TOMOGRAPHIC IMAGE RECONSTRUCTION VIA MACHINE LEARNING
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Abstract
Tomographic/tomosynthetic image reconstruction systems and methods in the framework of machine learning, such as deep learning, are provided. A machine learning algorithm can be used to obtain an improved tomographic image from raw data, processed data, or a preliminarily reconstructed intermediate image for biomedical imaging or any other imaging purpose. In certain cases, a single, conventional, non-deep-learning algorithm can be used on raw imaging data to obtain an initial image, and then a deep learning algorithm can be used on the initial image to obtain a final reconstructed image. All machine learning methods and systems for tomographic image reconstruction are covered, except for use of a single shallow network (three layers or less) for image reconstruction.
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Citations
48 Claims
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1-32. -32. (canceled)
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33. A method of reconstructing an image from tomographic data obtained by an imaging process, the method comprising:
performing at least one algorithm step on a raw data set or an intermediate data set of the tomographic data to obtain a final reconstructed image, the at least one algorithm comprising a deep learning algorithm. - View Dependent Claims (34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47)
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48. A system for reconstructing an image from raw data obtained by a medical imaging process, the system comprising:
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a subsystem for obtaining tomographic imaging data; at least one processor; and a machine-readable medium, in operable communication with the subsystem for obtaining tomographic imaging data and the at least one processor, having machine-executable instructions stored thereon that, when executed by the at least one processor, perform at least one algorithm step on a raw data set or an intermediate data set of the tomographic data to obtain a final reconstructed image, the at least one algorithm comprising a deep learning algorithm.
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Specification