Terahertz tomographic imaging of freshly excised human breast tissues

Analysis and design of terahertz reflectarrays based on graphene cell clusters

In this paper, the graphene cell-cluster is introduced, which is composed of an array of identical unit-cells placed in a geometrical configuration. Such graphene cell-clusters are then used for the realization of a reflectarray. To the best of our knowledge, identical unit-cells in a particular geometrical configuration have already been introduced, but the analytical formulas for this model have not been investigated so far. In this paper, the Fourier-optics and aperture field estimation methods are applied to investigate the effect of cell-cluster dimensions on the generation of specified far-field radiation patterns. Implementing cell-clusters in graphene reflectarrays and similar structures, and also applying the proposed formulas, lead to the simplicity of configuration and enhancing the design accuracy. First, the effect of cell-cluster dimensions on the reflectarray radiation pattern is investigated. Then, a reflectarray composed of graphene cell-clusters is designed. A new configuration of graphene unit-cell composed of two graphene layers is proposed, where a middle layer of metallic patch is inserted. In the common graphene unit-cells, the rate of amplitude variations is quite high and greatly depends on the variation of phase in the proposed unit-cell. However, the amplitude variation is quite smaller than the phase variations.

Deep Learning Classification of Breast Cancer Tissue from Terahertz Imaging Through Wavelet Synchro-Squeezed Transformation and Transfer Learning

Terahertz imaging and spectroscopy is an exciting technology that has the potential to provide insights in medical imaging. Prior research has leveraged statistical inference to classify tissue regions from terahertz images. To date, these approaches have shown that the segmentation problem is challenging for images of fresh tissue and for tumors that have invaded muscular regions. Artificial intelligence, particularly machine learning and deep learning, has been shown to improve performance in some medical imaging challenges. This paper builds on that literature by modifying a set of deep learning approaches to the challenge of classifying tissue regions of images captured by terahertz imaging and spectroscopy of freshly excised murine xenograft tissue. Our approach is to preprocess the images through a wavelet synchronous-squeezed transformation (WSST) to convert time-sequential terahertz data of each THz pixel to a spectrogram. Spectrograms are used as input tensors to a deep convolution neural network for pixel-wise classification. Based on the classification result of each pixel, a cancer tissue segmentation map is achieved. In experimentation, we adopt leave-one-sample-out cross-validation strategy, and evaluate our chosen networks and results using multiple metrics such as accuracy, precision, intersection, and size. The results from this experimentation demonstrate improvement in classification accuracy compared to statistical methods, an improvement to segmentation between muscle and cancerous regions in xenograft tumors, and identify areas to improve the imaging and classification methodology.

Hyperspectral terahertz imaging and optical clearance for cancer classification in breast tumor surgical specimen

Purpose: We investigate the enhancement in terahertz (THz) images of freshly excised breast tumors upon treatment with an optical clearance agent. The hyperspectral imaging and spectral classifications are used to quantitatively demonstrate the image enhancement. Glycerol solution with 60% concentration is applied to excised breast tumor specimens for various time durations to investigate the effectiveness on image enhancement. Approach: THz reflection spectroscopy is utilized to obtain the absorption coefficient and the index of refraction of untreated and glycerol-treated tissues at each frequency up to 3 THz. Two classifiers, spectral angular mapping (SAM) based on several kernels and Euclidean minimum distance (EMD) are implemented to evaluate the effectiveness of the treatment. The testing raw data is obtained from five breast cancer specimens: two untreated specimens and three specimens treated with glycerol solution for 20, 40, or 60 min. All tumors used in the testing data have healthy tissues adjacent to cancerous ones consistent with the challenge faced in lumpectomy surgeries. Results: The glycerol-treated tissues showed a decrease in the absorption coefficients compared with untreated tissues, especially as the period of treatment increased. Although the sensitivity metric of the classifier presented higher values in the untreated tissues compared with the treated ones, the specificity and accuracy metrics demonstrated higher values for the treated tissues compared with the untreated ones. Conclusions: The biocompatible glycerol solution is a potential optical clearance agent in THz imaging while keeping the histopathology imaging intact. The SAM technique provided a good classification of cancerous tissues despite the small amount of cancer in the training data (only 7%). The SAM exponential kernel and EMD presented classification accuracy of ∼ 80 % to 85% compared with linear and polynomial kernels that provided accuracy ranging from 70% to 80%. Overall, glycerol treatment provides a potential improvement in cancer classification in freshly excised breast tumors.

Terahertz Imaging for Breast Cancer Detection

Terahertz (THz) imaging has the potential to detect breast tumors during breast-conserving surgery accurately. Over the past decade, many research groups have extensively studied THz imaging and spectroscopy techniques for identifying breast tumors. This manuscript presents the recent development of THz imaging techniques for breast cancer detection. The dielectric properties of breast tissues in the THz range, THz imaging and spectroscopy systems, THz radiation sources, and THz breast imaging studies are discussed. In addition, numerous chemometrics methods applied to improve THz image resolution and data collection processing are summarized. Finally, challenges and future research directions of THz breast imaging are presented.

