Towards CT-quality Ultrasound Imaging using Deep Learning

Abstract

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.

References in corpus (4)

• Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
• Deep Convolutional Neural Network for Inverse Problems in Imaging
• Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
• CT Image Denoising with Perceptive Deep Neural Networks

Cited by in corpus (8)

• A Deep Learning Framework for Single-Sided Sound Speed Inversion in Medical Ultrasound
• CNN-Based Image Reconstruction Method for Ultrafast Ultrasound Imaging
• Learning beamforming in ultrasound imaging
• Non-rigid Registration Method between 3D CT Liver Data and 2D Ultrasonic Images based on Demons Model
• 3D B-mode ultrasound speckle reduction using deep learning for 3D registration applications
• High quality ultrasonic multi-line transmission through deep learning
• Current Advances in Computational Lung Ultrasound Imaging: A Review
• High frame-rate cardiac ultrasound imaging with deep learning