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  • Title: Ultrasound elastography using multiple images.
    Author: Rivaz H, Boctor EM, Choti MA, Hager GD.
    Journal: Med Image Anal; 2014 Feb; 18(2):314-29. PubMed ID: 24361599.
    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.
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