Pulsed terahertz imaging of breast cancer in freshly excised murine tumors

Statistical Analysis of Gastric Cancer Cells Response to Broadband Terahertz Radiation with and without Contrast Nanoparticles

The paper describes the statistical analysis of the response of gastric cancer cells and normal cells to broadband terahertz radiation up to 4 THz, both with and without the use of nanostructured contrast agents. The THz spectroscopy analysis was comparatively performed under the ATR procedure and transmission measurement procedure. The statistical analysis was conducted towards multiple pairwise comparisons, including a support medium (without cells) versus a support medium with nanoparticles, normal cells versus normal cells with nanoparticles, and, respectively, tumor cells versus tumor cells with nanoparticles. When generally comparing the ATR procedure and transmission measurement procedure for a broader frequency domain, the differentiation between normal and tumor cells in the presence of contrast agents is superior when using the ATR procedure. THz contrast enhancement by using contrast agents derived from MRI-related contrast agents leads to only limited benefits and only for narrow THz frequency ranges, a disadvantage for THz medical imaging.

Optimal frequency determination for terahertz technology-based detection of colitis-related cancer in mice

Colorectal cancer is a prevalent malignancy globally, often linked to chronic colitis. Terahertz technology, with its noninvasive and fingerprint spectroscopic properties, holds promise in disease diagnosis. This study aimed to explore terahertz technology's application in colitis-associated cancer using a mouse model. Mouse colorectal tissues were transformed into paraffin-embedded blocks for histopathological analysis using HE staining. Terahertz transmission spectroscopy was performed on the tissue blocks. By comparing terahertz absorption differences, specific frequency bands were identified as optimal for distinguishing cancerous and normal tissues. The study revealed that terahertz spectroscopy effectively differentiates colitis-related cancers from normal tissues. Remarkably, 1.8 THz emerged as a potential optimal frequency for diagnosing colorectal cancer in mice. This suggests the potential for rapid histopathological diagnosis of colorectal cancer using terahertz technology.

Biomedical application of terahertz imaging technology: a narrative review

Background and Objective

Terahertz (THz) imaging has wide applications in biomedical research due to its properties, such as non-ionizing, non-invasive and distinctive spectral fingerprints. Over the past 6 years, the application of THz imaging in tumor tissue has made encouraging progress. However, due to the strong absorption of THz by water, the large size, high cost, and low sensitivity of THz devices, it is still difficult to be widely used in clinical practice. This paper provides ideas for researchers and promotes the development of THz imaging in clinical research.

Methods

The literature search was conducted in the Web of Science and PubMed databases using the keywords "Terahertz imaging", "Breast", "Brain", "Skin" and "Cancer". A total of 94 English language articles from 1 January, 2017 to 30 December, 2022 were reviewed.

Key Content and Findings

In this review, we briefly introduced the recent advances in THz near-field imaging, single-pixel imaging and real-time imaging, the applications of THz imaging for detecting breast, brain and skin tissues in the last 6 years were reviewed, and the advantages and existing challenges were identified. It is necessary to combine machine learning and metamaterials to develop real-time THz devices with small size, low cost and high sensitivity that can be widely used in clinical practice. More powerful THz detectors can be developed by combining graphene, designing structures and other methods to improve the sensitivity of the devices and obtain more accurate information. Establishing a THz database is one of the important methods to improve the repeatability and accuracy of imaging results.

Conclusions

THz technology is an effective method for tumor imaging. We believe that with the joint efforts of researchers and clinicians, accurate, real-time, and safe THz imaging will be widely applied in clinical practice in the future.

