Ultrasound elastography using multiple images

Hassan Rivaz, Emad M. Boctor, Michael A. Choti, Gregory D. Hager

Research output: Contribution to journalArticle

28 Citations (Scopus)

Abstract

Displacement estimation is an essential step for ultrasound elastography and numerous techniques have been proposed to improve its quality using two frames of ultrasound RF data. This paper introduces a technique for calculating a displacement field from three (or multiple) frames of ultrasound RF data. To calculate a displacement field using three images, we first derive constraints on variations of the displacement field with time using mechanics of materials. These constraints are then used to generate a regularized cost function that incorporates amplitude similarity of three ultrasound images and displacement continuity. We optimize the cost function in an expectation maximization (EM) framework. Iteratively reweighted least squares (IRLS) is used to minimize the effect of outliers. An alternative approach for utilizing multiple images is to only consider two frames at any time and sequentially calculate the strains, which are then accumulated. We formally show that, compared to using two images or accumulating strains, the new algorithm reduces the noise and eliminates ambiguities in displacement estimation. The displacement field is used to generate strain images for quasi-static elastography. Simulation, phantom experiments and in vivo patient trials of imaging liver tumors and monitoring ablation therapy of liver cancer are presented for validation. We show that even with the challenging patient data, where it is likely to have one frame among the three that is not optimal for strain estimation, the introduction of physics-based prior as well as the simultaneous consideration of three images significantly improves the quality of strain images. Average values for strain images of two frames versus ElastMI are: 43 versus 73 for SNR (signal to noise ratio) in simulation data, 11 versus 15 for CNR (contrast to noise ratio) in phantom data, and 5.7 versus 7.3 for CNR in patient data. In addition, the improvement of ElastMI over both utilizing two images and accumulating strains is statistically significant in the patient data, with p-values of respectively 0.006 and 0.012.

Original languageEnglish (US)
Pages (from-to)314-329
Number of pages16
JournalMedical Image Analysis
Volume18
Issue number2
DOIs
StatePublished - 2014

Fingerprint

Elasticity Imaging Techniques
Ultrasonics
Noise
Costs and Cost Analysis
Physics
Cost functions
Liver
Signal-To-Noise Ratio
Liver Neoplasms
Mechanics
Least-Squares Analysis
Ablation
Tumors
Signal to noise ratio
Neoplasms
Imaging techniques
Monitoring

Keywords

  • Elasticity imaging
  • Expectation Maximization (EM)
  • Liver ablation
  • Strain imaging
  • Ultrasound elastography

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Radiology Nuclear Medicine and imaging
  • Health Informatics
  • Radiological and Ultrasound Technology
  • Medicine(all)

Cite this

Rivaz, H., Boctor, E. M., Choti, M. A., & Hager, G. D. (2014). Ultrasound elastography using multiple images. Medical Image Analysis, 18(2), 314-329. https://doi.org/10.1016/j.media.2013.11.002

Ultrasound elastography using multiple images. / Rivaz, Hassan; Boctor, Emad M.; Choti, Michael A.; Hager, Gregory D.

In: Medical Image Analysis, Vol. 18, No. 2, 2014, p. 314-329.

Research output: Contribution to journalArticle

Rivaz, H, Boctor, EM, Choti, MA & Hager, GD 2014, 'Ultrasound elastography using multiple images', Medical Image Analysis, vol. 18, no. 2, pp. 314-329. https://doi.org/10.1016/j.media.2013.11.002
Rivaz, Hassan ; Boctor, Emad M. ; Choti, Michael A. ; Hager, Gregory D. / Ultrasound elastography using multiple images. In: Medical Image Analysis. 2014 ; Vol. 18, No. 2. pp. 314-329.
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