High-Sensitive Multiwavelength Dynamic Diffuse Optical Tomography System: A Preliminary Investigation

Zhang, Limin; Cao, Bin; He, Xiangdong; Sun, Zhixiang; Li, Jiao; Zhou, Zhongxing; Gao, Feng

doi:10.3389/fphy.2020.600812

Cited by 3 publications

(1 citation statement)

References 24 publications

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“…The results indicate that extended-wavelength DRS, with a Sen of 94% and a Spec of 91%, reflects the superiority of applying diffuse reflectance modalities. Multi-wavelength interference, a prospective BC detection technique, makes advantage of the interference pattern produced by dividing a light beam into a reference beam and a sample beam 24 , 25 . By detecting the phase distinction between the two beams, BC and other tissue properties may be identified 26 , 27 .…”

Section: Introductionmentioning

confidence: 99%

Multi-wavelength interference phase imaging for automatic breast cancer detection and delineation using diffuse reflection imaging

Mahmoud,

El-Sharkawy

2024

Sci Rep

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Millions of women globally are impacted by the major health problem of breast cancer (BC). Early detection of BC is critical for successful treatment and improved survival rates. In this study, we provide a progressive approach for BC detection using multi-wavelength interference (MWI) phase imaging based on diffuse reflection hyperspectral (HS) imaging. The proposed findings are based on the measurement of the interference pattern between the blue (446.6 nm) and red (632 nm) wavelengths. We consider implementing a comprehensive image processing and categorization method based on the use of Fast Fourier (FF) transform analysis pertaining to a change in the refractive index between tumor and normal tissue. We observed that cancer growth affects tissue organization dramatically, as seen by persistently increased refractive index variance in tumors compared normal areas. Both malignant and normal tissue had different depth data collected from it that was analyzed. To enhance the categorization of ex-vivo BC tissue, we developed and validated a training classifier algorithm specifically designed for categorizing HS cube data. Following the application of signal normalization with the FF transform algorithm, our methodology achieved a high level of performance with a specificity (Spec) of 94% and a sensitivity (Sen) of 90.9% for the 632 nm acquired image categorization, based on preliminary findings from breast specimens under investigation. Notably, we successfully leveraged unstained tissue samples to create 3D phase-resolved images that effectively highlight the distinctions in diffuse reflectance features between cancerous and healthy tissue. Preliminary data revealed that our imaging method might be able to assist specialists in safely excising malignant areas and assessing the tumor bed following resection automatically at different depths. This preliminary investigation might result in an effective "in-vivo" disease description utilizing optical technology using a typical RGB camera with wavelength-specific operation with our quantitative phase MWI imaging methodology.

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Section: Introductionmentioning

confidence: 99%

Multi-wavelength interference phase imaging for automatic breast cancer detection and delineation using diffuse reflection imaging

Mahmoud,

El-Sharkawy

2024

Sci Rep

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Regression-based neural network for improving image reconstruction in diffuse optical tomography

Balasubramaniam

Arnon

2022

Biomed. Opt. Express

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Diffuse optical tomography (DOT) is a non-invasive imaging technique utilizing multi-scattered light at visible and infrared wavelengths to detect anomalies in tissues. However, the DOT image reconstruction is based on solving the inverse problem, which requires massive calculations and time. In this article, for the first time, to the best of our knowledge, a simple, regression-based cascaded feed-forward deep learning neural network is derived to solve the inverse problem of DOT in compressed breast geometry. The predicted data is subsequently utilized to visualize the breast tissues and their anomalies. The dataset in this study is created using a Monte-Carlo algorithm, which simulates the light propagation in the compressed breast placed inside a parallel plate source-detector geometry (forward process). The simulated DL-DOT system's performance is evaluated using the Pearson correlation coefficient (R) and the Mean squared error (MSE) metrics. Although a comparatively smaller dataset (50 nos.) is used, our simulation results show that the developed feed-forward network algorithm to solve the inverse problem delivers an increment of ∼30% over the analytical solution approach, in terms of R. Furthermore, the proposed network's MSE outperforms that of the analytical solution's MSE by a large margin revealing the robustness of the network and the adaptability of the system for potential applications in medical settings.

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Tikhonov regularization-based extended Kalman filter technique for robust and accurate reconstruction in diffuse optical tomography

Zhang¹,

Zhang

Liu³

et al. 2022

J. Opt. Soc. Am. A

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Diffuse optical tomography (DOT) is a non-invasive imaging modality that uses near-infrared light to probe the optical properties of tissue. In conventionally used deterministic methods for DOT inversion, the measurement errors were not taken into account, resulting in unsatisfactory noise robustness and, consequently, affecting the DOT image reconstruction quality. In order to overcome this defect, an extended Kalman filter (EKF)-based DOT reconstruction algorithm was introduced first, which improved the reconstruction results by incorporating a priori information and measurement errors to the model. Further, to mitigate the instability caused by the ill-condition of the observation matrix in the tomographic imaging problem, a new, to the best of our knowledge, estimation algorithm was derived by incorporating Tikhonov regularization to the EKF method. To verify the effectiveness of the EKF algorithm and Tikhonov regularization-based EKF algorithm for DOT imaging, a series of numerical simulations and phantom experiments were conducted, and the experimental results were quantitatively evaluated and compared with two conventionally used deterministic methods involving the algebraic reconstruction technique and Levenberg–Marquardt algorithm. The results show that the two EKF-based algorithms can accurately estimate the location and size of the target, and the imaging accuracy and noise robustness are obviously improved. Furthermore, the Tikhonov regularization-based EKF obtained optimal parameter estimations, especially under the circumstance of low absorption contrast (1.2) and high noise level (10%).

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High-Sensitive Multiwavelength Dynamic Diffuse Optical Tomography System: A Preliminary Investigation

Cited by 3 publications

References 24 publications

Multi-wavelength interference phase imaging for automatic breast cancer detection and delineation using diffuse reflection imaging

Multi-wavelength interference phase imaging for automatic breast cancer detection and delineation using diffuse reflection imaging

Regression-based neural network for improving image reconstruction in diffuse optical tomography

Tikhonov regularization-based extended Kalman filter technique for robust and accurate reconstruction in diffuse optical tomography

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