Image reconstruction by domain-transform manifold learning
Open accessAbstract
Image reconstruction is essential for imaging applications across the physical and life sciences, including optical and radar systems, magnetic resonance imaging, X-ray computed tomography, positron emission tomography, ultrasound imaging and radio astronomy. During image acquisition, the sensor encodes an intermediate representation of an object in the sensor domain, which is subsequently reconstructed into an image by an inversion of the encoding function. Image reconstruction is challenging because analytic knowledge of the exact inverse transform may not exist a priori, especially in the presence of sensor non-idealities and noise. Thus, the standard reconstruction approach involves approximating the inverse function with multiple ad hoc stages in a signal processing chain, the composition of which depends on the details of each acquisition strategy, and often requires expert parameter tuning to optimize reconstruction performance. Here we present a unified framework for image reconstruction-automated transform by manifold approximation (AUTOMAP)-which recasts image reconstruction as a data-driven supervised learning task that allows a mapping between the sensor and the image domain to emerge from an appropriate corpus of training data. We implement AUTOMAP with a deep neural network and exhibit its flexibility in learning reconstruction transforms for various magnetic resonance imaging acquisition strategies, using the same network architecture and hyperparameters. We further demonstrate that manifold learning during training results in sparse representations of domain transforms along low-dimensional data manifolds, and observe superior immunity to noise and a reduction in reconstruction artefacts compared with conventional handcrafted reconstruction methods. In addition to improving the reconstruction performance of existing acquisition methodologies, we anticipate that AUTOMAP and other learned reconstruction approaches will accelerate the development of new acquisition strategies across imaging modalities.
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AUTOMAP formulates image reconstruction generically as a data-driven supervised learning task that generates a mapping between the sensor and the image domain based on an appropriate collection of training data.
“A unified framework for image reconstruction [140], called automated transform by manifold approximation (AUTOMAP) consisting of a feedforward deep neural network with fully connected layers followed by a sparse convolutional autoencoder, formulate image reconstruction generically as a data-driven supervised learning task that generates a mapping between the sensor and the image domain based on an”
From An overview of deep learning in medical imaging focusing on MRI (2019) · cites this paper as [140]
The tremendous amounts of high-impact publications in top journals dealing with deep learning in medical imaging illustrate the value of the methods.
“This is illustrated by the tremendous amounts of high-impact publications in top-journals dealing with deep learning in medical imaging (for example [17, 21, 30, 32, 40, 90, 162, 137, 140, 273, 285], all published in 2018).”
From An overview of deep learning in medical imaging focusing on MRI (2019) · cites this paper as [140]
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- Authors
- Zhu, B., Liu, J. Z., Cauley, S. F., Rosen, B. R., & Rosen, M. S.
- Affiliations
- Harvard University (US)
- Corresponding author
- Rosen, Matthew S.
- Venue
- Nature
- Published
- 2018-03
- Volume
- 555
- Issue
- 7697
- Pages
- 487-492
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2611467245
- Cited by
- 1,694
- References
- 78
- Citations by year
- 2026: 79 · 2025: 165 · 2024: 187 · 2023: 232 · 2022: 247 · 2021: 280 · 2020: 251 · 2019: 195 · 2018: 52 · 2014: 1 · 2012: 1
- FWCI
- 134.55
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Institutes of Health (1s10rr019307-01, 1s10rr023043-01)
- National Institute of Mental Health (U01MH093765)
- National Institute of Biomedical Imaging and Bioengineering (P41EB015896)
- National Center for Research Resources (S10-RR-023043, S10RR023401)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Model Reduction and Neural Networks
- Keywords
- image reconstruction, AutoMap, magnetic resonance imaging, noise robustness, reconstruction artefacts, deep neural network, sparse representation, diagnostic imaging, astronomical imaging, computed tomography, positron emission tomography, ultrasonography, radio astronomy imaging, radar imaging, optical imaging
- MeSH terms
- Machine Learning, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Artifacts, Neural Networks, Computer, Positron-Emission Tomography
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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