Dual modality optical coherence tomography : Technology development and biomedical applications

Optical coherence tomography (OCT) is a cross-sectional imaging modality that is widely used in clinical ophthalmology and interventional cardiology. It is highly promising for in situ characterization of tumor tissues. OCT has high spatial resolution and high imaging speed to assist clinical decision making in real-time. OCT can be used in both structural imaging and mechanical characterization. Malignant tumor tissue alters morphology. Additionally, structural OCT imaging has limited tissue differentiation capability because of the complex and noisy nature of the OCT signal. Moreover, the contrast of structural OCT signal derived from tissue’s light scattering properties has little chemical specificity. Hence, interrogating additional tissue properties using OCT would improve the outcome of OCT’s clinical applications. In addition to morphological difference, pathological tissue such as cancer breast tissue usually possesses higher stiffness compared to the normal healthy tissue, which indicates a compelling reason for the specific combination of structural OCT imaging with stiffness assessment in the development of dual-modality OCT system for the characterization of the breast cancer diagnosis. This dissertation seeks to integrate the structural OCT imaging and the optical coherence elastography (OCE) for breast cancer tissue characterization. OCE is a functional extension of OCT. OCE measures the mechanical response (deformation, resonant frequency, elastic wave propagation) of biological tissues under external or internal mechanical stimulation and extracts the mechanical properties of tissue related to its pathological and physiological processes. Conventional OCE techniques (i.e., compression, surface acoustic wave, magnetomotive OCE) measure the strain field and the results of OCE measurement are different under different loading conditions. Inconsistency is observed between OCE characterization results from different measurement sessions. Therefore, a robust mechanical characterization is required for force/stress quantification. A quantitative optical coherence elastography (qOCE) that tracks both force and displacement is proposed and developed at NJIT. qOCE instrument is based on a fiber optic probe integrated with a Fabry-Perot force sensor and the miniature probe can be delivered to arbitrary locations within animal or human body. In this dissertation, the principle of qOCE technology is described. Experimental results are acquired to demonstrate the capability of qOCE in characterizing the elasticity of biological tissue. Moreover, a handheld optical instrument is developed to allow in vivo real-time OCE characterization based on an adaptive Doppler analysis algorithm to accurately track the motion of sample under compression. For the development of the dual modality OCT system, the structural OCT images exhibit additive and multiplicative noises that degrade the image quality. To suppress noise in OCT imaging, a noise adaptive wavelet thresholding (NAWT) algorithm is developed to remove the speckle noise in OCT images. NAWT algorithm characterizes the speckle noise in the wavelet domain adaptively and removes the speckle noise while preserving the sample structure. Furthermore, a novel denoising algorithm is also developed that adaptively eliminates the additive noise from the complex OCT using Doppler variation analysis

Simulation study on acousto-optics sensing of focused ultrasound

Abstract. The acousto-optics (AO) technique can provide a good contrast with high penetration depth (up to 5 cm) and can be potentially utilized in real time monitoring of the focused ultrasound (FUS) therapies. This work presents the AO simulation study on the interaction of light and FUS in the single-layer brain (SLB) medium and four-layer brain (FLB) medium. FUS pressure distribution at 0.5 MHz and 0.9 MHz frequency was simulated on k-Wave toolbox and the AO Monte Carlo (MC) algorithm was developed on MATLAB to simulate the AO effect in both mediums. The result for the SLB for both ultrasound (US) frequencies suggests that the modulation depth (MD) is high in the region of US focus with a magnitude of 2%-3% and <1% at 0.5 MHz and 0.9 MHz, respectively. Moreover, the MD decreases to 5 orders of magnitude at the source region. In the FLB, the MD decreased to 4–4.5 orders at the source and was present in the skull and US focus region with a magnitude of <1% at both US frequencies. These results suggest that AO can be utilized in sensing FUS effects on brain tissue and the AO signal-to-noise ratio (SNR) depends not only on the MD but also on the level of light intensity interacting with the US pressure

