A New Method for the Acquisition of Ultrasonic Strain Image Volumes

Housden, James; Chen, Lujie; Gee, Andrew H.; Treece, Graham M.; Uff, Chris; Fromageau, J.; Garcia, Leo; Prager, Richard W.; Dorward, Neil; Bamber, Jeffrey C.

doi:10.1016/j.ultrasmedbio.2010.12.009

Cited by 7 publications

(3 citation statements)

References 28 publications

(31 reference statements)

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“…The depths of the targets are 30 mm (Table 1). To our knowledge, this phantom and the 049A phantom by the same manufacturer, which is of a similar construction containing stepped cylindrical targets, are the only commercially available phantoms for sonoelastographic quality assurance and have been used in some basic studies in sonographic strain imaging 6 . In this study using the model 049 phantom, we measured the 4 types of phantom targets of different hardness, 1 to 4, and the background.…”

Section: Methodsmentioning

confidence: 99%

Reliable Measurement by Virtual Touch Tissue Quantification With Acoustic Radiation Force Impulse Imaging

Yamanaka

Kaminuma

Taketomi-Takahashi

et al. 2012

Journal of Ultrasound in Medicine

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Section: Methodsmentioning

confidence: 99%

Reliable Measurement by Virtual Touch Tissue Quantification With Acoustic Radiation Force Impulse Imaging

Yamanaka

Kaminuma

Taketomi-Takahashi

et al. 2012

Journal of Ultrasound in Medicine

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“…Although strain images are displayed at frame rates up to the ultrasound frame rate, the need to average over many frames while moving the tissue means that strain imaging is not as suitable as real-time ultrasonography for observing tissue motion or for rapidly exploring a volume. The latter problem may be solved by 3 D elastography [14].…”

Section: Strain Elastography (Se): Quasi-static Strain Imagingmentioning

confidence: 99%

EFSUMB Guidelines and Recommendations on the Clinical Use of Ultrasound Elastography. Part 1: Basic Principles and Technology

et al. 2013

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The technical part of these Guidelines and Recommendations, produced under the auspices of EFSUMB, provides an introduction to the physical principles and technology on which all forms of current commercially available ultrasound elastography are based. A difference in shear modulus is the common underlying physical mechanism that provides tissue contrast in all elastograms. The relationship between the alternative technologies is considered in terms of the method used to take advantage of this. The practical advantages and disadvantages associated with each of the techniques are described, and guidance is provided on optimisation of scanning technique, image display, image interpretation and some of the known image artefacts.

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“…Even though these automated methods reduce decorrelation caused by uncertainty of the US probe position, they require long acquisition times and are thus susceptible to motion artifacts in displacements estimated via speckle-tracking. Other methods of acquiring 3D RF echo data include the use of a mechanically swept 1D array (Sayed et al 2013, Sayed et al 2014, Fisher et al 2007, Bharat et al 2008, Treece et al 2008, Housden et al 2011 or a 2D matrix array (Deprez et al 2009, Papadacci et al 2016b, Papadacci et al 2016a, Gijsbertse et al 2016, Fisher et al 2010. Compared to data acquisition via free-hand scanning or an ABVS system, these methods are less susceptible to motion artifacts between pre-and post-deformation frames.…”

Section: Introductionmentioning

confidence: 99%

Physics-guided machine learning for 3-D quantitative quasi-static elasticity imaging

2020

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We present a 3D extension of the Autoprogressive Method (AutoP) for quantitative quasi-static ultrasonic elastography (QUSE) based on sparse sampling of force-displacement measurements. Compared to current model-based inverse methods, our approach requires neither geometric nor constitutive model assumptions. We build upon our previous report for 2D QUSE and demonstrate the feasibility of recovering the 3D linear-elastic material property distribution of gelatin phantoms under compressive loads. Measurements of boundary geometry, applied surface forces, and axial displacements enter into AutoP where a Cartesian neural network constitutive model (CaNNCM) interacts with finite element analyses to learn physically consistent material properties with no prior constitutive model assumption. We introduce a new regularization term uniquely suited to AutoP that improves the ability of CaNNCMs to extract information about spatial stress distributions from measurement data. Results of our study demonstrate that acquiring multiple sets of force-displacement measurements by moving the US probe to different locations on the phantom surface not only provides AutoP with the necessary information for a CaNNCM to learn the 3D material property distribution, but may significantly improve the accuracy of the Young’s modulus estimates. Furthermore, we investigate the trade-offs of decreasing the contact area between the US transducer and phantom surface in an effort to increase sensitivity to surface force variations without additional instrumentation. Each of these modifications improves the ability of CaNNCMs trained in AutoP to learn the spatial distribution of Young’s modulus from force-displacement measurements.

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A New Method for the Acquisition of Ultrasonic Strain Image Volumes

Cited by 7 publications

References 28 publications

Reliable Measurement by Virtual Touch Tissue Quantification With Acoustic Radiation Force Impulse Imaging

Reliable Measurement by Virtual Touch Tissue Quantification With Acoustic Radiation Force Impulse Imaging

EFSUMB Guidelines and Recommendations on the Clinical Use of Ultrasound Elastography. Part 1: Basic Principles and Technology

Physics-guided machine learning for 3-D quantitative quasi-static elasticity imaging

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