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AirNet: Fused Analytical and Iterative Reconstruction with Deep Learning Regularization of Densely Connected Deep Neural Networks for Sparse-Data CT

G Chen1,2*, Q Huang2, B Bradshaw Ghavidel1, T Liu1, H Gao1, (1) Shanghai Jiao Tong University, Shanghai, China, (2) Winship Cancer Institute of Emory University, Atlanta, GA

Presentations

(Sunday, 7/12/2020)   [Eastern Time (GMT-4)]

Room: AAPM ePoster Library

Purpose: Sparse-data CT frequently occurs, such as breast tomosynthesis, C-arm CT, on-board 4D CBCT, and industrial CT. However, sparse-data image reconstruction remains challenging due to incomplete data. This work develops an atlas-based image reconstruction method for sparse-data CT using deep neural networks (DNN).

Methods: The new method so-called AirNet is designed to incorporate the benefits from analytical reconstruction method (AR), iterative reconstruction method (IR), and DNN. It is built upon fused analytical and iterative reconstruction (AIR) that synergizes AR and IR via the optimization framework of modified proximal forward-backward splitting (PFBS). By unrolling PFBS into IR updates of CT data fidelity and DNN regularization with residual learning, AirNet utilizes AR such as FBP during the data fidelity, introduces dense connectivity into DNN regularization, and learns PFBS coefficients and DNN parameters that minimize the loss function during the training stage. And then AirNet with trained parameters can be used for end-to-end image reconstruction.

Results: A CT atlas of 100 prostate scans was used to validate the AirNet in comparison with state-of-art DNN-based post-processing and image reconstruction methods. The validation loss in AirNet had the fastest decreasing rate, owing to inherited fast convergence from AIR. AirNet was robust to noise in projection data and content differences between the training set and the images to be reconstructed.

Conclusions: A new image reconstruction AirNet is developed for sparse-data CT image reconstruction. AirNet achieved the best image reconstruction quality among all methods under comparison for all sparse-data scenarios (sparse-view and limited-angle).

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