with approximately 3% and 5% for four-fold acceleration,
respectively.
  Conclusion: The proposed architectural modifications allow for simple
adjustments on an already existing cascading framework to further improve the
resulting reconstructions.
