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Learning the Manifold of Quality Ultrasound Acquisition

  • Noha El-Zehiry
  • Michelle Yan
  • Sara Good
  • Tong Fang
  • S. Kevin Zhou
  • Leo Grady
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8149)

Abstract

Ultrasound acquisition is a challenging task that requires simultaneous adjustment of several acquisition parameters (the depth, the focus, the frequency and its operation mode). If the acquisition parameters are not properly chosen, the resulting image will have a poor quality and will degrade the patient diagnosis and treatment workflow. Several hardware-based systems for autotuning the acquisition parameters have been previously proposed, but these solutions were largely abandoned because they failed to properly account for tissue inhomogeneity and other patient-specific characteristics. Consequently, in routine practice the clinician either uses population-based parameter presets or manually adjusts the acquisition parameters for each patient during the scan. In this paper, we revisit the problem of autotuning the acquisition parameters by taking a completely novel approach and producing a solution based on image analytics. Our solution is inspired by the autofocus capability of conventional digital cameras, but is significantly more challenging because the number of acquisition parameters is large and the determination of “good quality” images is more difficult to assess. Surprisingly, we show that the set of acquisition parameters which produce images that are favored by clinicians comprise a 1D manifold, allowing for a real-time optimization to maximize image quality. We demonstrate our method for acquisition parameter autotuning on several live patients, showing that our system can start with a poor initial set of parameters and automatically optimize the parameters to produce high quality images.

Keywords

Acquisition Parameter Good Quality Image Image Quality Assessment Medical Ultrasound Imaging Ultrasound Acquisition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Noha El-Zehiry
    • 1
  • Michelle Yan
    • 1
  • Sara Good
    • 2
  • Tong Fang
    • 1
  • S. Kevin Zhou
    • 1
  • Leo Grady
    • 1
  1. 1.Corporate TechnologySiemens CorporationUSA
  2. 2.Siemens HealthcareUSA

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