Super-resolution image reconstruction using diffuse source models.

Journal Article (Journal Article)

Image reconstruction is central to many scientific fields, from medical ultrasound and sonar to computed tomography and computer vision. Although lenses play a critical reconstruction role in these fields, digital sensors enable more sophisticated computational approaches. A variety of computational methods have thus been developed, with the common goal of increasing contrast and resolution to extract the greatest possible information from raw data. This paper describes a new image reconstruction method named the Diffuse Time-domain Optimized Near-field Estimator (dTONE). dTONE represents each hypothetical target in the system model as a diffuse region of targets rather than a single discrete target, which more accurately represents the experimental data that arise from signal sources in continuous space, with no additional computational requirements at the time of image reconstruction. Simulation and experimental ultrasound images of animal tissues show that dTONE achieves image resolution and contrast far superior to those of conventional image reconstruction methods. We also demonstrate the increased robustness of the diffuse target model to major sources of image degradation through the addition of electronic noise, phase aberration and magnitude aberration to ultrasound simulations. Using experimental ultrasound data from a tissue-mimicking phantom containing a 3-mm-diameter anechoic cyst, the conventionally reconstructed image has a cystic contrast of -6.3 dB, whereas the dTONE image has a cystic contrast of -14.4 dB.

Full Text

Duke Authors

Cited Authors

  • Ellis, MA; Viola, F; Walker, WF

Published Date

  • June 2010

Published In

Volume / Issue

  • 36 / 6

Start / End Page

  • 967 - 977

PubMed ID

  • 20447760

Pubmed Central ID

  • PMC2878910

Electronic International Standard Serial Number (EISSN)

  • 1879-291X

International Standard Serial Number (ISSN)

  • 0301-5629

Digital Object Identifier (DOI)

  • 10.1016/j.ultrasmedbio.2010.03.002

Language

  • eng