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AI-Enabled Ultra-Low-Dose CT Reconstruction

arXiv 2106.09834 ↗First posted 17 Jun 2021eess.IV cs.CV cs.LGcited by 0

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Abstract (arXiv)

By the ALARA (As Low As Reasonably Achievable) principle, ultra-low-dose CT reconstruction is a holy grail to minimize cancer risks and genetic damages, especially for children. With the development of medical CT technologies, the iterative algorithms are widely used to reconstruct decent CT images from a low-dose scan. Recently, artificial intelligence (AI) techniques have shown a great promise in further reducing CT radiation dose to the next level. In this paper, we demonstrate that AI-powered CT reconstruction offers diagnostic image quality at an ultra-low-dose level comparable to that of radiography. Specifically, here we develop a Split Unrolled Grid-like Alternative Reconstruction (SUGAR) network, in which deep learning, physical modeling and image prior are integrated. The reconstruction results from clinical datasets show that excellent images can be reconstructed using SUGAR from 36 projections. This approach has a potential to change future healthcare.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 40 references that are in the map. First posted on arXiv 17 Jun 2021.

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15 of 40 references.

References · 15

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
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  9. 2020MetaInv-Net: Meta Inversion Network for Sparse View CT Image Reconstructioncited by 23
  10. 2020MAGIC: Manifold and Graph Integrative Convolutional Network for Low-Dose CT Reconstructioncited by 19
  11. 2020Do CNNs solve the CT inverse problem?cited by 13
  12. 2018Deep BCD-Net Using Identical Encoding-Decoding CNN Structures for Iterative Image Recoverycited by 11
3 more
  1. 2018Non-local Low-rank Cube-based Tensor Factorization for Spectral CT Reconstructioncited by 5
  2. 2020Limited View Tomographic Reconstruction Using a Deep Recurrent Framework with Residual Dense Spatial-Channel Attention Network and Sinogram Consistencycited by 4
  3. 2014How little data is enough? Phase-diagram analysis of sparsity-regularized X-ray CTcited by 1

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