Advancements and Breakthroughs in Ultrasound Imaging

Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world

Holographic Fourier domain diffuse correlation spectroscopy

Diffuse correlation spectroscopy (DCS) is a non-invasive optical modality which can be used to measure cerebral blood flow (CBF) in real-time. It has important potential applications in clinical monitoring, as well as in neuroscience and the development of a non-invasive brain-computer interface. However, a trade-off exists between the signal-to-noise ratio (SNR) and imaging depth, and thus CBF sensitivity, of this technique. Additionally, as DCS is a diffuse optical technique, it is limited by a lack of inherent depth discrimination within the illuminated region of each source-detector pair, and the CBF signal is therefore also prone to contamination by the extracerebral tissues which the light traverses. Placing a particular emphasis on scalability, affordability, and robustness to ambient light, in this work I demonstrate a novel approach which fuses the fields of digital holography and DCS: holographic Fourier domain DCS (FD-DCS). The mathematical formalism of FD-DCS is derived and validated, followed by the construction and validation (for both in vitro and in vivo experiments) of a holographic FD-DCS instrument. By undertaking a systematic SNR performance assessment and developing a novel multispeckle denoising algorithm, I demonstrate the highest SNR gain reported in the DCS literature to date, achieved using scalable and low-cost camera-based detection. With a view to generating a forward model for holographic FD-DCS, in this thesis I propose a novel framework to simulate statistically accurate time-integrated dynamic speckle patterns in biomedical optics. The solution that I propose to this previously unsolved problem is based on the Karhunen-Loève expansion of the electric field, and I validate this technique against novel expressions for speckle contrast for different forms of homogeneous field. I also show that this method can readily be extended to cases with spatially varying sample properties, and that it can also be used to model optical and acoustic parameters

Back to Basics: Fast Denoising Iterative Algorithm

We introduce Back to Basics (BTB), a fast iterative algorithm for noise reduction. Our method is computationally efficient, does not require training or ground truth data, and can be applied in the presence of independent noise, as well as correlated (coherent) noise, where the noise level is unknown. We examine three study cases: natural image denoising in the presence of additive white Gaussian noise, Poisson-distributed image denoising, and speckle suppression in optical coherence tomography (OCT). Experimental results demonstrate that the proposed approach can effectively improve image quality, in challenging noise settings. Theoretical guarantees are provided for convergence stability

A modeling-based assessment of acousto-optic sensing for monitoring high-intensity focused ultrasound lesion formation

Real-time acousto-optic (AO) sensing - a dual-wave modality that combines ultrasound with diffuse light to probe the optical properties of turbid media - has been demonstrated to non-invasively detect changes in ex vivo tissue optical properties during high-intensity focused ultrasound (HIFU) exposure. The AO signal indicates the onset of lesion formation and predicts resulting lesion volumes. Although proof-of-concept experiments have been successful, many of the underlying parameters and mechanisms affecting thermally induced optical property changes and the AO detectability of HIFU lesion formation are not well understood. In thesis, a numerical simulation was developed to model the AO sensing process and capture the relevant acoustic, thermal, and optical transport processes. The simulation required data that described how optical properties changed with heating. Experiments were carried out where excised chicken breast was exposed to thermal bath heating and changes in the optical absorption and scattering spectra (500 nm - 1100 nm) were measured using a scanning spectrophotometer and an integrating sphere assembly. Results showed that the standard thermal dose model currently used for guiding HIFU treatments needs to be adjusted to describe thermally induced optical property changes. To model the entire AO process, coupled models were used for ultrasound propagation, tissue heating, and diffusive light transport. The angular spectrum method was used to model the acoustic field from the HIFU source. Spatial-temporal temperature elevations induced by the absorption of ultrasound were modeled using a finite-difference time-domain solution to the Pennes bioheat equation. The thermal dose model was then used to determine optical properties based on the temperature history. The diffuse optical field in the tissue was then calculated using a GPU-accelerated Monte Carlo algorithm, which accounted for light-sound interactions and AO signal detection. The simulation was used to determine the optimal design for an AO guided HIFU system by evaluating the robustness of the systems signal to changes in tissue thickness, lesion optical contrast, and lesion location. It was determined that AO sensing is a clinically viable technique for guiding the ablation of large volumes and that real-time sensing may be feasible in the breast and prostate

A volume filtering and rendering system for an improved visual balance of feature preservation and noise suppression in medical imaging

Preserving or enhancing salient features whilst effectively suppressing noise-derived artifacts and extraneous detail have been two consistent yet competing objectives in volumetric medical image processing. Illustrative techniques (and methods inspired by them) can help to enhance and, if desired, isolate the depiction of specific regions of interest whilst retaining overall context. However, highlighting or enhancing specific features can have the undesirable side-effect of highlighting noise. Second-derivative based methods can be employed effectively in both the rendering and volume filtering stages of a visualisation pipeline to enhance the depiction of feature detail whilst minimising noise-based artifacts. We develop a new 3D anisotropic-diffusion PDE for an improved balance of feature-retention and noise reduction; furthermore, we present a feature-enhancing visualisation pipeline that can be applied to multiple modalities and has been shown to be particularly effective in the context of 3D ultrasound