Classification of terahertz-pulsed imaging data from excised breast tissue

Mutagenicity assessment of high-power 1.6-THz pulse laser radiation

The effect of terahertz (THz) radiation has been studied in medicine. However, there is a lack of scientific information regarding its possible mutagenicity. Therefore, the present study aimed to assess the mutagenicity of 1.6 THz laser irradiation. The Ames test was conducted using five bacterial tester strains. The bacteria were subjected to (i) 1.6 THz laser irradiation at 3.8 mW/cm2 for 60 min using a tabletop THz pulse laser system, (ii) ultraviolet irradiation, (iii) treatment with positive control chemicals (positive control) or (iv) treatment with the solvent used in the positive control (negative control). After treatment, the bacterial suspensions were cultured on minimal glucose agar to determine the number of revertant colonies. In addition, the comet assay was performed using fibroblasts (V79) to assess possible DNA damage caused by the THz laser irradiation. The Ames test demonstrated that the THz laser irradiation did not increase the number of revertant colonies compared to that in the negative control group, whereas the ultraviolet irradiation and positive control treatment increased the number of revertant colonies. Thus, 1.6 THz laser irradiation is unlikely to be mutagenic. The comet assay additionally suggests that the THz laser irradiation unlikely induce cellular DNA damage.

Design and characterization of a hyperbolic-elliptical lens pair in a rapid beam steering system for single-pixel terahertz spectral imaging of the cornea

Terahertz (THz) time-domain spectroscopy has been investigated for assessment of the hydration levels in the cornea, intraocular pressure, and changes in corneal topography. Previous efforts at THz imaging of the cornea have employed off-axis parabolic mirrors to achieve normal incidence along the spherical surface. However, this comes at the cost of an asymmetric field-of-view (FOV) and a long scan time because it requires raster-scanning of the collimated beam across the large mirror diameter. This paper proposes a solution by designing a pair of aspheric lenses that can provide a larger symmetric spherical FOV (9.6 mm) and reduce the scan time by two orders of magnitude using a novel beam-steering approach. A hyperbolic-elliptical lens was designed and optimized to achieve normal incidence and phase-front matching between the focused THz beam and the target curvature. The lenses were machined from a slab of high-density polyethylene and characterized in comparison to ray-tracing simulations by imaging several targets of similar sizes to the cornea. Our experimental results showed excellent agreement in the increased symmetric FOV and confirmed the reduction in scan time to about 3-4 seconds. In the future, this lens design process can be extended for imaging the sclera of the eye and other curved biological surfaces, such as the nose and fingers.

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.

Artificial Intelligence-Assisted Terahertz Imaging for Rapid and Label-Free Identification of Efficient Light Formula in Laser Therapy

Photodynamic therapy (PDT) is considered a promising noninvasive therapeutic strategy in biomedicine, especially by utilizing low-level laser therapy (LLLT) in visible and near-infrared spectra to trigger biological responses. The major challenge of PDT in applications is the complicated and time-consuming biological methodological measurements in identification of light formulas for different diseases. Here, we demonstrate a rapid and label-free identification method based on artificial intelligence (AI)-assisted terahertz imaging for efficient light formulas in LLLT of acute lung injury (ALI). The gray histogram of terahertz images is developed as the biophysical characteristics to identify the therapeutic effect. Label-free terahertz imaging is sequentially performed using rapid super-resolution imaging reconstruction and automatic identification algorithm based on a voting classifier. The results indicate that the therapeutic effect of LLLT with different light wavelengths and irradiation times for ALI can be identified using this method with a high accuracy of 91.22% in 33 s, which is more than 400 times faster than the biological methodology and more than 200 times faster than the scanning terahertz imaging technology. It may serve as a new tool for the development and application of PDT.

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.