Terahertz technology and its applications in head and neck diseases

The terahertz (THz) radiation refers to electromagnetic waves between infrared and millimeter waves. THz technology has shown a significant potential for medical diagnosis and biomedical applications over the past three decades. Therefore, exploring the biological effects of THz waves has become an important new field in life sciences. Specifically, THz radiation has been proved to be able to diagnose and treat several head and neck diseases. In this review, we primarily discuss the biological characteristics of THz waves and clinical applications of THz technology, focusing on the research progress of THz technology in head and neck diseases (brain cancer, hypopharyngeal cancer, oral diseases, thyroid nodules, Alzheimer's disease, eyes diseases, and otitis). The future application perspectives of THz technologies in head and neck diseases are also highlighted and proposed.

Making the Invisible Visible: Toward High-Quality Terahertz Tomographic Imaging via Physics-Guided Restoration

Terahertz (THz) tomographic imaging has recently attracted significant attention thanks to its non-invasive, non-destructive, non-ionizing, material-classification, and ultra-fast nature for object exploration and inspection. However, its strong water absorption nature and low noise tolerance lead to undesired blurs and distortions of reconstructed THz images. The diffraction-limited THz signals highly constrain the performances of existing restoration methods. To address the problem, we propose a novel multi-view Subspace-Attention-guided Restoration Network (SARNet) that fuses multi-view and multi-spectral features of THz images for effective image restoration and 3D tomographic reconstruction. To this end, SARNet uses multi-scale branches to extract intra-view spatio-spectral amplitude and phase features and fuse them via shared subspace projection and self-attention guidance. We then perform inter-view fusion to further improve the restoration of individual views by leveraging the redundancies between neighboring views. Here, we experimentally construct a THz time-domain spectroscopy (THz-TDS) system covering a broad frequency range from 0.1 to 4 THz for building up a temporal/spectral/spatial/material THz database of hidden 3D objects. Complementary to a quantitative evaluation, we demonstrate the effectiveness of our SARNet model on 3D THz tomographic reconstruction applications.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11263-023-01812-y.

Terahertz imaging of human skin pathologies using laser feedback interferometry with quantum cascade lasers

Early detection of skin pathologies with current clinical diagnostic tools is challenging, particularly when there are no visible colour changes or morphological cues present on the skin. In this study, we present a terahertz (THz) imaging technology based on a narrow band quantum cascade laser (QCL) at 2.8 THz for human skin pathology detection with diffraction limited spatial resolution. THz imaging was conducted for three different groups of unstained human skin samples (benign naevus, dysplastic naevus, and melanoma) and compared to the corresponding traditional histopathologic stained images. The minimum thickness of dehydrated human skin that can provide THz contrast was determined to be 50 µm, which is approximately one half-wavelength of the THz wave used. The THz images from different types of 50 µm-thick skin samples were well correlated with the histological findings. The per-sample locations of pathology vs healthy skin can be separated from the density distribution of the corresponding pixels in the THz amplitude-phase map. The possible THz contrast mechanisms relating to the origin of image contrast in addition to water content were analyzed from these dehydrated samples. Our findings suggest that THz imaging could provide a feasible imaging modality for skin cancer detection that is beyond the visible.

Revolutionary approaches for cancer diagnosis by terahertz-based spectroscopy and imaging

Most tumors are easily missed and misdiagnosed due to the lack of specific clinical signs and symptoms in the early stage. Thus, an accurate, rapid and reliable early tumor detection method is highly desirable. The application of terahertz (THz) spectroscopy and imaging in biomedicine has made remarkable progress in the past two decades, which addresses the shortcomings of existing technologies and provides an alternative for early tumor diagnosis. Although issues such as size mismatch and strong absorption of THz waves by water have set hurdles for cancer diagnosis by THz technology, innovative materials and biosensors in recent years have led to possibilities for new THz biosensing and imaging methods. In this article, we reviewed the issues that need to be solved before THz technology is used for tumor-related biological sample detection and clinical auxiliary diagnosis. We focused on the recent research progress of THz technology, with an emphasis on biosensing and imaging. Finally, the application of THz spectroscopy and imaging for tumor diagnosis in clinical practice and the main challenges in this process were also mentioned. Collectively, THz-based spectroscopy and imaging reviewed here is envisioned as a cutting-edge approach for cancer diagnosis.

