Analysis of sensitivity in quantitative micro-elastography

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

BACKGROUND AND OBJECTIVES

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.

METHODS

To construct the cGAN training and simulated test sets, we generated 30,000 artificial elasticity images using a parametric model and computed the corresponding phase difference images using finite element analysis to simulate compression applied to the artificial samples. We also imaged real MCF7 breast tumor spheroids embedded in hydrogel using mechano-microscopy to construct the experimental test set and evaluated the cGAN using the algebraic elasticity images and co-registered OCM and confocal fluorescence microscopy (CFM) images.

RESULTS

Comparison with the simulated test set ground truth elasticity images shows the cGAN produces a lower root mean square error (median: 3.47 kPa, 95 % confidence interval (CI) [3.41, 3.52]) than the algebraic method (median: 4.91 kPa, 95 % CI [4.85, 4.97]). For the experimental test set, the cGAN elasticity images contain features resembling stiff nuclei at locations corresponding to nuclei seen in the algebraic elasticity, OCM, and CFM images. Furthermore, the cGAN elasticity images are higher resolution and more robust to noise than the algebraic elasticity images.

CONCLUSIONS

The cGAN elasticity images exhibit better accuracy, spatial resolution, sensitivity, and robustness to noise than the algebraic elasticity images for both simulated and real experimental data.

In situ stress estimation in quantitative micro-elastography

In quantitative micro-elastography (QME), a pre-characterized compliant layer with a known stress-strain curve is utilized to map stress at the sample surface. However, differences in the boundary conditions of the compliant layer when it is mechanically characterized and when it is used in QME experiments lead to inconsistent stress estimation and consequently, inaccurate elasticity measurements. Here, we propose a novel in situ stress estimation method using an optical coherence tomography (OCT)-based uniaxial compression testing system integrated with the QME experimental setup. By combining OCT-measured axial strain with axial stress determined using a load cell in the QME experiments, we can estimate in situ stress for the compliant layer, more accurately considering its boundary conditions. Our proposed method shows improved accuracy, with an error below 10%, compared to 85% using the existing QME technique with no lubrication. Furthermore, demonstrations on hydrogels and cells indicate the potential of this approach for improving the characterization of the micro-scale mechanical properties of cells and their interactions with the surrounding biomaterial, which has potential for application in cell mechanobiology.

Analysis of friction in quantitative micro-elastography

Quantitative micro-elastography (QME) is a compression-based optical coherence elastography technique capable of measuring the mechanical properties of tissue on the micro-scale. As QME requires contact between the imaging window and the sample, the presence of friction affects the accuracy of the estimated elasticity. In previous implementations, a lubricant was applied at the contact surfaces, which was assumed to result in negligible friction. However, recently, errors in the estimation of elasticity caused by friction have been reported. This effect has yet to be characterized and is, therefore, not well understood. In this work, we present a systematic analysis of friction in QME using silicone phantoms. We demonstrate that friction, and, therefore, the elasticity accuracy, is influenced by several experimental factors, including the viscosity of the lubricant, the mechanical contrast between the compliant layer and the sample, and the time after the application of a compressive strain. Elasticity errors over an order of magnitude were observed in the absence of appropriate lubrication when compared to uniaxial compression testing. Using an optimized lubrication protocol, we demonstrate accurate elasticity estimation (<10% error) for nonlinear elastic samples with Young's moduli ranging from 3 kPa to 130 kPa. Finally, using a structured phantom, we demonstrate that friction can significantly reduce mechanical contrast in QME. We believe that the framework established in this study will facilitate more robust elasticity estimations in QME, as well as being readily adapted to understand the effects of friction in other contact elastography techniques.

Recent advances in optical elastography and emerging opportunities in the basic sciences and translational medicine [Invited]

Optical elastography offers a rich body of imaging capabilities that can serve as a bridge between organ-level medical elastography and single-molecule biophysics. We review the methodologies and recent developments in optical coherence elastography, Brillouin microscopy, optical microrheology, and photoacoustic elastography. With an outlook toward maximizing the basic science and translational clinical impact of optical elastography technologies, we discuss potential ways that these techniques can integrate not only with each other, but also with supporting technologies and capabilities in other biomedical fields. By embracing cross-modality and cross-disciplinary interactions with these parallel fields, optical elastography can greatly increase its potential to drive new discoveries in the biomedical sciences as well as the development of novel biomechanics-based clinical diagnostics and therapeutics.