Optimization for continuous-wave terahertz reflection imaging for biological tissues

Continuous-wave terahertz reflection imaging is a potential tool for biological tissues. Based on our home-made continuous-wave terahertz reflection imaging system, the effect of both polarization mode and reflection window on the imaging performance is studied theoretically and experimentally, showing good agreement. By taking obtaining sample information and image contrast into consideration, p-polarized terahertz waves are recommended. Moreover, considering the sample boundary identification and the image contrast, selection criteria for reflection window are proposed. This work will help to improve the performance of continuous-wave terahertz reflection imaging and accelerate the THz imaging in biological application.

A Review of Feasible Applications of THz Waves in Medical Diagnostics and Treatments

Introduction: Terahertz (THz) waves with frequencies in the range of 0.1 to 10 THz are electromagnetic radiation with growing applications in various fields of science and technology. Attractive features of this radiation have brought out many novel possibilities for medical diagnostics and treatments with considerable advantages compared to other conventional methods. Methods: In this paper, we present a review of more recent reports on practical applications of THz radiation for diagnostic, biosensing and clinical treatments. The review includes the diagnosis of breast, skin, mouth, cervical, lungs, small intestine, prostate, colon, and stomach cancers, the evaluation of biomolecules, the detection of genetic mutations, the determination of burn depth, the diagnosis of tooth decay, diabetes, and emotional-psychological states, the evaluation of corneal water to diagnose visual diseases, and wound healing monitoring. Further, it embraces the use of THz therapy in reducing the size of the tumor, treating skin cancer, and healing burn wounds, cardiovascular disease, corneal epithelium, angina, and THz heating. Results: This review has emphasized the capabilities of THz waves as a novel tool for future clinical diagnostics and treatments. Conclusion: The paper provides a comprehensive understanding of the feasible potential application of THz waves for clinical purposes and its advantages in comparison with other conventional tools.

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.

Using Attenuated Total Reflection (ATR) Apparatus to Investigate the Temperature Dependent Dielectric Properties of Water, Ice, and Tissue-Representative Fats

A novel method of investigating the temperature dependent variation of aspects of the complex refractive index n* in samples in the THz range using continuous, non-polarised, synchrotron radiation is presented. The method relies on the use of ATR apparatus, and retains the advantage of minimal sample preparation, which is a feature of ATR techniques. The method demonstrates a “proof of concept” of monitoring temperature reflectance whilst continuously heating or cooling samples by using a temperature variable Thermal Sample Stage. The method remains useful when the refractive index of the sample precludes attenuated total reflection study. This is demonstrated with the water reflectance experiments. The temperature dependent ATR reflectance of tissue-representative fats (lard and Lurpak® butter) was investigated with the novel approach. Both are within the ATR range of the diamond crystal in a “true” ATR mode. Lard showed no clear temperature variation between −15 °C and 24 °C at 0.7 to 1.15 THz or 1.70 to 2.25 THz. Lard can be regarded as having invariable, constant, dielectric properties within mixtures when biological substances are being assessed for temperature dependent dielectric variation within the stated THz ranges. Lurpak® butter (water content 14.7%) displayed temperature dependent reflected signal intensity features with a steady decline in reflectivity with increasing temperature. This is in line with the temperature-dependent behaviour of liquid water. There is no rapid change in reflected signal intensity even at −20 °C, suggesting that emulsified water retains liquid-water-like THz properties at freezing temperatures.

Terahertz radiation and the skin: a review

SIGNIFICANCE

Terahertz (THz) radiation has demonstrated a great potential in biomedical applications over the past three decades, mainly due to its non-invasive and label-free nature. Among all biological specimens, skin tissue is an optimal sample for the application of THz-based methods because it allows for overcoming some intrinsic limitations of the technique, such as a small penetration depth (0.1 to 0.3 mm for the skin, on average).

AIM

We summarize the modern research results achieved when THz technology was applied to the skin, considering applications in both imaging/detection and treatment/modulation of the skin constituents.

APPROACH

We perform a review of literature and analyze the recent research achievements in THz applications for skin diagnosis and investigation.