Advances in terahertz technology for cancer detection applications

Currently, there is an increasing demand for the diagnostic techniques that provide functional and morphological information with early cancer detection capability. Novel modern medical imaging systems driven by the recent advancements in technology such as terahertz (THz) and infrared radiation-based imaging technologies which are complementary to conventional modalities are being developed, investigated, and validated. The THz cancer imaging techniques offer novel opportunities for label free, non-ionizing, non-invasive and early cancer detection. The observed image contrast in THz cancer imaging studies has been mostly attributed to higher refractive index, absorption coefficient and dielectric properties in cancer tissue than that in the normal tissue due the local increase of the water molecule content in tissue and increased blood supply to the cancer affected tissue. Additional image contrast parameters and cancer biomarkers that have been reported to contribute to THz image contrast include cell structural changes, molecular density, interactions between agents (e.g., contrast agents and embedding agents) and biological tissue as well as tissue substances like proteins, fiber and fat etc. In this paper, we have presented a systematic and comprehensive review of the advancements in the technological development of THz technology for cancer imaging applications. Initially, the fundamentals principles and techniques for THz radiation generation and detection, imaging and spectroscopy are introduced. Further, the application of THz imaging for detection of various cancers tissues are presented, with more focus on the in vivo imaging of skin cancer. The data processing techniques for THz data are briefly discussed. Also, we identify the advantages and existing challenges in THz based cancer detection and report the performance improvement techniques. The recent advancements towards THz systems which are optimized and miniaturized are also reported. Finally, the integration of THz systems with artificial intelligent (AI), internet of things (IoT), cloud computing, big data analytics, robotics etc. for more sophisticated systems is proposed. This will facilitate the large-scale clinical applications of THz for smart and connected next generation healthcare systems and provide a roadmap for future research.

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.

In vivo Estimation of Breast Cancer Tissue Volume in Subcutaneous Xenotransplantation Mouse Models by Using a High-Sensitivity Fiber-Based Terahertz Scanning Imaging System

Absorption contrast between the terahertz (THz) frequency range of fatty and cancer tissues allows cancer diagnosis by THz imaging. We successfully demonstrated the ability of THz imaging to measure small breast cancer volume in the subcutaneous xenotransplantation mouse models even without external comparison. We estimated the volume detection limitation of the fiber-based THz scanning imaging system using a highly sensitive cryogenic-temperature-operated Schottky diode detector to be smaller than 1 mm³, thus showing the potential application of this technique in preliminary early cancer diagnosis.

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.

Disassembly of microtubules by intense terahertz pulses

The biological effects of terahertz (THz) radiation have been observed across multiple levels of biological organization, however the sub-cellular mechanisms underlying the phenotypic changes remain to be elucidated. Filamentous protein complexes such as microtubules are essential cytoskeletal structures that regulate diverse biological functions, and these may be an important target for THz interactions underlying THz-induced effects observed at the cellular or tissue level. Here, we show disassembly of microtubules within minutes of exposure to extended trains of intense, picosecond-duration THz pulses. Further, the rate of disassembly depends on THz intensity and spectral content. As inhibition of microtubule dynamics is a mechanism of clinically-utilized anti-cancer agents, disruption of microtubule networks may indicate a potential therapeutic mechanism of intense THz pulses.

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.

Terahertz Imaging and Spectroscopy in Cancer Diagnostics: A Technical Review

Terahertz (THz) waves are electromagnetic waves with frequency in the range from 0.1 to 10 THz. THz waves have great potential in the biomedical field, especially in cancer diagnosis, because they exhibit low ionization energy and can be used to discern most biomolecules based on their spectral fingerprints. In this paper, we review the recent progress in two applications of THz waves in cancer diagnosis: imaging and spectroscopy. THz imaging is expected to help researchers and doctors attain a direct intuitive understanding of a cancerous area. THz spectroscopy is an efficient tool for component analysis of tissue samples to identify cancer biomarkers. Additionally, the advantages and disadvantages of the developed technologies for cancer diagnosis are discussed. Furthermore, auxiliary techniques that have been used to enhance the spectral signal-to-noise ratio (SNR) are also reviewed.

