ArticleMedical physics2022
Report on the AAPM deep-learning sparse-view CT grand challenge.
Article in Medical physics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- CitrusGS: 3D Gaussian splatting for sparse-view CT reconstruction and precise morphological phenotyping of citrus fruit.Plant phenomics (Washington, D.C.) · 2026Article
- Review
- Deep Learning-Based Multi-View Projection Synthesis Approach for Improving the Quality of Sparse-View CBCT in Image-Guided Radiotherapy.Journal of imaging informatics in medicine · 2025Article
- Task based evaluation of sparse view CT reconstruction techniques for intracranial hemorrhage diagnosis using an AI observer model.Scientific reports · 2025Article
- Cascaded Multi-path Shortcut Diffusion Model for Medical Image Translation.Medical image analysis · 2024Article
- Spectral CT image reconstruction using a constrained optimization approach-An algorithm for AAPM 2022 spectral CT grand challenge and beyond.Medical physics · 2024Article
- Report on the AAPM deep-learning spectral CT Grand Challenge.Medical physics · 2024Article
- Transferring U-Net between low-dose CT denoising tasks: a validation study with varied spatial resolutions.Quantitative imaging in medicine and surgery · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
purposeThe purpose of the challenge is to find the deep-learning (DL) technique for sparse-view computed tomography (CT) image reconstruction that can yield the minimum root mean square error (RMSE) under ideal conditions, thereby addressing the question of whether or not DL can solve inverse problems in imaging.
methodsThe challenge setup involves a 2D breast CT simulation, where the simulated breast phantom has random fibro-glandular structure and high-contrast specks. The phantom allows for arbitrarily large training sets to be generated with perfectly known truth. The training set consists of 4000 cases where each case consists of the truth image, 128-view sinogram data, and the corresponding 128-view filtered back-projection (FBP) image. The networks are trained to predict the truth image from either the sinogram or FBP data. Geometry information is not provided. The participating algorithms are tested on a data set consisting of 100 new cases.
resultsAbout 60 groups participated in the challenge at the validation phase, and 25 groups submitted test-phase results along with reports on their DL methodology. The winning team improved reconstruction accuracy by two orders of magnitude over our previous convolutional neural network (CNN)-based study on a similar test problem.
conclusionsThe DL-sparse-view challenge provides a unique opportunity to examine the state-of-the-art in DL techniques for solving the sparse-view CT inverse problem.
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Registered trials
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.