Integrating a pressure sensor with an OCT handheld probe to facilitate imaging of microvascular information in skin tissue beds

Optical coherence tomography (OCT) and OCT angiography (OCTA) have been increasingly applied in skin imaging applications in dermatology, where the imaging is often performed with the OCT probe in contact with the skin surface. However, this contact mode imaging can introduce uncontrollable mechanical stress applied to the skin, inevitably complicating the interpretation of OCT/OCTA imaging results. There remains a need for a strategy for assessing local pressure applied on the skin during imaging acquisition. This study reports a handheld scanning probe integrated with built-in pressure sensors, allowing the operator to control the mechanical stress applied to the skin in real-time. With real time feedback information, the operator can easily determine whether the pressure applied to the skin would affect the imaging quality so as to obtain repeatable and reliable OCTA images for a more accurate investigation of skin conditions. Using this probe, imaging of palm skin was used in this study to demonstrate how the OCTA imaging would have been affected by different mechanical pressures ranging from 0 to 69 kPa. The results showed that OCTA imaging is relatively stable when the pressure is less than 11 kPa, and within this range, the change of vascular area density calculated from the OCTA imaging is below 0.13%. In addition, the probe was used to augment the OCT monitoring of blood flow changes during a reactive hyperemia experiment, in which the operator could properly control the amount of pressure applied to the skin surface and achieve full release after compression stimulation.

Three-dimensional mechanical characterization of murine skeletal muscle using quantitative micro-elastography

Skeletal muscle function is governed by both the mechanical and structural properties of its constituent tissues, which are both modified by disease. Characterizing the mechanical properties of skeletal muscle tissue at an intermediate scale, i.e., between that of cells and organs, can provide insight into diseases such as muscular dystrophies. In this study, we use quantitative micro-elastography (QME) to characterize the micro-scale elasticity of ex vivo murine skeletal muscle in three-dimensions in whole muscles. To address the challenge of achieving high QME image quality with samples featuring uneven surfaces and geometry, we encapsulate the muscles in transparent hydrogels with flat surfaces. Using this method, we study aging and disease in quadriceps tissue by comparing normal wild-type (C57BL/6J) mice with dysferlin-deficient BLAJ mice, a model for the muscular dystrophy dysferlinopathy, at 3, 10, and 24 months of age (sample size of three per group). We observe a 77% decrease in elasticity at 24 months in dysferlin-deficient quadriceps compared to wild-type quadriceps.

Biomechanical assessment of chronic liver injury using quantitative micro-elastography

Hepatocellular carcinoma is one of the most lethal cancers worldwide, causing almost 700,000 deaths annually. It mainly arises from cirrhosis, which, in turn, results from chronic injury to liver cells and corresponding fibrotic changes. Although it is known that chronic liver injury increases the elasticity of liver tissue, the role of increased elasticity of the microenvironment as a possible hepatocarcinogen is yet to be investigated. One reason for this is the paucity of imaging techniques capable of mapping the micro-scale elasticity variation in liver and correlating that with cancerous mechanisms on the cellular scale. The clinical techniques of ultrasound elastography and magnetic resonance elastography typically do not provide micro-scale resolution, while atomic force microscopy can only assess the elasticity of a limited number of cells. We propose quantitative micro-elastography (QME) for mapping the micro-scale elasticity of liver tissue into images known as micro-elastograms, and therefore, as a technique capable of correlating the micro-environment elasticity of tissue with cellular scale cancerous mechanisms in liver. We performed QME on 13 freshly excised healthy and diseased mouse livers and present micro-elastograms, together with co-registered histology, in four representative cases. Our results indicate a significant increase in the mean (×6.3) and standard deviation (×6.0) of elasticity caused by chronic liver injury and demonstrate that the onset and progression of pathological features such as fibrosis, hepatocyte damage, and immune cell infiltration correlate with localized variations in micro-elastograms.