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.

Plasmonics-enhanced photoconductive terahertz detector pumped by Ytterbium-doped fiber laser

We present a photoconductive terahertz detector operating at the 1 µm wavelength range at which high-power and compact Ytterbium-doped femtosecond fiber lasers are available. The detector utilizes an array of plasmonic nanoantennas to provide sub-picosecond transit time for the majority of photo-generated carriers to enable high-sensitivity terahertz detection without using a short-carrier-lifetime substrate. By using a high-mobility semiconductor substrate and preventing photocarrier recombination, the presented detector offers significantly higher sensitivity levels compared with previously demonstrated broadband photoconductive terahertz detectors operating at the 1 µm wavelength range. We demonstrate pulsed terahertz detection over a 4 THz bandwidth with a record-high signal-to-noise ratio of 95 dB at an average terahertz radiation power of 6.8 µW, when using an optical pump power of 30 mW.

Automatic recognition of breast invasive ductal carcinoma based on terahertz spectroscopy with wavelet packet transform and machine learning

We demonstrate an automatic recognition strategy for terahertz (THz) pulsed signals of breast invasive ductal carcinoma (IDC) based on a wavelet entropy feature extraction and a machine learning classifier. The wavelet packet transform was implemented into the complexity analysis of the transmission THz signal from a breast tissue sample. A novel index of energy to Shannon entropy ratio (ESER) was proposed to distinguish different tissues. Furthermore, the principal component analysis (PCA) method and machine learning classifier were further adopted and optimized for automatic classification of the THz signal from breast IDC sample. The areas under the receiver operating characteristic curves are all larger than 0.89 for the three adopted classifiers. The best breast IDC recognition performance is with the precision, sensitivity and specificity of 92.85%, 89.66% and 96.67%, respectively. The results demonstrate the effectiveness of the ESER index together with the machine learning classifier for automatically identifying different breast tissues.

A Comprehensive Review on Food Applications of Terahertz Spectroscopy and Imaging

Food product safety is a public health concern. Most of the food safety analytical and detection methods are expensive, labor intensive, and time consuming. A safe, rapid, reliable, and nondestructive detection method is needed to assure consumers that food products are safe to consume. Terahertz (THz) radiation, which has properties of both microwave and infrared, can penetrate and interact with many commonly used materials. Owing to the technological developments in sources and detectors, THz spectroscopic imaging has transitioned from a laboratory-scale technique into a versatile imaging tool with many practical applications. In recent years, THz imaging has been shown to have great potential as an emerging nondestructive tool for food inspection. THz spectroscopy provides qualitative and quantitative information about food samples. The main applications of THz in food industries include detection of moisture, foreign bodies, inspection, and quality control. Other applications of THz technology in the food industry include detection of harmful compounds, antibiotics, and microorganisms. THz spectroscopy is a great tool for characterization of carbohydrates, amino acids, fatty acids, and vitamins. Despite its potential applications, THz technology has some limitations, such as limited penetration, scattering effect, limited sensitivity, and low limit of detection. THz technology is still expensive, and there is no available THz database library for food compounds. The scanning speed needs to be improved in the future generations of THz systems. Although many technological aspects need to be improved, THz technology has already been established in the food industry as a powerful tool with great detection and quantification ability. This paper reviews various applications of THz spectroscopy and imaging in the food industry.

