Towards CT-quality Ultrasound Imaging using Deep Learning

The cost-effectiveness and practical harmlessness of ultrasound imaging have made it one of the most widespread tools for medical diagnosis. Unfortunately, the beam-forming based image formation produces granular speckle noise, blurring, shading and other artifacts. To overcome these effects, the ul...

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Bibliographic Details
Published in:arXiv.org 2017-10
Main Authors: Vedula, Sanketh, Ortal Senouf, Bronstein, Alex M, Michailovich, Oleg V, Zibulevsky, Michael
Format: Article
Language:English
Subjects:
Acoustic noise
Acoustic propagation
Acoustic properties
Artificial neural networks
Beamforming
Blurring
Computed tomography
Computer simulation
Deep learning
Image quality
Image reconstruction
Inverse problems
Medical imaging
Neural networks
Radio frequency
Shading
Ultrasonic imaging
Wave propagation
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Summary:The cost-effectiveness and practical harmlessness of ultrasound imaging have made it one of the most widespread tools for medical diagnosis. Unfortunately, the beam-forming based image formation produces granular speckle noise, blurring, shading and other artifacts. To overcome these effects, the ultimate goal would be to reconstruct the tissue acoustic properties by solving a full wave propagation inverse problem. In this work, we make a step towards this goal, using Multi-Resolution Convolutional Neural Networks (CNN). As a result, we are able to reconstruct CT-quality images from the reflected ultrasound radio-frequency(RF) data obtained by simulation from real CT scans of a human body. We also show that CNN is able to imitate existing computationally heavy despeckling methods, thereby saving orders of magnitude in computations and making them amenable to real-time applications.
ISSN:2331-8422