Journal Article

Image reconstruction by domain-transform manifold learning

Cauley, Stephen F., Rosen, Bruce R., Rosen, Matthew S., Liu, Jeremiah Z., Zhu, Bo

2018

Overview

Image reconstruction is reformulated using a data-driven, supervised machine learning framework that allows a mapping between sensor and image domains to emerge from even noisy and undersampled data, improving accuracy and reducing image artefacts. Machine learning improves image reconstruction Reconstructing images from data, whether for medical or astronomical purposes, hinges on well-defined steps. The data sensor encodes an intermediate representation of the observed object, which is converted into an image by a mathematical operation known as the inversion of the encoding function. This inversion is often plagued by sensor imperfections and noise, requiring extra technique-specific steps to correct them. Here, Matthew Rosen and colleagues present a more unified framework termed 'automated transform by manifold approximation' (AUTOMAP). AUTOMAP tackles image reconstruction as a supervised learning task, which uses appropriate training data to link the sensor data to the output image. The authors implemented AUTOMAP with a deep neural network and tested its flexibility in learning how to reconstruct images for various magnetic resonance imaging acquisition strategies. AUTOMAP reduced artefacts and improved accuracy in images reconstructed from noisy and undersampled acquisitions. The authors expect their framework to apply to other imaging methods. 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 , 2 , 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 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.

• Publisher: Nature Publishing Group UK,Nature Publishing Group
• Subject: 639/705/1042 / 639/766/259 / Architecture / Artefacts / Artificial neural networks / Astronomy / Computed tomography / Data processing / Euclidean space / Humanities and Social Sciences / Image acquisition / Image processing / Image reconstruction / Immunity / Learning / letter / Linear algebra / Machine learning / Magnetic fields / Magnetic resonance / Magnetic resonance imaging / Manifolds (Mathematics) / Mathematical analysis / Medical imaging / Methods / multidisciplinary / Neural networks / NMR / Noise / Noise reduction / Nuclear magnetic resonance / Positron emission / Positron emission tomography / Radar / Radar imaging / Radio astronomy / Representations / Science / Sensors / Signal processing / Tomography / Training / Transformations (mathematics) / Ultrasound