ArticleIEEE transactions on medical imaging2021
On Hallucinations in Tomographic Image Reconstruction.
Article in IEEE transactions on medical imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers.
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Who cites it
57 citing papers in PubMed.
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- Fluoroscopy-Guided Motion Management in Particle Therapy: Evolution, Challenges, and AI-Enabled Opportunities.Tomography (Ann Arbor, Mich.) · 2026Review
- Standardized Images and Evaluation Metrics for Tomography.Tomography (Ann Arbor, Mich.) · 2026Article
- Global-Token U-Net with Hybrid Loss for Trustworthy Medical Image Super-Resolution.Sensors (Basel, Switzerland) · 2026Article
- On Hallucinations in Artificial Intelligence-Generated Content for Nuclear Medicine Imaging (the DREAM Report).Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2026Review
- Context-specific image quality assessment for virtual histologic staining: checklist and guideline.Journal of medical imaging (Bellingham, Wash.) · 2026Article
- Hallucination at low radiation dose: Evaluation of two deep-learning reconstruction methods in high-resolution chest CT.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- Will AI Replace Physicians in the Near Future? AI Adoption Barriers in Medicine.Diagnostics (Basel, Switzerland) · 2026Review
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- MG-SpaIR: Multi-Grade Sparse-Guided Implicit Representation for Training-Data-Free Image Restoration.Journal of mathematical imaging and vision · 2026Article
- Learning a Filtered Backprojection Reconstruction Method for Photoacoustic Computed Tomography With Hemispherical Measurement Geometries.IEEE transactions on medical imaging · 2026Article
- LensPlus: a high space-bandwidth optical imaging technique.Biomedical optics express · 2026Article
- Survey of Deep Learning and Physics-Based Approaches in Computational Wave Imaging.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2025Article
- Noise-augmented deep denoising: A method to boost CT image denoising networks.Medical physics · 2025Article
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- Systematic Review and Meta-analysis of AI-driven MRI Motion Artifact Detection and Correction.ArXiv · 2025Article
- Fourier Diffusion Models: A Method to Control MTF and NPS in Score-Based Stochastic Image Generation.IEEE transactions on medical imaging · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Tomographic image reconstruction is generally an ill-posed linear inverse problem. Such ill-posed inverse problems are typically regularized using prior knowledge of the sought-after object property. Recently, deep neural networks have been actively investigated for regularizing image reconstruction problems by learning a prior for the object properties from training images. However, an analysis of the prior information learned by these deep networks and their ability to generalize to data that may lie outside the training distribution is still being explored. An inaccurate prior might lead to false structures being hallucinated in the reconstructed image and that is a cause for serious concern in medical imaging. In this work, we propose to illustrate the effect of the prior imposed by a reconstruction method by decomposing the image estimate into generalized measurement and null components. The concept of a hallucination map is introduced for the general purpose of understanding the effect of the prior in regularized reconstruction methods. Numerical studies are conducted corresponding to a stylized tomographic imaging modality. The behavior of different reconstruction methods under the proposed formalism is discussed with the help of the numerical studies.
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Registered trials
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