RESULTS

The reviewed results demonstrate the possibilities of THz spectroscopy and imaging, both pulsed and continuous, for diagnosis of skin melanoma and non-melanoma cancer, dysplasia, scars, and diabetic condition, mainly based on the analysis of THz optical properties. The possibility of modulating cell activity and treatment of various diseases by THz-wave exposure is shown as well.

CONCLUSIONS

The rapid development of THz technologies and the obtained research results for skin tissue highlight the potential of THz waves as a research and therapeutic instrument. The perspectives on the use of THz radiation are related to both non-invasive diagnostics and stimulation and control of different processes in a living skin tissue for regeneration and cancer treatment.

Development of oral cancer tissue-mimicking phantom based on polyvinyl chloride plastisol and graphite for terahertz frequencies

SIGNIFICANCE

A new concept of a biotissue phantom for terahertz (THz) biomedical applications is needed for reliable and long-term usage.

AIM

We aimed to develop a new type of biotissue phantom without water content and with controllable THz optical properties by applying graphite powders into a polyvinyl chloride plastisol (PVCP) matrix and to give a numerical description to the THz optical properties of the phantoms using the Bruggeman model (BM) of the effective medium theory (EMT).

APPROACH

The THz optical properties of graphite and the PVCP matrix were measured using THz time-domain spectroscopy, which works in the frequency range from 0.1 to 1 THz. Two phantoms with 10% and 12.5% graphite were fabricated to evaluate the feasibility of describing phantoms using the EMT. The EMT then was used to determine the concentration of graphite required to mimic the THz optical properties of human cancerous and healthy oral tissue.

RESULTS

The phantom with 16.7% of graphite has the similar THz optical properties as human cancerous oral tissue in the frequency range of 0.2 to 0.7 THz. The THz optical properties of the phantom with 21.9% of graphite are close to those of human healthy oral tissue in the bandwidth from 0.6 to 0.8 THz. Both the refractive index and absorption coefficient of the samples increase with an increase of graphite concentration. The BM of the EMT was used as the numerical model to describe the THz optical properties of the phantoms. The relative error of the BM for the refractive index estimation and the absorption coefficient is up to 4% and 8%, respectively.

CONCLUSIONS

A water-free biotissue phantom that mimics the THz optical properties of human cancerous oral tissue was developed. With 21.9% of graphite, the phantom also mimics human healthy oral tissue in a narrow frequency range. The BM proved to be a suitable numerical model of the phantom.

Detecting melanoma with a terahertz spectroscopy imaging technique

Transmission mode terahertz time-domain spectroscopy system was employed to image BALB/c mouse skin tissue slices containing melanoma. The melanoma was unambiguously identified in the frequency region of 0.6-1.8 THz because melanoma has a higher refractive index as well as a higher absorption coefficient than the normal region of the skin tissue. Based on the results of hematoxylin-eosin staining and mass weighing, it was further suggested that the higher density of nucleic acids, higher water content, and lower fat content in the melanoma compared to the normal region are major factors responsible for melanoma's higher refractive index and absorption coefficient than normal tissue. The present work validates that terahertz time-domain spectroscopy imaging technique is possible to be used for the diagnosis of melanoma.

Co-registered combined OCT and THz imaging to extract depth and refractive index of a tissue-equivalent test object

Terahertz (THz) imaging and optical coherence tomography (OCT) provide complementary information with similar length scales. In addition to OCT's extensive use in ophthalmology, both methods have shown some promise for other medical applications and non-destructive testing. In this paper, we present an iterative algorithm that combines the information from OCT and THz imaging at two different measurement locations within an object to determine both the depth of the reflecting layers at the two locations and the unknown refractive index of the medium for both the OCT wavelengths and THz frequencies. We validate this algorithm using a silicone test object with embedded layers and show that the depths and refractive index values obtained from the algorithm agreed with the measured values to within 3.3%. We further demonstrate for the first time that OCT and THz images can be co-registered and aligned using unsupervised image registration. Hence we show that a combined OCT/THz system can provide unique information beyond the capability of the separate modalities alone, with possible applications in the medical, industrial and pharmaceutical sectors.

