Terahertz time-gated spectral imaging for content extraction through layered structures

Spatial resolution, spectral contrast and occlusion are three major bottlenecks for non-invasive inspection of complex samples with current imaging technologies. We exploit the sub-picosecond time resolution along with spectral resolution provided by terahertz time-domain spectroscopy to computationally extract occluding content from layers whose thicknesses are wavelength comparable. The method uses the statistics of the reflected terahertz electric field at subwavelength gaps to lock into each layer position and then uses a time-gated spectral kurtosis to tune to highest spectral contrast of the content on that specific layer. To demonstrate, occluding textual content was successfully extracted from a packed stack of paper pages down to nine pages without human supervision. The method provides over an order of magnitude enhancement in the signal contrast and can impact inspection of structural defects in wooden objects, plastic components, composites, drugs and especially cultural artefacts with subwavelength or wavelength comparable layers

Pattern classification approaches for breast cancer identification via MRI: state‐of‐the‐art and vision for the future

Mining algorithms for Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCEMRI) of breast tissue are discussed. The algorithms are based on recent advances in multidimensional signal processing and aim to advance current state‐of‐the‐art computer‐aided detection and analysis of breast tumours when these are observed at various states of development. The topics discussed include image feature extraction, information fusion using radiomics, multi‐parametric computer‐aided classification and diagnosis using information fusion of tensorial datasets as well as Clifford algebra based classification approaches and convolutional neural network deep learning methodologies. The discussion also extends to semi‐supervised deep learning and self‐supervised strategies as well as generative adversarial networks and algorithms using generated confrontational learning approaches. In order to address the problem of weakly labelled tumour images, generative adversarial deep learning strategies are considered for the classification of different tumour types. The proposed data fusion approaches provide a novel Artificial Intelligence (AI) based framework for more robust image registration that can potentially advance the early identification of heterogeneous tumour types, even when the associated imaged organs are registered as separate entities embedded in more complex geometric spaces. Finally, the general structure of a high‐dimensional medical imaging analysis platform that is based on multi‐task detection and learning is proposed as a way forward. The proposed algorithm makes use of novel loss functions that form the building blocks for a generated confrontation learning methodology that can be used for tensorial DCE‐MRI. Since some of the approaches discussed are also based on time‐lapse imaging, conclusions on the rate of proliferation of the disease can be made possible. The proposed framework can potentially reduce the costs associated with the interpretation of medical images by providing automated, faster and more consistent diagnosis

Wavelet based local tomographic image using terahertz techniques

© 2008 Elsevier Inc. All rights reserved.Terahertz computed tomography has been developed based on coherent THz detection and filtered back projection (FBP) algorithms, which allows the global imaging of the internal structure and extraction of the frequency dependent properties. It offers a promising approach for achieving non-invasive inspection of solid materials. However, with traditional CT techniques, i.e. FBP algorithms, full exposure data are needed for inverting the Radon transform to produce cross sectional images. This remains true even if the region of interest is a small subset of the entire image. For time-domain terahertz measurements, the requirement for full exposure data is impractical due to the slow measurement process. This paper explores time domain reconstruction of terahertz measurements by applying wavelet-based filtered back projection algorithms for recovery of a local area of interest from terahertz measurements within its vicinity, and thus improves the feasibility of using terahertz imaging to detect defects in solid materials and diagnose disease states for clinical practise, to name a few applications. © 2008 Elsevier SAS. All rights reserved.Xiaoxia Yin, Brian W.-H. Ng, Brad Ferguson, Derek Abbot