Intraoperative Speckle Variance Optical Coherence Tomography for Tissue Temperature Monitoring During Cutaneous Laser Therapy

Image-Based Monitoring of Thermal Ablation

Thermal therapy is a commonly used local treatment technique in clinical practice. Monitoring the treatment process is essential for ensuring its success. In this review, we analyze recent image-based methods for thermal therapy monitoring, focusing particularly on their feasibility for synchronous or immediate postoperative monitoring. This includes thermography and other techniques that track the physical changes in tissue during thermal ablation. Potential directions and challenges for further clinical applications are also summarized.

Non-destructive viability assessment of cancer cell spheroids using dynamic optical coherence tomography with trypan blue validation

3D cell cultures are widely used in biomedical research for the recapitulation of in vivo microenvironments. Viability assessment and monitoring of these intricate conformations remain an open problem as standard cell viability protocols based on colorimetry or microscopy are not directly applicable to intact 3D samples. Optical coherence tomography (OCT) has been explored extensively for subsurface structural and quasi-functional analysis of 3D cell cultures and tissue. Recent studies of dynamic OCT as a source of cellular contrast have found qualitative associations with necrosis in cell spheroids, suggesting potential as a viability marker. We present empirical and validated evidence for dynamic OCT as a quantitative indicator of cell viability in 3D cultures. We analysed over 240 MCF-7 cancer cell spheroids with dynamic OCT and corresponding viability measurements using the trypan blue exclusion assay. Significant effects of common reagents dimethyl sulfoxide (DMSO) and phosphate-buffered saline (PBS) on OCT readouts were noted. We proposed a regression-based OCT brightness normalisation technique that removed reagent-induced OCT intensity biases and helped improve correspondence to the viability assay. These results offer a quantitative biological foundation for further advances of dynamic OCT as a novel non-invasive modality for 3D culture monitoring.

Speckle decorrelation rate as a robust indicator for visualizing the therapeutic thermal field with OCT

Optical coherence tomography (OCT) is evolving from a diagnostic imaging modality to one that also facilitates therapeutic procedures. However, visualizing the therapeutic thermal field during minimally invasive thermal treatments such as laser or radio frequency ablation is challenging. This difficulty arises because tissues show minimal optical property changes until they reach the coagulation threshold at approximately 50 ∘C. To address this, we introduce the speckle decorrelation rate as a new, to our knowledge, contrast mechanism for OCT, enhancing the visualization of the therapeutic thermal field. Through ex vivo tissue experiments on a laser ablation-OCT surveillance system, we demonstrate that the speckle decorrelation rate offers superior sensitivity to detect subtle temperature changes and is less sensitive to the selection of time intervals for decorrelation calculations compared to existing speckle decorrelation methods. Our approach, which is label-free and compatible with various OCT systems, has been validated across diverse biological tissues, showing potential to augment the precision and safety of thermal therapies. Additionally, we propose a GPU-accelerated pipeline to expedite processing time, making real-time thermal field visualization feasible.

Growing Trend to Adopt Speckle Variance Optical Coherence Tomography for Biological Tissue Assessments in Pre-Clinical Applications

Speckle patterns are a generic feature in coherent imaging techniques like optical coherence tomography (OCT). Although speckles are granular like noise texture, which degrades the image, they carry information that can be benefited by processing and thereby furnishing crucial information of sample structures, which can serve to provide significant important structural details of samples in in vivo longitudinal pre-clinical monitoring and assessments. Since the motions of tissue molecules are indicated through speckle patterns, speckle variance OCT (SV-OCT) can be well-utilized for quantitative assessments of speckle variance (SV) in biological tissues. SV-OCT has been acknowledged as a promising method for mapping microvasculature in transverse-directional blood vessels with high resolution in micrometers in both the transverse and depth directions. The fundamental scope of this article reviews the state-of-the-art and clinical benefits of SV-OCT to assess biological tissues for pre-clinical applications. In particular, focus on precise quantifications of in vivo vascular response, therapy assessments, and real-time temporal vascular effects of SV-OCT are primarily emphasized. Finally, SV-OCT-incorporating pre-clinical techniques with high potential are presented for future biomedical applications.