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.

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.

Imaging brain tissue slices with terahertz near-field microscopy

Photoconductive antenna microprobe (PCAM)-based terahertz (THz) near-field imaging technique is promising for biomedical detection due to its excellent biocompatibility and high resolution; yet it is limited by its imaging speed and the difficulty in the control of the PCAM tip-sample separation. In this work, we successfully realized imaging of mouse brain tissue slices using an improved home-built PCAM-based THz near-field microscope. In this system, the imaging speed was enhanced by designing and applying a voice coil motor-based delay-line. The tip-sample separation control was implemented by developing an image analysis-based technique. Compared with conventional PCAM-based THz near-field systems, our improved system is 100 times faster in imaging speed and the tip-sample separation can be controlled to a few micrometers (e.g., 3 μm), satisfying the requirements of THz near-field imaging of biological samples. It took about ~30 min (not the tens of hours it took to acquire the same kind of image previously) to collect a THz near-field image of brain tissue slices of BALb/c mice (500 μm × 500 μm) with pixel size of 20 μm × 20 μm. The results show that the mouse brain slices can be properly imaged and different regions in the slices (i.e., the corpus callosum region and the cerebrum region) can be identified unambiguously. Evidently, the work demonstrated here provides not only a convincing example but a useful technique for imaging biological samples with THz near-field microscopy. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 35: e2741, 2019.

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.

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.

Automatic evaluation of traumatic brain injury based on terahertz imaging with machine learning

The imaging diagnosis and prognostication of different degrees of traumatic brain injury (TBI) is very important for early care and clinical treatment. Especially, the exact recognition of mild TBI is the bottleneck for current label-free imaging technologies in neurosurgery. Here, we report an automatic evaluation method for TBI recognition with terahertz (THz) continuous-wave (CW) transmission imaging based on machine learning (ML). We propose a new feature extraction method for biological THz images combined with the transmittance distribution features in spatial domain and statistical distribution features in normalized gray histogram. Based on the extracted feature database, ML algorithms are performed for the classification of different degrees of TBI by feature selection and parameter optimization. The highest classification accuracy is up to 87.5%. The area under the curve (AUC) scores of the receiver operating characteristics (ROC) curve are all higher than 0.9, which shows this evaluation method has a good generalization ability. Furthermore, the excellent performance of the proposed system in the recognition of mild TBI is analyzed by different methodological parameters and diagnostic criteria. The system can be extensible to various diseases and will be a powerful tool in automatic biomedical diagnostics.

Concentration analysis of breast tissue phantoms with terahertz spectroscopy

Terahertz imaging has been previously shown to be capable of distinguishing normal breast tissue from its cancerous form, indicating its applicability to breast conserving surgery. The heterogeneous composition of breast tissue is among the main challenges to progressing this potential research towards a practical application. In this paper, two concentration analysis methods are proposed for analyzing phantoms mimicking breast tissue. The dielectric properties and the double Debye parameters were used to determine the phantom composition. The first method is wholly based on the conventional effective medium theory while the second one combines this theoretical model with empirical polynomial models. Through assessing the accuracy of these methods, their potential for application to quantifying breast tissue pathology was confirmed.

