Ultrasound transmission tomography image reconstruction with a fully convolutional neural network

Wenzhao Zhao, Hongjian Wang, Hartmut Gemmeke, Koen W.A. Van Dongen, Torsten Hopp, Jürgen Hesser

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)

Abstract

Image reconstruction of ultrasound computed tomography based on the wave equation is able to show much more structural details than simpler ray-based image reconstruction methods. However, to invert the wave-based forward model is computationally demanding. To address this problem, we develop an efficient fully learned image reconstruction method based on a convolutional neural network. The image is reconstructed via one forward propagation of the network given input sensor data, which is much faster than the reconstruction using conventional iterative optimization methods. To transform the ultrasound measured data in the sensor domain into the reconstructed image in the image domain, we apply multiple down-scaling and up-scaling convolutional units to efficiently increase the number of hidden layers with a large receptive and projective field that can cover all elements in inputs and outputs, respectively. For dataset generation, a paraxial approximation forward model is used to simulate ultrasound measurement data. The neural network is trained with a dataset derived from natural images in ImageNet and tested with a dataset derived from medical images in OA-Breast Phantom dataset. Test results show the superior efficiency of the proposed neural network to other reconstruction algorithms including popular neural networks. When compared with conventional iterative optimization algorithms, our neural network can reconstruct a 110 × 86 image more than 20 times faster on a CPU and 1000 times faster on a GPU with comparable image quality and is also more robust to noise.

Original languageEnglish
Article number235021
Number of pages15
JournalPhysics in Medicine and Biology
Volume65
Issue number23
DOIs
Publication statusPublished - 2020

Bibliographical note

Accepted Author Manuscript

Keywords

  • Breast cancer
  • Fully convolutional neural network
  • Image reconstruction
  • Paraxial approximation
  • Ultrasound transmission tomography

Fingerprint

Dive into the research topics of 'Ultrasound transmission tomography image reconstruction with a fully convolutional neural network'. Together they form a unique fingerprint.

Cite this