Automatic Generation of Boundary Conditions Using Demons Nonrigid Image Registration for Use in 3-D Modality-Independent Elastography

Pheiffer, Thomas S.; Ou, Jao J.; Ong, Rowena E.; Miga, Michael I.

doi:10.1109/tbme.2011.2159791

Cited by 12 publications

(9 citation statements)

References 30 publications

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“…However, this work goes further than the closely-related investigations of the elastography method called modality independent elastography (MIE) (Miga 2003, Miga et al 2005, Ou et al 2007, Pheiffer et al 2011, Weis et al 2013, 2015a). More specifically, previous MIE investigations were only successful in predicting soft-tissue stiffness ratios between adipose, fibroglandular, and tumor due to the use of indeterminate displacement boundary conditions.…”

Section: Discussionmentioning

confidence: 99%

Breast tissue stiffness estimation for surgical guidance using gravity-induced excitation

et al. 2017

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Tissue stiffness interrogation is fundamental in breast cancer diagnosis and treatment. Furthermore, biomechanical models for predicting breast deformations have been created for several breast cancer applications. Within these applications, constitutive mechanical properties must be defined and the accuracy of this estimation directly impacts the overall performance of the model. In this study, we present an image-derived computational framework to obtain quantitative, patient specific stiffness properties for application in image-guided breast cancer surgery and interventions. The method uses two MR acquisitions of the breast in different supine gravity-loaded configurations to fit mechanical properties to a biomechanical breast model. A reproducibility assessment of the method was performed in a test–retest study using healthy volunteers and was further characterized in simulation. In five human data sets, the within subject coefficient of variation ranged from 10.7% to 27% and the intraclass correlation coefficient ranged from 0.91–0.944 for assessment of fibroglandular and adipose tissue stiffness. In simulation, fibroglandular content and deformation magnitude were shown to have significant effects on the shape and convexity of the objective function defined by image similarity. These observations provide an important step forward in characterizing the use of nonrigid image registration methodologies in conjunction with biomechanical models to estimate tissue stiffness. In addition, the results suggest that stiffness estimation methods using gravity-induced excitation can reliably and feasibly be implemented in breast cancer surgery/intervention workflows.

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Section: Discussionmentioning

confidence: 99%

Breast tissue stiffness estimation for surgical guidance using gravity-induced excitation

et al. 2017

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“…While we have previously studied the use of the general MIE method, [39][40][41][42][43][44] we have recently fundamentally advanced the automation, translation, and application toward the preclinical setting. 45 MIE is now positioned as a noninvasive imaging methodology that can provide quantitative characterization of tissue mechanical status.…”

Section: Discussionmentioning

confidence: 99%

“…As shown in Ref. 44, this automatic boundary condition extraction process eliminates the problematic and tedious process of manual designation of boundary conditions, while maintaining desirable elasticity reconstruction results. Further details on the automated boundary condition extraction process can be found in Ref.…”

Section: Elasticity Reconstructionmentioning

confidence: 97%

“…Further details on the automated boundary condition extraction process can be found in Ref. 44. Deformation between the precompression image volume and the postcompression image volume is then simulated using a computational mechanical model that assumes isotropic Hookean linear elasticity.…”

Section: Elasticity Reconstructionmentioning

confidence: 99%

“…We have previously applied this general approach to x-ray computed tomography, MR and optical imaging data from both preclinical and clinical settings. [39][40][41][42][43][44][45] The MIE approach is a novel elastographic imaging technique that is automated, straightforward and capable of implementation across length scales and modalities.…”

Section: Introductionmentioning

confidence: 99%

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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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Deformable registration for quantifying longitudinal tumor changes during neoadjuvant chemotherapy

Weinstein

Conant

et al. 2014

Magnetic Resonance in Med

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Purpose To evaluate DRAMMS, an attribute-based deformable registration algorithm, compared to other intensity-based algorithms, for longitudinal breast MRI registration, and to show its applicability in quantifying tumor changes over the course of neoadjuvant chemotherapy. Methods Breast magnetic resonance images from 14 women undergoing neoadjuvant chemotherapy were analyzed. The accuracy of DRAMMS versus five intensity-based deformable registration methods was evaluated based on 2,380 landmarks independently annotated by two experts, for the entire image volume, different image subregions, and patient subgroups. The registration method with the smallest landmark error was used to quantify tumor changes, by calculating the Jacobian determinant maps of the registration deformation. Results DRAMMS had the smallest landmark errors (6.05 ± 4.86 mm), followed by the intensity-based methods CC-FFD (8.07 ± 3.86 mm), NMI-FFD (8.21 ± 3.81 mm), SSD-FFD (9.46 ± 4.55 mm), Demons (10.76 ± 6.01 mm), and Diffeomorphic Demons (10.82 ± 6.11 mm). Results show that registration accuracy also depends on tumor versus normal tissue regions and different patient subgroups. Conclusions The DRAMMS deformable registration method, driven by attribute-matching and mutual-saliency, can register longitudinal breast magnetic resonance images with a higher accuracy than several intensity-matching methods included in this article. As such, it could be valuable for more accurately quantifying heterogeneous tumor changes as a marker of response to treatment.

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Automatic Generation of Boundary Conditions Using Demons Nonrigid Image Registration for Use in 3-D Modality-Independent Elastography

Cited by 12 publications

References 30 publications

Breast tissue stiffness estimation for surgical guidance using gravity-induced excitation

Breast tissue stiffness estimation for surgical guidance using gravity-induced excitation

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

Deformable registration for quantifying longitudinal tumor changes during neoadjuvant chemotherapy

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