Enhancing the performance of model-based elastography by incorporating additional<i>a priori</i>information in the modulus image reconstruction process

Doyley, Marvin M.; Srinivasan, Sundar; Dimidenko, Eugene; Soni, Nirmal; Ophir, Jonathan

doi:10.1088/0031-9155/51/1/007

Cited by 36 publications

(32 citation statements)

References 40 publications

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“…(2) may not provide unique modulus elastograms 30 because the inverse elasticity problem is ill-posed; however, including geometric information in the modulus recovery process will transform the ill-posed problem to a well-posed one. [31][32][33][34][35] We included geometric information in the image reconstruction process by minimizing the following objective function:…”

Section: Soft Prior Reconstruction Methodsmentioning

confidence: 99%

Quantitative sparse array vascular elastography: the impact of tissue attenuation and modulus contrast on performance

2014

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Abstract. Quantitative sparse array vascular elastography visualizes the shear modulus distribution within vascular tissues, information that clinicans could use to reduce the number of strokes each year. However, the low transmit power sparse array (SA) imaging could hamper the clinical usefulness of the resulting elastograms. In this study, we evaluated the performance of modulus elastograms recovered from simulated and physical vessel phantoms with varying attenuation coefficients (0.6, 1.5, and 3.5 cm −1 ) and modulus contrasts (−12.04, −6.02, and −2.5 dB) using SA imaging relative to those obtained with conventional linear array (CLA) and plane-wave (PW) imaging techniques. Plaques were visible in all modulus elastograms, but those produced using SA and PW contained less artifacts. The modulus contrast-to-noise ratio decreased rapidly with increasing modulus contrast and attenuation coefficient, but more quickly when SA imaging was performed than for CLA or PW. The errors incurred varied from 10.9% to 24% (CLA), 1.8% to 12% (SA), and ≈4% (PW). Modulus elastograms produced with SA and PW imagings were not significantly different (p > 0.05). Despite the low transmit power, SA imaging can produce useful modulus elastograms in superficial organs, such as the carotid artery. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Section: Soft Prior Reconstruction Methodsmentioning

confidence: 99%

Quantitative sparse array vascular elastography: the impact of tissue attenuation and modulus contrast on performance

2014

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“…As other researchers have noted, the incorporation of a priori information can greatly enhance the performance of their elastography methods (Doyley et al 2005(Doyley et al , 2006. We recognize that the judicious use of information regarding lesion morphology as obtained from conjunctive imaging studies and post-processing would potentially aid MIE as well, especially in reducing the number of search parameters and improving initialization of the algorithm.…”

Section: Discussionmentioning

confidence: 87%

Evaluation of 3D modality-independent elastography for breast imaging: a simulation study

Ong

Yankeelov

et al. 2007

Phys. Med. Biol.

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This paper reports on the development and preliminary testing of a threedimensional implementation of an inverse problem technique for extracting soft-tissue elasticity information via non-rigid model-based image registration. The modality-independent elastography (MIE) algorithm adjusts the elastic properties of a biomechanical model to achieve maximal similarity between images acquired under different states of static loading. A series of simulation experiments with clinical image sets of human breasts were performed to test the ability of the method to identify and characterize a radiographically occult stiff lesion. Because boundary conditions are a critical input to the algorithm, a comparison of three methods for semi-automated surface point correspondence was conducted in the context of systematic and randomized noise processes. The results illustrate that 3D MIE was able to successfully reconstruct elasticity images using data obtained from both magnetic resonance and x-ray computed tomography systems. The lesion was localized correctly in all cases and its relative elasticity found to be reasonably close to the true values (3.5% with the use of spatial priors and 11.6% without). In addition, the inaccuracies of surface registration performed with thin-plate spline interpolation did not exceed empiric thresholds of unacceptable boundary condition error.

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“…Similar to previous work in elasticity imaging that utilizes spatial prior constraints to improve elasticity parameter reconstruction results, [60][61][62] we use anatomical information as a soft constraint, with a weighted penalty function applied to the calculated gradient during parameter inversion. As the penalized gradient term is used during iterative parameter estimation to calculate the spatial mechanical elasticity, this step penalizes large deviations within a tissue type via identified spatial priors.…”

Section: Enforcing Spatial Prior Constraintsmentioning

confidence: 99%

Assessing the accuracy and reproducibility of modality independent elastography in a murine model of breast cancer

et al. 2015

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Abstract. Cancer progression has been linked to mechanics. Therefore, there has been recent interest in developing noninvasive imaging tools for cancer assessment that are sensitive to changes in tissue mechanical properties. We have developed one such method, modality independent elastography (MIE), that estimates the relative elastic properties of tissue by fitting anatomical image volumes acquired before and after the application of compression to biomechanical models. The aim of this study was to assess the accuracy and reproducibility of the method using phantoms and a murine breast cancer model. Magnetic resonance imaging data were acquired, and the MIE method was used to estimate relative volumetric stiffness. Accuracy was assessed using phantom data by comparing to gold-standard mechanical testing of elasticity ratios. Validation error was <12%. Reproducibility analysis was performed on animal data, and within-subject coefficients of variation ranged from 2 to 13% at the bulk level and 32% at the voxel level. To our knowledge, this is the first study to assess the reproducibility of an elasticity imaging metric in a preclinical cancer model. Our results suggest that the MIE method can reproducibly generate accurate estimates of the relative mechanical stiffness and provide guidance on the degree of change needed in order to declare biological changes rather than experimental error in future therapeutic studies.

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Enhancing the performance of model-based elastography by incorporating additionala prioriinformation in the modulus image reconstruction process

Cited by 36 publications

References 40 publications

Quantitative sparse array vascular elastography: the impact of tissue attenuation and modulus contrast on performance

Quantitative sparse array vascular elastography: the impact of tissue attenuation and modulus contrast on performance

Evaluation of 3D modality-independent elastography for breast imaging: a simulation study

Assessing the accuracy and reproducibility of modality independent elastography in a murine model of breast cancer

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