Mammary tumors in Sprague Dawley rats induced by N-ethyl-N-nitrosourea for evaluating terahertz imaging of breast cancer

Purpose: The objective of this study is to quantitatively evaluate terahertz (THz) imaging for differentiating cancerous from non-cancerous tissues in mammary tumors developed in response to injection of N-ethyl-N-nitrosourea (ENU) in Sprague Dawley rats. Approach: While previous studies have investigated the biology of mammary tumors of this model, the current work is the first study to employ an imaging modality to visualize these tumors. A pulsed THz imaging system is utilized to experimentally collect the time-domain reflection signals from each pixel of the rat's excised tumor. A statistical segmentation algorithm based on the expectation-maximization (EM) classification method is implemented to quantitatively assess the obtained THz images. The model classification of cancer is reported in terms of the receiver operating characteristic (ROC) curves and the areas under the curves. Results: The obtained low-power microscopic images of 17 ENU-rat tumor sections exhibited the presence of healthy connective tissue adjacent to cancerous tissue. The results also demonstrated that high reflection THz signals were received from cancerous compared with non-cancerous tissues. Decent tumor classification was achieved using the EM method with values ranging from 83% to 96% in fresh tissues and 89% to 96% in formalin-fixed paraffin-embedded tissues. Conclusions: The proposed ENU breast tumor model of Sprague Dawley rats showed a potential to obtain cancerous tissues, such as human breast tumors, adjacent to healthy tissues. The implemented EM classification algorithm quantitatively demonstrated the ability of THz imaging in differentiating cancerous from non-cancerous tissues.

Diagnosis of Glioma Molecular Markers by Terahertz Technologies

This review considers glioma molecular markers in brain tissues and body fluids, shows the pathways of their formation, and describes traditional methods of analysis. The most important optical properties of glioma markers in the terahertz (THz) frequency range are also presented. New metamaterial-based technologies for molecular marker detection at THz frequencies are discussed. A variety of machine learning methods, which allow the marker detection sensitivity and differentiation of healthy and tumor tissues to be improved with the aid of THz tools, are considered. The actual results on the application of THz techniques in the intraoperative diagnosis of brain gliomas are shown. THz technologies’ potential in molecular marker detection and defining the boundaries of the glioma’s tissue is discussed.

Terahertz Imaging and Characterization Protocol for Freshly Excised Breast Cancer Tumors

This manuscript presents a protocol to handle, characterize, and image freshly excised human breast tumors using pulsed terahertz imaging and spectroscopy techniques. The protocol involves terahertz transmission mode at normal incidence and terahertz reflection mode at an oblique angle of 30°. The collected experimental data represent time domain pulses of the electric field. The terahertz electric field signal transmitted through a fixed point on the excised tissue is processed, through an analytical model, to extract the refractive index and absorption coefficient of the tissue. Utilizing a stepper motor scanner, the terahertz emitted pulse is reflected from each pixel on the tumor providing a planar image of different tissue regions. The image can be presented in time or frequency domain. Furthermore, the extracted data of the refractive index and absorption coefficient at each pixel are utilized to provide a tomographic terahertz image of the tumor. The protocol demonstrates clear differentiation between cancerous and healthy tissues. On the other hand, not adhering to the protocol can result in noisy or inaccurate images due to the presence of air bubbles and fluid remains on the tumor surface. The protocol provides a method for surgical margins assessment of breast tumors.

Breast Cancer Detection with Low-dimension Ordered Orthogonal Projection in Terahertz Imaging

This paper proposes a new dimension reduction algorithm based on low-dimension ordered orthogonal projection (LOOP), which is used for cancer detection with terahertz (THz) images of freshly excised human breast cancer tissues. A THz image can be represented by a data cube with each pixel containing a high dimension spectrum vector covering several THz frequencies, where each frequency represents a different dimension in the vector. The proposed algorithm projects the high-dimension spectrum vector of each pixel within the THz image into a low-dimension subspace that contains the majority of the unique features embedded in the image. The low-dimension subspace is constructed by sequentially identifying its orthonormal basis vectors, such that each newly chosen basis vector represents the most unique information not contained by existing basis vectors. A multivariate Gaussian mixture model is used to represent the statistical distributions of the low-dimension feature vectors obtained from the proposed dimension reduction algorithm. The model parameters are iteratively learned by using unsupervised learning methods such as Markov chain Monte Carlo or expectation maximization, and the results are used to classify the various regions within a tumor sample. Experiment results demonstrate that the proposed method achieves apparent performance improvement in human breast cancer tissue over existing approaches such as one-dimension Markov chain Monte Carlo. The results confirm that the dimension reduction algorithm presented in this paper is a promising technique for breast cancer detection with THz images, and the classification results present a good correlation with respect to the histopathology results of the analyzed samples.

Cancer detection in excised breast tumors using terahertz imaging and spectroscopy

Terahertz imaging and spectroscopy has demonstrated a potential for differentiating tissue types of excised breast cancer tumors. Pulsed terahertz technology provides a broadband frequency range from 0.1 THz to 4 THz for detecting cancerous tissue. Tumor tissue types of interest include cancer typically manifested as infiltrating ductal or lobular carcinomas, fibro-glandular (healthy connective tissues) and fat. In this work, images of breast tumors excised from human and animal models are reviewed. In addition to alternate fresh tissues, breast cancer tissue phantoms are developed to further evaluate terahertz imaging and the potential use of contrast agents. Terahertz results are successfully validated with pathology images, showing strong differentiation between cancerous and healthy tissues for all freshly excised tissues and types. The advantages, challenges and limitations of THz imaging of breast cancer are discussed.