Pulsed terahertz imaging of breast cancer in freshly excised murine tumors

This paper investigates terahertz (THz) imaging and classification of freshly excised murine xenograft breast cancer tumors. These tumors are grown via injection of E0771 breast adenocarcinoma cells into the flank of mice maintained on high-fat diet. Within 1 h of excision, the tumor and adjacent tissues are imaged using a pulsed THz system in the reflection mode. The THz images are classified using a statistical Bayesian mixture model with unsupervised and supervised approaches. Correlation with digitized pathology images is conducted using classification images assigned by a modal class decision rule. The corresponding receiver operating characteristic curves are obtained based on the classification results. A total of 13 tumor samples obtained from 9 tumors are investigated. The results show good correlation of THz images with pathology results in all samples of cancer and fat tissues. For tumor samples of cancer, fat, and muscle tissues, THz images show reasonable correlation with pathology where the primary challenge lies in the overlapping dielectric properties of cancer and muscle tissues. The use of a supervised regression approach shows improvement in the classification images although not consistently in all tissue regions. Advancing THz imaging of breast tumors from mice and the development of accurate statistical models will ultimately progress the technique for the assessment of human breast tumor margins.

Determination of terahertz permittivity of dehydrated biological samples

A key step in transforming terahertz imaging to a practical medical imaging modality lies in understanding the interactions between terahertz (THz) waves and biological tissues. Most of the models in the literature use the permittivity of liquid water to simulate the THz-tissue interactions, but they often neglect contributions from the biological background such as proteins and lipids because dehydrated biological samples are experimentally difficult to prepare. In this work, we present a method to prepare thin and flat dehydrated samples which can be easily handled and measured in a transmission setup. Our results will provide fundamental parameters for modelling THz-tissue interactions.

Composite multiscale entropy analysis of reflective terahertz signals for biological tissues

We demonstrate a composite multiscale entropy (CMSE) method of terahertz (THz) signal complexity analysis to distinguish different biological tissues. The THz signals reflected from fresh porcine skin and muscle tissues were measured and analyzed. The statistically significant difference and separation of the two tissues based on several parameters were analyzed and compared for THz spectroscopy and imaging, which verified the better performance of the CMSE method and further enhancement of the contrast among THz signals that interact with different tissues. This process provides a better analysis and discrimination method for THz spectroscopy and imaging in biomedical applications.

Use of a handheld terahertz pulsed imaging device to differentiate benign and malignant breast tissue

Since nearly 20% of breast-conserving surgeries (BCS) require re-operation, there is a clear need for developing new techniques to more accurately assess tumor resection margins intraoperatively. This study evaluates the diagnostic accuracy of a handheld terahertz pulsed imaging (TPI) system to discriminate benign from malignant breast tissue ex vivo. Forty six freshly excised breast cancer samples were scanned with a TPI handheld probe system, and histology was obtained for comparison. The image pixels on TPI were classified using (1) parameters in combination with support vector machine (SVM) and (2) Gaussian wavelet deconvolution in combination with Bayesian classification. The results were an accuracy, sensitivity, specificity of 75%, 86%, 66% for method 1, and 69%, 87%, 54% for method 2 respectively. This demonstrates the probe can discriminate invasive breast cancer from benign breast tissue with an encouraging degree of accuracy, warranting further study.

Terahertz imaging of metastatic lymph nodes using spectroscopic integration technique

Terahertz (THz) imaging was used to differentiate the metastatic states of frozen lymph nodes (LNs) by using spectroscopic integration technique (SIT). The metastatic states were classified into three groups: healthy LNs, completely metastatic LNs, and partially metastatic LNs, which were obtained from three mice without infection and six mice infected with murine melanoma cells for 30 days and 15 days, respectively. Under histological examination, the healthy LNs and completely metastatic LNs were found to have a homogeneous cellular structure but the partially metastatic LNs had interfaces of the melanoma and healthy tissue. THz signals between the experimental groups were not distinguished at room temperature due to high attenuation by water in the tissues. However, a signal gap between the healthy and completely metastatic LNs was detected at freezing temperature. The signal gap could be enhanced by using SIT that is a signal processing method dichotomizing the signal difference between the healthy cells and melanoma cells with their normalized spectral integration. This technique clearly imaged the interfaces in the partially metastatic LNs, which could not be achieved by existing methods using a peak point or spectral value. The image resolution was high enough to recognize a metastatic area of about 0.7 mm size in the partially metastatic LNs. Therefore, this pilot study demonstrated that THz imaging of the frozen specimen using SIT can be used to diagnose the metastatic state of LNs for clinical application.

