ArticleIEEE transactions on bio-medical engineering2021
Do CNNs Solve the CT Inverse Problem?
Article in IEEE transactions on bio-medical engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 46 citations in OpenAlex.
- Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems.Entropy (Basel, Switzerland) · 2025Article
- Accurate volume image reconstruction for digital breast tomosynthesis with directional-gradient and pixel sparsity regularization.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Report on the AAPM deep-learning spectral CT Grand Challenge.Medical physics · 2024Article
- Tomographic detection of photon pairs produced from high-energy X-rays for the monitoring of radiotherapy dosing.Nature biomedical engineering · 2023Article
- Iterative Image Reconstruction with Under-Sampled Data Assisted by a Neural Network.Journal of biotechnology and its applications · 2023Article
- Report on the AAPM deep-learning sparse-view CT grand challenge.Medical physics · 2022Article
- Emerging and future use of intra-surgical volumetric X-ray imaging and adjuvant tools for decision support in breast-conserving surgery.Current opinion in biomedical engineering · 2022Article
- Penalized-Likelihood PET Image Reconstruction Using Similarity-Driven Median Regularization.Tomography (Ann Arbor, Mich.) · 2022Article
- Low-dose CBCT reconstruction via joint non-local total variation denoising and cubic B-spline interpolation.Scientific reports · 2021Article
- Compressible Latent-Space Invertible Networks for Generative Model-Constrained Image Reconstruction.IEEE transactions on computational imaging · 2021Article
- Mutual Information-Based Non-Local Total Variation Denoiser for Low-Dose Cone-Beam Computed Tomography.Frontiers in oncology · 2021Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
objectiveThis work examines the claim made in the literature that the inverse problem associated with image reconstruction in sparse-view computed tomography (CT) can be solved with a convolutional neural network (CNN).
methodsTraining, and testing image/data pairs are generated in a dedicated breast CT simulation for sparse-view sampling, using two different object models. The trained CNN is tested to see if images can be accurately recovered from their corresponding sparse-view data. For reference, the same sparse-view CT data is reconstructed by the use of constrained total-variation (TV) minimization (TVmin), which exploits sparsity in the gradient magnitude image (GMI).
resultsThere is a significant discrepancy between the image obtained with the CNN and the image that generated the data. TVmin is able to accurately reconstruct the test images.
conclusionWe find that the sparse-view CT inverse problem cannot be solved for the particular published CNN-based methodology that we chose, and the particular object model that we tested. SIGNIFICANCE: The inability of the CNN to solve the inverse problem associated with sparse-view CT, for the specific conditions of the presented simulation, draws into question similar unsupported claims being made for the use of CNNs and deep-learning to solve inverse problems in medical imaging.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.