Journal
NATURE
Volume 555, Issue 7697, Pages 487-+Publisher
NATURE PORTFOLIO
DOI: 10.1038/nature25988
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Funding
- MGH Department of Radiology
- BWH Department of Radiology
- NVIDIA
- National Institutes of Health/National Institute of Biomedical Imaging and Bioengineering [EB022390]
- HCP
- MGH-USC Consortium [U01MH093765]
- NIH Blueprint Initiative for Neuroscience Research grant
- National Institutes of Health [P41EB015896]
- [S10RR023043]
- [1S10RR023401]
- [1S10RR019307]
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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(1-3). 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(4,5), the composition of which depends on the details of each acquisition strategy, and often requires expert parameter tuning to optimize reconstructionperformance. 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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