Terahertz tomographic imaging of freshly excised human breast tissues

Terahertz imaging and spectroscopy characterization of freshly excised breast cancer tumors are presented in the range 0.15 to 3.5 THz. Cancerous breast tissues were obtained from partial or full removal of malignant tumors while healthy breast tissues were obtained from breast reduction surgeries. The reflection spectroscopy to obtain the refractive index and absorption coefficient is performed on experimental data at each pixel of the tissue, forming tomographic images. The transmission spectroscopy of the refractive index and absorption coefficient are retrieved from experimental data at few tissue points. The average refractive index and absorption coefficients for cancer, fat, and collagen tissue regions are compared between transmission and reflection modes. The reflection mode offers the advantage of retrieving the electrical properties across a significantly greater number of points without the need for sectioning or altering the freshly excised tissue as in the transmission mode. The terahertz spectral power images and the tomographic images demonstrated good qualitative comparison with pathology.

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.

Assessment of Terahertz Imaging for Excised Breast Cancer Tumors with Image Morphing

This paper presents an image morphing algorithm for quantitative evaluation methodology of terahertz (THz) images of excised breast cancer tumors. Most current studies on the assessment of THz imaging rely on qualitative evaluation, and there is no established benchmark or procedure to quantify the THz imaging performance. The proposed morphing algorithm provides a tool to quantitatively align the THz image with the histopathology image. Freshly excised xenograft murine breast cancer tumors are imaged using the pulsed THz imaging and spectroscopy system in the reflection mode. Upon fixing the tumor tissue in formalin and embedding in paraffin, an FFPE tissue block is produced. A thin slice of the block is prepared for the pathology image while another THz reflection image is produced directly from the block. We developed an algorithm of mesh morphing using homography mapping of the histopathology image to adjust the alignment, shape, and resolution to match the external contour of the tissue in the THz image. Unlike conventional image morphing algorithms that rely on internal features of the source and target images, only the external contour of the tissue is used to avoid bias. Unsupervised Bayesian learning algorithm is applied to THz images to classify the tissue regions of cancer, fat, and muscles present in xenograft breast tumors. The results demonstrate that the proposed mesh morphing algorithm can provide more effective and accurate evaluation of THz imaging compared with existing algorithms. The results also showed that while THz images of FFPE tissue are highly in agreement with pathology images, challenges remain in assessing THz imaging of fresh tissue.

Pulsed Terahertz Reflection Imaging of tumors in a spontaneous model of breast cancer

We report the use of reflection-mode terahertz (THz) imaging in a transgenic mouse model of breast cancer. Unlike tumor xenografts that are grown from established cell lines, these tumors were spontaneously generated in the mammary fat pad of mice, and are a better representation of human breast cancer. THz imaging results from 7 tumors that recapitulate the compartmental complexity of breast cancer are presented here. Imaging was first performed on freshly excised tumors within an hour of excision and then repeated after fixation with formalin and paraffin. These THz images were then compared with histopathology to determine reflection-mode signals from specific regions within tumor. Our results demonstrate that the THz signal was consistently higher in cancerous tissue compared with fat, muscle, and fibrous tissue. Almost all tumors presented in this work demonstrated advanced stages where cancer infiltrated other tissues like fat and fibrous stroma. As the first known THz investigation in a transgenic model, these results hold promise for THz imaging at different stages of breast cancer.

Pilot study of freshly excised breast tissue response in the 300-600 GHz range

The failure to accurately define tumor margins during breast conserving surgery (BCS) results in a 20% re-excision rate. The present paper reports the investigation to evaluate the potential of terahertz imaging for breast tissue recognition within the under-explored 300 - 600 GHz range. Such a frequency window matches new BiCMOS technology capabilities and thus opens up the opportunity for near-field terahertz imaging using these devices. To assess the efficacy of this frequency band, data from 16 freshly excised breast tissue samples were collected and analyzed directly after excision. Complex refractive indices have been extracted over the as-mentioned frequency band, and amplitude frequency images show some contrast between tissue types. Principal component analysis (PCA) has also been applied to the data in an attempt to automate tissue classification. Our observations suggest that the dielectric response could potentially provide contrast for breast tissue recognition within the 300 - 600 GHz range. These results open the way for silicon-based terahertz subwavelength near field imager design, efficient up to 600 GHz to address ex vivo life-science applications.