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      MR-based synthetic CT generation using a deep convolutional neural network method

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      Medical Physics
      Wiley

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          Abstract

          Interests have been rapidly growing in the field of radiotherapy to replace CT with magnetic resonance imaging (MRI), due to superior soft tissue contrast offered by MRI and the desire to reduce unnecessary radiation dose. MR-only radiotherapy also simplifies clinical workflow and avoids uncertainties in aligning MR with CT. Methods, however, are needed to derive CT-equivalent representations, often known as synthetic CT (sCT), from patient MR images for dose calculation and DRR-based patient positioning. Synthetic CT estimation is also important for PET attenuation correction in hybrid PET-MR systems. We propose in this work a novel deep convolutional neural network (DCNN) method for sCT generation and evaluate its performance on a set of brain tumor patient images.

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          Most cited references54

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          Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
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              A Threshold Selection Method from Gray-Level Histograms

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                Author and article information

                Journal
                Medical Physics
                Med. Phys.
                Wiley
                00942405
                April 2017
                April 2017
                March 21 2017
                : 44
                : 4
                : 1408-1419
                Affiliations
                [1 ]Elekta Inc.; Maryland Heights MO 63043 USA
                Article
                10.1002/mp.12155
                28192624
                eb76277e-1388-481f-8eaf-4c2652d03581
                © 2017

                http://doi.wiley.com/10.1002/tdm_license_1

                http://onlinelibrary.wiley.com/termsAndConditions

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