Exploring the complementarity of THz pulse imaging and DCE-MRIs: Toward a unified multi-channel classification and a deep learning framework

We provide a comprehensive account of recent advances in biomedical image analysis and classification from two complementary imaging modalities: terahertz (THz) pulse imaging and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The work aims to highlight underlining commonalities in both data structures so that a common multi-channel data fusion framework can be developed. Signal pre-processing in both datasets is discussed briefly taking into consideration advances in multi-resolution analysis and model based fractional order calculus system identification. Developments in statistical signal processing using principal component and independent component analysis are also considered. These algorithms have been developed independently by the THz-pulse imaging and DCE-MRI communities, and there is scope to place them in a common multi-channel framework to provide better software standardization at the pre-processing de-noising stage. A comprehensive discussion of feature selection strategies is also provided and the importance of preserving textural information is highlighted. Feature extraction and classification methods taking into consideration recent advances in support vector machine (SVM) and extreme learning machine (ELM) classifiers and their complex extensions are presented. An outlook on Clifford algebra classifiers and deep learning techniques suitable to both types of datasets is also provided. The work points toward the direction of developing a new unified multi-channel signal processing framework for biomedical image analysis that will explore synergies from both sensing modalities for inferring disease proliferation.

Nondestructive Evaluation of Carbon Fiber Reinforced Polymer Composites Using Reflective Terahertz Imaging

Terahertz (THz) time-domain spectroscopy (TDS) imaging is considered a nondestructive evaluation method for composite materials used for examining various defects of carbon fiber reinforced polymer (CFRP) composites and fire-retardant coatings in the reflective imaging modality. We demonstrate that hidden defects simulated by Teflon artificial inserts are imaged clearly in the perpendicular polarization mode. The THz TDS technique is also used to measure the thickness of thin fire-retardant coatings on CFRP composites with a typical accuracy of about 10 micrometers. In addition, coating debonding is successfully imaged based on the time-delay difference of the time-domain waveforms between closely adhered and debonded sample locations.

Early detection of germinated wheat grains using terahertz image and chemometrics

In this paper, we propose a feasible tool that uses a terahertz (THz) imaging system for identifying wheat grains at different stages of germination. The THz spectra of the main changed components of wheat grains, maltose and starch, which were obtained by THz time spectroscopy, were distinctly different. Used for original data compression and feature extraction, principal component analysis (PCA) revealed the changes that occurred in the inner chemical structure during germination. Two thresholds, one indicating the start of the release of α-amylase and the second when it reaches the steady state, were obtained through the first five score images. Thus, the first five PCs were input for the partial least-squares regression (PLSR), least-squares support vector machine (LS-SVM), and back-propagation neural network (BPNN) models, which were used to classify seven different germination times between 0 and 48 h, with a prediction accuracy of 92.85%, 93.57%, and 90.71%, respectively. The experimental results indicated that the combination of THz imaging technology and chemometrics could be a new effective way to discriminate wheat grains at the early germination stage of approximately 6 h.

Pattern identification of biomedical images with time series: Contrasting THz pulse imaging with DCE-MRIs

OBJECTIVE

We provide a survey of recent advances in biomedical image analysis and classification from emergent imaging modalities such as terahertz (THz) pulse imaging (TPI) and dynamic contrast-enhanced magnetic resonance images (DCE-MRIs) and identification of their underlining commonalities.

METHODS

Both time and frequency domain signal pre-processing techniques are considered: noise removal, spectral analysis, principal component analysis (PCA) and wavelet transforms. Feature extraction and classification methods based on feature vectors using the above processing techniques are reviewed. A tensorial signal processing de-noising framework suitable for spatiotemporal association between features in MRI is also discussed.

VALIDATION

Examples where the proposed methodologies have been successful in classifying TPIs and DCE-MRIs are discussed.

