3-D Visualization and Non-linear Tissue Classification of Breast Tumors Using Ultrasound Elastography In Vivo

Sayed, Ahmed M.; Layne, Ginger P.; Abraham, Jame; Mukdadi, Osama M.

doi:10.1016/j.ultrasmedbio.2014.02.002

Cited by 17 publications

(32 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The same probe also acquires the imaging data, which are processed to construct strain images. With multicompression at different compression forces, it is possible to plot a load-strain difference curve, which determines the level of material nonlinearity that, in turn, reflects the benignity or malignancy of the suspected tumor, as reported by Sayed et al 33,34 in previous works. 33,34 As stated in these studies, the in vivo experimental data acquisition was approved by the Institutional Review Board before commencement, and informed consent was received from each patient before the US elastographic examination.…”

Section: Methodsmentioning

confidence: 99%

“…With multicompression at different compression forces, it is possible to plot a load-strain difference curve, which determines the level of material nonlinearity that, in turn, reflects the benignity or malignancy of the suspected tumor, as reported by Sayed et al 33,34 in previous works. 33,34 As stated in these studies, the in vivo experimental data acquisition was approved by the Institutional Review Board before commencement, and informed consent was received from each patient before the US elastographic examination. 33,34 Model Construction Different 3D breast models have been constructed by using the static structural design module in the ANSYS software package (ANSYS, Inc, Canonsburg, PA).…”

Section: Methodsmentioning

confidence: 99%

“…33,34 As stated in these studies, the in vivo experimental data acquisition was approved by the Institutional Review Board before commencement, and informed consent was received from each patient before the US elastographic examination. 33,34 Model Construction Different 3D breast models have been constructed by using the static structural design module in the ANSYS software package (ANSYS, Inc, Canonsburg, PA). Like any FEM problem, the modeling process follows the next 3 steps 51,52 :…”

Section: Methodsmentioning

confidence: 99%

“…25,31,32 Tissue nonlinearity detection using force-strain difference curves was proposed to augment the classic overlapping classification parameters, as it can produce a large separation margin between malignant and benign masses. 33,34 However, it requires the application of multicompression steps with a limited load range.…”

mentioning

confidence: 99%

“…In this study, we aimed to improve an earlier breast cancer diagnostic method by combining US elastographic data with 3D numeric models and simulations. 33,34 The models incorporated different breast tumor parameters, including position, size, and material properties. Two types of models were included in this work: models built by using virtual parameters for validation purposes and models constructed on the basis of previously reported in vivo patient data for verification.…”

mentioning

confidence: 99%

See 4 more Smart Citations

Breast Tumor Diagnosis Using Finite‐Element Modeling Based on Clinical in vivo Elastographic Data

Sayed

Naser

Wahba

et al. 2020

J of Ultrasound Medicine

View full text Add to dashboard Cite

Objectives-This study exploited finite-element modeling (FEM) to simulate breast tissue multicompression during ultrasound elastography to classify breast tumors based on their nonlinear biomechanical properties. Methods-Numeric simulations were first calculated by using 3-dimensional (3D) virtual models with an assumed tumor's geometric dimensions but with actual material properties to test and validate the FEM. Further numeric simulations were used to construct 3D models based on in vivo experimental data to verify our models. The models were designed for each individual in vivo case, emphasizing the geometry, position, and biomechanical properties of the breast tissue. At different compression levels, tissue strains were analyzed between the tumors and the background normal tissues to explore their nonlinearity and classify the tumor type. Tumor classification parameters were deduced by using a power-law relationship between the applied compressive forces and strain differences. Results-Classification parameters were compared between benign and malignant tumors, for which they were found to be statistically significant in classifying the tumor types (P < .05) by both the validation and verification of FEM. We compared the classification parameters between the in vivo and FEM classifications, for which they were found to be strongly correlated (R = 0.875; P < .001), with no statistical differences between their outcomes (P = .909). Conclusions-Good agreement between the model outcomes and the in vivo diagnostics was reported. The implemented models were validated and verified. The introduced 3D modeling method may augment elastographic methods to preliminary classify breast tumors at an early stage.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Breast Tumor Diagnosis Using Finite‐Element Modeling Based on Clinical in vivo Elastographic Data

Sayed

Naser

Wahba

et al. 2020

J of Ultrasound Medicine

View full text Add to dashboard Cite

show abstract

Ultrasound tissue classification: a review

et al. 2020

View full text Add to dashboard Cite

Ultrasound imaging is the most widespread medical imaging modality for creating images of the human body in clinical practice. Tissue classification in ultrasound has been established as one of the most active research areas, driven by many important clinical applications. In this paper, we present a survey on ultrasound tissue classification, focusing on recent advances in this area. We start with a brief review on the main clinical applications. We then introduce the traditional approaches, where the existing research on feature extraction and classifier design are reviewed. As deep learning approaches becoming popular for medical image analysis, the recent deep learning methods for tissue classification are also introduced. We briefly discuss the FDA-cleared techniques being used clinically. We conclude with the discussion on the challenges and research focus in future.

show abstract

Frontiers of cancer imaging and guided therapy using ultrasound, light, and microwaves

Witte

Karunakaran

Zuniga

et al. 2018

Clin Exp Metastasis

View full text Add to dashboard Cite

This review describes emerging techniques within the last 5 years that employ ultrasound for detecting and staging malignancy, tracking metastasis, and guiding treatment. Ultrasound elastography quantifies soft tissue elastic properties that change as a tumor grows and proliferates. Hybrid imaging modalities that combine ultrasound with light or microwave energy provide novel contrast for mapping blood oxygen saturation, transport of particles through lymphatic vessels and nodes, and real-time feedback for guiding needle biopsies. Combining these methods with smart nanoparticles and contrast agents further promotes new paradigms for cancer imaging and therapy.

show abstract

3-D Visualization and Non-linear Tissue Classification of Breast Tumors Using Ultrasound Elastography In Vivo

Cited by 17 publications

References 46 publications

Breast Tumor Diagnosis Using Finite‐Element Modeling Based on Clinical in vivo Elastographic Data

Breast Tumor Diagnosis Using Finite‐Element Modeling Based on Clinical in vivo Elastographic Data

Ultrasound tissue classification: a review

Frontiers of cancer imaging and guided therapy using ultrasound, light, and microwaves

Contact Info

Product

Resources

About