Towards super-resolved terahertz microscopy for cellular imaging.

Biomedical imaging includes the use of a variety of techniques to study organs and tissues. Some of the possible imaging modalities are more spread at clinical level (CT, MRI, PET), while others, such as light and electron microscopy are preferred in life sciences research. The choice of the imaging modalities can be based on the capability to study functional aspects of an organism, the delivered radiation dose to the patient, and the achievable resolution. In the last few decades, spectroscopists and imaging scientists have been interested in the use of terahertz (THz) frequencies (30 µm to 3 mm wavelength) due to the low photon energy associated (E∼1 meV, not causing breaking of the molecular bonds but still interacting with some vibrational modes) and the high penetration depth that is achievable. THz has been already adopted in security, quality control and material sciences. However, the adoption of THz frequencies for biological and clinical imaging means to face, as a major limitation, the very scarce resolution associated with the use of such long wavelengths. To address this aspect and reconcile the benefit of minimal harmfulness for bioimaging with the achievable resolving power, many attempts have been made. This review summarises the state-of-the-art of THz imaging applications aimed at achieving super-resolution, describing how practical aspects of optics and quasi-optics may be treated to efficaciously implement the use of THz as a new low-dose and versatile modality in biomedical imaging and clinical research.

A Review on the Rule-Based Filtering Structure with Applications on Computational Biomedical Images.

In this paper, we present rule-based fuzzy inference systems that consist of a series of mathematical representations based on fuzzy concepts in the filtering structure. It is crucial for understanding and discussing different principles associated with fuzzy filter design procedures. A number of typical fuzzy multichannel filtering approaches are provided in order to clarify the different fuzzy filter designs and compare different algorithms. In particular, in most practical applications (i.e., biomedical image analysis), the emphasis is placed primarily on fuzzy filtering algorithms, with the main advantages of restoration of corrupted medical images and the interpretation capability, along with the capability of edge preservation and relevant image information for accurate diagnosis of diseases.

Terahertz frequency-wavelet domain deconvolution for stratigraphic and subsurface investigation of art painting.

Terahertz frequency-wavelet deconvolution is utilized specifically for the stratigraphic and subsurface investigation of art paintings with terahertz reflective imaging. In order to resolve the optically thin paint layers, a deconvolution technique is enhanced by the combination of frequency-domain filtering and stationary wavelet shrinkage, and applied to investigate a mid-20th century Italian oil painting on paperboard, After Fishing, by Ausonio Tanda. Based on the deconvolved terahertz data, the stratigraphy of the painting including the paint layers is reconstructed and subsurface features are clearly revealed, demonstrating that terahertz frequency-wavelet deconvolution can be an effective tool to characterize stratified systems with optically thin layers.

Sub-surface terahertz imaging through uneven surfaces: visualizing Neolithic wall paintings in Çatalhöyük.

Pulsed terahertz imaging is being developed as a technique to image obscured mural paintings. Due to significant advances in terahertz technology, portable systems are now capable of operating in unregulated environments and this has prompted their use on archaeological excavations. August 2011 saw the first use of pulsed terahertz imaging at the archaeological site of Çatalhöyük, Turkey, where mural paintings dating from the Neolithic period are continuously being uncovered by archaeologists. In these particular paintings the paint is applied onto an uneven surface, and then covered by an equally uneven surface. Traditional terahertz data analysis has proven unsuccessful at sub-surface imaging of these paintings due to the effect of these uneven surfaces. For the first time, an image processing technique is presented, based around Gaussian beam-mode coupling, which enables the visualization of the obscured painting.

Terahertz deconvolution.

The ability to retrieve information from different layers within a stratified sample using terahertz pulsed reflection imaging and spectroscopy has traditionally been resolution limited by the pulse width available. In this paper, a deconvolution algorithm is presented which circumvents this resolution limit, enabling deep sub-wavelength and sub-pulse width depth resolution. The algorithm is explained through theoretical investigation, and demonstrated by reconstructing signals reflected from boundaries in stratified materials that cannot be resolved directly from the unprocessed time-domain reflection signal. Furthermore, the deconvolution technique has been used to recreate sub-surface images from a stratified sample: imaging the reverse side of a piece of paper.

Optimization of apodization functions in terahertz transient spectrometry.

A quadratic programming optimization procedure for designing asymmetric apodization windows tailored to the shape of time-domain sample waveforms recorded using a terahertz transient spectrometer is proposed. By artificially degrading the waveforms, the performance of the designed window in both the time and the frequency domains is compared with that of conventional rectangular, triangular (Mertz), and Hamming windows. Examples of window optimization assuming Gaussian functions as the building elements of the apodization window are provided. The formulation is sufficiently general to accommodate other basis functions.

Analysis of spectroscopic measurements of leaf water content at terahertz frequencies using linear transforms.

We provide a unified framework for a range of linear transforms that can be used for the analysis of terahertz spectroscopic data, with particular emphasis on their application to the measurement of leaf water content. The use of linear transforms for filtering, regression, and classification is discussed. For illustration, a classification problem involving leaves at three stages of drought and a prediction problem involving simulated spectra are presented. Issues resulting from scaling the data set are discussed. Using Lagrange multipliers, we arrive at the transform that yields the maximum separation between the spectra and show that this optimal transform is equivalent to computing the Euclidean distance between the samples. The optimal linear transform is compared with the average for all the spectra as well as with the Karhunen-Loève transform to discriminate a wet leaf from a dry leaf. We show that taking several principal components into account is equivalent to defining new axes in which data are to be analyzed. The procedure shows that the coefficients of the Karhunen-Loève transform are well suited to the process of classification of spectra. This is in line with expectations, as these coefficients are built from the statistical properties of the data set analyzed.