RESULTS

Identifying commonalities in the structure of such heterogeneous datasets potentially leads to a unified multi-channel signal processing framework for biomedical image analysis.

CONCLUSION

The proposed complex valued classification methodology enables fusion of entire datasets from a sequence of spatial images taken at different time stamps; this is of interest from the viewpoint of inferring disease proliferation. The approach is also of interest for other emergent multi-channel biomedical imaging modalities and of relevance across the biomedical signal processing community.

Breast Cancer classification using extracted parameters from a terahertz dielectric model of human breast tissue

Our previous study proposed a dielectric model for human breast tissue and provided initial analysis of classification potential of the eight model parameters and their multiparameter combinations with the support vector machine (SVM). A combination of three model parameters could achieve a leave-one-out cross validation accuracy of 93.2%. However, the SVM approach fails to exploit the combinations of more than three model parameters for classification improvement. Thus, the Bayesian neural network (BNN) method is employed to overcome this problem based on its advantages of handling our small data and high complexity of the multiparamter combinations. The BNN successfully classifies the data using the combinations of four model parameters with an accuracy, estimated by leave-one-out cross validation, of 97.3%. Overall performance assessed by leaveone-out and repeated random-subsampling cross validations for all examined combinations is also remarkably improved by BNN. The results indicate the advance of BNN as compared to SVM in utilising the model parameters for detecting tumour from normal breast tissue.

Development and testing of a single frequency terahertz imaging system for breast cancer detection

The ability to discern malignant from benign tissue in excised human breast specimens in Breast Conservation Surgery (BCS) was evaluated using single frequency terahertz radiation. Terahertz (THz) images of the specimens in reflection mode were obtained by employing a gas laser source and mechanical scanning. The images were correlated with optical histological micrographs of the same specimens, and a mean discrimination of 73% was found for five out of six samples using Receiver Operating Characteristic (ROC) analysis. The system design and characterization is discussed in detail. The initial results are encouraging but further development of the technology and clinical evaluation is needed to evaluate its feasibility in the clinical environment.

Terahertz time-domain spectroscopy of human blood

In the continuing development of terahertz technology to enable the determination of tissue pathologies in real-time during surgical procedures, it is important to distinguish the measured terahertz signal from biomaterials and fluids, such as blood, which may mask the signal from tissues of interest. In this paper, we present the frequency-dependent absorption coefficients, refractive indices, and Debye relaxation times of whole blood, red blood cells, plasma, and a thrombus.

A dielectric model of human breast tissue in terahertz regime

The double Debye model has been used to understand the dielectric response of different types of biological tissues at terahertz (THz) frequencies but fails in accurately simulating human breast tissue. This leads to limited knowledge about the structure, dynamics, and macroscopic behavior of breast tissue, and hence, constrains the potential of THz imaging in breast cancer detection. The first goal of this paper is to propose a new dielectric model capable of mimicking the spectra of human breast tissue's complex permittivity in THz regime. Namely, a non-Debye relaxation model is combined with a single Debye model to produce a mixture model of human breast tissue. A sampling gradient algorithm of nonsmooth optimization is applied to locate the optimal fitting solution. Samples of healthy breast tissue and breast tumor are used in the simulation to evaluate the effectiveness of the proposed model. Our simulation demonstrates exceptional fitting quality in all cases. The second goal is to confirm the potential of using the parameters of the proposed dielectric model to distinguish breast tumor from healthy breast tissue, especially fibrous tissue. Statistical measures are employed to analyze the discrimination capability of the model parameters while support vector machines are applied to assess the possibility of using the combinations of these parameters for higher classification accuracy. The obtained analysis confirms the classification potential of these features.

