The purpose of this thesis research was to explore the feasibility of spectral imaging using a microbolometer infrared camera and a step-scan Fourier transform infrared spectrometer (FTIR). Spectral imaging is usually carried out using cryogenically cooled semiconductor based focal plane arrays (FPAs) which provide higher sensitivity compared to microbolometer FPAs based on thermal sensors. The key advantage of spectral imaging is the ability to extract spatial variations of spectral information. During the measurement, images were collected as the moving mirror of the FTIR stepped across the zero crossings of the on-axis portion of the interferogram. The preliminary data indicate that interferograms can be successfully recorded using the microbolometer camera, and that data from individual pixels of the camera showed the expected intensity profile. The interferograms from the individual pixels were inverse Fourier transformed to recover the intensity of the broadband infrared source of the FTIR at different pixels. The initial data showed relatively low signal to noise ratio indicating that signal averaging is necessary at each mirror step by collecting several images as well as optimizing the image collecting optics.

A 3D wavelet based encoding spectroscopic method (WE-SI) is investigated and implemented on a 3 Tesla Siemens Scanner. Compared to CSI, the proposed method is able to reduce acquisition time, and preserves the spatial metabolite distribution. As expected, a decrease in Signal to Noise Ratio (SNR) is noticed in WE-SI data compared to CSI. The dissertation explores important physical principles in MRI and spectroscopic imaging as a background, following by introduction of the wavelet encoding theory and comparison to Fourier encoding.

A 3D wavelet based encoding spectroscopic method (WE-SI) is investigated and implemented on a 3 Tesla Siemens Scanner. Compared to CSI, the proposed method is able to reduce acquisition time, and preserves the spatial metabolite distribution. As expected, a decrease in Signal to Noise Ratio (SNR) is noticed in WE-SI data compared to CSI. The dissertation explores important physical principles in MRI and spectroscopic imaging as a background, following by introduction of the wavelet encoding theory and comparison to Fourier encoding.

Doctor of Philosophy / Biochemistry and Molecular Biophysics Interdepartmental Program / John M. Tomich / David L. Wetzel / The technique of near infrared chemical imaging has been widely used for many industrial applications. It offers selectivity and/or sensitivity for numerous organic functional groups. The advantage of the near infrared spectroscopic region is the linear relationship of absorbance and concentration that enables quantitation. This universally employed technique has been a boon for research studies in the industrial process of wheat milling for the production of flour. The milling process has numerous sequential grinding and sieving steps that enable selective physical segregation of a starch rich endosperm product from wheat. Thousands of spectra of purified endosperm and non-endosperm standards are collected to develop a spectral library. Quantitation of the purity of individual processing streams is accomplished by applying a partial least squares calibration that is based upon the spectral library. The quantitative chemical imaging technique is useful for determination of endosperm purity profiles for mill flour streams. These plots reveal purity changes as less pure streams are added to produce a flour blend. The chemical structural basis furthermore allows comparison of purity even with changes in the wheat blend being milled with representative standardization. Furthermore, whereas a certain section of sieves is responsible, for designating the material defined as flour, application of the spectroscopic method is obvious. Select examples of key processing streams were studied to show the possibility of sieve-by-sieve analysis of the physical separation to provide mill optimization. These novel methods of analysis would not be possible without the sensitive and selective method of quantitative chemical imaging. Application of this technique to a few select unit processes is projected to reasonably affect a 1% increase in the yield of high quality flour. This amounts to a significant financial gain against low profit margins.