Subcellular mechano-microscopy: high resolution three-dimensional elasticity mapping using optical coherence microscopy

The importance of cellular-scale mechanical properties is well-established, yet it is challenging to map subcellular elasticity in three dimensions. We present subcellular mechano-microscopy, an optical coherence microscopy (OCM)-based variant of three-dimensional (3-D) compression optical coherence elastography (OCE) that provides an elasticity system resolution of 5 × 5 × 5 µm: a 7-fold improvement in system resolution over previous OCE studies of cells. The improved resolution is achieved through a ∼5-fold improvement in optical resolution, refinement of the strain estimation algorithm, and demonstration that mechanical deformation of subcellular features provides feature resolution far greater than that demonstrated previously on larger features with diameter >250 µm. We use mechano-microscopy to image adipose-derived stem cells encapsulated in gelatin methacryloyl. We compare our results with compression OCE and demonstrate that mechano-microscopy can provide contrast from subcellular features not visible using OCE.

Analysis of strain estimation methods in phase-sensitive compression optical coherence elastography

In compression optical coherence elastography (OCE), deformation is quantified as the local strain at each pixel in the OCT field-of-view. A range of strain estimation methods have been demonstrated, yet it is unclear which method provides the best performance. Here, we analyze the two most prevalent strain estimation methods used in phase-sensitive compression OCE, i.e., weighted least squares (WLS) and the vector method. We introduce a framework to compare strain imaging metrics, incorporating strain sensitivity, strain signal-to-noise ratio (SNR), strain resolution, and strain accuracy. In addition, we propose a new phase unwrapping algorithm in OCE, fast phase unwrapping (FPU), and combine it with WLS, termed WLSFPU. Using the framework, we compare this new strain estimation method with both a current implementation of WLS that incorporates weighted phase unwrapping (WPU), termed WLSWPU, and the vector method. Our analysis reveals that the three methods provide similar strain sensitivity, strain SNR, and strain resolution, but that WLSFPU extends the dynamic range of accurate, measurable local strain, e.g., measuring a strain of 2.5 mɛ with ∼4% error, that is ×11 and ×15 smaller than the error measured using WLSWPU and the vector method, respectively. We also demonstrate, for the first time, the capability to detect sub-resolution contrast in compression OCE, i.e., changes in strain occurring within the strain axial resolution, and how this contrast varies between the different strain estimation methods. Lastly, we compare the performance of the three strain estimation methods on mouse skeletal muscle and human breast tissue and demonstrate that WLSFPU avoids strain imaging artifacts resulting from phase unwrapping errors in WLSWPU and provides improved contrast over the other two methods.

Introduction to optical coherence elastography: tutorial

Optical coherence elastography (OCE) has seen rapid growth since its introduction in 1998. The past few decades have seen tremendous advancements in the development of OCE technology and a wide range of applications, including the first clinical applications. This tutorial introduces the basics of solid mechanics, which form the foundation of all elastography methods. We then describe how OCE measurements of tissue motion can be used to quantify tissue biomechanical parameters. We also detail various types of excitation methods, imaging systems, acquisition schemes, and data processing algorithms and how various parameters associated with each step of OCE imaging can affect the final quantitation of biomechanical properties. Finally, we discuss the future of OCE, its potential, and the next steps required for OCE to become an established medical imaging technology.

Development of a handheld compression optical coherence elastography probe with a disposable stress sensor

Optical coherence elastography (OCE) is a functional extension of optical coherence tomography (OCT). OCE measures a sample's deformation under force stimuli. Compression is often used to generate the force stimuli in OCE. In this Letter, we report the development of a handheld quantitative compression OCE probe with a novel stress senor, dedicated to measuring the force. The stress sensor consists of a circular glass window and a metal ring which are connected with polyurethane spokes. This sensor is mounted on the tip of the OCT sample arm as an imaging window, so that the force applied to the sample through the window can be measured by detecting the window displacement from the OCT image. The force-displacement function was first developed through simulation on COMSOL Multiphysics and eventually calibrated experimentally. A phase-sensitive OCT technique was employed to measure both the window displacement and the sample deformation. The performance of an OCE probe with this stress sensor was evaluated on a two-layer phantom. The results show that it is extremely capable of measuring the sample Young's modulus. Finally, we successfully measured the elasticity of the human fingertip, indicating a good potential of this OCE probe for in vivo elastogram measurement on human skin.