Use of finite difference time domain simulations and Debye theory for modelling the terahertz reflection response of normal and tumour breast tissue

The aim of this work was to evaluate the capabilities of Debye theory combined with Finite Difference Time Domain (FDTD) methods to simulate the terahertz (THz) response of breast tissues. Being able to accurately model breast tissues in the THz regime would facilitate the understanding of image contrast parameters used in THz imaging of breast cancer. As a test case, the model was first validated using liquid water and simulated reflection pulses were compared to experimental measured pulses with very good agreement (p = 1.00). The responses of normal and cancerous breast tissues were simulated with Debye properties and the correlation with measured data was still high for tumour (p = 0.98) and less so for normal breast (p = 0.82). Sections of the time domain pulses showed clear differences that were also evident in the comparison of pulse parameter values. These deviations may arise from the presence of adipose and other inhomogeneities in the breast tissue that are not accounted for when using the Debye model. In conclusion, the study demonstrates the power of the model for simulating THz reflection imaging; however, for biological tissues extra Debye terms or a more detailed theory may be required to link THz image contrast to physiological composition and structural changes of breast tissue associated with differences between normal and tumour tissues.

In vivo analysis of THz wave irradiation induced acute inflammatory response in skin by laser-scanning confocal microscopy

The recent development of THz sources in a wide range of THz frequencies and power levels has led to greatly increased interest in potential biomedical applications such as cancer and burn wound diagnosis. However, despite its importance in realizing THz wave based applications, our knowledge of how THz wave irradiation can affect a live tissue at the cellular level is very limited. In this study, an acute inflammatory response caused by pulsed THz wave irradiation on the skin of a live mouse was analyzed at the cellular level using intravital laser-scanning confocal microscopy. Pulsed THz wave (2.7 THz, 4 μs pulsewidth, 61.4 μJ per pulse, 3Hz repetition), generated using compact FEL, was used to irradiate an anesthetized mouse's ear skin with an average power of 260 mW/cm(2) for 30 minutes using a high-precision focused THz wave irradiation setup. In contrast to in vitro analysis using cultured cells at similar power levels of CW THz wave irradiation, no temperature change at the surface of the ear skin was observed when skin was examined with an IR camera. To monitor any potential inflammatory response, resident neutrophils in the same area of ear skin were repeatedly visualized before and after THz wave irradiation using a custom-built laser-scanning confocal microscopy system optimized for in vivo visualization. While non-irradiated control skin area showed no changes in the number of resident neutrophils, a massive recruitment of newly infiltrated neutrophils was observed in the THz wave irradiated skin area after 6 hours, which suggests an induction of acute inflammatory response by the pulsed THz wave irradiation on the skin via a non-thermal process.

High-power femtosecond-terahertz pulse induces a wound response in mouse skin

Terahertz (THz) technology has emerged for biomedical applications such as scanning, molecular spectroscopy, and medical imaging. Although a thorough assessment to predict potential concerns has to precede before practical utilization of THz source, the biological effect of THz radiation is not yet fully understood with scant related investigations. Here, we applied a femtosecond-terahertz (fs-THz) pulse to mouse skin to evaluate non-thermal effects of THz radiation. Analysis of the genome-wide expression profile in fs-THz-irradiated skin indicated that wound responses were predominantly mediated by transforming growth factor-beta (TGF-β) signaling pathways. We validated NFκB1- and Smad3/4-mediated transcriptional activation in fs-THz-irradiated skin by chromatin immunoprecipitation assay. Repeated fs-THz radiation delayed the closure of mouse skin punch wounds due to up-regulation of TGF-β. These findings suggest that fs-THz radiation initiate a wound-like signal in skin with increased expression of TGF-β and activation of its downstream target genes, which perturbs the wound healing process in vivo.

Terahertz time-domain spectroscopy of human blood

In the continuing development of terahertz technology to enable the determination of tissue pathologies in real-time during surgical procedures, it is important to distinguish the measured terahertz signal from biomaterials and fluids, such as blood, which may mask the signal from tissues of interest. In this paper, we present the frequency-dependent absorption coefficients, refractive indices, and Debye relaxation times of whole blood, red blood cells, plasma, and a thrombus.