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Thesis (Ph.D.)--Tufts University, 2004. / Adviser: Sergio Fantini. Submitted to the Dept. of Electrical Engineering. Includes bibliographical references (leaves 201-202). Access restricted to members of the Tufts University community. Also available via the World Wide Web;

Proton echo planar spectroscopic imaging(PEPSI) is a novel and rapid technique of magnetic resonance spectroscopic imaging(MRSI). To analyze the metabolite in PEPSI by using LCModel, an automatic reconstruction system is necessary. Recently, many researches use graphic processing unit(GPU) to accelerate imaging reconstruction, and Compute Unified Device Architecture(CUDA) is developed by C language, so the programmers can write the program in parallel computing easily. PEPSI data acquisition includes non water suppression and water suppression scans, each scan contains odd and even echoes, these two data are reconstructed separately. The image reconstruction contains k-space filter, time-domain filter, three-dimension fast Fourier transform(FFT), phase correction and combine odd and even data. We use MATLAB, C++ and parallel computing to implement PEPSI reconstruction, and parallel computing applied CUDA which proposed by NVIDIA. In our study, the averaged non water suppression spectroscopic imaging executed by three different programming language are almost the same. In our data scale, the execution time of parallel computing is faster than MATLAB and C++, especially in the FFT step. Therefore, we simulated and compared the performance of one- to three-dimension FFT. Our result shows that accelerating performance of GPU depends on the number of data points according to the performance of FFT and the execution time of single coil PEPSI reconstruction. While the amount of data points is larger than 65536, as demonstrated in our study, parallel computing contribute in terms of computational acceleration.

A dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Master of Science. Johannesburg, 30th May 2014. / The Multi-Angle Imaging SpectroRadiometer (MISR) is an Earth observation instrument operated by NASA on its Terra satellite. The instrument is unique in imaging the Earth’s surface from nine cameras at different angles. An extended system MISR-HR, has been developed by the Joint Research Centre of the European Commission (JRC) and NASA, which derives many values describing the interaction between solar energy, the atmosphere and different surface characteristics. It also generates estimates of data at the native resolution of the instrument for 24 of the 36 camera bands for which on-board averaging has taken place prior to downloading of the data. MISR-HR data potentially yields high value information in agriculture, forestry, environmental studies, land management and other fields. The MISR-HR system and the data for the African continent have also been provided by NASA and the JRC to the South African National Space Agency (SANSA). Generally, satellite remote-sensing of the Earth’s surface is characterised by irregularity in the time-series of data due to atmospheric, environmental and other effects. Time-series methods, in particular for vegetation phenology applications, exist for estimating missing data values, filling gaps and discerning periodic structure in the data. Recent evaluations of the methods established a sound set of requirements that such methods should satisfy. Existing methods mostly meet the requirements, but choice of method would largely depend on the analysis goals and on the nature of the underlying processes. An alternative method for time-series exists in Gaussian Processes, a long established statistical method, but not previously a common method for satellite remote-sensing time-series. This dissertation asserts that Gaussian Process regression could also meet the aforementioned set of time-series requirements, and further provide benefits of a consistent framework rooted in Bayesian statistical methods. To assess this assertion, a data case study has been conducted for data provided by SANSA for the Kruger National Park in South Africa. The requirements have been posed as research questions and answered in the affirmative by analysing twelve years of historical data for seven sites differing in vegetation types, in and bordering the Park. A further contribution is made in that the data study was conducted using Gaussian Process software which was developed specifically for this project in the modern open language Julia. This software will be released in due course as open source.

Magnetic Resonance Spectroscopy (MRS) can be applied to probe noninvasively the concentrations and distribution of metabolites of human tissue in vivo. As the improving of hardware and localization techniques, MRS becomes more and more important in clinical applications. Furthermore, some post-processing software, like LCModel, provide a graphical user interface for efficient and convenient analysis of MR spectroscopic imaging and thus increase the value of MRS applications. Although LCModel provides an efficient analysis and produces stable results, it can not provide metabolite map to observe the distribution of metabolite concentrations. For this reason our study processes the output data of LCModel and Digital Imaging and Communications in Medicine (DICOM) format MR images for 2D/3D metabolite map displaying. Users can use this software to observe the metabolic distribution in AP, SI and RL slice of brain tissue. In the meanwhile, as the absolute quantification of MRS has played more and more important role in clinical applications, this study also provides the LCModel end users an easy way for interpretation.