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Foo, K. Y.

Publications and source records attributed to Foo, K. Y..

2 recordsLinked to original sources

Visualization of breast cancer using contrast-enhanced optical coherence elastography based on tissue heterogeneity

By mapping the mechanical properties of tissue, elastography can improve identification of breast cancer. On the macro-scale, ultrasound elastography and magnetic resonance elastography have emerged as effective clinical methods for the diagnosis of tumors. On the micro-scale, optical coherence elastography (OCE) shows promise for intraoperative tumor margin assessment during breast-conserving surgery. Whilst several OCE studies have demonstrated strong potential, the mechanical models used require the assumption of uniaxial stress throughout the sample. However, breast tissue is heterogeneous and contains compressible features (e.g., ducts and blood vessels) and collagen-rich fibrotic features (e.g., stroma). This heterogeneity can invalidate the assumption of uniaxial stress and reduce the accuracy of OCE, often making it challenging to interpret images. Here, we demonstrate a new variant of OCE based on mapping the Euler angle, i.e., the angle between the principal compression and the loading axis induced by tissue heterogeneity, which removes the assumption of uniaxial deformation. This is enabled by a hybrid three-dimensional (3-D) displacement estimation method that combines phase-sensitive detection and complex cross-correlation, providing access to the 3-D displacement and 3-D strain tensor on the micro-scale. We demonstrate this new OCE technique through experiments on phantoms and 10 fresh human breast specimens. Through close correspondence with histology, our results show that mapping the Euler angle provides additional contrast to both optical coherence tomography and a current OCE technique in identifying cancer. Mapping the Euler angle in breast tissue may provide a new biomarker for intraoperative tumor margin assessment.

bioengineering↗

Tumor spheroid elasticity estimation using mechano-microscopy combined with a conditional generative adversarial network

Techniques for imaging the mechanical properties of cells are needed to study how cell mechanics influence cell function and disease progression. Mechano-microscopy (a high-resolution variant of compression optical coherence elastography) generates elasticity images of a sample undergoing compression from the phase difference between optical coherence microscopy (OCM) B-scans. However, the existing mechano-microscopy signal processing chain (referred to as the algebraic method) assumes the sample stress is uniaxial and axially uniform, such that violation of these assumptions reduces the accuracy and precision of elasticity images. Furthermore, it does not account for prior information regarding the sample geometry or mechanical property distribution. In this study, we investigate the feasibility of training a conditional generative adversarial network (cGAN) to generate elasticity images from phase difference images of samples containing a cell spheroid embedded in a hydrogel. To train and test the cGAN, we constructed 30,000 elasticity and phase difference image pairs, where elasticity images were generated using a parametric model to simulate artificial samples, and phase difference images were computed using finite element analysis to simulate compression applied to the artificial samples. By applying both the cGAN and algebraic methods to simulated phase difference images, our results indicate the cGAN elasticity images exhibit better spatial resolution and sensitivity. We also evaluated the cGAN on experimental phase difference images of real spheroids embedded in hydrogels and compared the cGAN elasticity with the algebraic elasticity, OCM, and confocal fluorescence microscopy, and found the cGAN elasticity is often more robust to noise, especially within stiff nuclei.

bioengineering↗