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Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction

Abstract

Commercial iterative reconstruction techniques help to reduce the radiation dose of computed tomography (CT), but altered image appearance and artefacts can limit their adoptability and potential use. Deep learning has been investigated for low-dose CT (LDCT). Here, we design a modularized neural network for LDCT and compare it with commercial iterative reconstruction methods from three leading CT vendors. Although popular networks are trained for an end-to-end mapping, our network performs an end-to-process mapping so that intermediate denoised images are obtained with associated noise reduction directions towards a final denoised image. The learned workflow allows radiologists in the loop to optimize the denoising depth in a task-specific fashion. Our network was trained with the Mayo LDCT Dataset and tested on separate chest and abdominal CT exams from Massachusetts General Hospital. The best deep learning reconstructions were systematically compared to the best iterative reconstructions in a double-blinded reader study. This study confirms that our deep learning approach performs either favourably or comparably in terms of noise suppression and structural fidelity, and is much faster than commercial iterative reconstruction algorithms.

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Data availability

The training and validation sets were obtained from the 2016 NIH-AAPM-Mayo Clinic Low-Dose CT Grand Challenge, and are publicly available (http://www.aapm.org/GrandChallenge/LowDoseCT/). The testing data from MGH for this study are protected for patient privacy and will be available for data sharing upon request and after going through an external Institutional Review Board procedure at MGH.

Code availability

The source code and the trained models for this study are publicly available on Github (https://github.com/hmshan/MAP-NN).

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Acknowledgements

The authors thank M. Vannier and B. De Man for helpful discussions, and NVIDIA Corporation for the donation of GPUs used for this research. H.S. and G.W. were partially supported by the National Institutes of Health/National Institute of Biomedical Imaging and Bioengineering (U01EB017140).

Author information

G.W. initiated the project. G.W., H.S. and M.K.K. designed the experiments. H.S. and G.W. performed machine learning research. A.P. and F.H. collected CT data and conducted the reader studies. M.K.K., C.N. and R.D.K. were the readers. H.S., G.W., U.K. and M.K.K. analysed the data. H.S. and G.W. wrote the paper. M.K.K. and U.K. edited the paper.

Competing interests

G.W. and H.S. have received unrelated industrial research grants from General Electric and Hologic Inc. M.K.K. has received unrelated industrial research grants from Siemens Healthineers and Riverain Inc.

Correspondence to Mannudeep K. Kalra or Ge Wang.

Supplementary information

  1. Supplementary Information

    Supplementary Notes 1–3, Supplementary Figs. 1–4, Supplementary Tables 1 and 2

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Fig. 1: Proposed MAP-NN for LDCT denoising and progressively denoised images on LDCT and NDCT images with mapping depth D.
Fig. 2: Comparison between the best DL reconstruction and the best IR reconstruction for abdomen and chest regions across three major vendors (A, B and C) and three readers (R1, R2 and R3).
Fig. 3: Individual metric comparison between the best DL and best IR reconstructions keyed to body regions (abdomen and chest) and CT vendors (A, B and C).
Fig. 4: Best DL and best IR reconstructions, as well as NDCT FBP for abdomen and chest regions from three vendors.