Image reconstruction using spectroscopic and hyperspectral information for compressive terahertz imaging

Terahertz (THz) time-domain imaging is an emerging modality and has attracted a lot of interest. However, existing THz imaging systems often require a long scan time and sophisticated system design. Recently, a new design incorporating compressed sensing (CS) leads to a lower detector cost and shorter scan time, in exchange for computation in an image reconstruction step. In this paper, we develop two reconstruction algorithms that can estimate the underlying scene as accurately as possible. First is a single-band CS reconstruction method, where we show that by making use of prior information about the phase and the correlation between the spatial distributions of the amplitude and phase, the reconstruction quality can be significantly improved over previously published methods. Second, we develop a method that uses the multi-frequency nature of the THz pulse. Through effective use of the spatial sparsity, spectroscopic phase information, and correlations across the hyperspectral bands, our method can further enhance the recovered image quality. This is demonstrated by computation on a set of experimental THz data captured in a single-pixel THz system. © 2010 Optical Society of America.published_or_final_versio

Etalon Array Reconstructive Spectrometry.

Compact spectrometers are crucial in areas where size and weight may need to be minimized. These types of spectrometers often contain no moving parts, which makes for an instrument that can be highly durable. With the recent proliferation in low-cost and high-resolution cameras, camera-based spectrometry methods have the potential to make portable spectrometers small, ubiquitous, and cheap. Here, we demonstrate a novel method for compact spectrometry that uses an array of etalons to perform spectral encoding, and uses a reconstruction algorithm to recover the incident spectrum. This spectrometer has the unique capability for both high resolution and a large working bandwidth without sacrificing sensitivity, and we anticipate that its simplicity makes it an excellent candidate whenever a compact, robust, and flexible spectrometry solution is needed

Hyperspectral reconstruction in biomedical imaging using terahertz systems

Terahertz time-domain spectroscopy (THz-TDS) is an emerging modality for biomedical imaging. It is non-ionizing and can detect differences between water content and tissue density, but the detectors are rather expensive and the scan time tends to be long. Recently, it has been shown that the compressed sensing theory can lead to a radical re-design of the imaging system with lower detector cost and shorter scan time, in exchange for computation in the image reconstruction. We show in this paper that it is in fact possible to make use of the multi-frequency nature of the terahertz pulse to achieve hyperspectral reconstruction. Through effective use of the spatial sparsity, spectroscopic phase information, and correlations across the hyperspectral bands, our method can significantly improve the reconstructed image quality. This is demonstrated through using a set of experimental THz data captured in a single-pixel terahertz system. ©2010 IEEE.published_or_final_versionThe IEEE International Symposium on Circuits and Systems: Nano-Bio Circuit Fabrics and Systems (ISCAS 2010), Pars, France, 30 May-2 June 2010. In Proceedings of ISCAS, 2010, p. 2079-208

Hyperspectral reconstruction in biomedical imaging using terahertz systems

Terahertz time-domain spectroscopy (THz-TDS) is an emerging modality for biomedical imaging. It is non-ionizing and can detect differences between water content and tissue density, but the detectors are rather expensive and the scan time tends to be long. Recently, it has been shown that the compressed sensing theory can lead to a radical re-design of the imaging system with lower detector cost and shorter scan time, in exchange for computation in the image reconstruction. We show in this paper that it is in fact possible to make use of the multi-frequency nature of the terahertz pulse to achieve hyperspectral reconstruction. Through effective use of the spatial sparsity, spectroscopic phase information, and correlations across the hyperspectral bands, our method can significantly improve the reconstructed image quality. This is demonstrated through using a set of experimental THz data captured in a single-pixel terahertz system. ©2010 IEEE.published_or_final_versionThe IEEE International Symposium on Circuits and Systems: Nano-Bio Circuit Fabrics and Systems (ISCAS 2010), Pars, France, 30 May-2 June 2010. In Proceedings of ISCAS, 2010, p. 2079-208

Compressed Sensing Accelerated Magnetic Resonance Spectroscopic Imaging

abstract: Magnetic resonance spectroscopic imaging (MRSI) is a valuable technique for assessing the in vivo spatial profiles of metabolites like N-acetylaspartate (NAA), creatine, choline, and lactate. Changes in metabolite concentrations can help identify tissue heterogeneity, providing prognostic and diagnostic information to the clinician. The increased uptake of glucose by solid tumors as compared to normal tissues and its conversion to lactate can be exploited for tumor diagnostics, anti-cancer therapy, and in the detection of metastasis. Lactate levels in cancer cells are suggestive of altered metabolism, tumor recurrence, and poor outcome. A dedicated technique like MRSI could contribute to an improved assessment of metabolic abnormalities in the clinical setting, and introduce the possibility of employing non-invasive lactate imaging as a powerful prognostic marker. However, the long acquisition time in MRSI is a deterrent to its inclusion in clinical protocols due to associated costs, patient discomfort (especially in pediatric patients under anesthesia), and higher susceptibility to motion artifacts. Acceleration strategies like compressed sensing (CS) permit faithful reconstructions even when the k-space is undersampled well below the Nyquist limit. CS is apt for MRSI as spectroscopic data are inherently sparse in multiple dimensions of space and frequency in an appropriate transform domain, for e.g. the wavelet domain. The objective of this research was three-fold: firstly on the preclinical front, to prospectively speed-up spectrally-edited MRSI using CS for rapid mapping of lactate and capture associated changes in response to therapy. Secondly, to retrospectively evaluate CS-MRSI in pediatric patients scanned for various brain-related concerns. Thirdly, to implement prospective CS-MRSI acquisitions on a clinical magnetic resonance imaging (MRI) scanner for fast spectroscopic imaging studies. Both phantom and in vivo results demonstrated a reduction in the scan time by up to 80%, with the accelerated CS-MRSI reconstructions maintaining high spectral fidelity and statistically insignificant errors as compared to the fully sampled reference dataset. Optimization of CS parameters involved identifying an optimal sampling mask for CS-MRSI at each acceleration factor. It is envisioned that time-efficient MRSI realized with optimized CS acceleration would facilitate the clinical acceptance of routine MRSI exams for a quantitative mapping of important biomarkers.Dissertation/ThesisDoctoral Dissertation Bioengineering 201