Cross-attention learning enables real-time nonuniform rotational distortion correction in OCT

Nonuniform rotational distortion (NURD) correction is vital for endoscopic optical coherence tomography (OCT) imaging and its functional extensions, such as angiography and elastography. Current NURD correction methods require time-consuming feature tracking/registration or cross-correlation calculations and thus sacrifice temporal resolution. Here we propose a cross-attention learning method for the NURD correction in OCT. Our method is inspired by the recent success of the self-attention mechanism in natural language processing and computer vision. By leveraging its ability to model long-range dependencies, we can directly obtain the spatial correlation between OCT A-lines at any distance, thus accelerating the NURD correction. We develop an end-to-end stacked cross-attention network and design three types of optimization constraints. We compare our method with two traditional feature-based methods and a CNN-based method on two publicly-available endoscopic OCT datasets. We further verify the NURD correction performance of our method on 3D stent reconstruction using a home-built endoscopic OCT system. Our method achieves a ∼3 × speedup to real time (26 ± 3 fps), and superior correction performance.

Intelligent optical diagnosis and treatment system for automated image-guided laser ablation of tumors

PURPOSE

For tumor resections near critical structures, accurate identification of tumor boundaries and maximum removal are the keys to improve surgical outcome and patient survival rate, especially in neurosurgery. In this paper, we propose an intelligent optical diagnosis and treatment system for tumor removal, with automated lesion localization and laser ablation.

METHODS

The proposed system contains a laser ablation module, an optical coherence tomography (OCT) unit, and a robotic arm along with a stereo camera. The robotic arm can move the OCT sample arm and the laser ablation front-end to the suspected lesion area. The corresponding diagnosis and treatment procedures include computer-aided lesion segmentation using OCT, automated ablation planning, and laser control. The ablation process is controlled by a deflectable mirror, and a non-common-path ablation planning algorithm based on the transformation from lesion positions to mirror deflection angles is presented.

RESULTS

Phantom and animal experiments are carried out for system verification. The robot could reach the planned position with high precision, which is approximately 1.16 mm. Tissue classification with OCT images achieves 91.7% accuracy. The error of OCT-guided automated laser ablation is approximately 0.74 mm. Experiments on mouse brain tumors show that the proposed system is capable of clearing lesions efficiently and precisely. We also conducted an ex vivo porcine brain experiment to verify the whole process of the system.

CONCLUSION

An intelligent optical diagnosis and treatment system is proposed for tumor removal. Experimental results show that the proposed system and method are promising for precise and intelligent theranostics. Compared to conventional cancer diagnosis and treatment, the proposed system allows for automated operations monitored in real-time, with higher precision and efficiency.

Stabilizing the phase of swept-source optical coherence tomography by a wrapped Gaussian mixture model

The phase of an optical coherence tomography (OCT) signal carries critical information about particle micro-displacements. However, swept-source OCT (SSOCT) suffers from phase instability problems due to trigger jitters from the swept source. In this Letter, a wrapped Gaussian mixture model (WGMM) is proposed to stabilize the phase of SSOCT systems. A closed-form iteration solution of the WGMM is derived using the expectation-maximization algorithm. Necessary approximations are made for real-time graphic processing unit implementation. The performance of the proposed method is demonstrated through ex vivo, in vivo, and flow phantom experiments. The results show the robustness of the method in different application scenarios.

Optical flow optical coherence tomography for determining accurate velocity fields

Determining micron-scale fluid flow velocities using optical coherence tomography (OCT) is important in both biomedical research and clinical diagnosis. Numerous methods have been explored to quantify the flow information, which can be divided into either phase-based or amplitude-based methods. However, phase-based methods, such as Doppler methods, are less sensitive to transverse velocity components and suffer from wrapped phase and phase instability problems for axial velocity components. On the other hand, amplitude-based methods, such as speckle variance OCT, correlation mapping OCT and split-spectrum amplitude-decorrelation angiography, focus more on segmenting flow areas than quantifying flow velocities. In this paper, we propose optical flow OCT (OFOCT) to quantify accurate velocity fields. The equivalence between optical flow and real velocity fields is validated in OCT imaging. The sensitivity fall-off of a Fourier-domain OCT (FDOCT) system is considered in the modified optical flow continuity constraint. Spatial-temporal smoothness constraints are used to make the optical flow problem well-posed and reduce noises in the velocity fields. An iteration solution to the optical flow problem is implemented in a graphics processing unit (GPU) for real-time processing. The accuracy of the velocity fields is verified through phantom flow experiments by using a diluted milk powder solution as a scattering medium. Velocity fields are then used to detect flow turbulence and reconstruct flow trajectory. The results show that OFOCT is accurate in determining velocity fields and applicable to research concerning fluid dynamics.